What type of study is this?

This is a Observational study (also classified as: Literature Review).

August 20, 2025

Prognostic role of Ki-67 in colorectal carcinoma: Development and evaluation of machine learning prediction models

Key Points

Ki-67 ≥ 50% significantly predicts higher survival in colorectal carcinoma patients, indicating its importance in prognosis.
The support vector machine, random forest, and XGBoost models achieved training AUC values of 0.851, 0.948, and 0.872, showing strong predictive capability.
External validation of machine learning models yielded AUC values for predicting Ki-67 status, indicating consistent performance across datasets.
Significant associations were found between Ki-67 cutoffs and histological features, suggesting its clinical relevance in grading colorectal carcinoma.

Abstract

BACKGROUND Ki-67 is a routine test item in clinical pathology departments. However, its prognostic value requires further investigation, especially in the context of research using machine learning (ML), which remains relatively underdeveloped. AIM To investigate the prognostic value of Ki-67 in cases of colorectal carcinoma (CRC) and explore the potential application of ML algorithms to predict the Ki-67 index. METHODS Case data and pathological sections from two centers were systematically collected. To analyze the prognostic value of the Ki-67 index in CRC, multiple cutoff values were established. Meanwhile, by virtue of the histological features presented in the hematoxylin and eosin-stained CRC images, three mainstream ML algorithms, support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost) were employed to construct prediction models. Subsequently, the potential of these algorithms to classify and predict the Ki-67 index was explored. RESULTS Non-parametric tests revealed that Ki-67 ≥ 40% correlated with a high histological grade (P = 0.017), deficient mismatch repair protein status associated with ≥ 50%-90% cutoffs (all P ≤ 0.028), and ≥ 80% linked to lymph node metastasis (P = 0.006). Kaplan-Meier analysis showed that Ki-67 ≥ 50% predicted higher survival (log-rank P = 0.0299, hazard ratio = 2.142), with no differences for other cutoffs. COX regression identified the Ki-67 positive rate as a significant predictor (P = 0.027, hazard ratio = 2.583), while other variables had no association. In algorithmic model predictions, the SVM, RF, and XGBoost models achieved training area under the curve (AUC) values of 0.851, 0.948, and 0.872, respectively, with corresponding test set AUC values of 0.795, 0.755, and 0.750, respectively. During external validation, their AUC values for predicting Ki-67 status reached 0.757, 0.749, and 0.783, respectively. CONCLUSION In algorithmic model predictions, the SVM, RF, and XGBoost models achieved training AUC values of 0.851, 0.948, and 0.872, respectively, with corresponding test set AUC values of 0.795, 0.755, and 0.750, respectively. During external validation, their AUC values for predicting Ki-67 status reached 0.757, 0.749, and 0.783, respectively.

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