Cross-phase attention-based multi-phase CT deep learning for preoperative risk stratification of gastric gastrointestinal stromal tumors in a multicenter study.

PURPOSE: To develop and externally validate a multi-phase CT deep learning framework for preoperative risk stratification of 2-5 cm gastric gastrointestinal stromal tumors (GISTs). METHODS: This retrospective multicenter study included 427 patients with pathologically confirmed gastric GISTs. At Center A, patients treated from January 2015 to June 2022 formed the development set (n=240), in which all model selection, tuning, and early stopping were performed by five-fold cross-validation, and patients treated from July 2022 to December 2023 formed a temporally independent internal test set (n=59); Center B (n=128) served as the external validation set. Unenhanced, arterial, and venous CT images were analyzed with the Multi-Phase Fusion Network (MPFNet), which combines parallel ResNet50 encoders with a Cross-Phase Attention Module and phase-adaptive weighting. Incremental value over a combined clinical-radiomics reference model was assessed with the difference in AUC, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration intercept and slope, and decision curve analysis; three radiologists served as additional comparators. RESULTS: On the internal test set, MPFNet achieved an AUC of 0.935 (95% CI: 0.852-0.981) versus 0.872 for the combined model (P=0.016; NRI 0.58, IDI 0.112). On external validation, the AUC was 0.894 (95% CI: 0.827-0.942) versus 0.831 (P=0.091; NRI 0.41, IDI 0.078), with a calibration intercept of -0.14, slope of 0.87 (95% CI: 0.64-1.10), and Brier score of 0.118; net benefit exceeded that of the combined model at threshold probabilities of 0.30-0.50. The AUC was also higher than that of three radiologists (0.735-0.812 externally). External false negatives were concentrated in intermediate-risk tumors (9 of 11), and performance was lower on the external scanner than on the two Center A scanners. CONCLUSION: In retrospective external validation, MPFNet provided incremental discrimination over a combined clinical-radiomics model with acceptable calibration for preoperative risk stratification of 2-5 cm gastric GISTs. Its use as a decision-support tool for planning the resection approach requires prospective evaluation.