Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.

Journal: Diagnostics (Basel, Switzerland)
Published Date: May 29, 2025

Abstract

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, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learning in improving diagnostic accuracy for bone and soft tissue tumors. We examine integrating quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to enable non-invasive tumor profiling. AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation (e.g., Osteosarcoma vs. Ewing sarcoma), and predicting mutation signatures (e.g., TP53, RB1). Moreover, we highlight the use of liquid biopsy and circulating tumor DNA (ctDNA) as emerging diagnostic biomarkers, coupled with point-of-care molecular assays, to enable early and accurate detection in low-resource settings. The review concludes by discussing translational barriers, including data harmonization, regulatory challenges, and the need for multi-institutional datasets to validate AI-based diagnostic frameworks. This article synthesizes current advancements and provides a forward-looking view of precision diagnostics in musculoskeletal oncology.

Authors

  • Rahul Kumar
  • Kyle Sporn
    Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA.
  • Akshay Khanna
    Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA.
  • Phani Paladugu
    Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA.
  • Chirag Gowda
    Miller School of Medicine, University of Miami, Miami, FL 33146, USA.
  • Alex Ngo
    Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1600 NW 10th Ave, Miami, FL 33136, USA.
  • Ram Jagadeesan
    Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
  • Nasif Zaman
    Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, United States.
  • Alireza Tavakkoli
    Department of Computer Science and Engineering, University of Nevada School of Medicine, Reno, NV 89557, USA.

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