Deep learning radiomics analysis based on computed tomography for survival prediction in gastric neuroendocrine neoplasm: a multicenter study.

Background: Survival prediction is crucial for patients with gastric neuroendocrine neoplasms (gNENs) to assess the treatment programs and may guide personalized medicine. This study aimed to develop and evaluate a deep learning (DL) radiomics model to predict the overall survival (OS) in patients with gNENs. Methods: The retrospective analysis included 162 consecutive patients with gNENs from two hospitals, who were divided into a training cohort, internal validation cohort (The First Affiliated Hospital of Zhengzhou University; n=108), and an external validation cohort (The Henan Cancer Hospital; n=54). DL radiomics analysis was applied to computed tomography (CT) images of the arterial phase and venous phase, respectively. Based on pretreatment CT images, two DL radiomics signatures were developed to predict OS. The combined model incorporating the radiomics signatures and clinical factors was built through the multivariable Cox proportional hazards (CPH) method. The combined model was visualized into a radiomics nomogram for individualized OS estimation. Prediction performance was assessed with the concordance index (C-index) and the Kaplan-Meier (KM) estimator. Results: The DL-based radiomics signatures based on two phases were significantly correlated with OS in the training (C-index: 0.79-0.92; P<0.01), internal validation (C-index: 0.61-0.86; P<0.01), and external validation (C-index: 0.56-0.75; P<0.01) cohorts. The combined model integrating radiomics signatures with clinical factors showed a significant improvement in predictive performance compared to the clinical model in the training (C-index: 0.86 Conclusions: DL radiomics analysis could serve as a potential and noninvasive tool for prognostic prediction and risk stratification in patients with gNENs.