Preoperative Prediction of Ki-67 in Stage T1 Lung Adenocarcinoma Based on 2.5D and Ensemble Integrated Models: A Multicenter Study

Objective: In order to set up a predictive model which combines 2.5D deep learning, 2D deep learning, 3D deep learning, and an ensemble fusion model, which could be used for the accurate prediction of preoperative expression levels of ki-67 in patients diagnosed with stage T1 invasive lung adenocarcinoma(LUAD ). Patients and Methods: In total, 503 patients from our own institution and 102 patients with invasive LUAD of T1 stage from two other centers were included retrospectively. The subjects were then divided into a Ki-67 high-expression group and a Ki-67 low-expression group, comprising 254 and 351 subjects, respectively. The subject set from our own institution formed the training set, and the subject sets from the other two centers formed the test set. Three categories of deep learning(DL) models, 2D, 2.5D, and 3D models, and an ensemble fusion model were developed and used to evaluate the performance of each model in predicting Ki-67 expressions in subjects with T1 LUAD. Results: All of these models showed excellent discriminative performance for the training set. The Ensemble model showed the best discriminative performance with an AUC of 0.991 (95% CI: 0.9858–0.9964). The Support Vector Machine 3D model (SVM 3D) showed an AUC of 0.970 (95% CI: 0.9538–0.9866). The Multilayer Perceptron 2D model (MLP 2D; AUC = 0.931; 95% CI: 0.9093–0.9529) and Gradient Boosting Machine 2.5D model (GBM 2.5D; AUC = 0.908; 95% CI: 0.8811–0.9342) also showed excellent discriminative performance. For all of these models, AUC performance declined as expected for the independent test set; however, some of these models showed different degrees of performance declines. The Ensemble model showed excellent robustness to performance declines and showed an AUC of 0.870 (95% CI: 0.7943–0.9447). The GBM 2.5D model showed excellent robustness to performance declines and showed an AUC of 0.843 (95% CI: 0.7405–0.9450); it showed the smallest performance declines of all of the models. The MLP 2D model showed moderate performance and showed an AUC of 0.810 (95% CI: 0.7110–0.9099). On the other hand, it was evident that the performance of the SVM 3D model showed substantial declines; it showed an AUC of 0.745 (95% CI: 0.6338–0.8566); it showed the greatest performance declines of all of the models and possibly suffered from. Conclusion: The Ensemble model demonstrated high performance in the prediction of Ki-67 expression in stage T1 invasive LUAD, which confirms the feasibility of the proposed approach in the prediction of Ki-67.