[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124398-en":3,"doc-seo-124398-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124398,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Differentiating Between Renal Medullary and Clear Cell Renal Carcinoma With a Machine Learning Radiomics Approach","Objective: Develop and validate a radiomics-based machine learning model to distinguish renal medullary carcinoma (RMC) from clear cell renal carcinoma (ccRCC). Methods: Retrospective, IRB-approved analysis of pre-nephrectomy contrast-enhanced CT images and clinical data from 87 RMC and 93 ccRCC patients. Radiomics features (949) were extracted using PyRadiomics and evaluated via RadAR, then classified using extreme gradient boosting, generating three models using demographics, radiomics, and radiomics plus sickle cell trait. Results: AUC improved from 0.777 (demographics) to 0.915 (radiomics) and to 1.0 when sickle cell trait was incorporated.","The Texas Medical Center Library  \nDigitalCommons@TMC  \n\n| Faculty, Staff and Student Publications | MD Anderson UTHealth Houston Graduate School |\n| --- | --- |\n| 2-6-2025\u003Cbr>Differentiating Between Renal Medullary and Clear Cell Renal Carcinoma With a Machine Learning Radiomics Approach\u003Cbr>Rahim Jiwani\u003Cbr>Koustav Pal Iwan Paolucci\u003Cbr>Bruno Odisio\u003Cbr>Kristy Brock\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://digitalcommons.library.tmc.edu/uthgsbs_docs](https://digitalcommons.library.tmc.edu/uthgsbs_docs)\u003Cbr> Part of the Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, and the Oncology Commons |  |\n\nAuthors  \nRahim Jiwani, Koustav Pal, Iwan Paolucci, Bruno Odisio, Kristy Brock, Nizar M Tannir, Daniel D Shapiro, Pavlos Msaouel, and Rahul A Sheth  \nDifferentiating between renal medullary and clear cell renal carcinoma with a machine learning radiomics approach  \nRahim Jiwani1,‡, Koustav Pal1,‡,, Iwan Paolucci1,, Bruno Odisio1, Kristy Brock2, Nizar M. Tannir3, Daniel D. Shapiro4, Pavlos Msaouel3,5,6,, RahulA. Sheth*, 1  \n1Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States, 2Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States,  \n3Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States, 4Department of Urology, University of Wisconsin School of Medicine and Public Health, Madison, WI 77030, United States,  \n5Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States, 6David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States  \n* Corresponding author: Rahul A. Sheth, MD, Department of Interventional Radiology, 1515 Holcombe Blvd., Unit 1471, Houston, TX 77030-4009, USA. (rasheth@ [mdanderson.org](mdanderson.org)) .  \n‡Co-first authors.  \nAbstract  \nBackground: The objective of this study was to develop and validate a radiomics-based machine learning (ML) model to differentiate between renal medullary carcinoma (RMC) and clear cell renal carcinoma (ccRCC) .  \nMethods: This retrospective Institutional Review Board-approved study analyzed CT images and clinical data from patients with RMC (n = 87) and ccRCC (n = 93) . Patients without contrast-enhanced CT scans obtained before nephrectomy were excluded. A standard volumetric software package (MIM 7.1.4, MIM Software Inc.) was used for contouring, after which 949 radiomics features were extracted with PyRadiomics 3.1.0. Radiomics analysis was then performed with RadAR for differential radiomics analysis. ML was then performed with extreme gradient boosting (XGBoost 2.0.3) to differentiate between RMC and ccRCC. Three separate ML models were created to differentiate between ccRCC and RMC. These models were based on clinical demographics, radiomics, and radiomics incorporating hemoglobin electrophoresis for sickle cell trait, respectively.  \nResults: Performance metrics for the 3 developed ML models were as follows: demographic factors only (AUC = 0.777), calibrated radiomics (AUC = 0.915), and calibrated radiomics with sickle cell trait incorporated (AUC = 1.0) . The top 4 ranked features from differential radiomic analysis, ranked by their importance, were run entropy (preprocessing filter = original, AUC = 0.67), dependence entropy (preprocessing filter = wavelet, AUC = 0.67), zone entropy (preprocessing filter = original, AUC = 0.67), and dependence entropy (preprocessing filter = original, AUC = 0.66) .  \nConclusion: A radiomics-based machine learning model effectively differentiates between ccRCC and RMC. This tool can facilitate the radiologist’s ability to suspicion and decrease the misdiagnosis rate of RMC.  \nKey words: renal medullary","cbCaikmfGnuITwUq","https://ap.wps.com/l/cbCaikmfGnuITwUq","pdf",1194450,1,13,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Disease overview and clinical relevance\n# Implications for practice\n# Keywords","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To develop and validate a radiomics-based machine learning model that differentiates renal medullary carcinoma from clear cell renal carcinoma.\"},{\"question\":\"What data and imaging source were used to build the models?\",\"answer\":\"The models used retrospective CT images and clinical data from patients with RMC and ccRCC, using pre-nephrectomy contrast-enhanced CT scans.\"},{\"question\":\"How did model performance compare across the three approaches?\",\"answer\":\"Performance increased from a demographics-only model (AUC 0.777) to a radiomics-only model (AUC 0.915), and reached AUC 1.0 when radiomics was combined with sickle cell trait.\"}]","Differentiating Between Renal Medullary and Clear Cell Renal Carcinoma With a Machine Learning Radiomics Approach | 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was the main objective of the study?","Question",{"text":75,"@type":76},"To develop and validate a radiomics-based machine learning model that differentiates renal medullary carcinoma from clear cell renal carcinoma.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and imaging source were used to build the models?",{"text":80,"@type":76},"The models used retrospective CT images and clinical data from patients with RMC and ccRCC, using pre-nephrectomy contrast-enhanced CT scans.",{"name":82,"@type":73,"acceptedAnswer":83},"How did model performance compare across the three approaches?",{"text":84,"@type":76},"Performance increased from a demographics-only model (AUC 0.777) to a radiomics-only model (AUC 0.915), and reached AUC 1.0 when radiomics was combined with sickle cell 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