Development and interpretation of a multimodal predictive model for prognosis of gastrointestinal stromal tumor.

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal original tumor in gastrointestinal (GI) tract and is considered to have varying malignant potential. With the advancement of computer science, radiomics technology and deep learning had been applied in medical researches. It's vital to construct a more accurate and reliable multimodal predictive model for recurrence-free survival (RFS) aiding for clinical decision-making. A total of 254 patients underwent surgery and pathologically diagnosed with GIST in The First Hospital of China Medical University from 2019 to 2022 were included in the study. Preoperative contrast enhanced computerized tomography (CE-CT) and hematoxylin/eosin (H&E) stained whole slide images (WSI) were acquired for analysis. In the present study, we constructed a sum of 11 models while the multimodal model (average C-index of 0.917 on validation set in 10-fold cross validation) performed the best on external validation cohort with an average C-index of 0.864. The multimodal model also reached statistical significance when validated in the external validation cohort (n = 42) with a p-value of 0.0088 which pertained to the recurrence-free survival (RFS) comparison between the high and low groups using the optimal threshold on the predictive score. We also explored the biological significance of radiomics and pathomics features by visualization and quantitative analysis. In the present study, we constructed a multimodal model predicting RFS of GIST which was prior over unimodal models. We also proposed hypothesis on the correlation between morphology of tumor cell and prognosis.