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Updated: Jul 15, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
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Machine Learning for Predicting Ki-67 Expression in Renal Tumors: A Systematic Review And Meta-Analysis.

Yu Liu1, Guiqing Zhu2, Zhigang Xiu1

  • 1Department of Radiology, West China Longquan Hospital Sichuan University, Chengdu, China (Y.L., Z.X.).

Academic Radiology
|July 13, 2026
PubMed
Summary

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Machine learning (ML) models show moderate accuracy for predicting Ki-67 expression in renal tumors noninvasively. While not replacing pathology, these ML tools may aid diagnosis, but require further optimization and validation for clinical use.

Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Ki-67 expression is a key prognostic marker in renal tumors.
  • Accurate Ki-67 prediction is crucial for treatment decisions.
  • Noninvasive prediction methods are highly desirable to avoid tumor biopsy.

Purpose of the Study:

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

Main Methods:

  • Systematic literature search of major databases (PubMed, Web of Science, Embase, Cochrane Library) up to November 2025.
  • Inclusion of studies using ML to predict Ki-67 with immunohistochemistry as the reference standard.
Keywords:
Ki-67Machine learningMeta-analysisRenal cell carcinomaRenal tumor

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  • Bivariate random-effects modeling for pooled diagnostic metrics and meta-regression to explore heterogeneity.

    • Main Results:

    • Seven studies with 1176 training and 885 validation patients were included.
    • Pooled AUC was 0.85 for training and 0.86 for validation cohorts, with pooled sensitivity and specificity of 0.81/0.84 (training) and 0.83/0.73 (validation).
    • Heterogeneity was observed, influenced by tumor type, Ki-67 cut-off, feature extraction, and ML algorithm; XGBoost showed promising results.

    Conclusions:

    • ML models demonstrate moderate diagnostic performance for noninvasive Ki-67 prediction in renal tumors.
    • Accuracy is influenced by various factors, suggesting a need for standardization and optimization.
    • Current ML models may serve as complementary tools, with future research focusing on prospective validation for clinical applicability.