The value of dynamic enhanced MRI radiomics features based on habitat imaging technology for predicting pathological complete remission in neoadjuvant treatment of breast cancer
10.3760/cma.j.cn112149-20240510-00263
- VernacularTitle:基于生境成像技术的动态增强MRI影像组学特征预测乳腺癌新辅助治疗病理完全缓解的价值
- Author:
Deling SONG
1
;
Caiyun WEN
1
;
Yunpeng TAI
1
;
Jinjin LIU
1
;
Meihao WANG
1
;
Guoquan CAO
1
Author Information
1. 温州医科大学附属第一医院放射科，温州 325000
- Publication Type:Journal Article
- Keywords:
Breast neoplasms;
Magnetic resonance imaging;
Radiomics;
Neoadjuvant chemotherapy;
Habitat imaging
- From:
Chinese Journal of Radiology
2025;59(4):401-408
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the predictive value of radiomics features derived from dynamic contrast-enhanced MRI (DCE-MRI) based on habitat imaging technology for pathological complete response after neoadjuvant therapy (NAT) for breast cancer.Methods:All patients were female, aged 25-67 years. Patients were stratified into training ( n=83) and validation ( n=36) sets via stratified random sampling (7∶3 ratio). Pathological complete remission (pCR) and non-pathological complete remission (non-pCR) were defined using the Miller-Payne grading system. All patients underwent DCE-MRI before NAT. ITK-Snap software was used to outline the region of interest (ROI), the imaging histological features of the entire tumor region were extracted and screened, a traditional imaging histological model for predicting post-NAT pCR (ROI overall model) was constructed; the tumor region was divided into three subregions using habitat imaging technology, and the imaging histological features within ROI subregion 1, ROI subregion 2, and ROI subregion 3 were extracted and screened, and the habitat imaging model for predicting post-NAT pCR were constructed (ROI subregion 1 model, ROI subregion 2 model, ROI subregion 3 model). Univariate logistic regression identified clinical predictors of pCR for clinical model construction. Combined models integrating clinical predictors and habitat imaging features were established. The efficacy of each model in predicting pCR after NAT in breast cancer was evaluated using receiver operating characteristic curves and area under the curve (AUC), and the efficacy of clinical application of the models was evaluated using decision curve analysis (DCA). Results:Of the 119 patients, 74 were pCR patients, with 52 in the training set and 22 in the validation set, and 45 were non-pCR patients, with 31 in the training set and 14 in the validation set. Logistic regression analysis showed that human epidermal growth factor receptor 2 status ( OR=0.254, 95% CI 0.093-0.697, P=0.008) was an independent predictor of pCR after NAT, and this was used to construct a clinical prediction model. The predictive efficacy of ROI subregion 1 model and ROI subregion 2 model in the habitat model was higher than that of the traditional imaging histology model (ROI overall model), with AUCs of 0.805, 0.748,0.728 for the training set and 0.776,0.718,0.708 for the validation set, respectively. The combined clinical prediction model for predicting pCR after NAT in breast cancer had AUCs of 0.877 and 0.818 for the training and validation sets, respectively. DCA showed a higher net benefit for the combined model than for the traditional imaging histology model and the habitat imaging histology model. Conclusion:Compared with the traditional method of extracting the entire tumor region, extracting radiomics features from DCE-MRI subregions based on habitat imaging technology can improve the predictive performance of NAT efficacy in breast cancer.
