Relevant bibliographies by topics / Radiomic

Academic literature on the topic 'Radiomic'

Author: Grafiati
Published: 10 December 2022
Last updated: 14 March 2023

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Journal articles on the topic "Radiomic"

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Huang, Yi-Ching, Yi-Shan Tsai, Chung-I. Li, Ren-Hao Chan, Yu-Min Yeh, Po-Chuan Chen, Meng-Ru Shen, and Peng-Chan Lin. "Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer." Cancers 14, no. 8 (April 8, 2022): 1895. http://dx.doi.org/10.3390/cancers14081895.

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To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with PECAM1, PRDM1, AIF1, IL10, ISG20, and TLR8 expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets.
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Ryan, Sarah M., Tasha E. Fingerlin, Margaret Mroz, Briana Barkes, Nabeel Hamzeh, Lisa A. Maier, and Nichole E. Carlson. "Radiomic measures from chest high-resolution computed tomography associated with lung function in sarcoidosis." European Respiratory Journal 54, no. 2 (June 13, 2019): 1900371. http://dx.doi.org/10.1183/13993003.00371-2019.

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IntroductionPulmonary sarcoidosis is a rare heterogeneous lung disease of unknown aetiology, with limited treatment options. Phenotyping relies on clinical testing including visual scoring of chest radiographs. Objective radiomic measures from high-resolution computed tomography (HRCT) may provide additional information to assess disease status. As the first radiomics analysis in sarcoidosis, we investigate the potential of radiomic measures as biomarkers for sarcoidosis, by assessing 1) differences in HRCT between sarcoidosis subjects and healthy controls, 2) associations between radiomic measures and spirometry, and 3) trends between Scadding stages.MethodsRadiomic features were computed on HRCT in three anatomical planes. Linear regression compared global radiomic features between sarcoidosis subjects (n=73) and healthy controls (n=78), and identified associations with spirometry. Spatial differences in associations across the lung were investigated using functional data analysis. A subanalysis compared radiomic features between Scadding stages.ResultsGlobal radiomic measures differed significantly between sarcoidosis subjects and controls (p<0.001 for skewness, kurtosis, fractal dimension and Geary'sC), with differences in spatial radiomics most apparent in superior and lateral regions. In sarcoidosis subjects, there were significant associations between radiomic measures and spirometry, with a large association found between Geary'sCand forced vital capacity (FVC) (p=0.008). Global radiomic measures differed significantly between Scadding stages (p<0.032), albeit nonlinearly, with stage IV having more extreme radiomic values. Radiomics explained 71.1% of the variability in FVC compared with 51.4% by Scadding staging alone.ConclusionsRadiomic HRCT measures objectively differentiate disease abnormalities, associate with lung function and identify trends in Scadding stage, showing promise as quantitative biomarkers for pulmonary sarcoidosis.
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Hu, Yumin, Qiaoyou Weng, Haihong Xia, Tao Chen, Chunli Kong, Weiyue Chen, Peipei Pang, Min Xu, Chenying Lu, and Jiansong Ji. "A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer." Abdominal Radiology 46, no. 6 (June 2021): 2384–92. http://dx.doi.org/10.1007/s00261-021-03120-w.

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Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.
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Hu, Shuyi, Xiajie Lyu, Weifeng Li, Xiaohan Cui, Qiaoyu Liu, Xiaoliang Xu, Jincheng Wang, Lin Chen, Xudong Zhang, and Yin Yin. "Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)." Contrast Media & Molecular Imaging 2022 (June 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/7693631.

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Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups P < 0.05 . The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively. Conclusions. Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.
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Sherkhane, Umesh B., Ashish Kumar Jha, Sneha Mithun, Vinay Jaiswar, Alberto Traverso, Leonard Wee, Venkatesh Rangarajan, and Andre Dekker. "PyRadGUI: A GUI based radiomics extractor software." F1000Research 12 (March 10, 2023): 259. http://dx.doi.org/10.12688/f1000research.129826.1.

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Radiomics is the method of extracting high throughput mathematical and statistical features from medical images. These features have the potential to characterize the underlying pathology of the disease that is inappreciable to a trained human eye. There are several open-source and licensed tools to extract radiomic features such as pyradiomics, LIFEx, TexRAD, and RaCat. Although pyradiomics is a widely used radiomics package by researchers, this software is not very user-friendly and can be run using a command line. We have developed and validated the GUI tool, PyRadGUI to make the radiomics software easy to operate. This software adheres to IBSI radiomic feature definition and implements the radiomic pipeline in batch processing to extract radiomic features from multiple patient’s data and stores it in a comma separated value (CSV). We validated PyRadGUI software with the existing pyradiomic pipeline.
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Lei, Chu-qian, Wei Wei, Zhen-yu Liu, Qian-Qian Xiong, Ci-Qiu Yang, Teng Zhu, Liu-Lu Zhang, Mei Yang, Jie Tian, and Kun Wang. "Radiomics analysis for pathological classification prediction in BI-RADS category 4 mammographic calcifications." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e13055-e13055. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13055.

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e13055 Background: To establish and validate a radiomics-based imaging diagnostic model to predict Breast Imaging Reporting and Data System (BI-RADS) category 4 calcification of breast with mammographic images before biopsy and assess its value. Methods: A total of 212 BI-RADS category 4 pathology-proven mammographic calcifications without obvious mass on mammography were retrospectively enrolled (159 in primary cohort and 53 in validation cohort). All patients received ultrasound inspection and the results were available. 8286 radiomic features were extracted from each mammography images. We utilized machine learning to build a radiomic signature based on optimal features. Independent clinical factors were selected by multivariable logistic regression analysis, and we incorporated the radiomic signatures and risk clinical factors to build a radiomic nomogram. The performance of the radiomic nomogram were assessed by the area under the receiver-operating characteristic curve (AUC). Results: Six features were selected to develop the radiomic signatures based on the primary cohort. Combining with menopausal states, the individualized radiomic nomogram reached an AUC of 0.803 in the validation cohorts, and its clinical utility was confirmed by the decision curve analysis. The difference was significant between the AUC value of differentiating results of the radiomic nomogram compared with ultrasound, mammography and combined modality respectively(p < 0.05 in all three groups). Especially, for patients with MG+/US- calcifications, radiomics nomogram can be screen out benign calcifications. Conclusions: Based on mammographic radiomics, we developed a method for prediction of pathological classification in BI-RADS IV calcification, which has a certain predictive effect.
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Costa, Guido, Lara Cavinato, Chiara Masci, Francesco Fiz, Martina Sollini, Letterio Salvatore Politi, Arturo Chiti, et al. "Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases." Cancers 13, no. 12 (June 20, 2021): 3077. http://dx.doi.org/10.3390/cancers13123077.

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Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2–3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.
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Yin, Yunchao, Derya Yakar, Rudi A. J. O. Dierckx, Kim B. Mouridsen, Thomas C. Kwee, and Robbert J. de Haas. "Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging." Diagnostics 12, no. 2 (February 21, 2022): 550. http://dx.doi.org/10.3390/diagnostics12020550.

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Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.
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Gill, Andrew B., Leonardo Rundo, Jonathan C. M. Wan, Doreen Lau, Jeries P. Zawaideh, Ramona Woitek, Fulvio Zaccagna, et al. "Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma." Cancers 12, no. 12 (November 24, 2020): 3493. http://dx.doi.org/10.3390/cancers12123493.

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Clinical imaging methods, such as computed tomography (CT), are used for routine tumor response monitoring. Imaging can also reveal intratumoral, intermetastatic, and interpatient heterogeneity, which can be quantified using radiomics. Circulating tumor DNA (ctDNA) in the plasma is a sensitive and specific biomarker for response monitoring. Here we evaluated the interrelationship between circulating tumor DNA mutant allele fraction (ctDNAmaf), obtained by targeted amplicon sequencing and shallow whole genome sequencing, and radiomic measurements of CT heterogeneity in patients with stage IV melanoma. ctDNAmaf and radiomic observations were obtained from 15 patients with a total of 70 CT examinations acquired as part of a prospective trial. 26 of 39 radiomic features showed a significant relationship with log(ctDNAmaf). Principal component analysis was used to define a radiomics signature that predicted ctDNAmaf independent of lesion volume. This radiomics signature and serum lactate dehydrogenase were independent predictors of ctDNAmaf. Together, these results suggest that radiomic features and ctDNAmaf may serve as complementary clinical tools for treatment monitoring.
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Baine, Michael, Justin Burr, Qian Du, Chi Zhang, Xiaoying Liang, Luke Krajewski, Laura Zima, Gerard Rux, Chi Zhang, and Dandan Zheng. "The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients." Journal of Imaging 7, no. 2 (January 28, 2021): 17. http://dx.doi.org/10.3390/jimaging7020017.

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Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.
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Dissertations / Theses on the topic "Radiomic"

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Xu, Chongrui. "Quantitative Radiomic Analysis for Prognostic Medical Applications." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21517.

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Radiomics, a non-invasive and quantitative mining medical imaging information method, could extract molecular biological features and enormous feature combinations to customise individualised treatment and solve the problem of heterogeneity, satisfying the standards of precision medicine. However, it faces many challenges in the feature selection process, including redundant features, irrelevant features and the overfitting risk. More important, people know little about radiomics biological background and its connection to radiology, so it is difficult to apply radiology directly to medicine as it lacks interpretability. The core of this thesis is radiomic biology analysis that connects radiomic imaging information with molecular biology information to achieve a medical “gold standard” for cancer management. Developing methods to succeed in the feature selection process of data of varying dimensions is the main goal of this paper. Our major contributions in this thesis can be summarised as below: 1. We firstly proposed an unsupervised learning framework to guide supervised learning in the reduction of feature dimensions from large cohorts Non-Small Cell Lung Cancer data (NSCLC) on both clinical data and radiomic data for survival prediction. 2. An interpretable machine learning approach measures the contribution of features for each case and the connection of radiomics to its underlying biological features to make clinical decisions in leukemia and breast cancer cases. The weight of the feature can be estimated by measuring the distance of the approximate perturbation centre. 3. Based on the framework of feature selection that we proposed, to ensure the fairness and stability of the data split when processing classification results, cross-validation is embedded in the training process. We further propose a traversal selection method, optimising the computational complexity of the selection process to obtain the most robust feature set.
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Shafiq, ul Hassan Muhammad. "Characterization of Computed Tomography Radiomic Features using Texture Phantoms." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7642.

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Radiomics treats images as quantitative data and promises to improve cancer prediction in radiology and therapy response assessment in radiation oncology. However, there are a number of fundamental problems that need to be solved in order to potentially apply radiomic features in clinic. The first basic step in computed tomography (CT) radiomic analysis is the acquisition of images using selectable image acquisition and reconstruction parameters. Radiomic features have shown large variability due to variation of these parameters. Therefore, it is important to develop methods to address these variability issues in radiomic features due to each CT parameter. To this end, texture phantoms provide a stable geometry and Hounsfield Units (HU) to characterize the radiomic features with respect to image acquisition and reconstruction parameters. In this project, normalization methods were developed to address the variability issues in CT Radiomics using texture phantoms. In the first part of this project, variability in radiomic features due to voxel size variation was addressed. A voxel size resampling method is presented as a preprocessing step for imaging data acquired with variable voxel sizes. After resampling, variability due to variable voxel size in 42 radiomic features was reduced significantly. Voxel size normalization is presented to address the intrinsic dependence of some key radiomic features. After normalization, 10 features became robust as a function of voxel size. Some of these features were identified as predictive biomarkers in diagnostic imaging or useful in response assessment in radiation therapy. However, these key features were found to be intrinsically dependent on voxel size (which also implies dependence on lesion volume). The normalization factors are also developed to address the intrinsic dependence of texture features on the number of gray levels. After normalization, the variability due to gray levels in 17 texture features was reduced significantly. In the second part of the project, voxel size and gray level (GL) normalizations developed based on phantom studies, were tested on the actual lung cancer tumors. Eighteen patients with non-small cell lung cancer of varying tumor volumes were studied and compared with phantom scans acquired on 8 different CT scanners. Eight out of 10 features showed high (Rs > 0.9) and low (Rs < 0.5) Spearman rank correlations with voxel size before and after normalizations, respectively. Likewise, texture features were unstable (ICC < 0.6) and highly stable (ICC > 0.9) before and after gray level normalizations, respectively. This work showed that voxel size and GL normalizations derived from texture phantom also apply to lung cancer tumors. This work highlights the importance and utility of investigating the robustness of CT radiomic features using CT texture phantoms. Another contribution of this work is to develop correction factors to address the variability issues in radiomic features due to reconstruction kernels. Reconstruction kernels and tube current contribute to noise texture in CT. Most of texture features were sensitive to correlated noise texture due to reconstruction kernels. In this work, noise power spectra (NPS) was measured on 5 CT scanners using standard ACR phantom to quantify the correlated noise texture. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and the region of interest (ROI) maximum intensity as correction factors. Most texture features were radiation dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percent improvements in robustness of 19 features were in the range of 30% to 78% after corrections. In conclusion, most texture features are sensitive to imaging parameters such as reconstruction kernels, reconstruction Field of View (FOV), and slice thickness. All reconstruction parameters contribute to inherent noise in CT images. The problem can be partly solved by quantifying noise texture in CT radiomics using a texture phantom and an ACR phantom. Texture phantoms should be a pre-requisite to patient studies as they provide stable geometry and HU distribution to characterize the radiomic features and provide ground truths for multi-institutional validation studies.
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Carlini, Gianluca. "Prediction of cancer trajectories by statistical learning on radiomic features." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23134/.

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Radiomics refers to the analysis of quantitative features extracted from medical images including Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI), and other medical imaging techniques. Radiomic features can be used to build models providing diagnostic, prognostic, and predictive information. The aim of this work was to build a machine learning model able to predict survival probability in 85 cervical cancer patients, using the radiomics features extracted from CT and PET medical images. A thorough feature selection process was conducted employing different techniques to select the best predictors among the original features in the dataset. In particular, the Genetic Algorithm revealed to be the best of the feature selection methods employed, with promising applications in the field of radiomics. Two different survival models have been developed, a Cox proportional hazard model and a Random Survival Forest. A Decision Tree Classifier was also implemented as a further model to evaluate. All the models were trained on 80% of the available data and tested on the remaining 20%. The Concordance Index (CI) was used as the evaluation metric for the two survival models, while the area under the Receiver Operating Characteristic curve (ROC AUC) was used as the evaluation metric for the classifier. The Cox model trained using 9 selected CT features was superior to all the other models tested. It achieved a Concordance Index score of 0.71 on the test set, showing promising predictive capabilities on external data. Finally, the recurrence outcome was used as an additional feature, producing a general improvement of all the models.
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Biondi, Michelangelo. "A general method for radiomic features selection - A SPECT simulation study." Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1086938.

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Introduction There are several radiological techniques, and, in this study, we focused the Single Photon Emission Computed Tomography (SPECT) imaging. It is possible to reconstruct the unknown tracer distribution inside the body by applying tomographic reconstruction algorithms such as Filtered Back Projection (FBP) and Ordered Subset Expectation Maximisation (OSEM) to the acquired data. Nowadays, thanks to technological innovations, a new branch of research has rapidly evolved: the radiomics. In practice, radiomics tries to assess whether the “textural features” of images in regions related to specific diseases could provide added value in a diagnostic process, in the evaluation of prognosis or could guide therapeutic choices. A general concern that has to be accounted for when performing a clinical study is whether changes in acquisition and reconstruction parameters could affect the value of the features. To the best of our knowledge, in literature, there is no unique method for identifying robust features; here, a generalised method to study the effects of the variation of reconstruction parameters on radiomic features is proposed and applied to asses stability and reliability in SPECT imaging. Materials and methods Only simulation studies could asses the link between features extracted from reconstructed images and their original values. From a preliminary statistical analysis, it emerged that at least 66 phantoms (representing different original textures) were needed to achieve a statistical power higher than 90%. These synthetic phantoms derived from abdominal CT scans and “Visible Human Project” image sets. Then, using a proper model, we simulated SPECT acquisitions of each phantom and reconstructed the corresponding images changing parameters in FBP and OSEM tomographic algorithms. Features extraction was conducted with PyRadiomics, an open-source software. Six feature classes were considered, based on Intensity, Grey-Level Co-occurrence Matrix (GLCM), Grey Level Dependence Matrix (GLDM), Grey Level Run Length Matrix (GLRLM), Grey Level Size Zone Matrix (GLSZM) and Neighbourhood Grey-Tone Difference Matrix (NGTDM). Ultimately, 93 different radiomics features for each phantom were calculated. In this way, data-set has a series of repeated measurements and the method of Generalised Estimating Equations (GEE) is suitable for analysing databases with a similar structure. In this study, two different GEE models were developed: one to analyse if the radiomic features calculated in the reconstructed images (Vr) reproduce the same feature in the original VOI (Vo); another to study if they are stable or not with reconstruction parameters variations. Results 32 different reconstructions for each available phantom were obtained, for a total of 2112 images stacks. The results of the two GEE models, features could be classified according to four possible groups: a) feature with a correlation between Vo and Vr, without reconstruction parameters variation effect; b) features without a correlation between Vo and Vr and without a significant impact of the reconstruction parameters variation; c) features with a statistically significant correlation between Vo and Vr and with the effect of the reconstruction parameters variation on the Vr value; d) reconstruction parameters variation affects Vr. Moreover, there is not a correlation between the values obtained from the reconstructed images and Vo. Discussion In literature, as far as we know, there are no trustworthy works of the reproducibility or repeatability applied to SPECT imaging. Here, with software simulations, we tried to answer the following two questions: 1) Are the features extracted from the reconstructed images (Vr) correlated to those of the original images (Vo)? 2) Are the features extracted from the reconstructed images robust when the reconstruction parameters vary? To answer these questions, two GEE models were developed. Most features showed a correlation between Vo and Vr, but with a relevant impact of reconstruction parameters variation. For clinical studies, in our opinion, features like a) would be the optimal choice. However, also features like c) could be used, but researchers have to handle with care these features for which the reconstruction parameters variations affect Vr. Using the remaining features is not recommended as the lack of correlation between Vo and Vr makes random any link with clinical end-points, so it could be difficult to reproduce any result on cohorts of patients other than the one used to develop the radiomic model. Conclusions From this study, it emerges how reconstruction parameters could affect radiomic features in SPECT imaging. In our opinion, researchers should take into account this dependency in both retrospective and prospective radiomic studies. Ultimately, the method described in this work, although complicated, represents a logical approach to carry out propaedeutic evaluations about the selection of imaging parameters or radiomic features to be used for clinical studies.
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Penzias, Gregory. "Identifying the Histomorphometric Basis of Predictive Radiomic Markers for Characterization of Prostate Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1473415195867117.

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RINALDI, LISA. "FROM GREY-LEVELS TO NUMBERS: INVESTIGATION OF RADIOMIC FEATURE ROBUSTNESS IN CT IMAGES OF LUNG TUMOURS." Doctoral thesis, Università degli studi di Pavia, 2022. http://hdl.handle.net/11571/1462327.

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BIANCHINI, LINDA. "NOVEL PHANTOMS FOR ROBUST MRI-BASED RADIOMICS IN ONCOLOGY." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/772014.

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Radiomics is the process of converting medical images into minable high-dimensional data to support clinical decision. A dedicated software is exploited to extract many synthetic biomarkers, called radiomic features, which can be correlated with specific clinical outcomes and may uncover disease characteristics that failed to be appreciated by visual inspection of the images. In pelvic and breast cancer, MRI-based radiomics has been investigated for its potential to describe the tumour heterogeneity, thus improving diagnosis, prognosis and early therapy assessment, and it has shown promising results. However, the process lacks standardisation and harmonisation of the methods employed, from the image processing to the building of predictive models. Parts of these challenges can be investigated with phantom studies, which offer the possibility of repeated acquisition in controlled experimental setup. In this thesis, a pelvic phantom dedicated to MRI-based radiomics has been designed, developed and validated for the first time. A research has been conducted to identify the materials and geometry of the phantom in such a way that it could mimic the MR signal and texture of a tumour and its surrounding tissues, as seen in a set of representative patients. The phantom has been used for a multicentric evaluation of the repeatability and reproducibility of the radiomic features extracted from images acquired on three MR scanners of two vendors and two magnetic field strengths. This study allowed to identify a set of robust radiomic features to support studies on clinical datasets of patients with pelvic cancer. In addition, three radiomic software products for feature extraction have been tested to assess the impact of the choice of a specific tool on the value of the features. A prototype of a radiomic breast phantom has been assembled in the last part of the thesis work, including a 3D-printed insert to mimic a real tumour.
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Zdilar, Luka. "Evaluating the effect of right-censored endpoint transformation for dimensionality reduction of radiomic features of oropharyngeal cancer patients." Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6346.

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Radiomics is the process of extracting quantitative features from tomographic images (computed tomography [CT], magnetic resonance [MR], or positron emission tomography [PET] images). Thousands of features can be extracted via quantitative image analyses based on intensity, shape, size or volume, and texture. These radiomic features can then be used in combination with demographic, disease, and treatment indicators to increase precision in diagnosis, assessment of prognosis, and prediction of therapy response. However, for models to be effective and the analysis to be statistically sound, it is necessary to reduce the dimensionality of the data through feature selection or feature extraction. Supervised dimensionality reduction methods identify the most relevant features given a label or outcome such as overall survival (OS) or relapse-free survival (RFS) after treatment. For survival data, outcomes are represented using two variables: time-to-event and a censor flag. Patients that have not yet experienced an event are censored and their time-to-event is their follow up time. This research evaluates the effect of transforming a right-censored outcome into binary, continuous, and censored aware representations for dimensionality reduction of radiomic features to predict overall survival (OS) and relapse-free survival (RFS) of oropharyngeal cancer patients. Both feature selection and feature extraction are considered in this work. For feature selection, eight different methods were applied using a binary outcome indicating event occurrence prior to median follow-up time, a continuous outcome using the Martingale residuals from a proportional hazards model, and the raw right-censored time-to-event outcome. For feature extraction, a single covariate was extracted after clustering the patients according to radiomics data. Three different clustering techniques were applied using the same continuous outcome and raw right-censored outcome. The radiomic signatures are then combined with clinical variables for risk prediction. Three metrics for accuracy and calibration were used to evaluate the performance of five predictive models and an ensemble of the models. Analyses were performed across 529 patients and over 3800 radiomic features. The data was preprocessed to remove redundant and low variance features prior to either selection or clustering. The results show that including a radiomic signature or radiomic cluster label predicts better than using only clinical data. Randomly generating signatures or generating signatures without considering an outcome results in poor calibration scores. Random forest feature selectors with the continuous and right-censored outcomes give the best predictive scores for OS and RFS in terms of feature selection while hierarchical clustering for feature extraction gives similarly predictive scores with compact representation of the radiomic feature space.
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Pattiam, Giriprakash Pavithran. "Systemic Identification of Radiomic Features Resilient to Batch Effects and Acquisition Variations for Diagnosis of Active Crohn's Disease on CT Enterography." Cleveland State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=csu1629542175523398.

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ISAKSSON, LARS JOHANNES. "HYBRID DEEP LEARNING AND RADIOMICS MODELS FOR ASSESSMENT OF CLINICALLY RELEVANT PROSTATE CANCER." Doctoral thesis, Università degli Studi di Milano, 2022. https://hdl.handle.net/2434/946529.

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Precision medicine holds the potential to revolutionize healthcare by providing every patient with personalized treatments and decisions tailored to his or her individual needs. This might be enabled by the large influx of potentially diagnostic information from new sources such as genetics and modern imaging techniques, provided the relevant information can be extracted. One such framework that has started to demonstrate promise in radiology, especially in the assessment of cancer, is radiomics; the practice of characterizing images by extracting a substantial amount of quantitative mathematical descriptors. This success has largely been enabled by artificial intelligence (AI) and machine learning developments that are capable of handling the big data arrays. Using radiomics, researchers have been able to build prediction models capable of assisting and informing doctors in important decisions such as risk assessment or the choice of treatment. But even though radiomics has shown promise in preliminary studies, there is still a long way to go before radiomics and related AI applications can become routine tools in clinics. The road from patient admission to release is long, and all its intricate steps need to be studied in detail to establish the AI models' benefits and safety. Deep learning is an incredibly powerful AI technique that has revolutionized many areas of science and industry such as recommender systems and protein folding. The technique has demonstrated particular capabilities in image analysis, such as the ability to drive cars autonomously and generate realistic-looking images from scratch. However, the recent advances in deep learning have largely been segregated from the radiomics domain, even though they can synergize with radiomics by performing complementary tasks such as image segmentation and denoising. There is considerable potential for DL and radiomics to cooperatively reinforce each other that so far has been majorly unexplored. This thesis investigates the application of radiomics and deep learning in the context of prostate cancer. It focuses on the clinical perspective of where machine learning implementations are most likely to have a beneficial real-world impact. A key contribution is the deployment aspect: the models are not simply proofs of concept but are conceived and applied in a practical scenario, from patient admission to treatment decision. The specific areas studied include automatic organ segmentation in medical images, automatic quality assurance of segmentations, image processing, and radiomic feature analysis. Finally, a comprehensive study is performed on predicting essential pathological variables with AI, which so far has not been studied previously. Taken together, the methods outlined in this thesis constitute a concrete pathway of how AI can be used to bolster the steps along the patient's clinical trajectory. Successful applications of these methods hold the potential to reduce the workload of clinicians and improve patient outcomes.
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Books on the topic "Radiomic"

1

Novinarska radionica. 4th ed. Beograd: Zavod za Udžbenike i nastavna sredstva, 2000.

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Howland, Kenneth W. Radioman communications. [Pensacola, Fla.]: The Activity, 1994.

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Seth, Carl Magnus von. Radioliv: Dikter. Stockholm: Carlsson, 1995.

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Bush, Earl E. Radioman 1 & C. [Pensacola, Fla.]: The Center, 1986.

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Mohy-ud-Din, Hassan, and Saima Rathore, eds. Radiomics and Radiogenomics in Neuro-oncology. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40124-5.

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Tansley, David. Radionic healing: Is it for you? Dorset: Wessex Aquarian, 1995.

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Radionica za izradu naočara: Roman. Beograd: Prosveta, 1995.

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Vraneš, Aleksandra. Kreativne radionice u školskoj biblioteci. Beograd: Filološki fakultet univerziteta u Beogradu, 2012.

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Bi͡akov, V. M. Radioliz vody v i͡adernykh reaktorakh. Moskva: Ėnergoatomizdat, 1990.

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Khojazod, Saidmurodi. Taʺrikhi radioi Tojikiston. Dushanbe: Devashtich, 2006.

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Book chapters on the topic "Radiomic"

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Germanese, Danila, Sara Colantonio, Claudia Caudai, Maria Antonietta Pascali, Andrea Barucci, Nicola Zoppetti, Simone Agostini, et al. "May Radiomic Data Predict Prostate Cancer Aggressiveness?" In Computer Analysis of Images and Patterns, 65–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29930-9_7.

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Gupta, Nitika, and Priyanka Sharma. "A Review on Radiomic Analysis for Medical Imaging." In Algorithms for Intelligent Systems, 439–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6707-0_43.

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Sreekrishna, M., and T. Prem Jacob. "Imaging Radiomic-Driven Framework for Automated Cancer Investigation." In Artificial Intelligence on Medical Data, 85–94. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0151-5_7.

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Shallu, Pankaj Nanglia, Sumit Kumar, and Ashish Kumar Luhach. "Detection and Analysis of Lung Cancer Using Radiomic Approach." In Smart Computational Strategies: Theoretical and Practical Aspects, 13–24. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6295-8_2.

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Li, Hongwei, Fei-Fei Xue, Krishna Chaitanya, Shengda Luo, Ivan Ezhov, Benedikt Wiestler, Jianguo Zhang, and Bjoern Menze. "Imbalance-Aware Self-supervised Learning for 3D Radiomic Representations." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 36–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87196-3_4.

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Chaddad, Ahmad, Mingli Zhang, Christian Desrosiers, and Tamim Niazi. "Deep Radiomic Features from MRI Scans Predict Survival Outcome of Recurrent Glioblastoma." In Radiomics and Radiogenomics in Neuro-oncology, 36–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40124-5_4.

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Shaheen, Asma, Stefano Burigat, Ulas Bagci, and Hassan Mohy-ud-Din. "Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features." In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 259–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66843-3_25.

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Prazuch, Wojciech, Malgorzata Jelitto-Gorska, Agata Durawa, Katarzyna Dziadziuszko, and Joanna Polanska. "Radiomic-Based Lung Nodule Classification in Low-Dose Computed Tomography." In Bioinformatics and Biomedical Engineering, 357–63. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07704-3_29.

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Muscas, Giovanni, Simone Orlandini, Eleonora Becattini, Francesca Battista, Victor E. Staartjes, Carlo Serra, and Alessandro Della Puppa. "Radiomic Features Associated with Extent of Resection in Glioma Surgery." In Acta Neurochirurgica Supplement, 341–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85292-4_38.

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Neher, Helmut, John Arlette, and Alexander Wong. "Discovery Radiomics for Detection of Severely Atypical Melanocytic Lesions (SAML) from Skin Imaging via Deep Residual Group Convolutional Radiomic Sequencer." In Lecture Notes in Computer Science, 307–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27272-2_26.

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Conference papers on the topic "Radiomic"

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Papp, L., I. Rausch, M. Hacker, and T. Beyer. "Fuzzy Radiomics: A novel approach to minimize the effects of target delineation on radiomic models." In NuklearMedizin 2019. Georg Thieme Verlag KG, 2019. http://dx.doi.org/10.1055/s-0039-1683478.

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Wei, Jun, Heang-Ping Chan, Mark A. Helvie, Marilyn A. Roubidoux, Chuan Zhou, and Lubomir Hadjiiski. "Radiomic modeling of BI-RADS density categories." In SPIE Medical Imaging, edited by Samuel G. Armato and Nicholas A. Petrick. SPIE, 2017. http://dx.doi.org/10.1117/12.2255175.

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Ghouri, Muhammad Hamza, and Khan Bahadar Khan. "Radiomic Features Extraction Based on Genetic Algorithm." In 2020 IEEE 23rd International Multitopic Conference (INMIC). IEEE, 2020. http://dx.doi.org/10.1109/inmic50486.2020.9318183.

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Rifi, Amir L., Ines Dufait, Chaimae El Aisati, Mark De Ridder, and Kurt Barbe. "Unraveling the biological meaning of radiomic features." In 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2022. http://dx.doi.org/10.1109/memea54994.2022.9856571.

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Parekh, Vishwa S., and Michael A. Jacobs. "Radiomic Synthesis Using Deep Convolutional Neural Networks." In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI). IEEE, 2019. http://dx.doi.org/10.1109/isbi.2019.8759491.

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Haarburger, Christoph, Justus Schock, Daniel Truhn, Philippe Weitz, Gustav Mueller-Franzes, Leon Weninger, and Dorit Merhof. "Radiomic Feature Stability Analysis Based on Probabilistic Segmentations." In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020. http://dx.doi.org/10.1109/isbi45749.2020.9098674.

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Lukas, Brandon, Maria Tsoukas, and Kamran Avanaki. "Informative OCT radiomic features towards improved melanoma detection." In Photonics in Dermatology and Plastic Surgery 2022, edited by Bernard Choi and Haishan Zeng. SPIE, 2022. http://dx.doi.org/10.1117/12.2613020.

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Alhilali, Lea M., Shawn Stevens, Michael Lawton, Kaith Alfmety, Andrew Little, Kris Smith, Kevin King, Saeed Fakhran, and Randall Porter. "Radiomic Correlates of Hearing Loss in Vestibular Schwannomas." In 31st Annual Meeting North American Skull Base Society. Georg Thieme Verlag KG, 2022. http://dx.doi.org/10.1055/s-0042-1743613.

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Horng, Hannah, Apurva Singh, Bardia Yousefi, Eric A. Cohen, Babak Haghighi, Sharyn Katz, Peter B. Noël, Russell T. Shinohara, and Despina Kontos. "Iterative ComBat methods for harmonization of radiomic features." In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin, Karen Drukker, Maciej A. Mazurowski, Hongbing Lu, Chisako Muramatsu, and Ravi K. Samala. SPIE, 2022. http://dx.doi.org/10.1117/12.2610831.

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Brunese, Luca, Francesco Mercaldo, Alfonso Reginelli, and Antonella Santone. "Neural Networks for Lung Cancer Detection through Radiomic Features." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852169.

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Reports on the topic "Radiomic"

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Wang, Yingxuan, Cheng Yan, and Liqin Zhao. The value of radiomics-based machine learning for hepatocellular carcinoma after TACE: a systematic evaluation and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0100.

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Review question / Objective: Meta-analysis was performed to predict the efficacy and survival status of patients with hepatocellular carcinoma after the application of TACE, applying clinical models, radiomic models and combined models for non-invasive assessment.We performed a Meta-analysis on the prediction of efficacy and survival status after TACE for hepatocellular carcinoma. Condition being studied: Patients were scanned using CT or MR machines, and some patients had multiple follow-up records, and imaging feature extraction software was applied to extract regions of interest and build multiple prediction models.Literature screening was conducted by two reviewers independently, who had more than 3 years’ experience in imaging diagnosis and was cross-checked. Disagreements were settled by a third reviewer.
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Ouyang, Zhiqiang, Qian Li, Guangrong Zheng, Tengfei Ke, Jun Yang, and Chengde Liao. Radiomics for predicting tumor microenvironment phenotypes in non-small cell lung cance: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0060.

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Review question / Objective: Tumor microenvironment (TIME) phenotype is an important factor to affect the response and prognosis of immunotherapy in non-small cell lung cancer (NSCLC). Recently, accumulating studies have noninvasivly perdited the TIME phenotypes of NSCLC by using CT or PET/CT based radiomics. We will conduct this study by means of meta-analysis to eveluate the power and value of CT or PET/CT based radiomics for predicting TIME phenotypes in NSCLC patients. Condition being studied: At present, several recent prospective or retrospective cohort studies and randomized controlled studies have confirmed that CT or PET/CT-based radiomics were the potential tools to predict TIME phenotypes in NSCLC. However, this conclusion is controversial because of the difference of prediction profermance of different studies. The published and unpublished investigations will be included in this study. We will comprehensively evaluate the heterogeneity of these investigations, and the power and value of radiomics for predicting TIME phenotypes.
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Ford, Patrick, Jack Doyle, Shelia Schultz, R. G. Hoffman, and Steven E. Lammlein. Job Performance Measurement Test Package for the Navy Radiomen. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada213796.

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Lammlein, S. E., and H. G. Baker. Developing Performance Measures for the Navy Radioman (RM): Selecting Critical Tasks. Fort Belvoir, VA: Defense Technical Information Center, February 1987. http://dx.doi.org/10.21236/ada177887.

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Yang, Jiawen, Shuzong You, Limin Zhang, Huangqi Zhang, Binhao Zhang, Xue Dong, Wenting Pan, Shaofeng Duan, and Wenbin Ji. Prediction Power of Radiomics in Early Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0099.

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Di Campli San Vito, Patrizia, Stephen Brewster, Satvik Venkatesh, Eduardo Miranda, Alexis Kirke, David Moffat, Sube Banerjee, Alex Street, Jorg Fachner, and Helen Odell-Miller. RadioMe: Supporting Individuals with Dementia in Their Own Home... and Beyond? CHI '22 Workshop - Designing Ecosystems for Complex Health Needs, 2022. http://dx.doi.org/10.36399/gla.pubs.267520.

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Dementia is an illness with complex health needs, varying between individuals and increasing in severity over time. Approaches to use technology to aid people with dementia are often designed for a specific environment and/or purpose, such as the RadioMe system, a system designed to detect agitation in people with mild dementia living in their own home and calming them with music when agitation is detected. Both the monitoring and intervention components could potentially be beneficially used outside of the own home to aid people with dementia and carers in everyday life. But the adaptation could put additional burdens on the carer, as many decisions and the handling of the data and software could rely on their input. In this paper we discuss thoughts on the potential role of the carer for adaptations of specified system’s expansion to a larger ecosystem on the example of RadioMe.
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Partarrieu, I. X., N. Smith, and P. Harris. What Am I Measuring? Using Experimental Design to Understand and Improve Measurement Pipelines, with an Example Application to Radiomics Measurements. National Physical Laboratory, March 2022. http://dx.doi.org/10.47120/npl.ms35.

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zheng, xiushan. CT-based radiomics for prediction of lymph node metastasis in lung cancer A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0167.

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