Machine learning models for predicting the risk stratification of gastrointestinal stromal tumor based on the radiomic features of CT
10.3969/j.issn.1002-1671.2024.07.017
- VernacularTitle:基于CT影像组学特征的机器学习模型预测胃肠间质瘤的危险度
- Author:
Chenchen ZHANG
1
;
Hongkun YIN
;
Rui YU
;
Yiqing BAO
;
Shuo ZHAO
;
Guohua FAN
Author Information
1. 苏州大学附属第二医院放射科,江苏 苏州 215004
- Keywords:
gastrointestinal stromal tumor;
radiomics;
machine learning;
risk stratification
- From:
Journal of Practical Radiology
2024;40(7):1111-1115
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct the machine learning models based on the radiomic features of non-contrast and enhanced CT and to evaluate the predictive value in the risk stratification of gastrointestinal stromal tumor(GIST).Methods A total of 182 patients with pathologically confirmed GIST were randomly divided into a training set and a validation set at a ratio of 7∶3.The volume of interest(VOI)was outlined in the non-contrast phase,arterial phase and venous phase,and its radiomic features were extracted.The most valuable radiomic features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm.The logistic regression(LR)classifier was used to construct the prediction models based on single-phase or multi-phase images.The predictive efficacy of the different models was compared by using receiver operating characteristic(ROC)curves.Results Four,three,and four radiomic features were selected in the non-contrast phase,arterial phase and venous phase,and 4 models were constructed in total.Among the single-phase models,the venous phase had better predictive efficacy,with the area under the curve(AUC)of 0.932[95％confidence interval(CI)0.873-0.969]and 0.924(95％CI 0.819-0.979)in the training and validation sets.The predictive efficacy of the combined model was improved,with the AUC of 0.946(95％CI 0.891-0.978)and 0.938(95％CI 0.838-0.986).Conclusion The venous phase model can predict the risk stratification of GIST accurately,and the prediction efficacy can be improved by combining the non-contrast and arterial phases.
