RESUMO
PURPOSE: To investigate the effectiveness of an automatic analysis framework based on 3D-CT multi-scale features in predicting Ki67 expression levels in substantial renal cell carcinoma (RCC). METHODS: This retrospective study was conducted using multi-center cohorts consisting of 588 participants with pathologically confirmed RCC. The participants were divided into an internal training set (n = 485) and an external testing set (n = 103) from four and one local hospitals, respectively. The proposed automatic analytic framework comprised a 3D kidney and tumor segmentation model constructed by 3D UNet, a 3D-CT multi-scale features extractor based on the renal-tumor feature, and a low or high Ki67 prediction classifier using XGBoost. The framework was validated using a fivefold cross-validation strategy. The Shapley additive explanation (SHAP) method was used to determine the contribution of each feature. RESULTS: In the prediction of low or high Ki67, the combination of renal and tumor features achieved better performance than any single features. Internal validation using a fivefold cross-validation strategy yielded AUROC values of 0.75 ± 0.1, 0.75 ± 0.1, 0.83 ± 0.1, 0.77 ± 0.1, and 0.87 ± 0.1, respectively. The optimal model achieved an AUROC of 0.87 ± 0.1 and 0.82 ± 0.1 for low vs. high Ki67 prediction in the internal validation and external testing sets, respectively. Notably, the tumor first-order-10P was identified as the most influential feature in the model decision. CONCLUSIONS: Our study suggests that the proposed automatic analysis framework based on 3D-CT multi-scale features has great potential for accurately predicting Ki67 expression levels in substantial RCC. CRITICAL RELEVANCE STATEMENT: Automatic analysis framework based on 3D-CT multi-scale features provides reliable predictions for Ki67 expression levels in substantial RCC, indicating the potential usage of clinical applications.
