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

作者：Da-Tong Zeng, Min Li, Rui Lin, Weijian Huang, Shi-De Li, Wan‐Ying Huang, Bin Li, Qi Li, Gang Chen, Jia-Shu Jiang · 发表于：World Journal of Clinical Oncology · 年份：2025 · DOI：10.5306/wjco.v16.i8.107306 · 被引用次数：4 · 研究领域：AI in cancer detection、Colorectal Cancer Screening and Detection、Radiomics and Machine Learning in Medical Imaging

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 rati...