[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125331-en":3,"doc-seo-125331-105":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":4,"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},125331,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care","Musculoskeletal tumors pose diagnostic difficulty because of their rarity, histologic heterogeneity, and overlapping imaging appearances, which can force reliance on invasive biopsy and subjective radiologic interpretation. This review examines how radiogenomics and machine learning improve diagnostic accuracy for bone and soft-tissue tumors by integrating quantitative imaging from MRI, CT, and PET with genomic and transcriptomic data. AI approaches using CNNs and radiomics aim to support tumor grading, subtype differentiation, and mutation-signature prediction, while liquid biopsy and ctDNA enable emerging biomarker-based detection. The review also addresses translational barriers including data harmonization, regulatory issues, and the need for multi-institutional validation datasets.","Thomas Jefferson University  \nJefferson Digital Commons  \n\n| Department of Medicine Faculty Papers | Department of Medicine |\n| --- | --- |\n\n5-29-2025  \nIntegrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care  \nRahul Kumar  \nKyle Sporn Akshay Khanna Phani Paladugu Chirag Gowda  \nSee next page for additional authors  \nFollow this and additional works at: [https://jdc.jefferson.edu/medfp](https://jdc.jefferson.edu/medfp)  \n Part of the Computational Biology Commons, Diagnosis Commons, and the Radiology Commons Let us know how access to this document benefits you  \nThis Article is brought to you for free and open access by the Jefferson Digital Commons. The Jefferson Digital Commons is a service of Thomas Jefferson University's Center for Teaching and Learning (CTL) . The Commons is a showcase for Jefferson books and journals, peer-reviewed scholarly publications, unique historical collections from the University archives, and teaching tools. The Jefferson Digital Commons allows researchers and interested readers anywhere in the world to learn about and keep up to date with Jefferson scholarship. This article has been accepted for inclusion in Department of Medicine Faculty Papers by an authorized administrator of the Jefferson Digital Commons. For more information, please contact: [JeffersonDigitalCommons@jefferson.edu](JeffersonDigitalCommons@jefferson.edu).  \nAuthors  \nRahul Kumar, Kyle Sporn, Akshay Khanna, Phani Paladugu, Chirag Gowda, Alex Ngo, Ram Jagadeesan, Nasif Zaman, and Alireza Tavakkoli  \nReview  \nIntegrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care  \nRahul Kumar 1, *, Kyle Sporn 2, Akshay Khanna 3, Phani Paladugu 3,4, Chirag Gowda 1, Alex Ngo 1, Ram Jagadeesan 5,6, Nasif Zaman 7 and Alireza Tavakkoli 7  \nAcademic Editor: Dechang Chen  \nReceived: 4 May 2025  \nRevised: 21 May 2025  \nAccepted: 23 May 2025  \nPublished: 29 May 2025  \nCitation: Kumar, R.; Sporn, K.; Khanna, A.; Paladugu, P.; Gowda, C.; Ngo, A.; Jagadeesan, R.; Zaman, N.; Tavakkoli, A. Integrating Radiogenomicsand Machine Learning in Musculoskeletal Oncology Care. Diagnostics 2025, 15, 1377 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics15111377  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA; [gowdachirag24@gmail.com](gowdachirag24@gmail.com) (C.G.); [axn668@med.miami.edu](axn668@med.miami.edu) (A.N.)  \n2 Norton College of Medicine, Upstate Medical University, Syracuse, NY 13210, USA; [spornk@upstate.edu](spornk@upstate.edu)  \n3 Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA; [aya156@students.jefferson.edu](aya156@students.jefferson.edu) (A.K.); [phani.paladugu@students.jefferson.edu](phani.paladugu@students.jefferson.edu) (P.P.)  \n4 Brigham and Women’s Hospital, Boston, MA 02115, USA  \n5 Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; [ramjagad@cisco.com](ramjagad@cisco.com)  \n[6](6 Cisco AI Systems)[ Cisco AI Systems](6 Cisco AI Systems), [Cisco Inc](Cisco Inc)., [San Jose](San Jose), [CA 95134](CA 95134), [USA](USA)  \n7 Department of Computer Science, University of Nevada Reno, Reno, NV 89557, USA; [zaman@nevada.unr.edu](zaman@nevada.unr.edu) (N.Z.); [tavakkol@unr.edu](tavakkol@unr.edu) (A.T.)  \n* Correspondence: [rxk641@miami.edu](rxk641@miami.edu)  \nAbstract: Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis","cbCaioXmfvDw2Qyl","https://ap.wps.com/l/cbCaioXmfvDw2Qyl","pdf",2783643,1,37,"English","en",105,"# Introduction\n## Diagnostic challenges in musculoskeletal tumors\n## Need for objective, reproducible precision diagnostics\n# Radiogenomics and machine learning approaches\n## Quantitative imaging integration (MRI, CT, PET)\n## AI methods (CNNs, radiomic texture analysis)\n# Clinical applications and emerging biomarkers\n## Tumor grading and subtype differentiation\n## Predicting mutation signatures\n## Liquid biopsy and ctDNA in diagnosis\n# Translational considerations\n## Data harmonization and regulatory challenges\n## Validation with multi-institutional datasets","[{\"question\":\"Why are musculoskeletal tumors difficult to diagnose?\",\"answer\":\"They are clinically rare, show histological diversity, and often have overlapping imaging features, which complicates accurate interpretation.\"},{\"question\":\"How does the review describe using radiogenomics for diagnosis?\",\"answer\":\"It focuses on combining quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to support non-invasive tumor profiling.\"},{\"question\":\"What AI and biomarker approaches are highlighted for improved diagnostic accuracy?\",\"answer\":\"The review discusses convolutional neural networks and radiomic texture analysis for grading and subtype differentiation, as well as liquid biopsy with ctDNA and point-of-care molecular assays as emerging diagnostic biomarkers.\"}]","Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care | 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