Application of MR-based Nested Habitat Radiomics for Predicting 1-Year PFS in Soft Tissue Sarcoma Patients Receiving Preoperative Radiotherapy

Motivation: Predicting patient outcomes in soft tissue sarcoma remains challenging, as traditional prognostic factors often fail to capture tumor heterogeneity and aggressiveness accurately. Goal(s): This study investigated the use of MRI-based nested habitat radiomics to predict one-year progression-free survival in STS patients who received preoperative radiotherapy. Approach: By segmenting tumors into high-risk subregions (sub-ROIs and micro-ROIs), the model identified aggressive areas linked to early progression. Results: The nested habitat approach achieved superior predictive accuracy (AUC 0.83) compared to traditional models. Our findings suggest this method can aid in risk stratification, allowing clinicians to better tailor treatments and identify patients at high risk for recurrence. Impact: The nested habitat radiomics approach offers a non-invasive method to identify aggressive tumor regions, potentially transforming clinical practice by enabling personalized risk assessment. This could guide tailored treatment plans, improve patient outcomes, and reduce recurrence rates in soft tissue sarcoma management