Machine Learning for Predicting Ki-67 Expression in Renal Tumors: A Systematic Review And Meta-Analysis

To systematically evaluate the diagnostic performance of machine learning (ML) models for predicting Ki-67 expression in renal tumors and assess their potential for clinical translation.

We systematically searched PubMed, Web of Science, Cochrane Library, and Embase up to November 2025. Studies using ML to predict Ki-67 with immunohistochemistry as reference standard were included. Quality was assessed with QUADAS-2. A bivariate random-effects model pooled diagnostic metrics. Meta-regression and subgroup analyses explored heterogeneity.

Seven retrospective cohort studies were included, comprising 1176 patients in the training cohorts and 885 patients in the external validation cohorts. Pooled sensitivity, specificity, and area under the curve (AUC) were 0.81, 0.84, and 0.85 for training cohorts, and 0.83, 0.73, and 0.86 for validation cohorts. At 20% pretest probability, positive/negative predictions modified post-test probabilities to 55%/5%. Specificity showed substantial heterogeneity (I² > 74%). Meta-regression identified tumor type, Ki-67 cut-off, feature extraction software, and ML algorithm as heterogeneity sources. Subgroup analyses showed: RCC-specific studies and 5% Ki-67 cut-offs yielded higher estimates; PyRadiomics was most used (5 studies; sensitivity 0.79, specificity 0.85); eXtreme Gradient Boosting (XGBoost) showed numerically higher sensitivity and specificity than Random Forest (0.81/0.84 vs. 0.77/0.83).

ML demonstrates moderate diagnostic performance for noninvasive Ki-67 prediction in renal tumors. Diagnostic accuracy is influenced by tumor type, Ki-67 cut-off, feature extraction software, and algorithm selection. While current models cannot replace pathological assessment, they may serve as complementary tools. Future research should prioritize algorithm optimization, technical standardization, and prospective multi-center validation to enhance clinical applicability.