MRI-based habitat radiomics for assessing synchronous metastatic risk in renal cell carcinoma: a multicenter study.

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Abstract

To explore the role of MRI-based habitat radiomics in assessing the metastatic status of renal cell carcinoma (RCC). This study retrospectively collected 241 patients with RCC who underwent nephrectomy and lymphadenectomy at four centers. MRI data from the first center were split into a training set (n = 150) and an internal test set (n = 38); data from the other centers (n = 53) were used for external testing. Based on corticomedullary-phase enhancement and T2WI signal intensity, primary lesions were segmented into 15 habitat subregions. Radiomic features were extracted from the whole-tumor and habitat subregions, respectively. Machine learning algorithms were employed to construct models. Clinical indicators were then integrated to establish a combined model, with performance comparisons and subgroup analyses conducted based on both the internal and external test sets. Among the 241 patients (mean age 53 ± 13 years; 169 males), 36.1% exhibited distant or regional lymph node (RLN) metastases. The habitat models generally achieved higher area under the curves (AUCs) compared with the whole-tumor models in the internal and external test sets. By incorporating RLN size, the combined model outperformed the habitat model in the internal test set (AUC, 0.88 vs. 0.82, P = 0.020) and the clinical model integrating RLN size and hematuria in the external test set (AUC, 0.89 vs. 0.73, P = 0.012). Subgroup analyses showed that the combined model could independently identify distant and RLN metastases, unaffected by pathological subtypes. MRI-based habitat radiomics model provides a non-invasive tool for accurately assessing metastatic status in RCC.

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