Externally validated MRI radiomics machine learning models for differentiating between myxoid liposarcoma and myxoma.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
- School of Science, Engineering and Environment, University of Salford, Manchester, UK.
- Data Science and AI Hub, University of Salford, Manchester, UK.
- Institute for Diagnostic and Interventional Radiology, University Hospital of Cologne, University of Cologne, 50937, Cologne, Germany.
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Orthopedics, Yawkey Center for Outpatient Care, Suite 3700-3B, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.
- Georgetown University, Washington, DC, USA.
- Harvard Medical School, Boston, MA, USA.
- Department of Orthopedics, Tufts Medical Center, Boston, MA, USA.
- University of California San Diego, La Jolla, CA, USA.
- Department of Orthopedics, Yawkey Center for Outpatient Care, Suite 3700-3B, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA. [email protected].
Abstract
To develop and externally validate a radiomics-based machine learning model for differentiating myxoid liposarcoma (MLS) from myxoma on MRI and compare its diagnostic performance with that of experienced musculoskeletal radiologists. In this retrospective study, T1-weighted and fluid-sensitive MRI sequences from 109 patients with histologically confirmed MLS or myxoma were used to train support vector machine (SVM), random forest (RF), and XGBoost (XGB) classifiers. Models were evaluated in an independent external cohort of 32 patients from a second institution using different scanner hardware and protocols. Diagnostic performance was compared with that of two fellowship-trained senior musculoskeletal radiologists. Secondary analysis assessed MLS grading. The T1-weighted SVM achieved the strongest external binary performance (accuracy, 0.84; AUC, 0.91), outperforming RF and XGB (p = 0.005 and p < 0.001, respectively). Its performance did not differ significantly from that of radiologists (AUC, 0.88-0.94; p > 0.05), although the study was not powered to establish equivalence. Fluid-sensitive SVM performance was limited (accuracy, 0.56; AUC, 0.60), indicating sequence-dependent generalizability. Models and radiologists demonstrated limited MLS grading accuracy, reflecting tumor heterogeneity. Externally validated radiomics-based machine learning supported noninvasive differentiation of MLS from myxoma primarily on T1-weighted MRI. T1-weighted SVM performance was not significantly different from that of experienced radiologists, although equivalence was not established, whereas fluid-sensitive performance was substantially weaker. T1-weighted quantitative imaging biomarkers may complement soft tissue tumor characterization, but extrapolation to fluid-sensitive sequences requires caution.