Academic radiology · 2026
Prediction of Recurrence-Free Survival in Patients with Gastrointestinal Stromal Tumors via Mixed CT Radiomics-Postoperative Pathology Models.
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Liu Yun, He Changyin, Gong Chundan, Gao Yunhan, Shi Feng, Xia Yuwei, Li Chuanming
Аннотация
RATIONALE AND OBJECTIVES: The purpose of this study was to develop and validate preoperative and postoperative recurrence-free survival (RFS) prediction models for patients with gastrointestinal stromal tumors (GISTs) of all risk levels in the gastric, small intestine, colorectal and extragastrointestinal regions.
MATERIALS AND METHODS: In total, 269 patients with GIST from hospital 1 were included and randomly divided into a training cohort (n=215) and an internal validation cohort (n=54). Another 42 patients from hospital 2 comprised the external validation cohort. All patients were followed up for at least 60 months. The whole tumor was 3D segmented and delineated as the region of interest (ROI) slice by slice, and 851 radiomic features were extracted from each ROI. Preoperative models were established on the basis of radiomics, clinical characteristics and CT visual information for RFS prediction. After surgery, the postoperative prediction models were constructed on the basis of preoperative information and pathological information.
RESULTS: In the external validation, the C indices of the preoperative prediction models based of nonenhanced CT and contrast-enhanced CT of arterial phase, venous phase, delayed phase and combined phase were 0.696, 0.75, 0.771, 0.744 and 0.787, respectively; while the C indices of the postoperative prediction model were 0.776, 0.764, 0.783, 0.766, and 0.818, respectively. Non-enhanced CT could achieve effects similar to those of contrast-enhanced CT. A multipredictor nomogram was constructed for individualized estimation of RFS.
CONCLUSION: This study established preoperative and postoperative models for the RFS prediction in a whole GIST population with high, medium, low and extremely low risk levels. It could help clinicians develop personalized treatment plans to improve patient prognosis.
MATERIALS AND METHODS: In total, 269 patients with GIST from hospital 1 were included and randomly divided into a training cohort (n=215) and an internal validation cohort (n=54). Another 42 patients from hospital 2 comprised the external validation cohort. All patients were followed up for at least 60 months. The whole tumor was 3D segmented and delineated as the region of interest (ROI) slice by slice, and 851 radiomic features were extracted from each ROI. Preoperative models were established on the basis of radiomics, clinical characteristics and CT visual information for RFS prediction. After surgery, the postoperative prediction models were constructed on the basis of preoperative information and pathological information.
RESULTS: In the external validation, the C indices of the preoperative prediction models based of nonenhanced CT and contrast-enhanced CT of arterial phase, venous phase, delayed phase and combined phase were 0.696, 0.75, 0.771, 0.744 and 0.787, respectively; while the C indices of the postoperative prediction model were 0.776, 0.764, 0.783, 0.766, and 0.818, respectively. Non-enhanced CT could achieve effects similar to those of contrast-enhanced CT. A multipredictor nomogram was constructed for individualized estimation of RFS.
CONCLUSION: This study established preoperative and postoperative models for the RFS prediction in a whole GIST population with high, medium, low and extremely low risk levels. It could help clinicians develop personalized treatment plans to improve patient prognosis.