Background: Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy (NCT) is essential for personalized treatment of locally advanced gastric cancer (LAGC). The aim of this study was to identify a radioclinical signatures from pretreatment oversampled CT images to predict the response to NCT and prognosis of LAGC patients. Methods: LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. An SE-ResNet50-based chemotherapy response prediction system was developed from pretreatment CT images preprocessed with an imaging oversampling method (i.e., DeepSMOTE). Then, the Deep learning (DL) signature and clinic-based features were fed into the deep learning radioclinical signature (DLCS). The predictive performance of the model was evaluated based on discrimination, calibration and clinical usefulness. An additional model was built to predict overall survival (OS) and explore the survival benefit of the proposed DL signature and clinicopathological characteristics. Result: A total of 1060 LAGC patients were recruited from six hospitals, the training cohort (TC) and internal validation cohort (IVC) patients were randomly selected from center I. An external validation cohort (EVC) of 265 patients from 5 other centers was also included. The DLCS exhibited excellent performance in predicting the response to NCT in the IVC (AUC, 0.86) and EVC (AUC, 0.82), with good calibration in all cohorts (P>0.05). Moreover, the DLCS model outperformed the clinical model (P<0.05). Additionally, we found that the DL signature could serve as an independent factor for prognosis (HR, 0.828, P=0.004). The C-index, iAUC, and IBS for the OS model were 0.64, 1.24 and 0.71 in the test set. Conclusion: We proposed a DLCS model that combined imaging features with clinical risk factors to accurately predict tumor response and identify the risk of OS in LAGC patients priors to NCT that can then be used to guide personalized treatment plans with the help of computerized tumor-level characterization.

Introduction: The postoperative recurrence of gastric cancer (GC) has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of GC is crucial. Methods: This retrospective study gathered data from 2813 GC 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 nonrecurrent 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 computed tomography images, based on a pretrained 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 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.