Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT Imaging: a multicenter study.

ABSTRACT

INTRODUCTION: The postoperative recurrence of gastric cancer has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of gastric cancer is crucial. METHODS: This retrospective study gathered data from 2,813 gastric cancer patients who underwent radical surgery between 2011 and 2017 at two medical centers. Follow-up was extended until May 2023, and cases were categorized as recurrent or non-recurrent based on postoperative outcomes. Clinical pathological information and imaging data were collected for all patients. A new deep learning signature (DLS) was generated using pretreatment CT images, based on a pre-trained baseline (a customized Resnet50), for predicting postoperative recurrence. The deep learning fusion signature (DLFS) was created by combining the score of DLS with the weighted values of identified clinical features. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical usefulness. Survival curves were plotted to investigate the differences between DLFS and prognosis. RESULTS: In this study, 2813 patients with gastric cancer (GC) were recruited and allocated into training, internal validation, and external validation cohorts. The DLFS was developed and assessed for its capability in predicting the risk of postoperative recurrence. The DLFS exhibited excellent performance with AUCs of 0.833 (95% CI, 0.809-0.858) in the training set, 0.831 (95% CI, 0.792-0.871) in the internal validation set, and 0.859 (95% CI, 0.806-0.912) in the external validation set, along with satisfactory calibration across all cohorts (P>0.05). Furthermore, the DLFS model significantly outperformed both the clinical model and DLS (P<0.05). High-risk recurrent patients exhibit a significantly poorer prognosis compared to low-risk recurrent patients (P<0.05). CONCLUSIONS: The integrated model developed in this study, focusing on GC patients undergoing radical surgery, accurately identifies cases at high risk of postoperative recurrence and highlights the potential of DLFS as a prognostic factor for GC patients.