Gastric Neoplasm Detection on Contrast-enhanced CT with Deep Learning

Purpose To develop and validate a deep learning-based approach, gastric neoplasm detection with artificial intelligence (GANDA), for automated detection, diagnosis, and segmentation of gastric neoplasms on clinical routine contrast-enhanced CT. Materials and Methods In this retrospective study, GANDA, a joint segmentation and classification three-dimensional deep learning model, was developed by using CT data of 1,688 patients with or without gastric neoplasms from one hospital between January 2007 and June 2019. Performance was evaluated in an internal test cohort (January-June 2019), external test cohort (April 2015-December 2022) from four external centers, and real-world test cohort (March-May 2023) from one hospital. Model performance in tumor detection and diagnosis was assessed using receiver operating characteristic (ROC) analysis and compared with that of 10 board-certified radiologists (median experience, 8.5-years [IQR:5.25–14 years]). Model segmentation performance was assessed using the Dice coefficient. Results A total of 4,606 patients were included in the study (median age, 57 [IQR 48–66] years; 2,554 male). In the internal test cohort ( n = 266), GANDA achieved 87.3% sensitivity and 87.2% specificity for tumor detection. The model demonstrated significantly higher diagnostic accuracy (top-1 accuracy, 85.3%, 95%CI, 81.2–89.1%) compared with radiologists (mean accuracy, 74.2%, 95%CI, 70.5–77.6%, P = .002). In the external test cohort ( n = 2,657), GANDA distinguished between patients with gastric neoplasms and controls with 77.4% sensitivity and 89.8% specificity. The mean Dice coefficient in the internal test cohort was 0.52 for gastric cancer and 0.45 for non-gastric-cancer. In the real-world test cohort ( n = 7,695), GANDA achieved 83.2% sensitivity and 93.1% specificity for tumor detection. Conclusion GANDA enabled the detection and segmentation of gastric neoplasms on routine clinical CT scans. ©RSNA, 2025