Discussion
tumor, GISTs tumors, malignancy, gastric GISTs, GISTs, high-grade malignant GISTs
REGRESSION, TUMOR, GIST
To our best knowledge, this is the first research on the prediction of malignancy in gastric GISTs by machine learning classifiers. In addition, our report focuses on GISTs tumors of 1–5 cm in the gastric, which is different from studies that include large-size GISTs located in various sites of the gastrointestinal tract. This study has the largest sample size among relevant studies, so the reliability of the results can be guaranteed. Various qualitative and quantitative variables extracted from CT signs were inputted into LR, DT and GBDT models. The results of model evaluation were different but not inconsistent among the three cohorts. For the training cohort, GBDT had the greatest sensitivity, specificity, accuracy, PPV, NPV and AUC among the three models. What’s more, GBDT gained the largest AUC in the training and internal validation sets and performed best in all three cohorts in terms of accuracy, although the AUC was not as good as LR in the external test cohort. Furthermore, GBDT and LR showed better performance than the two radiologists. However, the performance of DT was not as outstanding as GBDT and LR. Therefore, GBDT and LR were suggested to be promising ML models for CT-based risk classification prediction of gastric GISTs due to the high accuracy and strong robustness.GBDT, an ensemble method based on bootstrap sampling, was demonstrated to be a favorable algorithm with high predictive efficiency, as reported in various previous researches [When it comes to feature variable selection, LD was found to be the only common CT feature between LR and GBDT that distinguished for high-grade malignant GISTs in this study. Several studies using multivariate logistic regression analysis revealed that the size of GISTs was the only independent risk factor for differentiation of the high-grade malignant GISTs [Artificially determined CT imaging features were used as the input variables for the three ML models to predict the risk classification of gastric GISTs in this study, and great results were obtained, especially in GBDT and LR. Compared with the diagnostic ability of radiologists, ML achieved more promising results, which may have a guiding prospect for doctors in daily diagnostic work, especially for the junior ones. It may promisingly provide theoretical and practical support for texture analysis or deep learning since ML may play an important role in feature selection.There are some limitations in our study. First, our sample size was small for ML. ML classifiers can highlight their advantages in the context of large data, amounts of predictor variables or complex relationship. Second, four risk grades were finally divided into two, so the results were unable to meet the requirement of each risk classification. Simple ML model cannot meet the needs of predicting four risk levels, but the convolutional neural network can, which puts the next step of research on the agenda. Third, only three simple ML models were implemented in our research, including the classic LR. We will try other more complex ML models to assess the risk stratification, such as random forest, support vector machine, k-nearest neighbors, etc. Fourth, radiomics, which transforms medical images into high-dimensional data by extracting tumor’s shape, intensity, and texture features, has recently shown great potential in aiding clinical decision making. Developing CT-based radiomics models for GIST risk stratifcation will be a future work.