Abstract
Background: The study aimed to evaluate the diagnostic performance of machine learning-based CT radiomics models for predicting the recurrence and metastasis of gastrointestinal stromal tumors (GISTs) preoperatively.Methods: A total of 382 patients with histopathological confirmed GISTs were retrospectively included. According to postoperative follow-up, patients were classified into non-recurrence and metastasis group (NRM) and recurrence or metastasis group (RM). Radiomics features were extracted from arterial and portal venous phase CT images. Four feature selection methods and ten machine learning techniques were used to train predicting models on training cohort with internal validation by 10-fold cross-validation. F1 score was used to evaluate the performance of the classification model. The best model of two phase were stacked to build an ensemble model. The area under the curve (AUC), recall, precision, accuracy, and F1 score were used to evaluate the performance of the models and compare with clinical criteria based on diameter.Results: Eighty machine learning models in two phases were built and the ensemble model was integrated by analysis of variance and Naive Bayes (ANOVA_NB) model in arterial phase which selected only 5 features provided the highest F1 Score of 0.560 and Kruskal Wallis and Adaptive Boosting (KW_ AdaBoost) model in venous phase which selected only 4 features provided the highest F1 Score of 0.500. The AUC of the generated ensemble model and the clinical criteria showed no difference (0.866 vs 0.857; DeLong Test, P = 0.865). But the ensemble model had higher accuracy (0.961), recall (0.826), precision (0.905), F1 Score (0.864), and the area under the Precision-Recall curve (0.774; 95%CI, 0.552 - 0.917), compared with clinical criteria, of which, the accuracy was 0.942, recall was 0.367, precision was 0.478, the F1 Score was 0.415 and the area under the Precision-Recall curve was 0.354(95%CI, 0.552 - 0.917).Conclusions: Our findings highlight the potential of machine learning techniques based on CT radiomics in the prediction of recurrence and metastasis of GISTs preoperatively.
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