A multimodal radiomics, deep learning, and pathomics signature for predicting the prognosis in central conventional...

This study aims to develop and validate a multimodal feature-integrated signature (MS) by combining radiomics, deep learning (DL), and pathomics features to predict prognosis in central conventional chondrosarcoma (CS) patients.

In this multicenter retrospective study, 166 central conventional CS patients who underwent surgery were enrolled from two institutions and divided into a training cohort (n = 117) and a validation cohort (n = 49). Using preoperative non-enhanced computed tomography (CT) images and postoperative whole slide imaging (WSI) of hematoxylin and eosin (H&E)-stained pathological sections, we extracted radiomics, DL, and pathomics features. These features were used to construct and validate 4 prognostic prediction signatures: the radiomics signature (RS), the DL signature (DS), the pathomics signature (PS), and the MS. Predictive performance was assessed via Harrell concordance index (C-index) and hazard ratio (HR). The SHapley Additive exPlanations (SHAP) analysis was applied to interpret the signature prediction process.

The RS, DS and PS demonstrated predictive ability, with C-indices of 0.735 (95% confidence interval‌ [CI]: 0.587-0.884), 0.767 (95% CI: 0.613-0.921) and 0.711 (95% CI: 0.555-0.867) in the validation cohort, respectively. The MS achieved the highest C-index among the signatures, with a C-index of 0.821 (95% CI: 0.660-0.982). Progression-free survival (PFS) significantly differed between high-risk and low-risk patients stratified by MS (log-rank test, P < 0.001). SHAP analysis quantified the contribution of individual features to the signature's predictions.

The MS showed promising prognostic value for prognostic prediction in central conventional CS. It may provide useful support for postoperative risk stratification.