Machine learning model based on MR T2WI and diffusion-weighted imaging radiomics for predicting perineural invasion of rectal cancer
10.13929/j.issn.1003-3289.2025.04.023
- VernacularTitle:基于MR T2WI及弥散加权成像影像组学构建机器学习模型预测直肠癌周围神经侵犯
- Author:
Honglin SHANG
1
;
Yuqi ZHAN
;
Shaoying MO
;
Yuhua FAN
;
Yunjun YANG
;
Hai ZHAO
;
Wei WANG
Author Information
1. 广东医科大学第一临床医学院,广东湛江 524023;广东医科大学佛山市第一人民医院影像科,广东佛山 528000
- Publication Type:Journal Article
- Keywords:
rectal neoplasms;
neoplasm invasiveness;
magnetic resonance imaging;
radiomics;
machine learning
- From:
Chinese Journal of Medical Imaging Technology
2025;41(4):616-621
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of machine learning model based on MR T2WI and diffusion weighted imaging(DWI)radiomics for predicting perineural invasion(PNI)of rectal cancer.Methods Totally 343 patients with rectal cancer were retrospectively collected and divided into training set(n=275,92 PNI[+]and 183 PNI[-])and test set(n=68,23 PNI[+]and 45 PNI[-])at the ratio of 8∶2.Univariate and multivariate logistic regression(LR)were used to analyze clinical data and screen the independent predictors of PNI in rectal cancer,so as to construct a clinical model.The best radiomics features were extracted and screened based on preoperative T2WI and DWI.Then extremely randomized trees,multilayer perceptron,light gradient boosting machine,extreme gradient boosting,support vector machine(SVM),LR,K-nearest neighbor and random forest algorithms were used to construct ML models,respectively,and the optimal ML model was selected to establish a clinical-radiomics ML model combined with clinical relevant independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Patients' age was the independent predictor of PNI of rectal cancer(OR=0.988,P＜0.001),and the area under the curve(AUC)of the clinical model constructed based on it was 0.435 and 0.458 in training and test sets,respectively.SVM model was the best one among 8 ML models,with AUC in training and test set of 0.887 and 0.854,respectively.The AUC of clinical-radiomics ML model in training and test sets was 0.887 and 0.860,respectively,not different with AUC of SVM model(both P＞0.05).Decision curve analysis showed that when the threshold value was 0.20-0.45,clinical net benefit of SVM model was higher than that of other models.Conclusion SVM model based on T2WI and DWI radiomics could effectively predict PNI of rectal cancer.
