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Objective <p>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.</p> Materials and methods <p>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<sub>radiomic</sub>) based on preoperative CECT was built to predict RFS, which was compared with the Armed Forces Institute of Pathology (AFIP) index (Model<sub>AFIP</sub>). The C-index and time-dependent receiver operating characteristic curves were estimated via the Kaplan-Meier method and compared via the log-rank test.</p> Results <p>The C-index values of the Model<sub>radiomic</sub> 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<sub>AFIP</sub> 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<sub>radiomic</sub> demonstrated a higher C-index than Model<sub>AFIP</sub> did in the training and external validation cohorts (<i>p</i> = 0.010 and 0.042, respectively). Model<sub>radiomic</sub> presented a greater AUC value than Model<sub>AFIP</sub> did at the 5th year in the training cohort (<i>p</i> = 0.009).</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Lian Zhao,
  • Liming Zhao,
  • Xiaonan Yin,
  • Enyu Yuan,
  • Yili Gu,
  • Xiaohua Zheng,
  • Bing Wu,
  • Yuan Yin,
  • Xijiao Liu

摘要

Objective

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.

Materials and methods

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 (Modelradiomic) based on preoperative CECT was built to predict RFS, which was compared with the Armed Forces Institute of Pathology (AFIP) index (ModelAFIP). The C-index and time-dependent receiver operating characteristic curves were estimated via the Kaplan-Meier method and compared via the log-rank test.

Results

The C-index values of the Modelradiomic 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 ModelAFIP 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. Modelradiomic demonstrated a higher C-index than ModelAFIP did in the training and external validation cohorts (p = 0.010 and 0.042, respectively). Modelradiomic presented a greater AUC value than ModelAFIP did at the 5th year in the training cohort (p = 0.009).

Conclusion

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.