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

Figure 1 Technical workflow for colorectal carcinoma Ki-67 predictive classification integrating case cohort assembly, whole-slide imaging, deep feature engineering, and machine learning modeling. CRC: Colorectal carcinoma; AI: Artificial intelligence; WSI: Whole-slide imaging.

Figure 2 Morpho-proliferative characterization of colorectal carcinoma specimens. A: Ki-67 immunohistochemistry (IHC), Ki-67 proliferation indices (positive cells/total): 10%; B: Ki-67 IHC, Ki-67 proliferation indices (positive cells/total): 30%; C: Ki-67 IHC, Ki-67 proliferation indices (positive cells/total): 50%; D: Ki-67 IHC, Ki-67 proliferation indices (positive cells/total): 90%; E: Hematoxylin and eosin (HE) histology at 400 × microscopic resolution, Ki-67 proliferation indices (positive cells/total): 10%; F: HE histology at 400 × microscopic resolution, Ki-67 proliferation indices (positive cells/total): 30%; G: HE histology at 400 × microscopic resolution, Ki-67 proliferation indices (positive cells/total): 50%; H: HE histology at 400 × microscopic resolution, Ki-67 proliferation indices (positive cells/total): 90%.

Figure 3 Kaplan-Meier survival curves for colorectal carcinoma patients stratified by Ki-67 40%, 50%, 60%, and 70%. Data from the external validation dataset (208 colorectal carcinoma patients from the Yulin Red Cross Hospital). A: Ki-67 < 40% vs Ki-67 ≥ 40% Kaplan-Meier analysis with log-rank test: P = 0.3490, hazard ratio (HR) = 1.451; B: Ki-67 < 50% vs Ki-67 ≥ 50% Kaplan-Meier analysis with log-rank test: P = 0.0299, HR = 2.142; C: Ki-67 < 60% vs Ki-67 ≥ 60% Kaplan-Meier analysis with log-rank test: P = 0.4259, HR = 1.400; D: Ki-67 < 70% vs Ki-67 ≥ 70% Kaplan-Meier analysis with log-rank test: P = 0.2614, HR = 2.026. HR: Hazard ratio.

Figure 4 Comparative predictive performance of Ki-67 classification models. A: Support vector machine with receiver operating characteristic (ROC) curves; B: Random forest with ROC curves; C: The eXtreme gradient boosting with ROC curves. ROC: Receiver operating characteristic; SVM: Support vector machine; XGBoost: eXtreme gradient boosting.

Figure 5 Confusion matrixes and precision-recall curves of Ki-67 classification models. A: Support vector machine in the train set and test set; B: Random forest in the train set and test set; C: The eXtreme gradient boosting in the train set and test set. SVM: Support vector machine; XGBoost: eXtreme gradient boosting; PR: Precision recall.

Figure 6 Comparative receiver operating characteristic analysis of prediction models in the external validation set. A: Support vector machine; B: Random forest; C: The eXtreme gradient boosting. ROC: Receiver operating characteristic; SVM: Support vector machine; XGBoost: eXtreme gradient boosting.

Figure 7 Confusion matrixes and precision-recall curves of Ki-67 classification models. A: Support vector machine in the external validation set; B: Random forest in the external validation set; C: The eXtreme gradient boosting in the external validation set. SVM: Support vector machine; XGBoost: eXtreme gradient boosting.

Figure 8 SHapley Additive exPlanation score of Ki-67 classification eXtreme gradient boosting models. SHAP: SHapley Additive exPlanation.