Table 1_Multiparametric MRI-based habitat and peritumoral radiomics integrating semi-automated segmentation for preoperative prediction of high-grade prostate cancer: a dual-center study.docx

Predictive classifiers were optimized through machine learning algorithms, particularly a multilayer perceptron (MLP) for the habitat signature. A combined clinical-radiomics nomogram was constructed, with SHapley Additive exPlanations (SHAP) employed to enhance model interpretability. Subgroup analyses were performed to assess the discriminative performance of the combined nomogram across the peripheral zone (PZ) and transition zone (TZ).</p>Results<p>The combined nomogram exhibited superior diagnostic performance, achieving AUCs of 0.914, 0.903, and 0.797 in the training cohort, internal validation cohort, and external test cohort, respectively.

The MLP-based habitat model showed good discriminative performance, with AUCs of 0.896, 0.897, and 0.782 across the training cohort, internal validation cohort, and external test cohort, respectively. Among the peritumoral regions, the 5-mm shell demonstrated the best discriminative performance. SHAP analysis identified Habitat 1 Shape Sphericity as the most influential feature.

Subgroup analysis demonstrated the discriminatory ability of the combined nomogram in both the PZ (AUC: 0.796) and TZ (AUC: 0.753) subgroups.</p>Conclusion<p>The integration of semi-automated segmentation-based habitat analysis and peritumoral radiomics provides a promising approach for preoperative risk stratification of prostate cancer.</p>