MRI-based habitat radiomics analysis for identifying molecular subtypes of endometrial cancer:a feasible study from two institutions
10.3969/j.issn.1672-8467.2024.06.003
- VernacularTitle:基于MRI的生境影像组学预测子宫内膜癌分子亚型的双中心临床研究
- Author:
Wen-Tao JIN
1
;
Tian-Ping WANG
;
Xiao-Jun CHEN
;
Guo-Fu ZHANG
;
Hai-Ming LI
;
He ZHANG
Author Information
1. 复旦大学附属妇产科医院放射科 上海 200011
- Keywords:
endometrial cancer(EC);
MRI;
prognosis model;
habitat;
radiomics
- From:
Fudan University Journal of Medical Sciences
2024;51(6):890-899
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop an MRI-based habitat radiomics model for the preoperative prediction of endometrial cancer(EC)molecular subtypes.Methods Patients with pathologically proven EC from two hospitals were included in the training(n=270)and testing(n=70)cohorts.All patients had preoperative MRI and histological and molecular diagnoses.First,the tumor was divided into habitat subregions based on diffusion-weighted imaging(DWI)and contrast-enhanced(CE)images.Subsequently,habitat radiomic features were extracted from different subregions of T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),DWI,and CE images.Three machine learning classifiers,including logistic regression,support vector machines,and random forests,were applied to develop predictive models for p53-abnormal endometrial cancer,with model performance validated.The model demonstrating the best overall predictive performance was selected as the habitat radiomics model.Using the same procedure,a whole-region radiomics model based on T1WI,T2WI,DWI,and CE sequences and a clinical model were constructed.The performance of the models was evaluated using receiver operating characteristic curves,and DeLong's test was employed to compare differences between the models.Decision curve analysis was used to assess the clinical benefits of the models'application.Results After feature selection,eight habitat radiomic features were retained to construct the habitat radiomics model,ten features for the whole-region radiomics model,and three clinical features for the clinical model.The habitat radiomics model achieved the highest area under the curve(AUC),with 0.855(0.788-0.922)in the training cohort and 0.769(0.631-0.907)in the testing cohort.DeLong's test showed that the habitat radiomics model outperformed the whole-region radiomics model in the training cohort(P=0.001),but there was no significant difference in the testing cohort(P=0.543).In both cohorts,the habitat radiomics model outperformed the clinical model(P=0.007,training cohort;P=0.038,testing cohort).Decision curve analysis(DCA)demonstrated that this model provided clinical benefit for diagnosis within a threshold probability range of approximately 0.2-0.8.Conclusion The MRI-based habitat radiomics model can accurately predict p53-abnormal EC,outperforming both the whole-region radiomics model and the clinical model,and is useful for the non-invasive molecular subtyping of endometrial cancer before surgery.
