A Robust and Explainable Radiomics Pipeline for Small Multimodal Datasets to Predict Metastasis in Patients with Soft Tissue Sarcoma

Preoperative radiotherapy and surgery are standard treatments for high-risk soft tissue sarcomas (STS), yet distant metastasis remains a significant clinical challenge. This study aims to develop a robust and interpretable multi-omics predictive model for metastasis, specifically addressing the limitations of small datasets. A retrospective analysis included 40 STS patients (23 with metastasis) treated with preoperative radiotherapy and surgery. Using PyRadiomics, 107 radiomic and dosiomic features were extracted from CT scans, dose distributions, and post-treatment contrast-enhanced T1-weighted MRI. A Logistic Regression classifier with L1 regularization was identified as optimal and rigorously evaluated via Stratified 5-Fold Cross-Validation repeated 100 times. Single-modality models demonstrated limited predictive performance, yielding mean AUC-ROC values of 0.52 for CT, 0.59 for dose distribution, and 0.62 for normalized MRI. Integrating these features into a combined multi-omics signature substantially improved the mean AUC-ROC to 0.75. Furthermore, model explainability was ensured using Log-Odds to identify key predictive features. Combining anatomical, dosiomic, and MRI-derived features significantly enhances the prediction of metastatic relapse in STS patients. This interpretable approach shows strong potential for guiding personalized clinical treatments.