Non-invasive prediction of Ki-67 expression in gastric cancer using AI-based dual-energy CT: a multicenter study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Henan International Joint Laboratory of Medical Imaging, Henan Key Laboratory of Digestive Tumor Imaging, Henan Key Laboratory of CT Imaging, Zhengzhou 450052, China.
- Department of Radiology, Xinxiang Central Hospital, The Fourth Clinical College of Henan Medical University, Xinxiang 453099, China.
- Medical Imaging Center, Shangqiu First People's Hospital, Shangqiu 476000, China.
- Department of Radiology, Xinxiang Central Hospital, The Fourth Clinical College of Henan Medical University, Xinxiang 453099, China. Electronic address: [email protected].
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Henan International Joint Laboratory of Medical Imaging, Henan Key Laboratory of Digestive Tumor Imaging, Henan Key Laboratory of CT Imaging, Zhengzhou 450052, China; Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou 450003, China. Electronic address: [email protected].
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Henan International Joint Laboratory of Medical Imaging, Henan Key Laboratory of Digestive Tumor Imaging, Henan Key Laboratory of CT Imaging, Zhengzhou 450052, China. Electronic address: [email protected].
Abstract
To develop and validate a machine learning model based on quantitative parameters of dual-energy CT (DECT) virtual monoenergetic images (VMIs) for the noninvasive preoperative prediction of Ki-67 expression status in gastric cancer. A total of 367 patients with pathologically confirmed gastric adenocarcinoma were enrolled and divided into a training cohort, two external validation cohorts, and a cross-platform validation cohort. Patients were classified into high or low Ki-67 expression groups based on a 70% cutoff. Quantitative parameters of VMI were measured and incorporated into machine learning algorithms to construct the DECT model. The optimal imaging model was combined with independent clinical predictors to develop a nomogram. Model performance was evaluated using ROC analysis, calibration curves, DCA, and Kaplan-Meier survival analysis. The logistic regression model was identified as the optimal DECT model. Its combination with clinical features yielded AUC values of 0.788 and 0.777 in the two DECT external validation cohorts, respectively. In the cross-platform validation cohort, the combined model achieved an AUC of 0.668. Calibration curves and DCA demonstrated good fitting and clinical usefulness of the integrated model in DECT cohorts. Stratified analysis confirmed that the model's performance was stable across different clinical characteristics. Furthermore, Kaplan-Meier analysis indicated that the combined model effectively stratified patients into high- and low-risk groups regarding progression-free survival (PFS). The individualized model based on DECT virtual monoenergetic images effectively predicts Ki-67 expression status and provides valuable prognostic risk stratification for gastric cancer patients.