Cross-phase attention-based multi-phase CT deep learning for preoperative risk stratification of gastric gastrointestinal stromal tumors in a multicenter study.
TL;DR
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 and its use as a decision-support tool for planning the resection approach requires prospective evaluation.
Abstract
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.