MRI Based Intratumoral-Peritumoral Habitat Radiomics for Prediction of Overall Survival in Rhabdomyosarcoma: A Multicenter Study.
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RATIONALE AND OBJECTIVES: This study aimed to develop and validate an MRI-based habitat radiomics model integrating intratumoral and peritumoral heterogeneity for predicting overall survival (OS) in rhabdomyosarcoma (RMS). MATERIALS AND METHODS: This retrospective study included 486 patients with histologically confirmed RMS, divided into training (n = 289), validation (n = 124), and test (n = 73) cohorts. All patients underwent standardized MRI scans, and T1 contrast-enhanced sequences were used for feature extraction. The tumor was segmented into intratumoral and peritumoral regions, and voxel-wise clustering using K-means was applied to identify subregions or "habitats" with similar imaging characteristics. A total of 1762 radiomic features were extracted from these regions and subregions, including texture, shape, and fractal features. Feature selection was performed using Spearman correlation, univariate Cox regression, and LASSO-Cox regression. Prognostic models were constructed using multivariate Cox proportional hazards models. The models were evaluated using concordance index (C-index), time-dependent AUC, Kaplan-Meier survival analysis, calibration curves, and decision curve analysis (DCA). RESULTS: The habitat-based models significantly outperformed conventional radiomics models. The intratumoral habitat model (Intra_Habitat) achieved a C-index of 0.876 in the training cohort, surpassing the whole-tumor model (C-index: 0.823). Similarly, the peritumoral habitat model (Peri_Habitat) demonstrated superior performance (C-index: 0.876). The integrated intratumoral-peritumoral habitat model (IntraPeri_Habitat) showed the best overall prognostic performance across all cohorts, with a C-index of 0.876 in the training cohort and 0.770 in the external test cohort. Time-dependent AUC for 5-year overall survival further confirmed its robust discriminative power. Kaplan-Meier analysis confirmed significant stratification between high- and low-risk groups (log-rank P < .001). Calibration curves showed excellent agreement between predicted and observed survival outcomes and DCA demonstrated superior net clinical benefit. CONCLUSION: MRI-based habitat analysis offers a refined method to capture tumor heterogeneity and provide insights into survival outcomes in RMS patients. The integrated intratumoral-peritumoral habitat model demonstrates promising prognostic value, potentially aiding personalized treatment strategies in the future.