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Objective: To establish and validate a radiomics nomogram that incorporated tumor habitat and peritumor features to predict tumor progression in patients with soft tissue sarcoma (STS). Methods: MRI data (fat-suppressed T2-weighted and contrast-enhanced fat-suppressed T1-weighted images) from 148 STS patients treated in four institutions were retrospectively enrolled. Patients were divided into a training cohort (n = 108) and validation cohort (n = 40). K-means clustering was applied to split intratumoral voxels into three habitats according to signal intensity values. A large number of radiomics features were extracted from numerous tumor-associated regions (tumor lesion, peritumor, tumor expansion, and intratumoral habitats) to construct a series of radiomics signatures. A nomogram integrating clinical predictors and radiomics signature was established and its value for predicting progression was validated. Results: The nomogram yielded superior prediction performance and less predictive error in the validation cohort (C-index, 0.777; median area under the receiver operating characteristic curve, 0.808; integrated Brier score, 0.135). When patients were stratified according to risk of progression (low and high) based on the nomogram in both the training and validation cohorts, Kaplan-Meier survival analysis demonstrated significant differences in progression-free survival between the groups. In addition, it could attach incremental value to histopathological grade system in progression risk evaluation. Conclusion: A nomogram based on intratumoral habitat and peritumor radiomics predicts tumor progression in STS patients and stratifies them according to risk of progression.
Liang et al. (2026) studied this question.