Journal articles on the topic 'Prediction Of Malignant'
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Sukanya L. "Risk of malignancy index (RMI) for prediction of malignancy in women with adnexal masses." International Journal of Research in Pharmaceutical Sciences 13, no. 3 (September 26, 2022): 339–42. http://dx.doi.org/10.26452/ijrps.v13i3.2733.
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Ovarian cancer is predominantly cancer in the perimenopausal and post- menopausal age group. A definitive biomarker has not been identified for malignant ovarian cancer and histopathology remains the diagnostic gold standard for this. Risk of Malignancy Index (RMI) in predicting malignant pelvic masses includes serum CA125 level, menopausal status, and ultrasonographic findings. The risk of malignancy index (RMI) was evaluated in the women presented with adnexal masses for its accuracy in predicting the malignancy. This was a retrospective study which included 120 women who presented with adnexal mass in a tertiary hospital. RMI scoring was done based on CA125 levels, ultrasound findings and postmenopausal status and RMI was correlated with the histopathological findings. Out of 120 subjects, 74.1% of subjects were proved to have malignant tumors. RMI in predicting malignancy showed a sensitivity of 88.76%, a specificity of 45.37%, a positive predictive value of 81.63%, a negative predictive value of 66.67% and an accuracy of RMI found to be 82.5%. The RMI is found to be a simple, cost-effective and reliable tool in predicting malignancy in women presenting with adnexal masses that helps in timely referral to a gynaecological oncology center for better management and survival. RMI scoring can be used as it is a better tool for analysing multiple parameters of the tumour.
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Wang, Xiuchao, Junjin Wang, Xi Wei, Lihui Zhao, Bo Ni, Zekun Li, Chuntao Gao, et al. "Preoperative ultrasound combined with routine blood tests in predicting the malignant risk of pancreatic cystic neoplasms." Cancer Biology & Medicine 19, no. 10 (November 1, 2022): 1503–16. http://dx.doi.org/10.20892/j.issn.2095-3941.2022.0258.
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Objective: Accurate preoperative identification of benign or malignant pancreatic cystic neoplasms (PCN) may help clinicians make better intervention choices and will be essential for individualized treatment. Methods: Preoperative ultrasound and laboratory examination findings, and demographic characteristics were collected from patients. Multiple logistic regression was used to identify independent risk factors associated with malignant PCN, which were then included in the nomogram and validated with an external cohort. The Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were calculated to evaluate the improvement in the predictive power of the new model with respect to that of a combined imaging and tumor marker prediction model. Results: Malignant PCN were found in 83 (40.7%) and 33 (38.7%) of the model and validation cohorts, respectively. Multivariate analysis identified age, tumor location, imaging of tumor boundary, blood type, mean hemoglobin concentration, neutrophil-to-lymphocyte ratio, carbohydrate antigen 19-9, and carcinoembryonic antigen as independent risk factors for malignant PCN. The calibration curve indicated that the predictions based on the nomogram were in excellent agreement with the actual observations. A nomogram score cutoff of 192.5 classified patients as having low vs. high risk of malignant PCN. The model achieved good C-statistics of 0.929 (95% CI 0.890–0.968, P < 0.05) and 0.951 (95% CI 0.903–0.998, P < 0.05) in predicting malignancy in the development and validation cohorts, respectively. NRI = 0.268; IDI = 0.271 (P < 0.001 for improvement). The DCA curve indicated that our model yielded greater clinical benefits than the comparator model. Conclusions: The nomogram showed excellent performance in predicting malignant PCN and may help surgeons select patients for detailed examination and surgery. The nomogram is freely available at https://wangjunjinnomogram.shinyapps.io/DynNomapp/.
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Li, Tingting, Yanjie Li, Yingqi Yang, Juan Li, ZiYue Hu, Lu Wang, Wei Pu, Ting Wei, and Man Lu. "Logistic regression analysis of ultrasound findings in predicting the malignant and benign phyllodes tumor of breast." PLOS ONE 17, no. 3 (March 24, 2022): e0265952. http://dx.doi.org/10.1371/journal.pone.0265952.
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Objective To evaluate ultrasound characteristics in the prediction of malignant and benign phyllodes tumor of the breast (PTB) by using Logistic regression analysis. Methods 79 lesions diagnosed as PTB by pathology were analyzed retrospectively. The ultrasound features of PTB were recorded and compared between benign and malignant tumors by using single factor and multiple stepwise Logistic regression analysis. Moreover, the Logistic regression model for malignancy prediction was also established. Results There were 79 patients with PTB, including 39 benign PTBs and 40 malignant PTBs (33 borderline PTBs and 7 malignant PTBs by pathologic classification). The area under the ROC curve (AUC) of lesion size and age were 0.737 and 0.850 respectively. There were significant differences in age, lesion size, shape, internal echo, liquefaction, and blood flow between malignant and benign PTBs by using single-factor analysis (P<0.05). Age, internal echo, and liquefaction were significant features by using Logistic regression analysis. The corresponding regression equation In (p/(1 − p) = -3.676+2.919 internal echo +3.029 liquefaction +4.346 age). Conclusion Internal echo, age, and liquefaction are independent ultrasound characteristics in predicting the malignancy of PTBs.
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Swan, Kristine Zøylner, Steen Joop Bonnema, Marie Louise Jespersen, and Viveque Egsgaard Nielsen. "Reappraisal of shear wave elastography as a diagnostic tool for identifying thyroid carcinoma." Endocrine Connections 8, no. 8 (August 2019): 1195–205. http://dx.doi.org/10.1530/ec-19-0324.
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Thyroid nodular disease is common, but predicting the risk of malignancy can be difficult. In this prospective study, we aimed to assess the diagnostic accuracy of shear wave elastography (SWE) in predicting thyroid malignancy. Patients with thyroid nodules were enrolled from a surgical tertiary unit. Elasticity index (EI) measured by SWE was registered for seven EI outcomes assessing nodular stiffness and heterogeneity. The diagnosis was determined histologically. In total, 329 patients (mean age: 55 ± 13 years) with 413 thyroid nodules (mean size: 32 ± 13 mm, 88 malignant) were enrolled. Values of SWE region of interest (ROI) for malignant and benign nodules were highly overlapping (ranges for SWE-ROImean: malignant 3–100 kPa; benign 4–182 kPa), and no difference between malignant and benign nodules was found for any other EI outcome investigated (P = 0.13–0.96). There was no association between EI and the histological diagnosis by receiver operating characteristics analysis (area under the curve: 0.51–0.56). Consequently, defining a cut-off point of EI for the prediction of malignancy was not clinically meaningful. Testing our data on previously proposed cut-off points revealed a low accuracy of SWE (56–80%). By regression analysis, factors affecting EI included nodule size >30 mm, heterogeneous echogenicity, micro- or macrocalcifications and solitary nodule. In conclusion, EI, measured by SWE, showed huge overlap between malignant and benign nodules, and low diagnostic accuracy in the prediction of thyroid malignancy. Our study supports that firmness of thyroid nodules, as assessed by SWE, should not be a key feature in the evaluation of such lesions.
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Carlsson, Leo S., Mikael Vejdemo-Johansson, Gunnar Carlsson, and Pär G. Jönsson. "Fibers of Failure: Classifying Errors in Predictive Processes." Algorithms 13, no. 6 (June 23, 2020): 150. http://dx.doi.org/10.3390/a13060150.
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Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FiFa), a method to classify failure modes of predictive processes. Our method uses Mapper, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode. We demonstrate FiFa on two scenarios: a convolutional neural network (CNN) predicting MNIST images with added noise, and an artificial neural network (ANN) predicting the electrical energy consumption of an electric arc furnace (EAF). The correction layer on the CNN model improved its prediction accuracy significantly while the inspection of failure modes for the EAF model provided guiding insights into the domain-specific reasons behind several high-error regions.
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Kaseb, Hatem, Ahmad Charifa, Rita Abi-Raad, Guoping Cai, Lynwood Hammers, Manju Prasad, and Adebowale Adeniran. "Concordance Between the TIRADS Ultrasound Scoring Criteria, Fine-Needle Aspiration Cytology, and Thyroid Final Resection Diagnosis." American Journal of Clinical Pathology 152, Supplement_1 (September 11, 2019): S92. http://dx.doi.org/10.1093/ajcp/aqz118.001.
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Abstract Objectives Thyroid imaging reporting and data system (TIRADS) criteria were recently introduced in our institution to aid in predicting diagnosis for various thyroid lesions. We evaluated the association of TIRADS imaging score and fine-needle aspiration (FNA) cytology with thyroid lesions that had a confirmed diagnosis at resection, with a focus on understanding the predictability of this diagnostic tool in malignancy prediction. Methods We assessed the concordance of TIRADS criteria and FNA diagnosis to the final anatomical diagnosis in the assessment of thyroid lesions. We retrieved the cases from the archives of the Yale pathology department between June 2017 and January 2018. Our inclusion criteria included patients who had a TIRADS score, cytology diagnosis, and final surgical resection diagnosis. A total of 65 subjects with an age range of 11 to 88 years were identified. Results The majority of the patients were females, 65% (42/65). Cases with TIRADS score 1-2 (likely benign) and Bethesda I/II nondiagnostic/benign were few since most of these cases did not go for surgical resection. The mildly suspicious TIRAD score 3 and FLUS category showed similar trends, 68% and 67%, respectively, in predicting malignant lesions. The TIRADS score 4 when compared to cytology (IV)/(V) demonstrated similar consistent results in malignancy prediction, both being high at 89% and 87%, respectively. The TIRADS score 5 demonstrated a 95% malignancy prediction. The overall sensitivity and specificity of TIRADS score in our cohort were 66% and 77%, respectively. The positive and negative predictive values of TIRADS score in our cohort were 89% and 39%, respectively. In comparison, the overall sensitivity and specificity of cytology assessment in our cohort were 91% and 44%, respectively. The positive and negative predictive values of cytology assessment in our cohort were 85% and 57%, respectively. Conclusion Our results demonstrated that both cytology and TIRAD score had similar trends in malignancy prediction. Cytological assessment had higher sensitivity but lower specificity compared to TIRADS score. While both techniques showed concordant high predictability of malignant lesions (approximately 91%), the use of both modalities adjunctively will be very useful in triaging cases for surgery. Overall, utilizing TIRADS score with cytology will help reduce the risk of unnecessary invasive procedures in patients with a low probability of malignant thyroid disease.
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Carter, J. R., J. M. Fowler, J. W. Carlson, L. F. Carson, L. L. Adcock, and L. B. Twiggs. "Prediction of malignancy using transvaginal color flow Doppler in patients with gynecologic tumors." International Journal of Gynecologic Cancer 3, no. 5 (1993): 279–84. http://dx.doi.org/10.1046/j.1525-1438.1993.03050279.x.
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Eighty-five patients referred to the Women's Cancer Center, University of Minnesota had transvaginal color flow Doppler performed to determine if pelvic malignancy could be predicted by blood flow assessment. Their mean age was 49 years (range 21–86 years). Thirty-five patients were subsequently found to have malignant tumors of the cervix, uterus or ovary. The presence of increased intratumoral blood flow as depicted by color flow Doppler had a sensitivity of 83%, specificity of 100%, positive predictive value (PPV) of 100% and negative predictive value (NPV) of 89% for malignancy. The mean intratumoral Pulsatility Index (PI) of the patients with malignant tumors was 0.81 (SD 0.24; range 0.3–1.2), which was significantly lower than for the benign group (P= 0.001). A PI of ≤ 1.0 had a sensitivity of 96.3%, specificity of 94.3%, PPV of 89.7% and NPV of 98% for predicting malignancy. Transvaginal color flow Doppler shows promise as a method of predicting malignancy in patients with gynecologic pathology.
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Ohno, Riki, Ryuichi Kawamoto, Mami Kanamoto, Jota Watanabe, Masahiko Fujii, Hiromi Ohtani, Masamitsu Harada, Teru Kumagi, and Hideki Kawasaki. "Neutrophil to Lymphocyte Ratio is a Predictive Factor of Malignant Potential for Intraductal Papillary Mucinous Neoplasms of the pancreas." Biomarker Insights 14 (January 2019): 117727191985150. http://dx.doi.org/10.1177/1177271919851505.
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Intraductal papillary mucinous neoplasms (IPMNs) are cystic neoplasms with the potential for progression to pancreatic cancer. Accurate prediction of the malignant potential is challenging and a proper treatment strategy has not been well established. Preoperative neutrophil-to-lymphocyte ratio (NLR) is a biomarker of the malignant potential in patients with several types of malignancy. We explored malignant potential in patients with IPMN. The present study included 56 patients aged of 73 ± 9 years (mean ± standard deviation) who underwent curative resection for IPMN from 1996 to 2017. We analyzed the relationship between the characteristics including NLR and malignant component for predicting pathological results. The nonmalignant IPMN group (N = 21) included patients with low-grade dysplasia (LGD) and intermediate-grade dysplasia (IGD), and the malignant IPMN group (N = 35) included patients with high-grade dysplasia (HGD) and invasive carcinoma. In a univariate analysis, NLR ⩾ 2.2 ( P = .001), prognostic nutritional index (PNI) < 45 ( P = .016), CA 19-9 > 37 U/mL ( P = .039), and cystic diameter ⩾ 30 mm ( P = .010), and mural nodule ( P = .010) were significantly different between the malignant IPMN and the nonmalignant IPMN groups. Multivariate analysis showed that high NLR (⩾2.2) (odds ratio 9.79; 95% confidence interval: 2.06-45.6), cystic diameter ⩾ 30 mm (4.65; 1.14-18.9), and mural nodule (4.91; 1.20-20.1) were independently predictive of malignant IPMN. These results suggest that preoperative NLR is a useful predictive biomarker for evaluating malignant potential in patients with IPMN.1
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Yamanaka, Shoichiro, Naoki Kawahara, Ryuji Kawaguchi, Keita Waki, Tomoka Maehana, Yosuke Fukui, Ryuta Miyake, Yuki Yamada, Hiroshi Kobayashi, and Fuminori Kimura. "The Comparison of Three Predictive Indexes to Discriminate Malignant Ovarian Tumors from Benign Ovarian Endometrioma: The Characteristics and Efficacy." Diagnostics 12, no. 5 (May 12, 2022): 1212. http://dx.doi.org/10.3390/diagnostics12051212.
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This study aimed to evaluate the prediction efficacy of malignant transformation of ovarian endometrioma (OE) using the Copenhagen Index (CPH-I), the risk of ovarian malignancy algorithm (ROMA), and the R2 predictive index. This retrospective study was conducted at the Department of Gynecology, Nara Medical University Hospital, from January 2008 to July 2021. A total of 171 patients were included in the study. In the current study, cases were divided into three cohorts: pre-menopausal, post-menopausal, and a combined cohort. Patients with benign ovarian tumor mainly received laparoscopic surgery, and patients with suspected malignant tumors underwent laparotomy. Information from a review chart of the patients’ medical records was collected. In the combined cohort, a multivariate analysis confirmed that the ROMA index, the R2 predictive index, and tumor laterality were extracted as independent factors for predicting malignant tumors (hazard ratio (HR): 222.14, 95% confidence interval (CI): 22.27–2215.50, p < 0.001; HR: 9.80, 95% CI: 2.90–33.13, p < 0.001; HR: 0.15, 95% CI: 0.03–0.75, p = 0.021, respectively). In the pre-menopausal cohort, a multivariate analysis confirmed that the CPH index and the R2 predictive index were extracted as independent factors for predicting malignant tumors (HR: 6.45, 95% CI: 1.47–28.22, p = 0.013; HR: 31.19, 95% CI: 8.48–114.74, p < 0.001, respectively). Moreover, the R2 predictive index was only extracted as an independent factor for predicting borderline tumors (HR: 45.00, 95% CI: 7.43–272.52, p < 0.001) in the combined cohort. In pre-menopausal cases or borderline cases, the R2 predictive index is useful; while, in post-menopausal cases, the ROMA index is better than the other indexes.
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Assegie, Tsehay Admassu, R. Lakshmi Tulasi, and N. Komal Kumar. "Breast cancer prediction model with decision tree and adaptive boosting." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 184. http://dx.doi.org/10.11591/ijai.v10.i1.pp184-190.
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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.
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Hebbar, Shripad, and Vijaya Bharathi K. "Validation of a new ovarian malignancy suspicion index for preoperative evaluation of adnexal masses." International Journal of Reproduction, Contraception, Obstetrics and Gynecology 6, no. 1 (December 20, 2016): 240. http://dx.doi.org/10.18203/2320-1770.ijrcog20164666.
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Background: The currently available ovarian malignancy probability scores incorporate biochemical markers such as CA 125 (Carbohydrate Antigen 125), which is not routinely available in peripheral centers. There is a need for tumour marker independent prediction model to differentiate malignant ovarian masses from their benign counterparts in order to plan appropriate surgery. To formulate and prospectively validate a new Ovarian Malignancy Suspicion Index (OMSI) independent of serum CA 125 level, in preoperative evaluation of adnexal masses admitted for surgery.Methods: This was a combined retrospective and prospective cohort study conducted in a tertiary referral hospital over a period of one and half years. Retrospective sample included 100 subjects who had undergone surgery for adnexal masses and who had definite histopathological report. Detailed data were obtained with respect to age, menopausal status, sonographic findings including solid areas, ascites, mean diameter, bilateralism, and presence of septa. A logistic multivariate regression analysis was carried out to find the best prediction score (OMSI - Ovarian Malignancy Suspicion Index). This model was further evaluated prospectively in 60 subjects for its diagnostic ability to identify benign and malignant ovarian pathology.Results: OMSI at the cut off value of 3.9 differentiated effectively malignant ovarian mass from benign variety with a good diagnostic performance (Sensitivity 100%, Specificity 90.5%, Positive Predictive Value 81.8% and Negative Predictive Value 100%) as good as currently recommended RMI (Risk Malignancy Index) score. It was also found that OMSI > 3.9 was associated with positive ultrasound evidence for ovarian malignancy such as presence of thick septae (90%), solid areas within the tumour (93.8%), papillary projections (100%), bilaterality (90%) and ascites (100%).Conclusions: This study shows that it is possible to derive ovarian malignancy prediction model such as OMSI without including CA 125 with diagnostic ability in par with risk scoring systems such as WHO recommended RMI. Using this model, physicians working in peripheral centers without facilities for estimating serum tumour markers can arrive at the possible diagnosis and plan appropriate management strategies.
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Sharma, Abha, Richa Sharma, and Ashita Gulati. "Comparison of ovarian crescent sign and risk of malignancy index for prediction of ovarian malignancy in adnexal masses." International Journal of Reproduction, Contraception, Obstetrics and Gynecology 10, no. 2 (January 28, 2021): 541. http://dx.doi.org/10.18203/2320-1770.ijrcog20210298.
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Background: Objective of the study was to evaluate ovarian crescent sign (OCS) as a sonographic parameter for prediction of ovarian cancer in adnexal masses suspicious of ovarian malignancy and to compare it with risk of malignancy index (RMI).Methods: Presence of OCS and calculation of RMI was done for 50 cases of adnexal masses scheduled to undergo surgery taking histopathology as gold standard.Results: 18% (9/50) of adnexal masses were malignant. OCS was absent in all malignant lesions, giving a sensitivity and negative predictive value of 100%. OCS was present in 33/41 of benign masses (specificity 80.4%). Relation of OCS to mass size<10 cm and menopausal status was significant (p<0.001). RMI≥200 could not diagnose malignancy in 4/9 cases (sensitivity 55.5%). RMI had specificity and negative predictive value of 95.1% and 90.7% respectively. Combining OCS and RMI had a lower specificity. Sequential application using OCS as first node and RMI as second node failed to diagnose 44.4% (4/9) cases as malignant.Conclusions: OCS is cheaper, easy to perform and appears to be a better test than RMI to differentiate between benign and early-stage malignant ovarian tumors. It can be used for triaging patient for referral.
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Heřman, Jan, Zuzana Sedláčková, Tomáš Fürst, Jaromír Vachutka, Richard Salzman, Jaroslav Vomáčka, and Miroslav Heřman. "The Role of Ultrasound and Shear-Wave Elastography in Evaluation of Cervical Lymph Nodes." BioMed Research International 2019 (April 30, 2019): 1–6. http://dx.doi.org/10.1155/2019/4318251.
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Aim. To evaluate the prognostic value of ultrasound and shear-wave elastography (SWE) in diagnosing malignant cervical lymph nodes. Methods. A total of 99 patients with enlarged lymph nodes (99 lymph nodes presenting as a neck mass) were examined clinically with conventional ultrasound including Doppler examination and shear-wave elastography. The results of the examinations were compared with the final diagnosis. Results. There were 43 benign and 56 malignant lymph nodes in our cohort. Age and sex were significant predictors of malignancy. The standard ultrasound parameters—node size, long/short axis ratio, hilum, vascularization, and the presence of microcalcifications—were also statistically significant. Lymph node volume combined with age showed the best predictive power. The maximum stiffness found on SWE was also a significant predictor of malignancy. The combination of epidemiologic, classic ultrasound, and elastographic parameters yielded the highest sensitivity and specificity in the prediction of malignancy; however, the additional impact of elastographic parameters was low. Conclusion. A combination of epidemiologic and classic ultrasound parameters can discriminate between malignant and benign lymph nodes with satisfactory sensitivity and specificity. Examining the stiffness of lymph nodes by means of SWE does not add much new predictive power.
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Fujibuchi, Taketsugu, Joji Miyawaki, Teruki Kidani, Hiroshi Imai, and Hiromasa Miura. "Prediction of Soft Tissue Sarcoma from Clinical Characteristics and Laboratory Data." Cancers 12, no. 3 (March 13, 2020): 679. http://dx.doi.org/10.3390/cancers12030679.
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The accurate diagnosis of soft tissue tumors may be difficult. Simple clinical characteristics or laboratory data that can predict tumor malignancy can be useful tools for diagnosing soft tissue tumors. Between 2003 and 2018, 588 patients with primary soft tissue tumors were retrospectively reviewed. Their clinical characteristics and laboratory data were evaluated to determine their association with the diagnosis of benign, intermediate, or malignant tumor. Multivariable analysis revealed that tumor size ≥ 5.6 cm (odds ratio (OR), 6.15; p < 0.001), white blood cell (WBC) count ≥ 5700/µL (OR, 2.49; p = 0.002), hemoglobin (Hb) count ≤ 12.4 g/dL (OR, 2.56; p = 0.004), C-reactive protein (CRP) level ≥ 0.17 mg/dL (OR, 2.64; p < 0.001), and lactate dehydrogenase (LDH) level ≥ 240 IU/L (OR, 4.94; p < 0.001) were significant predictive factors for sarcoma. The sensitivity and specificity in the presence of three or more predictive factors for detecting malignant tumors were 0.58 and 0.90 respectively, and it was an appropriate threshold with the maximum Youden’s index of 0.49. Simple clinical and laboratory data were useful tools for predicting whether the tumor is malignant. Patients with soft tissue tumors that meet any three or more predictive factors should be referred to a specialist.
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Bianconi, Francesco, Isabella Palumbo, Mario Luca Fravolini, Maria Rondini, Matteo Minestrini, Giulia Pascoletti, Susanna Nuvoli, et al. "Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans." Sensors 22, no. 13 (July 4, 2022): 5044. http://dx.doi.org/10.3390/s22135044.
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Indeterminate lung nodules detected on CT scans are common findings in clinical practice. Their correct assessment is critical, as early diagnosis of malignancy is crucial to maximise the treatment outcome. In this work, we evaluated the role of form factors as imaging biomarkers to differentiate benign vs. malignant lung lesions on CT scans. We tested a total of three conventional imaging features, six form factors, and two shape features for significant differences between benign and malignant lung lesions on CT scans. The study population consisted of 192 lung nodules from two independent datasets, containing 109 (38 benign, 71 malignant) and 83 (42 benign, 41 malignant) lung lesions, respectively. The standard of reference was either histological evaluation or stability on radiological followup. The statistical significance was determined via the Mann–Whitney U nonparametric test, and the ability of the form factors to discriminate a benign vs. a malignant lesion was assessed through multivariate prediction models based on Support Vector Machines. The univariate analysis returned four form factors (Angelidakis compactness and flatness, Kong flatness, and maximum projection sphericity) that were significantly different between the benign and malignant group in both datasets. In particular, we found that the benign lesions were on average flatter than the malignant ones; conversely, the malignant ones were on average more compact (isotropic) than the benign ones. The multivariate prediction models showed that adding form factors to conventional imaging features improved the prediction accuracy by up to 14.5 pp. We conclude that form factors evaluated on lung nodules on CT scans can improve the differential diagnosis between benign and malignant lesions.
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Wu, Simiao, Ruozhen Yuan, Yanan Wang, Chenchen Wei, Shihong Zhang, Xiaoyan Yang, Bo Wu, and Ming Liu. "Early Prediction of Malignant Brain Edema After Ischemic Stroke." Stroke 49, no. 12 (December 2018): 2918–27. http://dx.doi.org/10.1161/strokeaha.118.022001.
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Background and Purpose— Malignant brain edema after ischemic stroke has high mortality but limited treatment. Therefore, early prediction is important, and we systematically reviewed predictors and predictive models to identify reliable markers for the development of malignant edema. Methods— We searched Medline and Embase from inception to March 2018 and included studies assessing predictors or predictive models for malignant brain edema after ischemic stroke. Study quality was assessed by a 17-item tool. Odds ratios, mean differences, or standardized mean differences were pooled in random-effects modeling. Predictive models were descriptively analyzed. Results— We included 38 studies (3278 patients) with 24 clinical factors, 7 domains of imaging markers, 13 serum biomarkers, and 4 models. Generally, the included studies were small and showed potential publication bias. Malignant edema was associated with younger age (n=2075; mean difference, −4.42; 95% CI, −6.63 to −2.22), higher admission National Institutes of Health Stroke Scale scores (n=807, median 17–20 versus 5.5–15), and parenchymal hypoattenuation >50% of the middle cerebral artery territory on initial computed tomography (n=420; odds ratio, 5.33; 95% CI, 2.93–9.68). Revascularization (n=1600, odds ratio, 0.37; 95% CI, 0.24–0.57) were associated with a lower risk for malignant edema. Four predictive models all showed an overall C statistic >0.70, with a risk of overfitting. Conclusions— Younger age, higher National Institutes of Health Stroke Scale, and larger parenchymal hypoattenuation on computed tomography are reliable early predictors for malignant edema. Revascularization reduces the risk of malignant edema. Future studies with robust design are needed to explore optimal cutoff age and National Institutes of Health Stroke Scale scores and to validate and improve existing models.
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Shin, Kyung-Hwa, Hyung-Hoi Kim, Hyung Joon Yoon, Eun Taeg Kim, Dong Soo Suh, and Ki Hyung Kim. "The Discrepancy between Preoperative Tumor Markers and Imaging Outcomes in Predicting Ovarian Malignancy." Cancers 14, no. 23 (November 25, 2022): 5821. http://dx.doi.org/10.3390/cancers14235821.
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Preoperative tumor markers and imaging often differ in predicting whether an ovarian tumor is malignant. Therefore, we evaluated the correlation between the predictive values of imaging and tumor markers for diagnosing ovarian tumors, especially when there were discrepancies between the two. We enrolled 1047 patients with ovarian tumors. The predictive values and concordance rates between the preoperative risk of ovarian malignancy algorithm (ROMA) and imaging, including CT and MRI, were evaluated. Diagnoses of 561 CT (77.9%) and 322 MRI group (69.2%) participants were consistent with the ROMA. Among them, 96.4% of the CT (541/561) and 92.5% of the MRI (298/322) group predicted an accurate diagnosis. In contrast, 67.3% (101/150) of CT and 75.2% (100/133) of MRI cases accurately predicted the diagnosis in cases with discrepancies between ROMA and CT or MRI; a total of 32% (48/150) of the CT and 25.5% (34/133) of the MRI group showed an accurate ROMA diagnosis in cases with discrepancies between ROMA and imaging. In the event of a discrepancy between ROMA and imaging when ovarian tumor malignancy prediction, the question is which method should take precedence. This study demonstrates that MRI has the greatest diagnostic accuracy, followed by CT and ROMA. It is also important to understand underlying diseases and benign conditions and rare histopathologies of malignant tumors.
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Luo, Hui, Ziqing Lin, Lijuan Wu, Yuying Wang, Haojie Ning, Yanping Feng, Yulu Cheng, Xiaoyi Wen, and Xiaoyan Liu. "Application of O-RADS Ultrasound Lexicon-Based Logistic Regression Analysis Model in the Diagnosis of Solid Component-Containing Ovarian Malignancies." BioMed Research International 2022 (October 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/7187334.
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Objective. To use the logistic regression model to evaluate the value of ultrasound characteristics in the Ovarian-Adnexal Reporting and Data System ultrasound lexicon in determining ovarian solid component-containing mass benignancy/malignancy. Methods. We retrospectively analyzed the data of 172 patients with adnexal masses discovered by ultrasound, and diagnosis was confirmed by postoperative pathological tests from January 2019 to December 2021. Thirteen ovarian tumor-related parameters in the benign and malignant ovarian tumor groups were selected for univariate analyses. Statistically significant parameters were included in multivariate logistic regression analyses to construct a logistic regression diagnosis model, and the diagnostic performance of the model in predicting ovarian malignancies was calculated. Results. Of the 172 adnexal tumors, 104 were benign, and 68 were malignant. There were differences in cancer antigen 125, maximum mass diameter, maximum solid component diameter, multilocular cyst with solid component, external contour, whether acoustic shadows were present in the solid component, number of papillae, vascularity, presence/absence of ascites, and presence/absence of peritoneal thickening or nodules between the benign ovarian tumor and malignancy groups ( p < 0.05 ). Logistic regression analyses showed that maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites were included in the logistic regression model, and the area under the receiver operating characteristic curve for this regression model in predicting ovarian malignancy was 0.962 (95% confidence interval: 0.933~0.990; p < 0.001 ). Logit p ≥ − 0.02 was used as the cutoff value, and the prediction accuracy, sensitivity, specificity, positive predictive value, and negative predictive values were 93.6%, 86.8%, 98.1%, 96.7%, and 91.9%, respectively. Conclusion. The logistic regression model containing the maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites can help in determining the benignancy/malignancy of solid component-containing masses.
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Matos Pedreira, Camilla, José Alves Barros Filho, Carolina Pereira, Thamine Lessa Andrade, Ricardo Mingarini Terra, Sergio Tadeu Lima Fortunato Pereira, and Gustavo Almeida Fortunato. "The Impact Prediction Models of Neoplasia for Lung Nodules in High-Risk Patients." Revista Científica Hospital Santa Izabel 3, no. 3 (May 9, 2020): 138–46. http://dx.doi.org/10.35753/rchsi.v3i3.47.
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Objectives: This study aims to evaluate the impact of using three predictive models of lung nodule malignancy in a population of patients at high-risk for neoplasia according to previous analysis by physicians, as well as evaluate the clinical and radiological malignancy-predictors of the images. Material and Methods: This is a retrospective cohort study, with 135 patients, undergone surgical in the period from 01/07/2013 to 10/05/2016. The study included nodules with dimensions between 5mm and 30mm, excluding multiple nodules, alveolar consolidation, pleural effusion, and lymph node enlargement. The main variables analyzed were age, sex, smoking history, extrathoracic cancer, diameter, location, and presence of spiculation. The calculation of the area under the ROC curve assessed the accuracy of each prediction model. Results: The study analyzed 135 individuals, of which 96 (71.1%) had malignant nodules. The areas under the ROC curves for each prediction model were: Swensen 0.657; Brock 0.662; and Herder 0.633. The models Swensen, Brock, and Herder presented positive predictive values in high-risk patients, corresponding to 83.3%, 81.8%, and 82.9%, respectively. Patients with the intermediate and low-risk presented a high malignant nodule rate, ranging from 69.3-72.5% and 42.8-52.6%, respectively. Conclusion: None of the three quantitative models analyzed in this study was considered satisfactory (AUC> 0.7) and should be used with caution after specialized evaluation to avoid underestimation of the risk of neoplasia. The pretest calculations might not contemplate other factors than those predicted in the regressions, that could present a role in the clinical decision of resection.
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liang, Ling, Haiyan zhang, Haike Lei, Hong Zhou, Yongzhong Wu, and Jiang shen. "Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters." Technology in Cancer Research & Treatment 21 (January 2022): 153303382211197. http://dx.doi.org/10.1177/15330338221119748.
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Objective: To assess the clinical value of a radiomics model based on low-dose computed tomography (LDCT) in diagnosing benign and malignant pulmonary ground-glass nodules. Methods: A retrospective analysis was performed on 274 patients who underwent LDCT scanning with the identification of pulmonary ground-glass nodules from January 2018 to March 2021. All patients had complete clinical and pathological data. The cases were randomly divided into 191 cases in a training set and 83 cases in a validation set using the random sampling method and a 7:3 ratio. Based on the predictor sources, we established clinical, radiomics, and combined prediction models in the training set. A receiver operating characteristic (ROC) curve was generated for the training and validation sets, the predictive abilities of the different models for benign and malignant nodules were compared according to the area under the curve (AUC), and the model with the best predictive ability was selected. A calibration curve was plotted to test the good-of-fitness of the model in the validation set. Results: Of the 274 patients (84 males and 190 females), 156 had malignant, and 118 had benign nodules. The univariate analysis showed a statistically significant difference in nodule position between benign nodules and lung adenocarcinoma in both data sets ( P <.001 and .021). In the training set, when the nodule diameter was >8 mm, the probability of nodule malignancy increased ( P < .001). The results showed that the combined model had a higher prediction ability than the other two models. The combined model could distinguish between benign and malignant pulmonary nodules in the training set (AUC: 0.711; 95%CI: 0.634-0.787; ACC: 0.696; sensitivity: 0.617; specificity: 0.816; PPV:0.835; NPV: 0.585). Moreover, this model could predict benign and malignant nodules in the validation set (AUC: 0.695; 95%CI: 0.574-0.816; ACC: 9.747; sensitivity: 0.694; specificity: 0.824; PPV: 0.850; NPV: 0.651). The calibration curve had a P value of 0.775, indicating that in the validation set, there was no difference between the value predicted by the combined model and the actual observed value and that the result was a good fit. Conclusion: The prediction model combining clinical information and radiomics parameters had a good ability to distinguish benign and malignant pulmonary ground-glass nodules.
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Lin, Ching-Kai, Lih-Yu Chang, Kai-Lun Yu, Yueh-Feng Wen, Hung-Jen Fan, and Chao-Chi Ho. "Differentiating metastatic lymph nodes in lung cancer patients based on endobronchial ultrasonography features." Medical Ultrasonography 20, no. 2 (May 2, 2018): 154. http://dx.doi.org/10.11152/mu-1282.
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Aim: The aim of this study was to identify easy and relatively effective ultrasound criteria for metastatic mediastinal lymph node prediction. Materials and methods: A retrospective chart review of patients who underwent endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) from March 2014 to September 2016 was performed. We used the following EBUS sonographic features for metastatic lymph node prediction: 1) length of the short axis, 2) shape, 3) margin, 4) echogenicity, 5) central hilar structure, and 6) coagulation necrosis sign. These sonographic findings were compared with the final pathology results or clinical follow-up. Results: A total of 227 lymph nodes were retrospectively evaluated in 133 lung cancer patients; 72% of the lymph nodes had been proven to be malignant metastasis. Logistic regression analysis revealed that the length of the short axis, shape, margin, and echogenicity were independent predictive factors for metastasis. We developed a sum score based on these four sonographic features. A larger sum score trended toward a greater possibility of malignancy. If all four predictive factors were preserved, the diagnostic accuracy, the value of the specificity and the positive predictive value of the sonographic feature would be higher than 90%. Conclusions: The sonographic features of EBUS are valuable tools in predicting metastatic lymph nodes in lung cancer patients.
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Sun, Guoxin, Liying Cai, Xiong Yan, Weihong Nie, Xin Liu, Jing Xu, and Xiao Zou. "A prediction model based on digital breast pathology image information." PLOS ONE 19, no. 5 (May 17, 2024): e0294923. http://dx.doi.org/10.1371/journal.pone.0294923.
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Background The workload of breast cancer pathological diagnosis is very heavy. The purpose of this study is to establish a nomogram model based on pathological images to predict the benign and malignant nature of breast diseases and to validate its predictive performance. Methods In retrospect, a total of 2,723 H&E-stained pathological images were collected from 1,474 patients at Qingdao Central Hospital between 2019 and 2022. The dataset consisted of 509 benign tumor images (adenosis and fibroadenoma) and 2,214 malignant tumor images (infiltrating ductal carcinoma). The images were divided into a training set (1,907) and a validation set (816). Python3.7 was used to extract the values of the R channel, G channel, B channel, and one-dimensional information entropy from all images. Multivariable logistic regression was used to select variables and establish the breast tissue pathological image prediction model. Results The R channel value, B channel value, and one-dimensional information entropy of the images were identified as independent predictive factors for the classification of benign and malignant pathological images (P < 0.05). The area under the curve (AUC) of the nomogram model in the training set was 0.889 (95% CI: 0.869, 0.909), and the AUC in the validation set was 0.838 (95% CI: 0.7980.877). The calibration curve results showed that the calibration curve of this nomogram model was close to the ideal curve. The decision curve results indicated that the predictive model curve had a high value for auxiliary diagnosis. Conclusion The nomogram model for the prediction of benign and malignant breast diseases based on pathological images demonstrates good predictive performance. This model can assist in the diagnosis of breast tissue pathological images.
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Aziz, Aliya B., and Nida Najmi. "Is Risk Malignancy Index a Useful Tool for Predicting Malignant Ovarian Masses in Developing Countries?" Obstetrics and Gynecology International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/951256.
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Introduction. Risk of Malignancy Index (RMI) is widely studied for prediction of malignant pelvic masses in Western population. However, little is known regarding its implication in the developing countries. The objective of this study is to determine how accurately the RMI can predict the malignant pelvic masses.Materials and Methods. The study is a retrospective review of patients attending the gynecological clinic between January 2004 and December 2008 with adnexal masses. Information on demographic characteristics, ultrasound findings, menopausal status, CA125, and histopathology was collected. RMI score for each patient in the study group was calculated.Results. The study group included a total of 283 patients. Analysis of the individual parameters of RMI revealed that ultrasound was the best predictor of malignancy with a sensitivity, specificity, and positive likelihood ratio of 78.3%, 81.5%, and 4.2, respectively. At a standard cut-off value of 250, RMI had a positive likelihood ratio of 8.1, while it was 6.8 at a cut-off of 200, albeit with comparable sensitivity and specificity.Conclusion. RMI is a sensitive tool in predicting malignant adnexal masses. A cut-off of 200 may be suitable in developing countries for triaging and early referral to tertiary care centers.
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Minnerup, Jens, Heike Wersching, E. Bernd Ringelstein, Walter Heindel, Thomas Niederstadt, Matthias Schilling, Wolf-Rüdiger Schäbitz, and André Kemmling. "Prediction of Malignant Middle Cerebral Artery Infarction Using Computed Tomography-Based Intracranial Volume Reserve Measurements." Stroke 42, no. 12 (December 2011): 3403–9. http://dx.doi.org/10.1161/strokeaha.111.619734.
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Background and Purpose— Early decompressive surgery in patients with malignant middle cerebral artery (MCA) infarction improves outcome. Elevation of intracranial pressure depends on both the space occupying brain edema and the intracranial volume reserve (cerebrospinal fluid [CSF]). However, CSF volume was not investigated as a predictor of malignant infarction so far. We hypothesize that assessment of CSF volume in addition to admission infarct size improves early prediction of malignant MCA infarction. Methods— Stroke patients with carotid-T or MCA main stem occlusion and ischemic lesion (reduced cerebral blood volume [CBV]) on perfusion CT were considered for the analysis. The end point malignant MCA infarction was defined by clinical signs of herniation. Volumes of CSF and CBV lesion were determined on admission. Receiver-operator characteristics analysis was used to calculate predictive values for radiological and clinical measurements. Results— Of 52 patients included, 26 (50%) developed malignant MCA infarction. Age, a decreased level of consciousness on admission, CBV lesion volume, CSF volume, and the ratio of CBV lesion volume to CSF volume were significantly different between malignant and nonmalignant groups. The best predictor of a malignant course was the ratio of CBV lesion volume to CSF volume with a cut-off value of 0.92 (96.2% sensitivity, 96.2% specificity, 96.2% positive predictive value, and 96.2% negative predictive value). Conclusions— Based on admission native CT and perfusion CT measurements, the ratio of ischemic lesion volume to CSF volume predicts the development of malignant MCA infarction with higher accuracy than other known predictors, including ischemic lesion volume or clinical characteristics.
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Larach, M. G., J. R. Landis, and S. J. Shirk. "PREDICTION OF MALIGNANT HYPERTHERMIA SUSCEPTIBILITY IN MAN." Anesthesiology 77, Supplement (September 1992): A1052. http://dx.doi.org/10.1097/00000542-199209001-01052.
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McColl, Kenneth EL, and Derek Gillen. "Prediction of Malignant Potential in Reflux Disease." American Journal of Gastroenterology 100, no. 5 (May 2005): 1019–20. http://dx.doi.org/10.1111/j.1572-0241.2005.41985.x.
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Yu, Shuang-Ni, Joshua Li, Sio-In Wong, Julia Y. S. Tsang, Yun-Bi Ni, Jie Chen, and Gary M. Tse. "Atypical aspirates of the breast: a dilemma in current cytology practice." Journal of Clinical Pathology 70, no. 12 (May 29, 2017): 1024–32. http://dx.doi.org/10.1136/jclinpath-2016-204138.
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AimsThe probabilistic approach is widely adopted for breast fine needle aspiration cytology. However, a definite cytological diagnosis is not always possible for C3 (atypia) cases, which poses a management dilemma as this represents a mixed category of benign and malignant cases. It would be beneficial to be able to predict malignancy based on specific cytological features in C3 aspirates.MethodsA comprehensive panel of cytological features (including quantitative, cytomorphological and background features) in a large cohort of C3 breast aspirates with subsequent histological excisions was evaluated to identify relevant morphological criteria predicting the risk of subsequent malignancy.ResultsA total of 229 C3 specimens with histological follow-up were included. Malignant outcome was found in 30.1% of specimens and the majority were invasive cancers. Features that showed a significant association with malignant outcome included older age (p=0.001), lower percentage of epithelial cell clusters and high percentage of single cells (p=0.002), cribriform architecture in cell clusters (p=0.034), presence of intracellular mucin (p=0.027), increased cell clusters without myoepithelial cells (p=0.048), diminished fibromyxoid stromal fragments (p=0.001), reduced bipolar nuclei (p=0.021) and the presence of necrosis (p=0.023). Except for the percentages of single cells and cell clusters without myoepithelial cells, all other features were shown to be independent risk predictors in multivariate analysis.ConclusionsC3 aspirates were associated with a significant probability of histological malignancy. Certain quantitative, cytomorphological and background features were potentially helpful in predicting the risk of a malignant outcome. The prediction could be clinically useful in the management of C3 cases.
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Priya F., Margaret Harriet, Vanusha, and N. Hephzibah Kirubamani. "Clinical correlation of ovarian mass with ultrasound findings and histopathology report." International Journal of Reproduction, Contraception, Obstetrics and Gynecology 6, no. 12 (November 23, 2017): 5230. http://dx.doi.org/10.18203/2320-1770.ijrcog20175058.
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Background: As the ovarian malignancy is most common among genital malignancy, the diagnosis of malignant ovarian tumour helps us to plan the treatment modality like neoadjuvant chemotherapy, chemoradiation, radiotherapy, surgery and fertility sparing surgery depending upon stage of the disease and age of the patient. This study correlates between the clinical and ultrasound findings of ovarian tumours to diagnose the nature of the tumour whether benign or malignant and offer appropriate treatment and finally correlated with histopathology report. The aim of this study was to correlate clinical, USG morphology, colour doppler indices in ovarian mass with histopathology report.Methods: This is a prospective observational study conducted at Saveetha Medical college and hospital between June 2016 to May 2017 for women who were clinically diagnosed to have ovarian mass and operated for it. These patients underwent trans vaginal (if married) or trans abdominal ultrasound and Doppler using GE S7 expert or Sonoline Acuson x300 (siemens) or Philips HD 11xE. Based on clinical findings and on the characterization of the image in USG and colour doppler findings it will be concluded whether the mass is benign or malignant. This is correlated with HPE report.Results: Out of 113 women studied ovarian mass diagnosed clinically as benign in 78%and malignant in 21%. USG prediction of ovarian cancer was 88.00% sensitivity, 80.68% specificity. When Doppler findings were included sensitivity was 91.43% and specificity was 91.03%. The combined use of clinical and USG with Doppler for diagnosis of ovarian malignancy was 92.31% sensitive and 95.95% specific. The positive predictive value of combined use of clinical and USG with Doppler for diagnosis of ovarian malignancy was 92.31%.Conclusions: From this study clinical, USG and Doppler are important modalities in diagnosing benign or malignant ovarian tumour. When both are combined the diagnostic value is extremely high. This aids in planning the management.
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Wu, Ching-Yang, Jui-Ying Fu, Ching-Feng Wu, Ming-Ju Hsieh, Yun-Hen Liu, Hui-Ping Liu, Jason Chia-Hsun Hsieh, and Yang-Teng Peng. "Malignancy Prediction Capacity and Possible Prediction Model of Circulating Tumor Cells for Suspicious Pulmonary Lesions." Journal of Personalized Medicine 11, no. 6 (May 21, 2021): 444. http://dx.doi.org/10.3390/jpm11060444.
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More and more undetermined lung lesions are being identified in routine lung cancer screening. The aim of this study was to try to establish a malignancy prediction model according to the tumor presentations. From January 2017 to December 2018, 50 consecutive patients who were identified with suspicious lung lesions were enrolled into this study. Medical records were reviewed and tumor macroscopic and microscopic presentations were collected for analysis. Circulating tumor cells (CTC) were found to differ between benign and malignant lesions (p = 0.03) and also constituted the highest area under the receiver operation curve other than tumor presentations (p = 0.001). Since tumor size showed the highest sensitivity and CTC revealed the best specificity, a malignancy prediction model was proposed. Akaike information criterion (A.I.C.) of the combined malignancy prediction model was 26.73, which was lower than for tumor size or CTCs alone. Logistic regression revealed that the combined malignancy prediction model showed marginal statistical trends (p = 0.0518). In addition, the 95% confidence interval of combined malignancy prediction model showed less wide range than tumor size ≥ 0.7 cm alone. The calculated probability of malignancy in patients with tumor size ≥ 0.7 cm and CTC > 3 was 97.9%. By contrast, the probability of malignancy in patients whose tumor size was < 0.7 cm, and CTC ≤ 3 was 22.5%. A combined malignancy prediction model involving tumor size followed by the CTC count may provide additional information to assist decision making. For patients who present with tumor size ≥ 0.7 cm and CTC counts > 3, aggressive management should be considered, since the calculated probability of malignancy was 97.9%.
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Bialecka, Monika, Joaquin Montilla-Rojo, Bernard A. J. Roelen, Ad J. Gillis, Leendert H. J. Looijenga, and Daniela C. F. Salvatori. "Humanised Mice and Immunodeficient Mice (NSG) Are Equally Sensitive for Prediction of Stem Cell Malignancy in the Teratoma Assay." International Journal of Molecular Sciences 23, no. 9 (April 23, 2022): 4680. http://dx.doi.org/10.3390/ijms23094680.
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The use of human pluripotent stem cells (hPSCs) in regenerative medicine has great potential. However, it is important to exclude that these cells can undergo malignant transformation, which could lead to the development of malignant tumours. This property of hPSCs is currently being tested using the teratoma assay, through which cells are injected into immunodeficient mice. Transplantation of stem cells in immunocompromised recipient animals certainly has a much higher incidence of tumour formation. On the other hand, the results obtained in immunodeficient mice could indicate a risk of tumour formation that is practically not present in the human immunocompetent recipient. The presence of a humanised immune system might be more representative of the human situation; therefore, we investigated if the demonstrated malignant features of chosen and well-characterised stem cell lines could be retrieved and if new features could arise in a humanised mouse model. Hu-CD34NSGTM (HIS) mice were compared side by side with immunocompromised mice (NSG) after injection of a set of benign (LU07) and malignant (LU07+dox and 2102Ep) cell lines. Analysis of the tumour development, histological composition, pathology evaluation, and malignancy-associated miRNA expression levels, both in tumour and plasma samples, revealed no differences among mouse groups. This indicates that the HIS mouse model is comparable to, but not more sensitive than, the NSG immunodeficient model for studying the malignancy of stem cells. Since in vivo teratoma assay is cumbersome, in vitro methods for the detection of malignancy are urgently needed.
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Verstraelen, Tom E., Freyja H. M. van Lint, Laurens P. Bosman, Remco de Brouwer, Virginnio M. Proost, Bob G. S. Abeln, Karim Taha, et al. "Prediction of ventricular arrhythmia in phospholamban p.Arg14del mutation carriers–reaching the frontiers of individual risk prediction." European Heart Journal 42, no. 29 (June 11, 2021): 2842–50. http://dx.doi.org/10.1093/eurheartj/ehab294.
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Abstract Aims This study aims to improve risk stratification for primary prevention implantable cardioverter defibrillator (ICD) implantation by developing a new mutation-specific prediction model for malignant ventricular arrhythmia (VA) in phospholamban (PLN) p.Arg14del mutation carriers. The proposed model is compared to an existing PLN risk model. Methods and results Data were collected from PLN p.Arg14del mutation carriers with no history of malignant VA at baseline, identified between 2009 and 2020. Malignant VA was defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. A prediction model was developed using Cox regression. The study cohort consisted of 679 PLN p.Arg14del mutation carriers, with a minority of index patients (17%) and male sex (43%), and a median age of 42 years [interquartile range (IQR) 27–55]. During a median follow-up of 4.3 years (IQR 1.7–7.4), 72 (10.6%) carriers experienced malignant VA. Significant predictors were left ventricular ejection fraction, premature ventricular contraction count/24 h, amount of negative T waves, and presence of low-voltage electrocardiogram. The multivariable model had an excellent discriminative ability {C-statistic 0.83 [95% confidence interval (CI) 0.78–0.88]}. Applying the existing PLN risk model to the complete cohort yielded a C-statistic of 0.68 (95% CI 0.61–0.75). Conclusion This new mutation-specific prediction model for individual VA risk in PLN p.Arg14del mutation carriers is superior to the existing PLN risk model, suggesting that risk prediction using mutation-specific phenotypic features can improve accuracy compared to a more generic approach.
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Laurent, I., C. Balleyguier, R. Rouzier, F. André, H. Marsiglia, M. Spielmann, P. Vielh, and S. Delaloge. "A mathematical model to predict for pre-malignant or malignant diagnosis among patients with Birad 4 breast lesions." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 10578. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.10578.
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10578 Background: Preoperative cytological or histological diagnosis of breast lesions is mandatory in order to avoid unnecessary surgical biopsies, but on the other side preoperative work-up may dangerously delay specific care of breast cancer. Solid lesions or microcalcifications (M) scored as Birad 4 are increasingly prevalent in western countries. A highly variable proportion of these lesions (20–80%) are breast cancer. Tools to help clinicians recognize cancers and preneoplastic lesions among true benign conditions may be very helpful in clinical practice. Methods: Radiological, clinical and pathological data of consecutive patients with Birad 4 breast M (N = 384 biopsies among 354 patients) or nodular lesions (N = 172 FNAC among 167 patients) seen in a multidisciplinary breast clinic were prospectively recorded. A multivariate analysis of factors predicting for a final cancer or pre-malignant diagnosis was performed and two nomograms were constructed using the R statistical package for both nodular lesions and M. They were validated by bootstrapping. Variables tested included age, size and palpability of lesion, Gail score, menopausal status, HRT use, progression of lesion (M), and presence of associated symptoms. Results: Median age was 57 years (18–92) for the entire population. Patients with nodular lesions were menopausal in 64.5%, median size of their lesion was 12 mm (4–50), 32% were palpable; 43% had a final diagnosis of breast cancer and 3.5% of atypical hyperplasia or LCIS. 69% of patients with M were menopausal, 31.25% had a final diagnosis of breast cancer and 9.8% of atypical hyperplasia or LCIS. Among patients with nodular lesions, age and palpability were the sole independent predictors of cancer or precancerous lesions (p = 0.04 and 0.004), but the other variables (Gail, menopause, HRT) added discrimination with a concordance index of 0.71. Among patients with M, the only independent predictive variable was the recent progression of the lesions (p = 0.01). The nomogram had a concordance index of 0.69. Conclusion: Our study provides two original nomograms for the prediction of the pre-malignant or malignant nature of recently discovered solid breast lesions and M. Gail model alone is not a highly useful tool in daily individual cancer prediction. No significant financial relationships to disclose.
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Shinohara, Shinji, Takeshi Hanagiri, Masaru Takenaka, Yasuhiro Chikaishi, Soich Oka, Hidehiko Shimokawa, Makoto Nakagawa, et al. "Evaluation of undiagnosed solitary lung nodules according to the probability of malignancy in the American College of Chest Physicians (ACCP) evidence-based clinical practice guidelines." Radiology and Oncology 48, no. 1 (March 1, 2014): 50–55. http://dx.doi.org/10.2478/raon-2013-0064.
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Abstract Background. This study retrospectively investigated the clinical significance of undiagnosed solitary lung nodules removed by surgical resection. Patients and methods. We retrospectively collected data on the age, smoking, cancer history, nodule size, location and spiculation of 241 patients who had nodules measuring 7 mm to 30 mm and a final diagnosis established by histopathology. We compared the final diagnosis of each patient with the probability of malignancy (POM) which was proposed by the American College of Chest Physicians (ACCP) guidelines. Results. Of the 241 patients, 203 patients were diagnosed to have a malignant lung tumor, while 38 patients were diagnosed with benign disease. There were significant differences in the patients with malignant and benign disease in terms of their age, smoking history, nodule size and spiculation. The mean value and the standard deviation of the POM in patients with malignant tumors were 51.7 + 26.1%, and that of patients with benign lesions was 34.6 + 26.7%. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.67. The best cut-off value provided from the ROC curve was 22.6. When the cut-off value was set at 22.6, the sensitivity was 83%, specificity 52%, positive predictive value 90%, negative predictive value 36% and accuracy 77%, respectively. Conclusions. The clinical prediction model proposed in the ACCP guidelines showed unsatisfactory results in terms of the differential diagnosis between malignant disease and benign disease of solitary lung nodules in our study, because the specificity, negative predictive value and AUC were relatively low.
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Kelagade, Mahadev, Ruchi N. Thakur, and Sonali Deshmukh. "The study of the correlation between international ovarian tumour analysis classification, risk of malignancy index and clinicopathological findings of adnexal masses." International Journal of Reproduction, Contraception, Obstetrics and Gynecology 13, no. 5 (April 26, 2024): 1250–55. http://dx.doi.org/10.18203/2320-1770.ijrcog20241075.
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Background: Adnexal masses of ovarian origin are of growing concern these days due to high fatality associated with ovarian malignancy because they are diagnosed at advanced stage due to vague symptoms and absence of recommended screening tests. The present study aimed to assess the prediction potential of IOTA classification and RMI to clinicopathological findings of adnexal masses and calculate the sensitivity and specificity of same. Methods: This was a prospective observational study carried out on 96 non pregnant women presenting with adnexal mass to gynaecology OPD of a tertiary care hospital from 2020 to 2022. They were evaluated preoperatively with complete history, examination, ultrasound, and tumor markers. IOTA score and RMI was calculated for all patients. Following surgery, histopathology results were compared with preoperative evaluation. Statistical Analysis was done. Results: Mucinous cyst adenoma was the most common benign ovarian tumour, serous cystadenocarcinoma being the most common malignant ovarian tumour. Patients with malignancy were older and mostly postmenopausal. IOTA was found better than RMI with higher sensitivity 98.5% and high PPV 98.5%. Similarly, IOTA had higher specificity 91.7% and higher NPV 91.7% for identifying and prediction of benign patients. Conclusions: IOTA guidelines to describe sonographic features of adnexal masses have shown a high sensitivity and specificity for prediction of malignancy in adnexal masses and is a more reliable diagnostic tool over RMI tool for differentiation between benign and malignant adnexal masses.
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Park, Hye Sun, Hee Jung Shin, Ki Chang Shin, Joo Hee Cha, Eun Young Chae, Woo Jung Choi, and Hak Hee Kim. "Comparison of peritumoral stromal tissue stiffness obtained by shear wave elastography between benign and malignant breast lesions." Acta Radiologica 59, no. 10 (January 23, 2018): 1168–75. http://dx.doi.org/10.1177/0284185117753728.
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Background Aggressive breast cancers produce abnormal peritumoral stiff areas, which can differ between benign and malignant lesions and between different subtypes of breast cancer. Purpose To compare the tissue stiffness of the inner tumor, tumor border, and peritumoral stroma (PS) between benign and malignant breast masses by shear wave elastography (SWE). Material and Methods We enrolled 133 consecutive patients who underwent preoperative SWE. Using OsiriX commercial software, we generated multiple 2-mm regions of interest (ROIs) in a linear arrangement on the inner tumor, tumor border, and PS. We obtained the mean elasticity value (Emean) of each ROI, and compared the Emean between benign and malignant tumors. Odds ratios (ORs) for prediction of malignancy were calculated. Subgroup analyses were performed among tumor subtypes. Results There were 85 malignant and 48 benign masses. The Emean of the tumor border and PS were significantly different between benign and malignant masses ( P < 0.05 for all). ORs for malignancy were 1.06, 1.08, 1.05, and 1.04 for stiffness of the tumor border, proximal PS, middle PS, and distal PS, respectively ( P < 0.05 for all). Malignant masses with a stiff rim were significantly larger than malignant masses without a stiff rim, and were more commonly associated with the luminal B and triple negative subtypes. Conclusion Stiffness of the tumor border and PS obtained by SWE were significantly different between benign and malignant masses. Malignant masses with a stiff rim were larger in size and associated with more aggressive pathologic subtypes.
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Peisen, Felix, Annika Gerken, Alessa Hering, Isabel Dahm, Konstantin Nikolaou, Sergios Gatidis, Thomas K. Eigentler, Teresa Amaral, Jan H. Moltz, and Ahmed E. Othman. "Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study." Diagnostics 13, no. 20 (October 14, 2023): 3210. http://dx.doi.org/10.3390/diagnostics13203210.
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Background: The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy. Methods: A baseline machine-learning model using clinical variables (demographic parameters and tumor markers) was compared with an extended model using clinical variables and radiomic features of the whole tumor burden, utilizing repeated five-fold cross-validation. Baseline CTs of 91 stage IV malignant melanoma patients, all treated in the same university hospital, were identified in the Central Malignant Melanoma Registry and all metastases were volumetrically segmented (n = 4727). Results: Compared with the baseline model, the extended radiomics model did not add significantly more information to the best-response prediction (AUC [95% CI] 0.548 (0.188, 0.808) vs. 0.487 (0.139, 0.743)), the prediction of PFS after six months (AUC [95% CI] 0.699 (0.436, 0.958) vs. 0.604 (0.373, 0.867)), or the overall survival prediction after six and twelve months (AUC [95% CI] 0.685 (0.188, 0.967) vs. 0.766 (0.433, 1.000) and AUC [95% CI] 0.554 (0.163, 0.781) vs. 0.616 (0.271, 1.000), respectively). Conclusions: The results showed no additional value of baseline whole-body CT radiomics for best-response prediction, progression-free survival prediction for six months, or six-month and twelve-month overall survival prediction for stage IV melanoma patients receiving first-line targeted therapy. These results need to be validated in a larger cohort.
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Goel, Sarika, Ankur Malhotra, Arjit Agarwal, Shruti Chandak, Ashutosh Kumar, and Adil Khan. "Comparative Efficacy of Ultrasonography and Acoustic Radiation Force Impulse (ARFI) Elastography in Prediction of Malignancy in Thyroid Nodules." Journal of Diagnostic Medical Sonography 36, no. 5 (June 21, 2020): 433–43. http://dx.doi.org/10.1177/8756479320931354.
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Objective: The incidence of malignancy in thyroid nodules is infrequent, but this trend may be reversing. The present study was conducted to emphasize the diagnostic accuracy of acoustic radiation force impulse (ARFI) imaging, in addition to conventional gray-scale ultrasonography (US), for differentiating benign and malignant thyroid nodules. Methods: A total of 141 patients with thyroid nodules (≥10 mm) were included in the study and were evaluated with US, Doppler, and ARFI elastography using Siemens S2000 Acuson ultrasound equipment. Results: The sonographic patterns most predictive and indicative of malignancy included irregular margins and presence of microcalcifications. The Doppler findings in isolation were not extremely sensitive in the detection of malignancy. The shear wave velocity cutoff value on ARFI imaging using receiver operating characteristic curves for differentiation of benign and malignant nodules were noted at 2.87 m/s. ARFI imaging performed better than US and Doppler with sensitivity of 75%, specificity of 96%, and accuracy of 94%. Conclusion: ARFI elastography could be utilized as a reliable initial screening test for detection of malignancy in thyroid nodules.
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Eckel, K. T., A. Pfahlberg, O. Gefeller, and T. Hothorn. "Flexible Modeling of Malignant Melanoma Survival." Methods of Information in Medicine 47, no. 01 (2008): 47–55. http://dx.doi.org/10.3414/me0450.
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Summary Objectives: This paper compares the diagnostic capabilities of flexible ensemble methods modeling the survival time of melanoma patients in comparison to the well established proportional hazards model. Both a random forest type algorithm for censored data as well as a model combination of the proportional hazards model with recursive partitioning are investigated. Methods: Benchmark experiments utilizing the integrated Brier score as a measure for goodness of prediction are the basis of the performance assessment for all competing algorithms. For the purpose of comparing regression relationships represented by the models under test, we describe fitted conditional survival functions by a univariate measure derived from the area under the curve. Based on this measure, we adapt a visualization technique useful for the inspection and comparison of model fits. Results: For the data of malignant melanoma patients the predictive performance of the competing models is on par, allowing for a fair comparison of the fitted relationships. Newly introduced MODplots visualize differences in the fitting structure of the underlying models. Conclusion: The paper provides a framework for comparing the predictive and diagnostic performance of a parametric, a non-parametric and a combined approach.
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Miller, Ronald D., MARILYN GREEN LARACH, HENRY ROSENBERG, DAVID R. LARACH, and A. MICHAEL BROENNLE. "Prediction of Malignant Hyperthermia Susceptibility by Clinical Signs." Anesthesiology 66, no. 4 (April 1, 1987): 547–50. http://dx.doi.org/10.1097/00000542-198704000-00017.
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Puesken, Michael, Boris Buerke, Joachim Gerss, Barbara Frisch, Florian Beyer, Matthias Weckesser, Harald Seifarth, Walter Heindel, and Johannes Wessling. "Prediction of Lymph Node Manifestations in Malignant Lymphoma." Journal of Computer Assisted Tomography 34, no. 4 (July 2010): 564–69. http://dx.doi.org/10.1097/rct.0b013e3181db2901.
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Burt, Bryan M., Hyun-Sung Lee, Anjali C. Raghuram, Chad Strange, James Mason, Taylor Strange, Juan Delgado, and David J. Sugarbaker. "Preoperative prediction of unresectability in malignant pleural mesothelioma." Journal of Thoracic and Cardiovascular Surgery 159, no. 6 (June 2020): 2512–20. http://dx.doi.org/10.1016/j.jtcvs.2019.11.035.
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Wagner, J. D. "Prediction of malignant hyperthermia susceptibility by clinical signs." Journal of Oral and Maxillofacial Surgery 46, no. 2 (February 1988): 169–70. http://dx.doi.org/10.1016/0278-2391(88)90279-0.
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McLaren, Christine E., Wen-Pin Chen, Ke Nie, and Min-Ying Su. "Prediction of Malignant Breast Lesions from MRI Features." Academic Radiology 16, no. 7 (July 2009): 842–51. http://dx.doi.org/10.1016/j.acra.2009.01.029.
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Phruttinarakorn, Bantita, Sirithep Plumworasawat, Jitchai Kayankarnnavee, Jirasit Lualon, and Atcharaporn Pongtippan. "Application of the Paris Reporting System for Urine Cytology: The Three-Year Experience of a Single Tertiary Care Institute in Thailand." Acta Cytologica 66, no. 2 (2022): 134–41. http://dx.doi.org/10.1159/000521139.
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<b><i>Introduction:</i></b> Urothelial carcinoma is one of the most common human cancers, both in Thailand and worldwide. Urine cytology is a screening tool used to detect urothelial carcinoma. The Paris System for Reporting Urinary Cytology (TPSRUC) was first published in 2016 to standardize the procedures, reporting, and management of urothelial carcinoma. Diagnostic categories include negative for high-grade urothelial carcinoma (NHGUC), atypical urothelial cells (AUCs), suspicious for HGUC (SHGUC), HGUC, low-grade urothelial neoplasm, and other malignancies. <b><i>Material and Methods:</i></b> In a retrospective review, urine cytology specimens from 2016 to 2019 were reevaluated using the TPSRUC. The risk of high-grade malignant neoplasm (ROHM) for each diagnostic category was calculated. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of prediction of high-grade malignant neoplasms were evaluated for cases with histological follow-up specimens. <b><i>Results:</i></b> In total, 2,178 urine cytology specimens were evaluated, of which 456 cases had follow-up histological specimens. The ROHM in each diagnostic category was as follows: NHGUC, 17.4%; AUC, 49.9%; SHGUC, 81.2%; HGUC, 91.3%; and other malignant neoplasms, 87.5%. The sensitivity, specificity, PPV, NPV, and accuracy for high-grade malignant neoplasm prediction were 63%, 92.8%, 89%, 73.1%, and 78.5% when AUC was included as malignant in the comparison and 82.6%, 74.7%, 75.1%, 82.3%, and 78.5% when AUC was not considered malignant. <b><i>Conclusions:</i></b> TPSRUC provides reliable results that are reproducible by different interpreters and is a helpful tool for the detection of HGUC.
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Mohammed Danfulani and Shamsuddeen Ahmad Aliyu. "The role of multi-parametric magnetic resonance imaging (MRI) in early prediction of malignant transformation of low-grade Gliomas (A Systematic review)." GSC Advanced Research and Reviews 5, no. 3 (December 30, 2020): 014–29. http://dx.doi.org/10.30574/gscarr.2020.5.3.0122.
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Introduction: Low-grade gliomas is the most common primary brain tumour, although the presentation may take up to two decades, there is high tendency of early malignant transformation which raise a growing concern. Multi-parametric MRI studies have the potential for predicting the early malignant transformation. Methods: A comprehensive electronic search of various databases was conducted together with forward tracking of the reference list to retrieve relevant qualitative primary studies. Moreover, hand search for journal that was not available electronically was also conducted. Through assessment of the relevant studies was ensured and the included studies were carefully selected. The relevant data was extracted by data extraction form recommended by Cochrane collaborations. Results: The search yielded 1158 which was narrowed down to eight (8) studies that satisfied the inclusion criteria. These studies are assessing the role of different MRI parameters in predicting the early malignant transformation of Low-grade gliomas. The risk of bias and the applicability concern of the included studies are low. Conclusion: Based on the findings of this review; Multi-parametric MRI studies have the potential of predicting the early malignant transformation of low-grade gliomas. There is need for high quality large scale, prospective studies on the role of multi-parametric MRI studies in early prediction of malignant transformation of LGGs and meta-analysis of these studies is highly recommended.
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Ramadhan, Nur Ghaniaviyanto, and Faisal Dharma Adhinata. "TEKNIK SMOTE DAN GINI SCORE DALAM KLASIFIKASI KANKER PAYUDARA." RADIAL : Jurnal Peradaban Sains, Rekayasa dan Teknologi 9, no. 2 (December 18, 2021): 125–34. http://dx.doi.org/10.37971/radial.v9i2.229.
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Breast cancer is a malignancy in breast tissue that can originate from the epithelium of the ducts and lobules. WHO says 30% - 50% of cancer cases can be prevented. Breast cancer prevention can be done utilizing screening or early diagnosis. The purpose of the initial diagnosis is that if a lump appears, predictions can be made whether it is classified as malignant or benign. Breast cancer prediction can be done using a dataset containing cancer-related parameters. However, sometimes the dataset used also has problems such as the amount of data is not balanced and the use of irrelevant features. This study aims to improve breast cancer prediction results by balancing the number of data classes and using the rank feature. The method used is SMOTE for imbalanced data and Gini score for rank features. The classification model used is random forest and naïve Bayes. The results obtained by the random forest classification model are superior to Naïve Bayes.
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Nasien, Dewi, Veren Enjeslina, M. Hasmil Adiya, and Zirawani Baharum. "Breast Cancer Prediction Using Artificial Neural Networks Back Propagation Method." Journal of Physics: Conference Series 2319, no. 1 (August 1, 2022): 012025. http://dx.doi.org/10.1088/1742-6596/2319/1/012025.
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Abstract Research on breast cancer has been widely conducted and previously studied with various methods or algorithms to categorize it into benign and malignant groups. In ANN algorithm, one method called back propagation network is utilized to solve complex problems related to identification, pattern recognition prediction, and so forth. The objective of the present study is to investigate the level of accuracy and performance by ANN back propagation in predicting breast cancer. Several stages for this study are formulating the problem, collecting and processing the Wisconsin breast cancer dataset from the Kaggle site. Designing and creating an ANN algorithm system to classify cancer into malignant and benign, then examining the system to perceive the prediction accuracy, and conclude it. The results of the numerical simulation indicate that the created system of MATLAB R2016a software obtained an accuracy of 96.929% with an error of 3.071% by a combination of training parameters with epoch 1000, learning rate 0.01, goal 0.001, and hidden layer 5.
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Sathishkumar J and Dr. Venkatasalam K. "Prediction and Classifications of Breast Cancer using Enhanced Convolutional Neural Network Approaches." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (April 23, 2024): 1045–53. http://dx.doi.org/10.47392/irjaeh.2024.0145.
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In worldwide women mortality increases extremely every year due to breast cancer and diagnosis of the issue through prediction is very much imperative for healthy lifespan. Here precision of cancer extrapolation is an essential thing for survivability of patient with appropriate treatment. Deep learning algorithms have materialised as influential tool for predicting breast cancer in medical image processing, which leverages capabilities of artificial neural networks (ANN) that are intended to mimic an architecture and functionalities of human brain. Superior features of convolutional neural network (CNN) in deep learning for handling image-based data like, exploiting spatial information, hierarchical feature learning, parameter sharing and data augmentation are important parameters in medical image processing. In this paper CNN algorithm is incorporated for predicting breast cancer in earlier and malignant stage, the results are compared with other deep learning algorithms and our proposed algorithm is expected to give better performance in parameters like accuracy testing, image classifiers, gene sequence classifiers and malignancy detection.
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Rapelli Ramakrishna and Madhavi Thatipamula. "Efficacy of Ultrasound Intrinsic Compression Strain Elastography in Prediction of Malignancy in Thyroid Nodules with Fine Needle Aspiration Cytology Correlation." Asian Journal of Medical Radiological Research 8, no. 2 (December 30, 2020): 9–17. http://dx.doi.org/10.47009/ajmrr.2020.8.2.2.
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Background: Different diagnostic modalities are used to evaluate and diagnose efficiently thyroid nodules. These include Clinical Examinations, Thyroid Function Test (TFT), Scintiscan, Ultrasonography (USG), Fine Needle Aspiration Cytology (FNAC), and Histopathological examination. However, clinical assessment, TFT and USG have been poor parameters for assessing thyroid nodules. The objective is to this study was aimed to evaluate the efficacy of Ultrasound Elastography for the prediction of malignancy in thyroid nodule. Subjects and Methods:After obtaining written informed consent, demographic data such as age, sex and clinical features like, swelling, mode of onset, difficulty in swallowing, difficulty in breathing, hoarseness of voice obtained through an interview and recorded on predesigned and pretested proforma (Annexure II). Further these patients were subjected Grayscale Ultrasound, Ultrasound Elastography and FNAC. Results: Malignant lesions were noted in 19 patients on FNAC. Among them, 16 (84.21%) patients had malignant lesions while 3 (15.79%) patients had benign lesions based on combined USE and ECI criteria. This difference was statistically significant (p<0.001). The sensitivity of combined USE and ECI criteria in the diagnosis of malignant lesions was 84.21% with Specificity of 81.69%, PPV 55.17% and NPV 95.08%. Conclusion: Based on the findings of this study it may be concluded that, USE as determined by the Ragos criteria, TI RADS score are highly associated with malignant thyroid lesions and useful in differentiating the malignant thyroid lesions from benign ones.
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Chen, Qian, Peng Hao, Chipiu Wong, Xiaoxin Zhong, Qing He, and Yantao Chen. "Development and validation of a novel nomogram of 1-year mortality in the elderly with hip fracture: a study of the MIMIC-III database." BMJ Open 13, no. 5 (May 2023): e068465. http://dx.doi.org/10.1136/bmjopen-2022-068465.
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ObjectiveHip fracture is a prevalent condition with a significant death rate among the elderly. We sought to develop a nomogram-based survival prediction model for older patients with hip fracture.DesignA retrospective case–control study.SettingThe data from Medical Information Mart for Intensive Care III (MIMIC-III V.1.4).ParticipantsThe clinical features of elderly patients with hip fracture, including basic information, comorbidities, severity score, laboratory tests and therapy, were filtered out based on the MIMIC-III V.1.4.Methods and main outcome measuresAll patients included in the study were from critical care and randomly divided into training and validation sets (7:3). On the basis of retrieved data, the least absolute shrinkage and selection operator (LASSO) regression and multiple logistic regression analysis were used to identify independent predictive variables of 1-year mortality, and then constructed a risk prediction nomogram. The predictive values of the nomogram model were evaluated by the concordance indexes (C-indexes), receiver operating characteristic curve, decision curve analysis (DCA) and calibration curve.ResultsA total of 341 elderly patients with hip fracture were included in this study; 121 cases died within 1 year. After LASSO regression and multiple logistic regression analysis, a novel nomogram contained the predictive variables of age, weight, the proportion of lymphocyte count, liver disease, malignant tumour and congestive heart failure. The constructed model proved satisfactory discrimination with C-indexes of 0.738 (95% CI 0.674 to 0.802) in the training set and 0.713 (95% CI 0.608 to 0.819) in the validation set. The calibration curve shows a good degree of fitting between the predicted and observed probabilities and the DCA confirms the model’s clinical practicability.ConclusionsThe novel prediction model provides personalised predictions for 1-year mortality in elderly patients with hip fractures. Compared with other hip fracture models, our nomogram is particularly suitable for predicting long-term mortality in critical patients.