Machine learning based on preoperative CT for noninvasive prediction of recurrence-free survival in gastrointestinal stromal tumors

Clinicians need a reliable, noninvasive tool that can predict the risk of gastrointestinal stromal tumor (GIST) recurrence preoperatively. We aimed to develop a machine learning model based on preoperative contrast-enhanced CT (CECT) to predict recurrence-free survival (RFS) in GIST patients who underwent radical resection. A total of 192 patients with intermediate- and high-risk GISTs who underwent radical resection and subsequently received adjuvant imatinib were included, with a minimum follow-up duration of 24 months. A deep learning model (Model radiomic ) based on preoperative CECT was built to predict RFS, which was compared with the Armed Forces Institute of Pathology (AFIP) index (Model AFIP ). The C-index and time-dependent receiver operating characteristic curves were estimated via the Kaplan-Meier method and compared via the log-rank test. The C-index values of the Model radiomic in the training, testing and validation cohorts ranged from 0.678 to 0.763, whereas the areas under the curves (AUCs) at 3 and 5 years were 0.744–0.790 and 0.703–0.833, respectively. The C-index values of Model AFIP in the above datasets were 0.539–0.660, and the AUCs at 3 and 5 years were 0.497–0.649 and 0.503–0.630. Model radiomic demonstrated a higher C-index than Model AFIP did in the training and external validation cohorts ( p = 0.010 and 0.042, respectively). Model radiomic presented a greater AUC value than Model AFIP did at the 5th year in the training cohort ( p = 0.009). We found the machine learning-based preoperative CECT performed better than the AFIP index in prediction of RFS of GIST patients, especially at the 5th year, predicting recurrence risk in patients who underwent radical resection and receiving adjuvant therapy. This model may serve as a non-invasive tool to identify high-risk individuals who require more intensive surveillance and personalized management following radical resection.