Journal articles on the topic 'AUC'
To see the other types of publications on this topic, follow the link: AUC.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'AUC.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Porrata, Luis F., Kay M. Ristow, Joseph P. Colgan, Thomas M. Habermann, Thomas E. Witzig, David James Inwards, Stephen M. Ansell, et al. "Peripheral Blood Lymphocyte/Monocyte Ratio At Diagnosis Is Independent of the Cell of Origin in Predicting Survival in Diffuse Large B-Cell Lymphoma,." Blood 118, no. 21 (November 18, 2011): 3652. http://dx.doi.org/10.1182/blood.v118.21.3652.3652.
Full textAbstract:
Abstract Abstract 3652 Purpose: Gene expression profiling has shown biologically distinct cell of origin categories for diffuse large B-cell lymphoma (DLBCL): germinal center B-cell like (GCB), and activated-B-cell like (ABC). GCB, DLBCL patients experienced better clinical outcomes compared with ABC, DLBCL patients. However, gene microarray technology is not broadly available in a non-research setting. Absolute lymphocyte count (ALC) at diagnosis is a prognostic factor for survival in DLBCL. Recently, gene-expression profiling and immunohistochemistry-based studies in non-Hosgkin's lymphoma demonstrate the important role of monocytes and their progeny, particularly lymphoma-associated macrophages, in promoting lymphomagenesis. thus, we studied if the peripheral blood absolute lymphocyte count/absolute monocyte count at diagnosis (ALC/AMC-DX), as a surrogate biomarker of the host response against the cell of origin in DLBCL, affects survival in DLBCL. Patients and Methods: We perfromed a retrospective analysis of the association between ALC/AMC-DX and survival in 131 consecutive DLBCL patients that were followed at Mayo Clinic from 2004 to 2010, with available tissue immunostaining for CD10, BCL6, MUM 1, and BCL 2 (Hans' algorithm) to identified GCB and ABC DLBCL patients. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses were used for ALC/AMC-DX cut-off value determientaion and proportional-hazards models wee used to compare survival based on the ALC/AMC-DX. Results: The cohort included 91 (70%) GCb DLBCL patients and 40 (30%) ABC, DLBCL patients. All patients were treated with rituximab, cyclosphosphamide, adriamycin, vincristine, and prednisone (R-CHOP-21). The median follow-up period was 2.1 years (range 0.1–6.9 years). An ALC/AMC-DX >= 1.5 was the best cut-off value for survival with an empircal AUC of 0.83 (95% CI, 0.77 to 0.89), a sensitivity of 83% (95% CL, 72% to 92%) and specificity of 79% (95%CI, 72% to 85%). The cut-off value for ALC/AMC-DX >= 1.5 was validated by the k-fold cross validation method, showing a cross validation ROC with an AUC of 0.89 (95%CI, 0.80 to 0.95) for an ALC/AMC-DX >=1.5. Using Kaplan-Meier analysis, the overall survival (OS), defined as the time from diagnosis to last follow-up or death due to any cause; and progression-free survival (PFS), defined as the time from diagnosis to death of any cause, relapse, progression, or last follow-up, based on ALC/AMC-DX >= 1.5 were evaluated. patients with an ALC/AMC-DX >=1.5 experienced a superior OS and PFS compared with patients with an ALC/AMC-DX < 1.5: [OS: median was not reached vs 20.8 months, 5-years OS rates of 83% (95%CI, 70% to 95%) vs 36% (95%CI, 20% to 55%), p < 0.0001, respectively; and PFS: median was not reached vs 10.8 months, 5-years PFS rates of 70% (95%CI, 58% to 88%) vs 28% (95% CI, 17% to 46%), p < 0.0001, respectively]. Multivariate Cox stepwise regression analysis identified cell of origin, International Prognostic Index (IPI) and ALC/AMC-DX as the strongest predictors for OS and PFS, with ALC/AMC-DX out-performing cell of origin and IPI: OS [HR = 0.17, 95%CI, 0.07 to 0.36, p < 0.0001] and PFS [HR=0.21, 955CI, 0.11 to 0.39, p < 0.001]. conclusion: ALC/AMC-DX is independent of the cell of origin to predict survival and provides a single biomarker to predict clinical outcomes in DLBCL. Confirmatory studies are required. Disclosures: No relevant conflicts of interest to declare.
2
Zhao, Songzhu, Mingjia Li, Daniel Spakowicz, Sandip H. Patel, Andrew Johns, Madison Grogan, Abdul Miah, et al. "Neutrophil-lymphocyte score: A novel prognostic scoring system that utilized the dynamic change of neutrophil, lymphocyte, and albumin and its comparison to other indices." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 3048. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.3048.
Full textAbstract:
3048 Background: Indications for immune checkpoint inhibitor (ICI) in cancer care are expanding rapidly. There is increasing need for accurate decision tool to better guide treatment. We have constructed a new prognostic scoring system, neutrophil-lymphocyte score (NRS), based on the nonlinear dynamic change of neutrophil to lymphocyte ratio (NLR) in relation to survival over the first cycle of ICI treatment. We compared this novel system to existing indices such as NLR, lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR), Advanced Lung Cancer Inflammation Index (ALI), and Systemic Immune-inflammation Index (SII). Methods: This is a retrospective analysis of 837 patients at Ohio State University from 2011-18. Neutrophil (ANC), lymphocyte (ALC), platelet (plt), monocyte (AMC), albumin (alb), and body mass index (BMI) were collected at baseline. Repeat labs were collected at cycle 2. NLR = ANC/ALC, ALI = BMI x alb / NLR, LMR = ALC/AMC, SII = platelet x NLR, PLR = plt/ALC. NLR Ratio = baseline NLR / repeat NLR. Based on the association between NLR and the overall survival, we assigned 1 point (p) for baseline NLR < 0.7, 6p for 0.7 to < 2, 5p for 2 to < 3, 4 p for 3 to < 4, 3 for 4 to 5, 2p for 5 to < 9, and 1p for ≥9. We also assigned 1p for NLR ratio < 0.6, 2p for 0.6 to < 0.8, 3p for 0.8 to < 1.2, 5p for 1.25 to < 1.4, 3p for 1.4 to < 1.6, and 2p for ≥1.6. NLS = sum of these 2 scores . NLS_A = NLS*alb. Time-dependent receiver operator characteristic (ROC) curves with integrated time-dependent area under the curve (TD AUC) values were used to evaluate the predictive accuracy of each index for survival. Results: For baseline and repeat values, all indices were statistically significant (P < 0.001) in predicting survival. Baseline integrated TD AUC were: ALI 0.704, NLR 0.692, SII 0.663, LMR 0.645, and PLR 0.612. All of the repeat indices at cycle 2 had higher prognostic value than their baseline counterparts. Integrated TD AUC for indices at cycle 2 were: ALI 0.740 (with baseline BMI), NLR 0.729, SII 0.694, LMR 0.671, and PLR 0.652. NLS_A was a composite score based on the dynamic change of NLR from cycle 1 to 2 and the treatment alb with integrated TD-AUC at 0.754. Conclusions: Indices constructed from ANC, ALC, AMC, Plt, alb, and BMI can be obtained inexpensively and provide great prognostic value for pts on ICI. We have constructed a novel scoring system (NLS_A) and demonstrated its improvement over the current prognostic indices. Studies with a larger cohort are needed to further improve and validate this system.
3
Gómez-Veiras, Javier, Ángel Salgado-Barreira, José Luis Vázquez, Margarita Montero-Sánchez, José Ramón Fernández-Lorenzo, and Marcos Prada-Arias. "Value of Fibrinogen to Discriminate Appendicitis from Nonspecific Abdominal Pain in Preschool Children." European Journal of Pediatric Surgery 30, no. 04 (June 12, 2019): 357–63. http://dx.doi.org/10.1055/s-0039-1692166.
Full textAbstract:
Introduction The aim of this study was to assess the diagnostic value of the biomarker fibrinogen (FB), along with the markers white blood cell (WBC) count, absolute neutrophil count (ANC), and C-reactive protein (CRP), to discriminate appendicitis from nonspecific abdominal pain (NSAP) in preschool children. Materials and Methods We prospectively evaluated all children aged <5 years admitted for suspected appendicitis at an academic pediatric emergency department during 5 years. Diagnostic accuracy of FB (prothrombin time–derived method), WBC, ANC, and CRP were assessed by the area under the curve (AUC) of the receiver-operating characteristic curve. Results A total of 82 patients were enrolled in the study (27 NSAP, 17 uncomplicated, and 38 complicated appendicitides). WBC and ANC had moderate diagnostic accuracy for appendicitis versus NSAP (WBC: AUC 0.66, ANC: AUC 0.67). CRP and FB had good diagnostic accuracy for appendicitis versus NSAP (CRP: AUC 0.78, FB: AUC 0.77). WBC and ANC are not useful to discriminate complicated versus uncomplicated appendicitis (WBC: AUC 0.43, ANC: AUC 0.45). CPR and FB had good diagnostic accuracy for complicated versus uncomplicated appendicitis (CRP: AUC 0.80, FB: AUC 0.73). Conclusion CRP and FB are more useful than WBC and ANC to discriminate appendicitis from NSAP in preschool children. CRP and FB are especially useful to discriminate complicated from uncomplicated appendicitis and NSAP. In a child with suspected appendicitis, a plasma FB level (prothrombin time–derived method) >540 mg/dL is associated with an increased likelihood of complicated appendicitis.
4
Owusu-Boadu, Bridgitte, Isaac Kofi Nti, Owusu Nyarko-Boateng, Justice Aning, and Victoria Boafo. "Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features." Applied Computer Systems 26, no. 2 (December 1, 2021): 122–31. http://dx.doi.org/10.2478/acss-2021-0015.
Full textAbstract:
Abstract The academic performance of students is essential for academic progression at all levels of education. However, the availability of several cognitive and non-cognitive factors that influence students’ academic performance makes it challenging for academic authorities to use conventional analytical tools to extract hidden knowledge in educational data. Therefore, Educational Data Mining (EDM) requires computational techniques to simplify planning and determining students who might be at risk of failing or dropping from school due to academic performance, thus helping resolve student retention. The paper studies several cognitive and non-cognitive factors such as academic, demographic, social and behavioural and their effect on student academic performance using machine learning algorithms. Heterogenous lazy and eager machine learning classifiers, including Decision Tree (DT), K-Nearest-Neighbour (KNN), Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), AdaBoost and Support Vector Machine (SVM) were adopted and training was performed based on k-fold (k = 10) and leave-one-out cross-validation. We evaluated their predictive performance using well-known evaluation metrics like Area under Curve (AUC), F-1 score, Precision, Accuracy, Kappa, Matthew’s correlation coefficient (MCC) and Recall. The study outcome shows that Student Absence Days (SAD) are the most significant predictor of students’ academic performance. In terms of prediction accuracy and AUC, the RF (Acc = 0.771, AUC = 0.903), LR (Acc = 0.779, AUC = 0.90) and ANN (Acc = 0.760, AUC = 0.895) outperformed all other algorithms (KNN (Acc = 0.638, AUC = 0.826), SVM (Acc = 0.727, AUC = 0.80), DT (Acc = 0.733, AUC = 0.876) and AdaBoost (Acc = 0.748, AUC = 0.808)), making them more suitable for predicting students’ academic performance.
5
Ramesh, Jayanthy, Johann Varghese, S. L. Sagar Reddy, and Moganti Rajesh. "Systemic inflammatory index a simple marker of thrombo-inflammation and prognosis in severe COVID-19 patients." International Journal of Advances in Medicine 8, no. 9 (August 21, 2021): 1335. http://dx.doi.org/10.18203/2349-3933.ijam20213165.
Full textAbstract:
Background: COVID-19 pandemic has challenged the healthcare resources globally, inspiring the need for identifying simple, economical biomarkers. COVID-19 is an immune-inflammatory disorder and systemic inflammatory index (SII) derived from the peripheral blood has been proposed as a marker.Methods: Retrospective study of severe COVID-19 hospitalized patients (total N=154 including diabetic subset N=57). Data regarding hematological variables such as absolute neutrophil count (ANC), absolute lymphocyte count (ALC), platelet count along with thrombo-inflammatory proteins, D-dimer, C-reactive protein (CRP) were extracted from medical records. SII was calculated from ANC×platelets/lymphocyte count. Clinically applicable cut-offs were derived using the receiver operating characteristic curve (ROC) analysis for SII, CRP and D-dimer. Correlations between hematological parameters and D-dimer, CRP were analyzed to validate them as biomarkers of thrombo-inflammation and as predictors of clinical outcome.Results: Among 154 severe COVID-19 patients, significant association with mortality was seen with respect to ANC (p<0.001), SII (p=0.01), CRP (p=0.004) and D-dimer (p=0.001). In the total COHORT, based on ROC curve, applicable cut-off for outcome prediction were for SII 14.85×105 (area under curve (AUC)-0.691, sensitivity-67%, specificity-64%,odds ratio (OR)-3.44), CRP 19.7 mg/l (AUC-0.718, OR-5.71), D-dimer 0.285 mcg/ml (AUC-0.773, OR-6.94) respectively. In the diabetic subset, the cut-offs for SII 14.85×105 (AUC-0.68, sensitivity-80%, specificity-54%, OR-4.7), CRP 52.5 mg/l (AUC-0.723, OR-5.36) and D-dimer 0.285 mcg/ml (AUC-0.771, OR-11.3) respectively.Conclusions: Clinically applicable thresholds for SII serve as reliable biomarkers of thrombo-inflammation and prognosis in severe COVID-19 patients. Diabetic patients with similar thresholds had higher risk and prediction for mortality. In the resource constrained health care settings, who might not afford D-dimer, SII may serve as an economical bio-maker.
6
Madrazo, Ivan, Monserrat Fabiola Vélez, Josue Jonathan Hidalgo, Ginna Ortiz, Juan José Suárez, Leonardo M. Porchia, M. Elba Gonzalez-Mejia, and Esther López-Bayghen. "Prediction of severe ovarian hyperstimulation syndrome in women undergoing in vitro fertilization using estradiol levels, collected ova, and number of follicles." Journal of International Medical Research 48, no. 8 (August 2020): 030006052094555. http://dx.doi.org/10.1177/0300060520945551.
Full textAbstract:
Objective Our objective was to determine whether estradiol (E2) levels (Day 3 and fold change to Day 10), antral follicle count (AFC), and number of ova collected could predict ovarian hyperstimulation syndrome (OHSS) and culdocentesis intervention. Methods We conducted a retrospective review of patient charts between January 2008 and December 2017. OHSS was defined using American Society for Reproductive Medicine criteria. Predictability was evaluated by measuring the area under the receiver operating characteristic curve (AUC). Results The cohort included 319 women (166 controls, 153 OHSS, of whom 54 had severe OHSS). The OHSS group had higher E2Day 3 (249 ± 177 vs. 150 ± 230 ng/L), E2FoldChange (32.2 ± 29.1 vs. 20.1 ± 23.8), AFC (18.2 ± 9.1 vs. 11.6 ± 8.3), and number of ova collected (21.1 ± 9.0 vs. 10.1 ± 6.5). E2Day 3 (AUC = 0.76, 95%CI: 0.71–0.82), E2FoldChange (AUC = 0.71, 95%CI: 0.65–0.77), AFC (AUC = 0.75, 95%CI: 0.70–0.81), and number of ova collected (AUC = 0.85, 95%CI: 0.81–0.89) were predictive for OHSS. All variables were predictive for culdocentesis intervention (E2Day 3: AUC = 0.63, 95%CI: 0.55–0.70; E2FoldChange: AUC = 0.63, 95%CI: 0.55–0.71; AFC: AUC = 0.74, 95%CI: 0.68–0.80; number of ova collected: AUC = 0.80, 95%CI: 0.75–0.85). Conclusions Day 3 E2 levels and number of ova collected predict patients who could develop OHSS and may require culdocentesis.
7
Desiato, Vincenzo, Alan S. Rosman, Elliot Newman, Russell S. Berman, H. Leon Pachter, and Marcovalerio Melis. "Changes in apparent diffusion coefficient evaluated with diffusion-weighted MRI to predict complete pathologic response after neoadjuvant therapy for rectal cancer: Literature review and meta-analysis." Journal of Clinical Oncology 34, no. 4_suppl (February 1, 2016): 503. http://dx.doi.org/10.1200/jco.2016.34.4_suppl.503.
Full textAbstract:
503 Background: A complete pathological response (pCR) is observed in 9-38% of all patients undergoing neo-adjuvant chemo-radiation therapy (CRT) for locally advanced rectal cancer (ARC). Imaging techniques that can reliably assess CRT results may enhance identification of those pCR patients for which surgery may potentially be avoided. Recently, several studies have suggested that diffusion-weighted magnetic resonance imaging (DW-MRI) may predict pathologic response by measuring tumor apparent diffusion coefficient (ADC). ADC can be measured before (pre-ADC) and/or after CRT (post-ADC). Both pre- and post-ADC, as well as the variation between pre- and post-ADC (Δ-ADC) can be used to assess pCR. We aimed to assess the reliability of ADC at predicting pCR in ARC patients treated with CRT. To determine the most effective ADC timing to evaluate pCR. Methods: A systematic review of available literature was conducted to compare all the studies of DW-MRI for identification of pCR after CRT for ARC. For each parameter (pre-ADC, post-ADC and D-ADC) we pooled sensitivity and specificity and calculated the area (AUC) under the summary receiver operating characteristics (sROC) curve. Results: We found 10 prospective and 8 retrospective studies examining correlation of ADC and CRT results. Overall, pCR rate was 25%. Pooled sensitivity, specificity, and AUC were: 0.743, 0.755, and 0.841 for pre-ADC; 0.745, 0.706, and 0.782 for post-ADC; and 0.832, 0.806, and 0.895 for D-ADC. Conclusions: Our meta-analysis confirms that at least 25% of patients with ARC experiences pCR after CRT. DW-MRI is a promising technique for assessment of CRT results and D-ADC appears to be the most effective parameter for prediction of pCR.
8
Di Donna, Mariano Catello, Giuseppe Cucinella, Giulia Zaccaria, Giuseppe Lo Re, Agata Crapanzano, Sergio Salerno, Vincenzo Giallombardo, et al. "Concordance of Radiological, Laparoscopic and Laparotomic Scoring to Predict Complete Cytoreduction in Women with Advanced Ovarian Cancer." Cancers 15, no. 2 (January 13, 2023): 500. http://dx.doi.org/10.3390/cancers15020500.
Full textAbstract:
Objective: To identify the best method among the radiologic, laparoscopic and laparotomic scoring assessment to predict the outcomes of cytoreductive surgery in patients with advanced ovarian cancer (AOC). Methods: Patients with AOC who underwent pre-operative computed tomography (CT) scan, laparoscopic evaluation, and cytoreductive surgery between August 2016 and February 2021 were retrospectively reviewed. Predictive Index (PI) score and Peritoneal Cancer Index (PCI) scores were used to estimate the tumor load and predict the residual disease in the primary debulking surgery (PDS) and interval debulking surgery (IDS) after neoadjuvant chemotherapy (NACT) groups. Concordance percentages were calculated between the two scores. Results: Among 100 eligible patients, 69 underwent PDS, and 31 underwent NACT and IDS. Complete cytoreduction was achieved in 72.5% of patients in the PDS group and 77.4% in the IDS. In patients undergoing PDS, the laparoscopic PI and the laparotomic PCI had the best accuracies for complete cytoreduction (R0) [area under the curve (AUC) = 0.78 and AUC = 0.83, respectively]. In the IDS group, the laparotomic PI (AUC = 0.75) and the laparoscopic PCI (AUC= 0.87) were associated with the best accuracy in R0 prediction. Furthermore, radiological assessment, through PI and PCI, was associated with the worst accuracy in either PDS or IDS group (PI in PDS: AUC = 0.64; PCI in PDS: AUC = 0.64; PI in IDS: AUC = 0.46; PCI in IDS: AUC = 0.47). Conclusion: The laparoscopic score assessment had high accuracy for optimal cytoreduction in AOC patients undergoing PDS or IDS. Integrating diagnostic laparoscopy in the decision-making algorithm to accurately triage AOC patients to different treatment strategies seems necessary.
9
Taffel, Myles T., Lyndon Luk, Justin M. Ream, and Andrew B. Rosenkrantz. "Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy." Canadian Association of Radiologists Journal 70, no. 4 (November 2019): 416–23. http://dx.doi.org/10.1016/j.carj.2019.07.001.
Full textAbstract:
Purpose To evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy. Methods Forty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics’ diagnostic performance for lesion histology, T-stage, N-stage, and grade. Results Whole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers ( P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean25-50) (AUC = 0.829-0.829). For mean10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10−3 mm2/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N1 than N0 for both readers ( P ≤ .043) were mean0-10 (AUC = 0.789-0.822) and mean10-25 (AUC = 0.800-0.822). For mean10-25 (metric with highest AUC for identifying N0), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage ( P > .195) or grade ( P > .215). Conclusion Volumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.
10
Huang, Junpeng, Sixiang Ling, Xiyong Wu, and Rui Deng. "GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility." Land 11, no. 3 (March 17, 2022): 436. http://dx.doi.org/10.3390/land11030436.
Full textAbstract:
Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to Siguniang Mountain (DS) railway, China. Therefore, four advanced machine learning models, namely, the Bayesian network (BN), decision table (DTable), radial basis function network (RBFN), and stochastic gradient descent (SGD), are proposed in this study to delineate landslide susceptibility zones. First, a landslide inventory map was randomly divided into 828 (75%) samples and 276 (25%) samples for training and validation, respectively. Second, the One-R technique was utilized to analyze the importance of 14 variables. Then, the prediction capability of the four models was validated and compared in terms of different statistical indices (accuracy (ACC) and Cohen’s kappa coefficient (k)) and the areas under the curve (AUC) in the receiver operating characteristic curve. The results showed that the SGD model performed best (AUC = 0.897, ACC = 80.98%, and k = 0.62), followed by the BN (AUC = 0.863, ACC = 78.80%, and k = 0.58), RBFN (AUC = 0.846, ACC = 77.36%, and k = 0.55), and DTable (AUC = 0.843, ACC = 76.45%, and k = 0.53) models. The susceptibility maps revealed that the DS railway segments from Puyang town to Dengsheng village are in high and very high-susceptibility zones.
11
Choi, Sun Young, Moo-Hyun Kim, Kwang-Min Lee, Yeo-Gyeong Ko, Chan-Ho Yoon, Min-Kyeong Jo, and Sung-Cheol Yun. "Comparison of Performance between ARC-HBR Criteria and PRECISE-DAPT Score in Patients Undergoing Percutaneous Coronary Intervention." Journal of Clinical Medicine 10, no. 12 (June 10, 2021): 2566. http://dx.doi.org/10.3390/jcm10122566.
Full textAbstract:
The proper management of bleeding risk in patients undergoing percutaneous coronary intervention (PCI) is critical. Recently, the Academic Research Consortium for High Bleeding Risk (ARC-HBR) criteria have been proposed as a standardized tool for predicting bleeding risk. We sought to compare the predictive performance of ARC-HBR criteria and the PRECISE-DAPT score for bleeding in Korean patients undergoing PCI. We recruited 1418 consecutive patients undergoing PCI from January 2012 through December 2018 (Dong-A University Medical Center, Busan, Korea). The ARC-HBR and PRECISE-DAPT scores showed a high AUC for three bleeding definitions (AUC 0.75 and 0.77 for BARC 3 to 5; AUC 0.68 and 0.71 for TIMI minor to major; AUC 0.81 and 0.82 for GUSTO moderate to severe, respectively) and all-cause death (AUC 0.82 and 0.82, respectively). When compared with the ARC-HBR score, the discriminant ability of the PRECISE-DAPT score was not significantly different for bleeding events and all-cause death. The ARC-HBR criteria and PRECISE-DAPT scores demonstrated reasonably good discriminatory capacity with respect to 1-year bleeding events in Korean patients treated with DAPT, regardless of the bleeding definition. Our findings also suggest that the simple PRECISE-DAPT score is as useful as ARC-HBR criteria in predicting bleeding and all-cause death after PCI.
12
Baget-Bernaldiz, Marc, Romero-Aroca Pedro, Esther Santos-Blanco, Raul Navarro-Gil, Aida Valls, Antonio Moreno, Hatem A. Rashwan, and Domenec Puig. "Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database." Diagnostics 11, no. 8 (July 31, 2021): 1385. http://dx.doi.org/10.3390/diagnostics11081385.
Full textAbstract:
Background: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). Results: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. Conclusions: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.
13
Porrata, Luis F., Kay M. Ristow, Joseph P. Colgan, Thomas M. Habermann, Thomas E. Witzig, David James Inwards, Stephen M. Ansell, et al. "Peripheral Blood Lymphocyte/Monocyte Ratio At Diagnosis and Survival in Nodular Lymphocyte-Predominant Hodgkin's Lymphoma,." Blood 118, no. 21 (November 18, 2011): 3642. http://dx.doi.org/10.1182/blood.v118.21.3642.3642.
Full textAbstract:
Abstract Abstract 3642 Purpose: The reactive pathologic background in nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) consists of lymphocytes and histocytes. Lymphopenia is a negative prognostic factor in NLPHL as it is also in Classical Hodgkin Lymphoma (cHL). In cHL, tumor-associated macrophages recruited from circulating monocytes are a negative prognostic factor for survival. Thus, we studied if the peripheral blood absolute lymphocyte count/absolute monocyte count ratio at diagnosis (ALC/AMC-DX), as a surrogate biomarker of the host response against the neoplastic lymphocytic and/or histiocytic (L&H) cells, affects survival in NLPHL. Patients and Methods: We performed a retrospective analysis of the association between ALC/AMC-DX and survival in 103 consecutive NLPHL patients that were followed at Mayo Clinic from 1974 to 2010. Receiver operating characteristic (ROC) and area under the curve (AUC) were used for ALC/AMC-DX cut-off value analysis and proportional-hazards models were used to compare survival based on the ALC/AMC-DX ratio. Results: The cohort included 67 (65%) males and 36 (35%) females. Eighty-three percent of patients presented with stage I/II and 17% with stage III/IV. Sixty-five (63%) patients were treated with radiation alone; 20 (20%) patients with chemotherapy alone; and 18 (17%) patients with chemotherapy and radiation. The median follow-up period was 8.9 years (range: 0.3–31 years). An ALC/AMC-DX ≥ 2.1 was the best cut-off value for survival with an empirical AUC of 0.82 (95% confidence interval [CI], 0.78 to 0.88), a sensitivity of 70% (95% CI, 61% to 76%) and specificity of 84% (95%CI, 59% to 82%). The cut-off value for ALC/AMC-DX ≥ 2.1 was validated by the k-fold cross validation method, showing a cross validation ROC with an AUC of 0.82 (95% CI, 0.72 to 0.93) for an ALC/AMC-DX ≥ 2.1. Using Kaplan-Meier analysis, we studied overall survival (OS), lymphoma-specific survival (LSS), progression-free survival (PFS), and time to progression (TTP) based on ALC/AMC-DX ≥ 2.1. Patients with an ALC/AMC-DX ≥ 2.1 experienced an superior OS, LSS, PFS, and TTP compared with patients with an ALC/AMC-DX < 2.1: [OS: median was 22.1 years vs 6.3 years, 10-years OS rates of 89% (95%CI, 80% to 98%) vs 44% (95%CI, 27% to 65%), p < 0.0001, respectively; LSS: median was not reached vs 7.3 years, 10-years LSS rates of 99% (95%CI, 90% to 100%) vs 45% (95%CI, 29% to 67%), p < 0.0001, respectively; PFS: median was 20.6 years vs 4.6 years, 10-years PFS rates of 84% (95%CI, 80% to 97%) vs 26% (95%CI, 18% to 55%), p < 0.0001, respectively; and TTP: median was not reached vs 5.5 years, 10-years TTP rates of 93% (95%CI, 85% to 99%) vs 29% (95%CI, 19% to 57%), p < 0.0001, respectively]. After adjusting for the International Prognostic Factors and the International Prognostic Score, ALC/AMC-DX remained an independent prognostic factor for OS {hazard ratio (HR), 0.42, 95%CI, 0.23 to 0.64, p< 0.004]; LSS [HR, 0.14; 95%CI, 0.04 to 0.23, p <0.0004]; PFS [HR, 0.14; 95%CI, 0.03 to 0.34, p < 0.001], and TTP [HR, 0.16, 95%CI, 0.04–0.34, p <0.002]. Conclusion: ALC/AMC-DX is an independent prognostic factor for survival and provides a single biomarker to predict clinical outcomes in NLPHL. Disclosures: No relevant conflicts of interest to declare.
14
Yin, Ping, Ning Mao, Sicong Wang, Chao Sun, and Nan Hong. "Clinical-radiomics nomograms for pre-operative differentiation of sacral chordoma and sacral giant cell tumor based on 3D computed tomography and multiparametric magnetic resonance imaging." British Journal of Radiology 92, no. 1101 (September 2019): 20190155. http://dx.doi.org/10.1259/bjr.20190155.
Full textAbstract:
Objective: To develop and validate clinical-radiomics nomograms based on three-dimensional CT and multiparametric MRI (mpMRI) for pre-operative differentiation of sacral chordoma (SC) and sacral giant cell tumor (SGCT). Methods: A total of 83 SC and 54 SGCT patients diagnosed through surgical pathology were retrospectively analyzed. We built six models based on CT, CT enhancement (CTE), T1 weighted, T2 weighted, diffusion-weighted imaging (DWI), and contrast-enhanced T1 weighted features, two radiomics nomograms and two clinical-radiomics nomograms combined radiomics mixed features with clinical data. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) analysis were used to assess the performance of the models. Results: SC and SGCT presented significant differences in terms of age, sex, and tumor location (tage = 9.00, χ2sex = 10.86, χ2location = 26.20; p < 0.01). For individual scan, the radiomics model based on diffusion-weighted imaging features yielded the highest AUC of 0.889 and ACC of 0.885, followed by CT (AUC = 0.857; ACC = 0.846) and CT enhancement (AUC = 0.833; ACC = 0.769). For the combined features, the radiomics model based on mixed CT features exhibited a better AUC of 0.942 and ACC of 0.880, whereas mixed MRI features achieved a lower performance than the individual scan. The clinical-radiomics nomogram based on combined CT features achieved the highest AUC of 0.948 and ACC of 0.920. Conclusions: The radiomics model based on CT and multiparametricMRI present a certain predictive value in distinguishing SC and SGCT, which can be used for auxiliary diagnosis before operation. The clinical-radiomics nomograms performed better than radiomics nomograms. Advances in knowledge: Clinical-radiomics nomograms based on CT and mpMRI features can be used for preoperative differentiation of SC and SGCT.
15
Juraschek, Stephen P., Michael W. Steffes, and Elizabeth Selvin. "Associations of Alternative Markers of Glycemia with Hemoglobin A1c and Fasting Glucose." Clinical Chemistry 58, no. 12 (December 1, 2012): 1648–55. http://dx.doi.org/10.1373/clinchem.2012.188367.
Full textAbstract:
BACKGROUND 1,5-Anhydroglucitol (1,5-AG), fructosamine, and glycated albumin are of increasing interest as alternative measures of hyperglycemia. We characterize the associations of these nontraditional glycemic markers with hemoglobin A1c (Hb A1c) and fasting glucose and assess their ability to identify people with diabetes. METHODS We conducted a cross-sectional comparison of 1,5-AG, fructosamine, and glycated albumin with Hb A1c and fasting glucose measurements in 1719 participants from the Atherosclerosis Risk in Communities Study. We evaluated nonlinear relationships using R2 and F-statistics. Performance for identification of cases of diabetes was determined using the area under the curve (AUC). Diabetes was defined by Hb A1c ≥6.5%, fasting glucose ≥126 mg/dL (≥6.99 mmol/L), and/or a self-reported history of diagnosed diabetes. RESULTS Median values of Hb A1c and fasting glucose were 5.8% and 109 mg/dL (6.05 mmol/L), respectively; 17.3% of the study population had diagnosed diabetes. Glycated albumin, fructosamine, and 1,5-AG were more strongly correlated with Hb A1c compared with fasting glucose (all P values <0.05). Nonlinear models provided the best fit for describing the relationships of the alternative markers to Hb A1c. When diabetes was defined by an Hb A1c ≥6.5%, fructosamine (AUC 0.83; 95% CI, 0.79–0.87) and glycated albumin (AUC 0.87; 95% CI, 0.83–0.90) performed comparably to fasting glucose (AUC 0.83; 95% CI, 0.79–0.87), while 1,5-AG performed worse (AUC 0.74; 95% CI, 0.69–0.78) for identifying cases of undiagnosed diabetes. CONCLUSIONS Fructosamine and glycated albumin may be useful adjuncts to Hb A1c and fasting glucose. Future studies should examine these markers in situations in which fasting glucose or Hb A1c measurements are invalid or not available.
16
俞, 岚. "Discussion on the Definition and Unit of AUC/MIC (AUIC)." Pharmacy Information 06, no. 01 (2017): 12–16. http://dx.doi.org/10.12677/pi.2017.61003.
Full text
17
Diaz-Ledema, Claudio, and Francisco Bengoa. "Derivaciones quirúrgicas para artroplastia total de cadera desde la atención primaria: la utilidad de los Criterios de Uso Apropiado de la AAOS." Revista Chilena de Ortopedia y Traumatología 64, no. 01 (April 2023): e23-e29. http://dx.doi.org/10.1055/s-0043-57254.
Full textAbstract:
Resumen Antecedentes Se ha comprobado que los médicos de atención primaria (MAPs) tienen falta de claridad respecto a las indicaciones para la artroplastia total de cadera (ATC), lo que hace que el proceso de derivación quirúrgica sea propenso a la variabilidad y la inconsistencia. Los Criterios de Uso Apropiado (Appropriate Use Criteria, AUC, en inglés) de la American Academy of Orthopaedic Surgeons (AAOS-AUC) son una herramienta de apoyo a la toma de decisiones basada en la evidencia que ayuda a los médicos a seleccionar para quién debe indicarse el tratamiento. Este estudio tiene como objetivo comparar la tasa de referencias quirúrgicas de ATC realizadas por MAPs utilizando la herramienta AAOS-AUC y la tasa de referencias resultantes después de la educación formal del médico basada en los estándares actuales de tratamiento de la osteoartritis. Materiales y Métodos Usando un diseño cruzado, 22 MAPs evaluaron 2 rondas de 10 casos clínicos cada una, generando 440 encuentros clínicos simulados de pacientes con osteoartritis de cadera. En 220 encuentros simulados, el MAP decidió si una derivación quirúrgica era apropiada mediante el uso de la herramienta AAOS-AUC. En los otros 220 encuentros simulados, esa decisión se tomó utilizando el conocimiento adquirido después de la educación médica formal. Se comparó la tasa de derivaciones quirúrgicas generadas por ambas estrategias. Resultados No hubo diferencia en la tasa de derivaciones quirúrgicas al comparar encuentros simulados utilizando la herramienta AAOS-AUC (57,3 %) y aquellos que utilizaron el juicio clínico después de la educación formal (62,7 %; p = 0,2). Tampoco se encontraron diferencias al comparar MAPs que usaron la herramienta AAOS-AUC durante su primera o segunda ronda de casos (60,7% versus 58,8%, respectivamente; p = 0,68) Conclusión En manos de MAPs, la herramienta en línea AAOS-AUC funciona tan bien como la educación formal del médico durante el proceso de derivación quirúrgica para ATC. Es plausible considerar la AAOS-AUC una herramienta práctica de apoyo a la toma de decisiones para pacientes con artrosis de cadera evaluados en atención primaria. Nivel de evidencia Nivel V.
18
Bahlinger, Veronika, Annalena Branz, Pamela Strissel, Reiner Strick, Fabienne Lange, Carol-Immanuel Geppert, Niklas Klümper, et al. "Associations of TACSTD2/TROP2 and NECTIN-4/NECTIN-4 with molecular subtypes, PD-L1 expression and FGFR3 mutational status in two advanced urothelial bladder cancer cohorts." Journal of Clinical Oncology 41, no. 6_suppl (February 20, 2023): 554. http://dx.doi.org/10.1200/jco.2023.41.6_suppl.554.
Full textAbstract:
554 Background: Treatment options for advanced urothelial carcinoma (aUC) rapidly evolved in recent years. Besides immunomodulative therapeutic options like anti-PD-(L)1 inhibitors and inhibitors targeting FGFR alterations, two new antibody-drug conjugates (ADC), sacituzumab govitecan (SG) and enfortumab vedotin (EV), have been approved for treatment. However, little is known about associations of specific aUC properties and the surface target expression of TROP2 and NECTIN-4. This study characterizes associations of TACSTD2/TROP2 and NECTIN-4/NECTIN-4 gene and protein expression with morphomolecular and clinico-pathological characteristics of aUC in two large independent cohorts. Methods: The TCGA BLCA (n=405) and the CCC-EMN (n=247) cohorts were retrospectively analyzed. Expression of mRNA and protein for TACSTD2/TROP2 and NECTIN-4/NECTIN-4 was measured and correlated with clinico-pathological characteristics, molecular subtypes, FGFR3 alterations and PD-L1 expression. Results: TACSTD2/TROP2 and NECTIN-4/NECTIN-4are highly expressed at protein and transcript level in aUC, and their expression status did not correlate with patient survival in two independent cohorts. NECTIN-4/NECTIN-4 expression was higher in luminal tumors and reduced in squamous aUCs. NECTIN-4 was negative in 10.6% of samples, and 18.4% of samples had low expression (H-Score < 15). TROP2 negativity rate amounted to 6.5%. TACSTD2 and NECTIN-4 expression was reduced in neuroendocrine-like and/or protein-based double negative tumors. TROP2 and NECTIN-4 negative tumors (protein level) included one sarcomatoid and four neuroendocrine aUC. FGFR3 alterations and PD-L1 expression on tumor and immune cells did not associate with TROP2 or NECTIN-4 expression. Conclusions: TACSTD2/TROP2 and NECTIN-4/NECTIN-4 are widely expressed in aUC, independent of FGFR3 alterations or PD-L1 expression. Expression loss is associated with aggressive morphomolecular aUC subtypes, i.e. neuroendocrine(-like) and sarcomatoid aUC. TROP2 and NECTIN-4 are widely expressed in aUC thus representing suitable targets for novel ADC treatment for the majority of aUC patients.
19
Partridge, Savannah C., Zheng Zhang, David C. Newitt, Jessica E. Gibbs, Thomas L. Chenevert, Mark Alan Rosen, Patrick J. Bolan, Helga Marques, Laura Esserman, and Nola M. Hylton. "ACRIN 6698 trial: Quantitative diffusion-weighted MRI to predict pathologic response in neoadjuvant chemotherapy treatment of breast cancer." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 11520. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.11520.
Full textAbstract:
11520 Background: Diffusion-weighted (DW) MRI is a non-contrast technique that can reflect treatment-induced alterations in tumor microstructure and cellularity. ACRIN 6698 was performed as a sub-study of the I-SPY 2 TRIAL to evaluate quantitative DW MRI for early assessment of breast cancer response to neoadjuvant chemotherapy (NAC) in a multisite, multiplatform trial. Methods: The IRB-approved trial was performed at ten institutions. Of 406 enrolled breast cancer patients, 272 were randomized to treatment (12 weekly cycles paclitaxel+/-experimental agent, followed by AC) and underwent breast DW MRI at four time points: pre-NAC (T1), early-NAC after 3 cycles paclitaxel (T2), mid-NAC between paclitaxel and AC (T3) and post-NAC (T4). Tumor apparent diffusion coefficient (ADC) was measured at each time point and compared for patients with and without complete pathologic response (pCR) by Wilcoxon signed rank test. Exploratory analyses were performed across subtypes defined by hormone receptor (HR) and HER2 expression. Performance for predicting pCR was assessed by calculating the area under the ROC curve (AUC). Results: Of 272 treated patients, 227 comprised the final cohort (14 were excluded for missing MRI exams, 31 for poor image quality). Median patient age was 48 (range, 25-77) years, and 71/227 (31.3%) achieved pCR. Subtype groups were HR+/HER2+ (n = 38), HR+/HER2- (n = 95), HR-/HER2+ (n = 20), and HR-/HER2- (n = 74). For the full cohort (all subtypes and treatments), both ADC and change in ADC from T1 were significantly predictive of pCR at T3 (AUC = 0.63, 95% CI 0.55-0.71; AUC = 0.62, 95% CI 0.53-0.70, respectively), and also at T4. ADC measures were not predictive of pCR at either T1or T2. Stratifying by subtype showed change in ADC at T3 was more predictive in HR-/HER2+ (AUC = 0.86) and HR+/HER2- (AUC = 0.75) tumors than HR-/HER2- and HR+/HER2+ tumors (AUC = 0.59 and 0.56, respectively). Conclusions: DW MRI reflects cytotoxic effects of chemotherapy, and mid-treatment ADC was a predictive marker of pCR. The predictive value of ADC varied across biologic subtypes. Further work is needed to determine the comparative predictive value of ADC to other imaging metrics. Clinical trial information: NCT01564368.
20
Son, Seung Ha, In Ho Lee, Jung Soo Park, In Sool Yoo, Seung Whan Kim, Jin Woong Lee, Seung Ryu, et al. "Does Combining Biomarkers and Brain Images Provide Improved Prognostic Predictive Performance for Out-Of-Hospital Cardiac Arrest Survivors before Target Temperature Management?" Journal of Clinical Medicine 9, no. 3 (March 10, 2020): 744. http://dx.doi.org/10.3390/jcm9030744.
Full textAbstract:
We examined whether combining biomarkers measurements and brain images early after the return of spontaneous circulation improves prognostic performance compared with the use of either biomarkers or brain images for patients with cardiac arrest following target temperature management (TTM). This retrospective observational study involved comatose out-of-hospital cardiac arrest survivors. We analyzed neuron-specific enolase levels in serum (NSE) or cerebrospinal fluid (CSF), grey-to-white matter ratio by brain computed tomography, presence of high signal intensity (HSI) in diffusion-weighted imaging (DWI), and voxel-based apparent diffusion coefficient (ADC). Of the 58 patients, 33 (56.9%) had poor neurologic outcomes. CSF NSE levels showed better prognostic performance (area under the curve (AUC) 0.873, 95% confidence interval (CI) 0.749–0.950) than serum NSE levels (AUC 0.792, 95% CI 0.644–0.888). HSI in DWI showed the best prognostic performance (AUC 0.833, 95% CI 0.711–0.919). Combining CSF NSE levels and HSI in DWI had better prognostic performance (AUC 0.925, 95% CI 0.813–0.981) than each individual method, followed by the combination of serum NSE levels and HSI on DWI and that of CSF NSE levels and the percentage of voxels of ADC (AUC 0.901, 95% CI 0.792–0.965; AUC 0.849, 95% CI 0.717–0.935, respectively). Combining CSF/serum NSE levels and HSI in DWI before TTM improved the prognostic performance compared to either each individual method or other combinations.
21
Innocenti, Federico, Deanna L. Kroetz, Erin Schuetz, M. Eileen Dolan, Jacqueline Ramírez, Mary Relling, Peixian Chen, Soma Das, Gary L. Rosner, and Mark J. Ratain. "Comprehensive Pharmacogenetic Analysis of Irinotecan Neutropenia and Pharmacokinetics." Journal of Clinical Oncology 27, no. 16 (June 1, 2009): 2604–14. http://dx.doi.org/10.1200/jco.2008.20.6300.
Full textAbstract:
Purpose We aim to identify genetic variation, in addition to the UGT1A1*28 polymorphism, that can explain the variability in irinotecan (CPT-11) pharmacokinetics and neutropenia in cancer patients. Patients and Methods Pharmacokinetic, genetic, and clinical data were obtained from 85 advanced cancer patients treated with single-agent CPT-11 every 3 weeks at doses of 300 mg/m2 (n = 20) and 350 mg/m2 (n = 65). Forty-two common variants were genotyped in 12 candidate genes of the CPT-11 pathway using several methodologies. Univariate and multivariate models of absolute neutrophil count (ANC) nadir and pharmacokinetic parameters were evaluated. Results Almost 50% of the variation in ANC nadir is explained by UGT1A1*93, ABCC1 IVS11 –48C>T, SLCO1B1*1b, ANC baseline levels, sex, and race (P < .0001). More than 40% of the variation in CPT-11 area under the curve (AUC) is explained by ABCC2 –24C>T, SLCO1B1*5, HNF1A 79A>C, age, and CPT-11 dose (P < .0001). Almost 30% of the variability in SN-38 (the active metabolite of CPT-11) AUC is explained by ABCC1 1684T>C, ABCB1 IVS9 –44A>G, and UGT1A1*93 (P = .004). Other models explained 17%, 23%, and 27% of the variation in APC (a metabolite of CPT-11), SN-38 glucuronide (SN-38G), and SN-38G/SN-38 AUCs, respectively. When tested in univariate models, pretreatment total bilirubin was able to modify the existing associations between genotypes and phenotypes. Conclusion On the basis of this exploratory analysis, common polymorphisms in genes encoding for ABC and SLC transporters may have a significant impact on the pharmacokinetics and pharmacodynamics of CPT-11. Confirmatory studies are required.
22
Syed, Aaquib, Richard Adam, Thomas Ren, Jinyu Lu, Takouhie Maldjian, and Tim Q. Duong. "Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer." PLOS ONE 18, no. 1 (January 17, 2023): e0280320. http://dx.doi.org/10.1371/journal.pone.0280320.
Full textAbstract:
Purpose To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. Material and methods This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy. Data types used included tumor ADC values, diffusion-weighted and dynamic-contrast-enhanced MRI at three treatment timepoints, and patient demographics and tumor data. GLCM textural analysis was performed on MRI data. An extreme gradient boosting machine learning algorithm was used to predict pCR. Prediction performance was evaluated using the area under the curve (AUC) of the receiver operating curve along with precision and recall. Results Prediction using texture features of DWI and DCE images at multiple treatment time points (AUC = 0.871; 95% CI: (0.768, 0.974; p<0.001) and (AUC = 0.903 95% CI: 0.854, 0.952; p<0.001) respectively), outperformed that using mean tumor ADC (AUC = 0.850 (95% CI: 0.764, 0.936; p<0.001)). The AUC using all MRI data was 0.933 (95% CI: 0.836, 1.03; p<0.001). The AUC using non-MRI data was 0.919 (95% CI: 0.848, 0.99; p<0.001). The highest AUC of 0.951 (95% CI: 0.909, 0.993; p<0.001) was achieved with all MRI and all non-MRI data at all time points as inputs. Conclusion Using XGBoost on extracted GLCM features and non-imaging data accurately predicts pCR. This early prediction of response can minimize exposure to toxic chemotherapy, allowing regimen modification mid-treatment and ultimately achieving better outcomes.
23
Salimova, M. D., A. V. Atalyan, Ya G. Nadelyaeva, I. N. Danusevich, L. M. Lazareva, N. A. Kurashova, M. A. Darenskaya, et al. "Ceruloplasmin and complement C3 are markers of diminished ovarian reserve in premenopausal women." Fundamental and Clinical Medicine 8, no. 1 (March 31, 2023): 8–20. http://dx.doi.org/10.23946/2500-0764-2023-8-1-8-20.
Full textAbstract:
Aim. To establish cut-off values for the concentrations of complement C3 and ceruloplasmin, diagnostic markers of reduced antral follicle count (AFC) and anti-Müllerian hormone (AMH) which both indicate diminished ovarian reserve, in women of reproductive age.Materials and Methods. Here we enrolled 864 women (18-40 years of age, average age 31.70 ± 5.14 years) who underwent an annual medical examination in 2017–2019 in the Irkutsk Region and the Republic of Buryatia. Reduced AFC was defined as ≤ 5 antral follicles in each ovary at pelvic ultrasound examination whilst reduced AMH was defined as < 1.2 ng/mL. In total, 112 women had reduced ovarian reserve and 752 were included into the control group. In addition to AMH, we also measured serum prolactin, gonadotropins, inhibin B, estradiol, complement C3, and ceruloplasmin using enzyme-linked immunosorbent assay. The cut-off values were determined by plotting a receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC).Results. The cut-off level of complement C3 was 894 (867; 1355.5) mg/mL [AUC: 0.769 (0.635; 0.904)] in women with reduced AFC (≤ 5) and 981.5 (916.5; 1467.5) mg/mL [AUC: 0.62 (0.493; 0.746)] in women with reduced AMH (< 1.2 ng/mL). The cut-off level of ceruloplasmin was 1.745 (1.625; 1.975) mg/mL [AUC: 0.859 (0.759; 0.96)] in women with reduced AFC (≤ 5) and 1.975 (1.665; 2.15) mg/mL, [AUC: 0.662 (0.542; 0.782)] in women with reduced AMH (< 1.2 ng/mL).Conclusion. We have established the cut-off values for the serum complement C3 and ceruloplasmin in women with reduced AFC and AMH, indicators defining diminished ovarian reserve in women of reproductive age.
24
Chen, Xiao Yu, Bo Liu, and Xin Xia. "Ensemble Learning in Data Mining of Fetal Cardiotocograms." Advanced Materials Research 945-949 (June 2014): 2505–8. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2505.
Full textAbstract:
ReliefF feature selection and LogitBoost ensemble learning method are employed in the data mining procedure of 2126 fetal cardiotocograms (CTGs). Based on 10 critical features selected by ReliefF and the full 21 features, LogitBoost algorithm almost outperforms the other three ensemble learning methods of Stacking, Bagging and AdaBoostM1 in ACC (%) and AUC in classification, and the ACC (%) and AUC of LogitBoost algorithm are achieved to 94.45% and 0.977 based on the critical features from ReliefF.
25
K., Brindha, Kumar Manickam, Santhakumari Ulagaratchagan, Mohan Kumar, Sowmya Sampath, and Shobhana Sivathanu. "Serum procacitonin as a diagnostic marker of bacterial infection in febrile children." International Journal of Contemporary Pediatrics 4, no. 4 (June 21, 2017): 1381. http://dx.doi.org/10.18203/2349-3291.ijcp20172670.
Full textAbstract:
Background: Early and accurate diagnosis of bacterial infections in children is important as the outcome is dependent on it. Various tests and biomarkers have been used for this among which serum procalcitonin shows a lot of promise. The aim of the study was to determine the role of serum procalcitonin as a diagnostic marker of bacterial infection in febrile children.Methods: All acutely febrile children between 6 months to 12 years of age were enrolled in this prospective study. The efficacy of procalcitonin (PCT), highly sensitive C-reactive protein (hs CRP) and absolute neutrophil count (ANC) in diagnosing bacterial infections was compared.Results: Among the three parameters, PCT has the highest area under the receiver operating characteristic curve (AUC) (O.755), followed by CRP (AUC 0.717) and ANC (AUC 0.628).Conclusions: In summary, our study showed that PCT performs better than hs CRP and ANC in detecting bacterial infection in febrile children.
26
Gambale, Elisabetta, Marco Maruzzo, Carlo Messina, Irene De Gennaro Aquino, Ismaela Anna Vascotto, Virginia Rossi, Davide Bimbatti, et al. "Blood eosinophils as prognostic biomarkers for advanced urothelial carcinoma in patients treated with avelumab: First results of the MALVA study (Meet-URO 25)." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e16570-e16570. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e16570.
Full textAbstract:
e16570 Background: Platinum-based chemotherapies (CTs) represent the standard first-line treatment of advanced urothelial carcinoma (aUC). However, the development of resistance and toxicities to these regimens is responsible for poor progression-free survival (PFS) and overall survival (OS). Maintenance treatment with the programmed death-ligand 1 (PD-L1) inhibitor avelumab after initial response to CT improved significantly OS. Despite the survival advantage, only a limited percentage of patients (pts) benefits from immunotherapy. Therefore, prognostic/predictive factors for immunotherapy are needed. Studies carried out on small cohorts of pts with melanoma, renal cell cancer or lung carcinoma proved that eosinophil count or variations could be used as prognostic factors during immunotherapy. Thus, the aim of the present study is to evaluate eosinophil levels as potential biomarker for outcome among aUC pts enrolled in the MALVA (Maintenance with AVeLumAb in advanced urothelial neoplasms in response to first-line CT: an observational retrospective and prospective study) ongoing study (Meet-URO 25). Methods: The MALVA study is an ongoing real-life multicentric retro-/prospective observational study on aUC pts receiving avelumab maintenance after response to platinum-based first-line CT. The co-primary endpoints are OS and PFS. We present here data of the first 100 enrolled aUC pts who received avelumab as maintenance therapy after response to platinum-based CT between January 2021 and January 2023. Absolute Eosinophil Counts (AEC) were registered at baseline (week 0) and at time of the first tumor assessment (week 12). This study aims to evaluate whether the AEC could be a predictive biomarker of efficacy in pts with aUC treated with avelumab. Results: One-hundred pts (median age, 72 years), 71.4% of whom were men, were enrolled (data cut off, January 2, 2023). Median follow-up time was 8.5 months. Median duration of avelumab treatment was 5.9 months. Median PFS for the entire population was 8.7 months (95% CI; 5.7 months-not reached). Predefined subgroup analyses showed PFS and OS improvement (5.1 months vs NR, p = 0.0031; 12.9 months vs NR, p = 0.056 respectively) in pts with high AEC at week 0 vs low AEC pts. Additionally, a pilot analysis was conducted for 34 pts, for whom PFS and OS were stratified by AEC at first tumor assessment (week 12). PFS was longer (6.4 months vs NR, p = 0.045) for pts with high AEC vs low AEC pts. Conclusions: In order to optimize treatment efficacy in aUC, reliable biomarkers are required. In this study, we provide data from a homogeneous cohort investigating the relevance of eosinophils in aUC pts receiving avelumab, suggesting that AEC could be an easily accessible and reproducible prognostic biomarker that warrants further studies.
27
Meng, Yang, Guoxin Liang, and Mei Yue. "Deep Learning-Based Arrhythmia Detection in Electrocardiograph." Scientific Programming 2021 (May 13, 2021): 1–7. http://dx.doi.org/10.1155/2021/9926769.
Full textAbstract:
This study aimed to explore the application of electrocardiograph (ECG) in the diagnosis of arrhythmia based on the deep convolutional neural network (DCNN). ECG was classified and recognized with the DCNN. The specificity (Spe), sensitivity (Sen), accuracy (Acc), and area under curve (AUC) of the DCNN were evaluated in the Chinese Cardiovascular Disease Database (CCDD) and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, respectively. The results showed that in the CCDD, the original model tested by the small sample set had an accuracy (Acc) of 82.78% and AUC of 0.882, while the Acc and AUC of the translated model were 85.69% and 0.893, respectively, so the difference was notable ( P < 0.05); the Acc of the original model and the translated model was 80.12% and 82.63%, respectively, in the large sample set, so the difference was obvious ( P < 0.05). In the MIT-BIH database, the Acc of normal (N) heart beat (HB) (99.38%) was higher than that of the atrial premature beat (APB) (87.45%) ( P < 0.05). In a word, applying the DCNN could improve the Acc of ECG for classification and recognition, so it could be well applied to ECG signal classification.
28
Meng, Xiaoyan, Shichao Li, Kangwen He, Henglong Hu, Cui Feng, Zhen Li, and Yanchun Wang. "Evaluation of Whole-Tumor Texture Analysis Based on MRI Diffusion Kurtosis and Biparametric VI-RADS Model for Staging and Grading Bladder Cancer." Bioengineering 10, no. 7 (June 21, 2023): 745. http://dx.doi.org/10.3390/bioengineering10070745.
Full textAbstract:
Background: to evaluate the feasibility of texture analysis (TA) based on diffusion kurtosis imaging (DKI) in staging and grading bladder cancer (BC) and to compare it with apparent diffusion coefficient (ADC) and biparametric vesical imaging reporting and data system (VI-RADS). Materials and Methods: In this retrospective study, 101 patients with pathologically confirmed BC underwent MRI with multiple-b values ranging from 0 to 2000 s/mm2. ADC- and DKI-derived parameters, including mean kurtosis (MK) and mean diffusivity (MD), were obtained. First-order texture histogram parameters of MK and MD, including the mean; 5th, 25th, 50th, 75th, and 90th percentiles; inhomogeneity; skewness: kurtosis; and entropy; were extracted. The VI-RADS score was evaluated based on the T2WI and DWI. The Mann–Whitney U-test was used to compare the texture parameters and ADC values between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), as well as between low and high grades. Receiver operating characteristic analysis was used to evaluate the diagnostic performance of each significant parameter and their combinations. Results: The NMIBC and low-grade group had higher MDmean, MD5th, MD25th, MD50th, MD75th, MD90th, and ADC values than those of the MIBC and the high-grade group. The NMIBC and low-grade group yielded lower MKmean, MK25th, MK50th, MK75th, and MK90th than the MIBC and high-grade group. Among all histogram parameters, MD75th and MD90th yielded the highest AUC in differentiating MIBC from NMIBC (both AUCs were 0.87), while the AUC for ADC was 0.86. The MK75th and MK90th had the highest AUC (both 0.79) in differentiating low- from high-grade BC, while ADC had an AUC of 0.68. The AUC (0.92) of the combination of DKI histogram parameters (MD75th, MD90th, and MK90th) with biparametric VI-RADS in staging BC was higher than that of the biparametric VI-RADS (0.89). Conclusions: Texture-analysis-derived DKI is useful in evaluating both the staging and grading of bladder cancer; in addition, the histogram parameters of the DKI (MD75th, MD90th, and MK90th) can provide additional value to VI-RADS.
29
Mchenga, Martina, Ronelle Burger, and Dieter von Fintel. "Can women’s reports in client exit interviews be used to measure and track progress of antenatal care services quality? Evidence from a facility assessment census in Malawi." PLOS ONE 18, no. 7 (July 31, 2023): e0274650. http://dx.doi.org/10.1371/journal.pone.0274650.
Full textAbstract:
Introduction Unlike household surveys, client exit interviews are conducted immediately after a consultation and therefore provides an opportunity to capture routine performance and level of service quality. This study examines the validity and reliability of women’s reports on selected ANC interventions in exit interviews conducted in Malawi. Methods Using data from the 2013–2014 Malawi service provision facility census, we compared women’s reports in exit interviews regarding the contents of ANC received with reports obtained through direct observation by a trained healthcare professional. The validity of six indicators was tested using two measures: the area under the receiver operating characteristic curve (AUC), and the inflation factor (IF). Reliability of women’s reports was measured using the Kappa coefficient (κ) and the prevalence-adjusted bias-adjusted kappa (PABAK). Finally, we examined whether reporting reliability varied significantly by individual and facility characteristics. Results Of the six indicators, two concrete and observable measures had high reporting accuracy and met the validity criteria for both AUC ≥ 0.7 and 0.75>IF>1.25, namely whether the provider prescribed or gave malaria prophylaxis (AUC: 0.84, 95% CI: 0.83–0.86; IF: 0.96) or iron/folic tablets (AUC: (0.84 95% CI: 0.81–0.87; IF:1.00). Whereas four measures related to counselling had lower reporting accuracy: whether the provider offered counselling about nutrition in pregnancy (AUC: 0.69, 95%CI: 0.67–0.71; IF = 1.26), delivery preparation (AUC: 0.62, 95% CI: 0.60–065; IF = 0.99), pregnancy related complications (AUC: 0.59, 95%CI: 0.56–0.61; IF = 1.11), and iron/folic acid side effects (AUC:0.58, 95% CI: 0.55–0.60; IF = 1.42). Similarly, the observable measures had high reliability with both κ and PABAK values in the ranges of ≥ 0.61 and ≥ 0.80. Respondent’s age, primiparous status, number of antenatal visits, and the type of health provider increased the likelihood of reporting reliability. Conclusion In order to enhance the measurement of quality of ANC services, our study emphasizes the importance of carefully considering the type of information women are asked to recall and the timing of the interviews. While household survey programmes such as the demographic health survey and multiple indicator cluster survey are commonly used as data sources for measuring intervention coverage and quality, policy makers should complement such data with more reliable sources like routine data from health information systems.
30
Tang, Tielong, Chao Yang, Ham Ebo Brown, and Jing Huang. "Circulating Heat Shock Protein 70 Is a Novel Biomarker for Early Diagnosis of Lung Cancer." Disease Markers 2018 (August 29, 2018): 1–8. http://dx.doi.org/10.1155/2018/6184162.
Full textAbstract:
Heat shock protein 70 (HSP70) was a highly conserved protein which was significantly induced in response to cellular stresses. HSP70 played an important role in the pathogenesis of cancer which stabilized the production of large amount of oncogenic proteins and finally supported growth and survival of tumor. However, there was no report about the diagnosis of circulating HSP70 in lung cancer patients. In this study, a total of 297 participants (lung cancer: 197, healthy control: 100) were enrolled in the detection of circulating HSP70 level in plasma by ELISA assay. The results indicated that circulating HSP70 significantly decreased in lung cancer patients compared to healthy controls (P<0.0001). Receiver operating characteristic (ROC) analysis showed that HSP70 (AUC: 82.2%, SN: 74.1%, SP: 80.0%) had higher diagnosis value than clinical existing biomarkers CEA (AUC: 80.1%, SN: 76.8%, SP: 67.3%) and CA 19-9 (AUC: 63.7%, SN: 64.2%, SP: 54.0%). In the analysis of early lung cancer patients, ROC results also revealed that HSP70 (AUC: 83.8%, SN: 71.2%, SP: 84.0%) have higher sensitivity, specificity, and AUC than CEA (AUC: 73.7%, SN: 73.2%, SP: 69.1%) and CA 19-9 (AUC: 61.5%, SN: 69.4%, SP: 53.4%). In analysis of specific histological classifications, HSP70 showed more valuable in the diagnosis of SCC (AUC: 85.9%, SN: 86.1.9%, SP: 81.0%) than ADC (AUC: 81.0%, SN: 69.1%, SP: 81.0%). Combined analysis of HSP70 and existing biomarker: CEA and CA 19-9 exhibited that HSP70 combined CEA and CA 19-9 showed the highest AUC (0.945, 95% CI, 0.855–1.000). The importance of our results was that we found decreased circulating HSP70, in combination with elevated CEA and CA 19-9, could be utilized in the diagnosis of early (stage I and II) lung cancer.
31
Anwar, Shayan Sirat Maheen, Mirza Zain Baig, Altaf Ali Laghari, Fatima Mubarak, Muhammad Shahzad Shamim, Umaima Ayesha Jilani, and Muhammad Usman Khalid. "Accuracy of apparent diffusion coefficients and enhancement ratios on magnetic resonance imaging in differentiating primary cerebral lymphomas from glioblastoma." Neuroradiology Journal 32, no. 5 (June 12, 2019): 328–34. http://dx.doi.org/10.1177/1971400919857556.
Full textAbstract:
Background and purposeThis study aimed to determine the accuracy of apparent diffusion coefficient (ADC) and enhancement ratio (ER) in discriminating primary cerebral lymphomas (PCL) and glioblastomas.Materials and methodsCircular regions of interest were randomly placed centrally within the largest solid-enhancing area of all lymphomas and glioblastomas on both post-contrast T1-weighted images and corresponding ADC maps. Regions of interest were also drawn in the contralateral hemisphere to obtain enhancement and ADC values of normal-appearing white matter. This helped us to calculate the ER and ADC ratio.ResultsMean enhancement and ADC (mm2/s) values for PCL were 2220.56 ± 2948.30 and 712.00 ± 137.87, respectively. Mean enhancement and ADC values for glioblastoma were 1537.07 ± 1668.33 and 1037.93 ± 280.52, respectively. Differences in ADC values, ratios and ERs were all statistically significant between the two groups ( p < 0.05). ADC values correctly predicted 71.4% of the lesions as glioblastoma and 83.3% as PCL (area under the curve (AUC) = 0.86 on receiver operating characteristic curve analysis). ADC ratios correctly predicted 85.7% of the lesions as glioblastoma and 100% as PCL (AUC = 0.93). ERs correctly predicted 71.4% of the lesions as glioblastoma and 88.9% as PCL (AUC = 0.92). The combination of ADC ratio and ER correctly predicted 100% tumour type in both patient subgroups.ConclusionsADC values, ADC ratios and ERs may serve as useful variables to distinguish PCL from glioblastoma. The combination of ADC ratio and ER yielded the best results in identification of both patient subgroups.
32
Santucci, Domiziana, Eliodoro Faiella, Alessandro Calabrese, Bruno Beomonte Zobel, Andrea Ascione, Bruna Cerbelli, Giulio Iannello, Paolo Soda, and Carlo de Felice. "On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior." Cancers 13, no. 20 (October 15, 2021): 5167. http://dx.doi.org/10.3390/cancers13205167.
Full textAbstract:
Background: to evaluate whether Apparent Diffusion Coefficient (ADC) values of invasive breast cancer, provided by 3T Diffusion Weighted-Images (DWI), may represent a non-invasive predictor of pathophysiologic tumor aggressiveness. Methods: 100 Patients with histologically proven invasive breast cancers who underwent a 3T-MRI examination were included in the study. All MRI examinations included dynamic contrast-enhanced and DWI/ADC sequences. ADC value were calculated for each lesion. Tumor grade was determined according to the Nottingham Grading System, and immuno-histochemical analysis was performed to assess molecular receptors, cellularity rate, on both biopsy and surgical specimens, and proliferation rate (Ki-67 index). Spearman’s Rho test was used to correlate ADC values with histological (grading, Ki-67 index and cellularity) and MRI features. ADC values were compared among the different grading (G1, G2, G3), Ki-67 (<20% and >20%) and cellularity groups (<50%, 50–70% and >70%), using Mann–Whitney and Kruskal-Wallis tests. ROC curves were performed to demonstrate the accuracy of the ADC values in predicting the grading, Ki-67 index and cellularity groups. Results: ADC values correlated significantly with grading, ER receptor status, Ki-67 index and cellularity rates. ADC values were significantly higher for G1 compared with G2 and for G1 compared with G3 and for Ki-67 < 20% than Ki-67 > 20%. The Kruskal-Wallis test showed that ADC values were significantly different among the three grading groups, the three biopsy cellularity groups and the three surgical cellularity groups. The best ROC curves were obtained for the G3 group (AUC of 0.720), for G2 + G3 (AUC of 0.835), for Ki-67 > 20% (AUC of 0.679) and for surgical cellularity rate > 70% (AUC of 0.805). Conclusions: 3T-DWI ADC is a direct predictor of cellular aggressiveness and proliferation in invasive breast carcinoma, and can be used as a supporting non-invasive factor to characterize macroscopic lesion behavior especially before surgery.
33
Yamamoto, Yoshiaki, Ryouichi Tsunedomi, Yoshihisa Kawai, Hiroaki Matsumoto, Shoichi Hazama, Hiroaki Nagano, and Hideyasu Matsuyama. "Pharmacogenetic-based area-under-curve model to predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma." Journal of Clinical Oncology 36, no. 6_suppl (February 20, 2018): 625. http://dx.doi.org/10.1200/jco.2018.36.6_suppl.625.
Full textAbstract:
625 Background: Although titrated-dose axitinib offers a good objective response rate (ORR) for metastatic renal cell carcinoma (RCC), its optimal initial dose is unclear because its dosing regimen is based on a hypertensive reaction. We investigated whether axitinib pharmacogenetics were related to clinical efficacy/adverse events, and calculated a model to predict clinical efficacy and adverse events, using pharmacokinetic data based on gene polymorphisms in a phase 2 trial. Methods: We prospectively evaluated objective response and adverse events to establish a prediction model in 44 consecutive patients with advanced RCC, treated with axitinib between October 2013 and March 2017. Gene polymorphisms, including ABC transporters ( BCRP and MDR1) and UGT1A were analyzed by whole-exome sequencing, Sanger sequencing, and DNA chips. To construct the prediction model for area under curve (AUC), we used an exponential regression model with gene polymorphisms and dosage as covariates. To further validate this prediction model, we prospectively compared the calculated AUC in this model with actual AUCs in 13 additional consecutive patients. Results: Actual AUC was significantly correlated with the best ORR ( P = 0.0002), and adverse events (grade 2–3 hand–foot syndrome [ P = 0.0055]; grade 2 hypothyroidism [ P = 0.0381]); it was also significantly correlated with calculated AUC ( P < 0.0001). Calculated AUC was also significantly associated with best ORR ( P = 0.0044), grade 2–3 hand–foot syndrome ( P = 0.0191) and grade 2 hypothyroidism ( P = 0.0082). Surprisingly, hypertension was associated with neither ORR nor AUC. In the validation study, calculated AUC before axitinib treatment precisely predicted actual AUC ( P = 0.0079) in 13 additional consecutive patients. Conclusions: Our pharmacogenetics-based AUC model may offer a more benign method to determine optimal initial axitinib doses than hypertension, and could contribute to more precise treatment of individuals with advanced RCC. Clinical trial information: UMIN000011147.
34
Li, Linfang, Shan Xing, Miantao Wu, Yufeng Ao, Xin Zheng, Rongzeng Cai, Runkun Han, Jingcong Li, Xiaohui Li, and Qiuyao Zeng. "Fecal CEA Has an Advantage in the Diagnosis of Colorectal Cancer at Early Stage." Cancer Control 28 (January 2021): 107327482110482. http://dx.doi.org/10.1177/10732748211048292.
Full textAbstract:
Purpose Serum carcinoembryonic antigen (SCEA) level is often measured in patients with CRC but suffers from poor sensitivity and specificity as a diagnostic biomarker. CEA is more abundant in stool than in serum, but it has not been widely studied. This study aimed to elucidate the efficacy of fecal CEA (FCEA) as a potential non-invasive biomarker for early diagnosis of CRC. Materials and Methods We retrospectively analyzed the determination of FCEA and SCEA levels by electrochemiluminescence. We evaluated the diagnostic accuracy of FCEA and SCEA levels in early-stage CRC patients and healthy controls using ROC curve. Results A total of 298 people were included: 115 patients with CRC, 35 patients with adenomatous polyp (APC), 46 patients with non-gastrointestinal cancer (NGC), and 102 healthy controls (HC). The FCEA concentrations in CRC and APC patients were significantly higher than that of NGC and HC, and this is different from SCEA expression in APC and NGC. As a diagnostic biomarker of CRC, FCEA had significantly larger AUC compared with SCEA (.802 vs .735, P < .001). For identifying early-stage colorectal cancer, FCEA showed better diagnostic efficacy (AUC: .831) than SCEA (AUC: .750), and the combination of the 2 biomarkers was even higher (AUC: .896). The sensitivity of FCEA was higher than that of SCEA (78.7% vs 29.8%). When SCEA was negative, 80.3% of CRC and 54.6% of APC cases could be identified by FCEA. Conclusion Compared with SCEA, FCEA has more advantages in the diagnosis of the early stage of colorectal cancer and adenomatous polyps.
35
Lei, Qiang, Qi Wan, Lishan Liu, Jianfeng Hu, Wei Zuo, Jianneng Li, Guihua Jiang, and Xinchun Li. "Values of Apparent Diffusion Coefficient and Lesion-to-Spinal Cord Signal Intensity in Diagnosing Solitary Pulmonary Lesions: Turbo Spin-Echo versus Echo-Planar Imaging Diffusion-Weighted Imaging." BioMed Research International 2021 (August 10, 2021): 1–8. http://dx.doi.org/10.1155/2021/3345953.
Full textAbstract:
Objective. This study is aimed at comparing the image quality and diagnostic performance of mean apparent diffusion coefficient (ADC) and lesion-to-spinal cord signal intensity ratio (LSR) derived from turbo spin-echo diffusion-weighted imaging (TSE-DWI) and echo-planar imaging- (EPI-) DWI in patients with a solitary pulmonary lesion (SPL). Methods. 33 patients with SPL underwent chest imaging using EPI-DWI and TSE-DWI with b = 600 s/mm2 in free breathing. A comparison of the distortion ratio (DR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) was drawn between the two techniques using a Wilcoxon signed-rank test. The interprotocol reproducibility between quantitative parameters of EPI-DWI and TSE-DWI was evaluated using a Bland-Altman plot. ADCs and LSRs derived from EPI-DWI and TSE-DWI were calculated and compared between malignant and benign groups using the Mann–Whitney test. Results. TSE-DWI had similar SNR and CNR compared with EPI-DWI. DR was significantly lower on TSE-DWI than EPI-DWI. ADC and LSR showed slightly higher values with TSE-DWI, while the Bland-Altman analysis showed unacceptable limits of agreement between the two sequences. ADC and LSR of both DWI techniques differed significantly between lung cancer and benign lesions ( P < 0.05 ). The LSR(EPI-DWI) showed the highest area under the curve ( AUC = 0.818 ), followed by ADC(EPI-DWI) ( AUC = 0.789 ), ADC(TSE-DWI) ( AUC = 0.781 ), and LSR(TSE-DWI) ( AUC = 0.748 ), respectively. Among these parameters, the difference in diagnostic accuracy was not statistically significant. Conclusions. TSE-DWI provides significantly improved image quality in patients with SPL as compared with EPI-DWI. However, there was no difference in diagnostic efficacy between these two techniques, according to ADC and LSR.
36
Norton, Matthew, and Stan Uryasev. "Maximization of AUC and Buffered AUC in binary classification." Mathematical Programming 174, no. 1-2 (July 5, 2018): 575–612. http://dx.doi.org/10.1007/s10107-018-1312-2.
Full text
37
Karkra, Rohan, Chaya Sindaghatta Krishnarao, Jayaraj Biligere Siddaiah, and Mahesh Padukudru Anand. "Hematological Parameters for Predicting Mortality in Acute Exacerbation of Chronic Obstructive Pulmonary Disease." Journal of Clinical Medicine 12, no. 13 (June 23, 2023): 4227. http://dx.doi.org/10.3390/jcm12134227.
Full textAbstract:
(1) Introduction: COPD is a common and serious condition affecting a significant proportion of the population globally. Patients often suffer from exacerbations which lead to the worsening of their health status and respiratory function, and can often lead to death. Quick and cheap investigations are required that are capable of predicting mortality in patients with acute exacerbations that can be applied in low resource settings. (2) Materials and methods: This was a retrospective study carried out using hospital records of patients admitted for AECOPD from 1 January 2017 to 30 November 2022. Chi-square test (for sex) and Student’s t-test were used to look for significant associations. Receiver Operating Characteristics (ROC) curves were plotted and Area Under Curve (AUC) values were calculated for various hematological parameters. Youden’s J was used to identify the ideal cut-off with optimal sensitivity and specificity. Multivariate Cox regression was used to identify independent hematological predictors of mortality. Kaplan–Meir survival plots for neutrophil lymphocyte ratio (NLR) with the optimal cut-off were plotted. (3) Results: Amongst the 500 patients, 42 died while 458 survived, giving a mortality rate of 8.4%. NLR had the strongest association with mortality. The cut-off for various parameters were: NLR 14.83 (AUC 0.73), total leukocyte count (TLC) 13,640 cells/mm3 (AUC 0.60), absolute neutrophil count (ANC) 12,556 cells/mm3 (AUC 0.62), derived NLR (dNLR) 9.989 (AUC 0.73), hemoglobin 11.8 mg/dL (AUC 0.59), packed cell volume (PCV) 36.6% (AUC 0.60), and platelet lymphocyte ratio (PLR) 451.32 (AUC 0.55). (4) Conclusions: In patients with acute exacerbation of COPD, NLR was strongly associated with mortality, followed by dNLR. Cox regression identified NLR as an independent predictor of mortality.
38
Li, Wen, Nu N. Le, Natsuko Onishi, David C. Newitt, Lisa J. Wilmes, Jessica E. Gibbs, Julia Carmona-Bozo, et al. "Diffusion-Weighted MRI for Predicting Pathologic Complete Response in Neoadjuvant Immunotherapy." Cancers 14, no. 18 (September 13, 2022): 4436. http://dx.doi.org/10.3390/cancers14184436.
Full textAbstract:
This study tested the hypothesis that a change in the apparent diffusion coefficient (ADC) measured in diffusion-weighted MRI (DWI) is an independent imaging marker, and ADC performs better than functional tumor volume (FTV) for assessing treatment response in patients with locally advanced breast cancer receiving neoadjuvant immunotherapy. A total of 249 patients were randomized to standard neoadjuvant chemotherapy with pembrolizumab (pembro) or without pembrolizumab (control). DCE-MRI and DWI, performed prior to and 3 weeks after the start of treatment, were analyzed. Percent changes of tumor ADC metrics (mean, 5th to 95th percentiles of ADC histogram) and FTV were evaluated for the prediction of pathologic complete response (pCR) using a logistic regression model. The area under the ROC curve (AUC) estimated for the percent change in mean ADC was higher in the pembro cohort (0.73, 95% confidence interval [CI]: 0.52 to 0.93) than in the control cohort (0.63, 95% CI: 0.43 to 0.83). In the control cohort, the percent change of the 95th percentile ADC achieved the highest AUC, 0.69 (95% CI: 0.52 to 0.85). In the pembro cohort, the percent change of the 25th percentile ADC achieved the highest AUC, 0.75 (95% CI: 0.55 to 0.95). AUCs estimated for percent change of FTV were 0.61 (95% CI: 0.39 to 0.83) and 0.66 (95% CI: 0.47 to 0.85) for the pembro and control cohorts, respectively. Tumor ADC may perform better than FTV to predict pCR at an early treatment time-point during neoadjuvant immunotherapy.
39
Barella, Susanna, Ramon Simon-Lopez, Nicola Di Gaetano, and Renzo Galanello. "Beta Thalassemia Trait: How the New Information Provided by the Routine Hematology Analysers May Help in Its Differential Diagnosis or Flagging." Blood 120, no. 21 (November 16, 2012): 5186. http://dx.doi.org/10.1182/blood.v120.21.5186.5186.
Full textAbstract:
Abstract Abstract 5186 Introduction: Beta Thalassemia (β-thalassemia) is one of the more common hemoglobinopathies worldwide, being the heterozygous variant, called Beta Thalassemia Trait, a benign variant, but important to diagnose, for genetic counseling, trying to avoid the homozygous variant, called major. Diagnostic of Beta Thalassemia Trait: Classic testing for β-thalassemia includes: hematologic testing of red blood cell indices, peripheral blood smear (prewsence of target cells and RBC with basophilic stippling, etc.), and qualitative and quantitative hemoglobin analysis. Have been proposed too Discriminant functions, like the one published many years ago, by England and Fraser. Objective: Recently have been developed new parameters and information in the new automated hematology analyzer called DxH8008™ from Beckman Coulter as @MSCV, @RSF, @MAF, @ LHD% and many morphological parameters for RBC and Reticulocytes calles Cell Population Data. All this parameters may be used to create flagging for laboratory use only (LUO) or Research use only (RUO). The purpose of this study is to investigate the possible use or utility of this new information for the screening/flagging of Beta Thalassemia Trait. Patient and Methods: We have collected 30 patients with Beta Thalassemia Trait. All of them were confirmed by red cell morphology, Hgb Electroforesis, cromatography in liquid phase in human whole blood for the determination of Hemoglobin A2, F, A1c, and identification of abnormal hemoglobins and DNA analysis (DNA Analysis by GAP-PCR). We have compared these patients with a control group (184 individuals) and with other anemias (see Table 1). Results: Using ROC analysis, the best parameters differentiating the Beta Thalassemia Trait from the normals were: MCV (AUC 1. 000), MRV (AUC 0. 999), @MAF(AUC 0. 999), @MCNRET (AUC 0. 997), RDW (AUC 0. 957), HGB (AUC 0. 915), RBC(AUC 0. 912). Using ROC analysis, the best parameters differentiating the Beta Thalassemia Trait from other anemias (excluding normals) were: RDW-SD (AUC 0. 937), DF Eng-Fra (AUC 0. 779), RDW (AUC 0. 766), RBC (AUC 0. 734) Disclosures: Simon-Lopez: Beckman Coulter: @LHD, @MAF, @RSF, @LHD, @MAF, @RSF Patents & Royalties, Employment. Di Gaetano:Instrumentation Laboratory spa: Work for a distributor of Beckman Coulter Instruments in Italy Other. Galanello:Ferrokin: Research Funding; Apopharma: Research Funding, Speakers Bureau; Novartis: Research Funding, Speakers Bureau.
40
Won Park, Yae, Sung Soo Ahn, Dongmin Choi, and Hwiyoung Kim. "CMET-04. RADIOMICS FEATURES CAN DIFFERENTIATE THE EGFR MUTATION STATUS OF BRAIN METASTASES FROM NON-SMALL CELL LUNG CANCER." Neuro-Oncology 21, Supplement_6 (November 2019): vi51. http://dx.doi.org/10.1093/neuonc/noz175.205.
Full textAbstract:
Abstract BACKGROUND AND PURPOSE To assess whether radiomics features on DTI and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) molecular status in brain metastases from non-small cell lung cancer (NSCLC). MATERIALS AND METHODS Radiomics features (n = 5046) were extracted from preoperative MRI including T1C and DTI from pathologically confirmed brain metastases of 59 patients with underlying NSCLC and known EGFR mutation status (31 EGFR wild type, 28 EGFR mutant). A subset of 4317 features (85.6%) with high stability (intraclass correlation coefficient > 0.9) were selected for further analysis. After feature selection by the least absolute shrinkage and selection operator, the radiomics classifiers were constructed by various machine learning algorithms. The prediction performance of the classifier was validated by using leave-one-out cross-validation. Diagnostic performance was compared between multiparametric MRI radiomics models and single imaging radiomics models using the area under the curve (AUC) from ROC analysis. RESULTS Thirty-seven significant radiomics features (6 from ADC, 6 from fractional anisotropy [FA], 25 from T1C) were selected. The best performing multiparametric radiomics model (AUC 0.97, 95% CI 0.94–1) showed better performance than any single radiomics model using ADC (AUC 0.79, p = 0.007), FA (AUC 0.75, p = 0.001), or T1C (AUC 0.96, p = 0.678); the accuracy, sensitivity, and specificity of this model were 94.4%, 96.6%, and 92.0%, respectively. CONCLUSION Radiomics classifiers integrating multiparametric MRI parameters may be useful to differentiate the EGFR mutation status in brain metastases from lung cancer.
41
Martín-Montero, Adrián, Gonzalo C. Gutiérrez-Tobal, David Gozal, Verónica Barroso-García, Daniel Álvarez, Félix del Campo, Leila Kheirandish-Gozal, and Roberto Hornero. "Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea." Entropy 23, no. 8 (August 6, 2021): 1016. http://dx.doi.org/10.3390/e23081016.
Full textAbstract:
Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0–13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0–0.04 Hz; low frequency: 0.04–0.15 Hz; and high frequency: 0.15–0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001–0.005 Hz; BW2: 0.028–0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.
42
Anđelić, Nikola, Sandi Baressi Šegota, Matko Glučina, and Ivan Lorencin. "Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier." Machines 11, no. 1 (January 12, 2023): 105. http://dx.doi.org/10.3390/machines11010105.
Full textAbstract:
The navigation of mobile robots throughout the surrounding environment without collisions is one of the mandatory behaviors in the field of mobile robotics. The movement of the robot through its surrounding environment is achieved using sensors and a control system. The application of artificial intelligence could potentially predict the possible movement of a mobile robot if a robot encounters potential obstacles. The data used in this paper is obtained from a wall-following robot that navigates through the room following the wall in a clockwise direction with the use of 24 ultrasound sensors. The idea of this paper is to apply genetic programming symbolic classifier (GPSC) with random hyperparameter search and 5-fold cross-validation to investigate if these methods could classify the movement in the correct category (move forward, slight right turn, sharp right turn, and slight left turn) with high accuracy. Since the original dataset is imbalanced, oversampling methods (ADASYN, SMOTE, and BorderlineSMOTE) were applied to achieve the balance between class samples. These over-sampled dataset variations were used to train the GPSC algorithm with a random hyperparameter search and 5-fold cross-validation. The mean and standard deviation of accuracy (ACC), the area under the receiver operating characteristic (AUC), precision, recall, and F1−score values were used to measure the classification performance of the obtained symbolic expressions. The investigation showed that the best symbolic expressions were obtained on a dataset balanced with the BorderlineSMOTE method with ACC¯±SD(ACC), AUC¯macro±SD(AUC), Precision¯macro±SD(Precision), Recall¯macro±SD(Recall), and F1−score¯macro±SD(F1−score) equal to 0.975×1.81×10−3, 0.997±6.37×10−4, 0.975±1.82×10−3, 0.976±1.59×10−3, and 0.9785±1.74×10−3, respectively. The final test was to use the set of best symbolic expressions and apply them to the original dataset. In this case the ACC¯±SD(ACC), AUC¯±SD(AUC), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1−score¯±SD(F1−Score) are equal to 0.956±0.05, 0.9536±0.057, 0.9507±0.0275, 0.9809±0.01, 0.9698±0.00725, respectively. The results of the investigation showed that this simple, non-linearly separable classification task could be solved using the GPSC algorithm with high accuracy.
43
Barella, Susanna, Ramon Simon-Lopez, Nicola Di Gaetano, and Renzo Galanello. "Alfa Thalassemia Intermedia (HbH disease): How the New Information Provided by the Routine Hematology Analysers May Help in Its Differential Diagnosis or Flagging." Blood 120, no. 21 (November 16, 2012): 5180. http://dx.doi.org/10.1182/blood.v120.21.5180.5180.
Full textAbstract:
Abstract Abstract 5180 Alpha-thalassemia (α-thalassemia) has two clinically significant forms: hemoglobin Bart hydrops fetalis (Hb Bart) syndrome and hemoglobin H (HbH) disease. HbH disease is characterized by microcytic hypochromic hemolytic anemia, hepatosplenomegaly, mild jaundice, and sometimes thalassemia-like bone changes. Diagnostic of Alfa Thalassemia: Classic testing for α-thalassemia includes: hematologic testing of red blood cell indices, peripheral blood smear, supravital stain to detect RBC inclusion bodies, and qualitative and quantitative hemoglobin analysis. HBA1, the gene encoding α1-globin, and HBA2, the gene encoding α2-globin, are the two genes most commonly associated with α-thalassemia. Molecular genetic testing of HBA1 and HBA2 detects deletions in about 90% and point mutations in about 10% of affected individuals. Objective: Recently have been developed new parameters and information in the new automated hematology analyzer called DxH8008™ from Beckman Coulter as @MSCV, @RSF, @MAF, @LHD% and many morphological parameters for RBC and Reticulocytes calles Cell Population Data. All this parameters may be used to create flagging for laboratory use only (LUO) or Research use only (RUO). The purpose of this study is to investigate the possible use or utility of this new information for the screening/flagging of Alfa Thalassemia. Patient and Methods: We have collected 129 patients with Alfa Thalassemia Intermedia (HbH disease). All of them were confirmed by red cell morphology, Hgb Electroforesis, cromatography in liquid phase in human whole blood for the determination of Hemoglobin A2, F, A1c, and identification of abnormal hemoglobins and DNA analysis (DNA Analysis by GAP-PCR). We have compared these patients with a control group (184 individuals) and with other anemias (see Table 1). Results: Using ROC analysis, the best parameters differentiating the HbH Disease from the normals were: RDW (AUC 1. 000), @LHD(AUC 1. 000), @MAF(AUC 1. 000), @MCNRET (AUC 1. 000), MCV (AUC 0. 999), @MCRET (AUC 0. 999), @RSF (AUC 0. 998), HGB (AUC 0. 996), @MSCV (AUC 0. 995). Using ROC analysis, the best parameters differentiating the HbH Disease from other anemias (excluding normals) were: @LHD(AUC 0. 957), @MCNRET (AUC 0. 946), @MCRET (AUC 0. 902), @MAF(AUC 0. 873), MCV (AUC 0. 869). Using logistic regression we found a discrminant function that permits to differentiate/flag perfectly the patients with HbH disease from other anemias, and of course from normals: AUC 0. 996) Sensitivity: 91. 47% Specificity 94. 68% with a percent of cases correctly classified of: 93. 67 %. Disclosures: Simon-Lopez: Beckman Coulter: @LHD, @MAF, @RSF, @LHD, @MAF, @RSF Patents & Royalties, Employment. Di Gaetano:Instrumentation Laboratory spa: Work for a distributor of Beckman Coulter Instruments in Italy Other. Galanello:Novartis: Research Funding, Speakers Bureau; Apopharma: Research Funding, Speakers Bureau; Ferrokin: Research Funding.
44
Khodadadi, Babak, Nazanin Mousavi, Mahshad Mousavi, Parastoo Baharvand, and Seyyed Amir Yasin Ahmadi. "Diagnosis and predictive clinical and para-clinical cutoffs for diabetes complications in Lur and Lak populations of Iran; a ROC curve analysis to design a regional guideline." Journal of Nephropharmacology 7, no. 2 (August 17, 2018): 83–89. http://dx.doi.org/10.15171/npj.2018.19.
Full textAbstract:
Introduction: American Diabetes Association updates its guideline every year. However this guideline can be changed for different populations based on their cultural and genetic status. Objectives: We intend to design a regional study in Lur and Lak populations of Iran using receiver operating characteristics (ROC) curve model. Patients and Methods: A total of 133 diabetes mellitus (DM) patients were enrolled in this study. The collected information for each patient were gender, age, body mass index (BMI), DM type, DM duration, fasting blood sugar (FBS), hemoglobin A1c (HbA1c), lipid profile, type of treatments, type of statin and dose, documented neuropathy, documented nephropathy, symptomatic retinopathy, peripheral vessel disease (PVD), documented cardiovascular disease (CVD), food ulcer history, dawn effect, systolic blood pressure (SBP), and diastolic blood pressure (DBP). ROC curve was used and area under curve (AUC) was reported. Results: For neuropathy, age was the most accurate diagnostic index (area under curve [AUC] = 79%). For nephropathy SBP was the most accurate diagnostic index (AUC= 88%). For symptomatic retinopathy DM duration was the most accurate diagnostic index (AUC= 81%). For PVD, HDL-C was the most accurate diagnostic index (reverse AUC= 67%). For CVD age was the most accurate diagnostic index (AUC= 81%). For foot ulcer history age was the most accurate diagnostic index (AUC= 85%). Conclusion: The final suggested guideline is like the international guidelines. However some unique points should be regarded. Blood pressure >165/110 mm Hg is diagnostic of diabetic nephropathy. Additionally serum high-density lipoprotein (HDL-C) >48 mg/dL is strongly suggested.
45
Chen, Sixuan, Yue Xu, Meiping Ye, Yang Li, Yu Sun, Jiawei Liang, Jiaming Lu, et al. "Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics." Journal of Clinical Medicine 11, no. 12 (June 15, 2022): 3445. http://dx.doi.org/10.3390/jcm11123445.
Full textAbstract:
This study aimed to investigate the feasibility of predicting oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in diffuse gliomas by developing a deep learning approach using MRI radiomics. A total of 111 patients with diffuse gliomas participated in the retrospective study (56 patients with MGMT promoter methylation and 55 patients with MGMT promoter unmethylation). The radiomics features of the two regions of interest (ROI) (the whole tumor area and the tumor core area) for four sequences, including T1 weighted image (T1WI), T2 weighted image (T2WI), apparent diffusion coefficient (ADC) maps, and T1 contrast-enhanced (T1CE) MR images were extracted and jointly fed into the residual network. Then the deep learning method was developed and evaluated with a five-fold cross-validation, where in each fold, the dataset was randomly divided into training (80%) and validation (20%) cohorts. We compared the performance of all models using area under the curve (AUC) and average accuracy of validation cohorts and calculated the 10 most important features of the best model via a class activation map. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0.85. Based on the ROI of the tumor core, the T1CE and ADC model achieved the highest AUC value of 0.90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core exhibited the best performance, with the highest average accuracy (0.91) and AUC (0.90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas.
46
Zhou, Wei-Neng, Yan-Min Zhang, Xin Qiao, Jing Pan, Ling-Feng Yin, Lu Zhu, Jun-Nan Zhao, et al. "Virtual Screening Strategy Combined Bayesian Classification Model, Molecular Docking for Acetyl-CoA Carboxylases Inhibitors." Current Computer-Aided Drug Design 15, no. 3 (April 10, 2019): 193–205. http://dx.doi.org/10.2174/1573409914666181109110030.
Full textAbstract:
Introduction: Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases. Methods: In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again. Results: Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library. Conclusion: In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.</P>
47
Anđelić, Nikola, Ivan Lorencin, Sandi Baressi Šegota, and Zlatan Car. "The Development of Symbolic Expressions for the Detection of Hepatitis C Patients and the Disease Progression from Blood Parameters Using Genetic Programming-Symbolic Classification Algorithm." Applied Sciences 13, no. 1 (December 31, 2022): 574. http://dx.doi.org/10.3390/app13010574.
Full textAbstract:
Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to detect HCV patients with high accuracy based on the enzymes, proteins, and biomarker values contained in a patient’s blood sample using genetic programming symbolic classification (GPSC) algorithm. Not only that, but the idea was also to obtain a mathematical equation that could detect the progress of the disease i.e., Hepatitis C, Fibrosis, and Cirrhosis using the GPSC algorithm. Since the original dataset was imbalanced (a large number of healthy patients versus a small number of Hepatitis C/Fibrosis/Cirrhosis patients) the dataset was balanced using random oversampling, SMOTE, ADSYN, and Borderline SMOTE methods. The symbolic expressions (mathematical equations) were obtained using the GPSC algorithm using a rigorous process of 5-fold cross-validation with a random hyperparameter search method which had to be developed for this problem. To evaluate each symbolic expression generated with GPSC the mean and standard deviation values of accuracy (ACC), the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score were obtained. In a simple binary case (healthy vs. Hepatitis C patients) the best case was achieved with a dataset balanced with the Borderline SMOTE method. The results are ACC¯±SD(ACC), AUC¯±SD(AUC), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1−score¯±SD(F1−score) equal to 0.99±5.8×10−3, 0.99±5.4×10−3, 0.998±1.3×10−3, 0.98±1.19×10−3, and 0.99±5.39×10−3, respectively. For the multiclass problem, OneVsRestClassifer was used in combination with GPSC 5-fold cross-validation and random hyperparameter search, and the best case was achieved with a dataset balanced with the Borderline SMOTE method. To evaluate symbolic expressions obtained in this case previous evaluation metric methods were used however for AUC, Precision, Recall, and F1−score the macro values were computed since this method calculates metrics for each label, and find their unweighted mean value. In multiclass case the ACC¯±SD(ACC), AUC¯macro±SD(AUC), Precision¯macro±SD(Precision), Recall¯macro±SD(Recall), and F1−score¯macro±SD(F1−score) are equal to 0.934±9×10−3, 0.987±1.8×10−3, 0.942±6.9×10−3, 0.934±7.84×10−3 and 0.932±8.4×10−3, respectively. For the best binary and multi-class cases, the symbolic expressions are shown and evaluated on the original dataset.
48
Dong, Xueyan, Qiang Hou, Yueming Chen, and Xianjun Wang. "Diagnostic Value of the Methylation of Multiple Gene Promoters in Serum in Hepatitis B Virus-Related Hepatocellular Carcinoma." Disease Markers 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/2929381.
Full textAbstract:
This study sought to evaluate the diagnostic value of the methylation of multiple gene promoters in serum in hepatitis B virus- (HBV-) related hepatocellular carcinoma (HCC). A total of 343 participants were enrolled, including 98 patients with HCC, 75 patients with liver cirrhosis (LC), 90 patients with chronic hepatitis B (CHB), and 80 healthy individuals. RASSF1A, APC, BVES, TIMP3, GSTP1, and HOXA9 were selected as the candidate genes. The MethyLight method was used to assay promoter methylation statuses. The diagnostic performances of markers were assessed by constructing receiver operating characteristic (ROC) curves. The prevalences of methylation for RASSF1A, APC, BVES, HOXA9, GSTP1, and TIMP3 were 52.04%, 36.73%, 29.59%, 20.41%, 17.35%, and 11.22%, respectively. APC methylation completely overlapped with RASSF1A methylation. The area under the curve (AUC) for RASSF1A methylation (0.718) was better than the corresponding AUC for AFP (0.609) in distinguishing HCC from CHB. When RASSF1A, BVES, HOXA9, and AFP were combined, the AUC was 0.852 (95% CI = 0.796–0.908, P=0.028), and the sensitivity and specificity were 83.7% and 78.9%, respectively. In conclusion, an assay that combines methylation of the RASSF1A, BVES, and HOXA9 gene promoters in serum and AFP could significantly improve HBV-related HCC diagnoses.
49
Rengo, Marco, Alessandro Onori, Damiano Caruso, Davide Bellini, Francesco Carbonetti, Domenico De Santis, Simone Vicini, et al. "Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors." Journal of Personalized Medicine 13, no. 5 (April 24, 2023): 717. http://dx.doi.org/10.3390/jpm13050717.
Full textAbstract:
Background: preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST’s prognosis as determined by the Miettinen classification. Methods: patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated. Results: 52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations. Conclusions: the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs.
50
Flynn, Sinéad, Seán Millar, Claire Buckley, Kate Junker, Catherine Phillips, and Janas Harrington. "Comparing non-invasive diabetes risk scores for detecting patients in clinical practice: a cross-sectional validation study." HRB Open Research 4 (July 7, 2021): 70. http://dx.doi.org/10.12688/hrbopenres.13254.1.
Full textAbstract:
Background: Type 2 diabetes (T2DM) is a significant cause of morbidity and mortality, thus early identification is of paramount importance. A high proportion of T2DM cases are undiagnosed highlighting the importance of effective detection methods such as non-invasive diabetes risk scores (DRSs). Thus far, no DRS has been validated in an Irish population. Therefore, the aim of this study was to compare the ability of nine DRSs to detect T2DM cases in an Irish population. Methods: This was a cross-sectional study of 1,990 men and women aged 46–73 years. Data on DRS components were collected from questionnaires and clinical examinations. T2DM was determined according to a fasting plasma glucose level ≥7.0 mmol/l or a glycated haemoglobin A1c level ≥6.5% (≥48 mmol/mol). Receiver operating characteristic curve analysis assessed the ability of DRSs and their components to discriminate T2DM cases. Results: Among the examined scores, area under the curve (AUC) values ranged from 0.71–0.78, with the Cambridge Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Leicester Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Rotterdam Predictive Model 2 (AUC=0.78, 95% CI: 0.74–0.82) and the U.S. Diabetes Risk Score (AUC=0.78, 95% CI: 0.74–0.81) demonstrating the largest AUC values as continuous variables and at optimal cut-offs. Regarding individual DRS components, anthropometric measures displayed the largest AUC values. Conclusions: The best performing DRSs were broadly similar in terms of their components; all incorporated variables for age, sex, BMI, hypertension and family diabetes history. The Cambridge Diabetes Risk Score, had the largest AUC value at an optimal cut-off, can be easily accessed online for use in a clinical setting and may be the most appropriate and cost-effective method for case-finding in an Irish population.