The value of MRI radiomics model for predicting pathologic response to neoadjuvant therapy in human epidermal growth factor receptor 2-positive breast cancer
10.3760/cma.j.cn112149-20241214-00735
- VernacularTitle:MRI影像组学模型评估人表皮生长因子受体2阳性乳腺癌新辅助治疗疗效的价值
- Author:
Junjie ZHANG
1
;
Yanfen CUI
1
;
Ruirui SONG
1
;
Jianxin ZHANG
1
;
Xiaotang YANG
1
Author Information
1. 山西省肿瘤医院 中国医学科学院肿瘤医院山西医院 山西医科大学附属肿瘤医院医学影像科，太原 030013
- Publication Type:Journal Article
- Keywords:
Breast neoplasms;
Magnetic resonance imaging;
Radiomics;
Human epidermal growth factor receptor 2;
Neoadjuvant therapy
- From:
Chinese Journal of Radiology
2025;59(9):1046-1054
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of MRI radiomics model in evaluating the pathological complete response (pCR) status of human epidermal growth factor receptor 2(HER-2) positive breast cancer after neoadjuvant therapy.Methods:The study was a cross-sectional study. The clinical, pathological, and MRI data of 243 HER-2 positive breast cancer patients who received neoadjuvant therapy in Shanxi Province Cancer Hospital from January 2021 to June 2023 were retrospectively analyzed. All patients were female, aged 26?75 years. All patients were randomly divided into training set (146 cases) and validation set (97 cases) at a ratio of 6∶4 according to the simple random sampling method. Univariate and multivariate logistic regression were used to screen independent predictors of pCR. Radiomics features were extracted from the early-phase (the 2nd phase) images of breast dynamic contrast-enhanced-MRI after neoadjuvant therapy.The four-step procedure was adopted for feature screening. The radiomics model was constructed by logistic regression. A combined model was constructed by integrating radiomics features and independent predictors. Two radiologists (Reader 1 with 10 years experience and Reader 2 with 13 years experience) who major in breast MRI visually evaluated the pCR status of breast cancer after neoadjuvant therapy. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the efficacy of Reader 1, Reader 2, the radiomics model, and the combined model in predicting pCR status. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration of the model.Results:Among 243 HER-2 positive breast cancer patients, totally 118 achieved pCR. In clinical and pathological features, HER-2 3+ was an independent predictor of pCR ( OR=2.71, 95% CI 1.03?7.12, P=0.043). In the training set and validation set, the AUCs of the radiomics model in predicting pCR status were 0.899 and 0.853, respectively.The AUCs of the combined model were 0.917 and 0.890, respectively. In the validation set, the AUC value of the radiomics model in predicting pCR status was higher than that of Reader 1 and Reader 2. Hosmer-Lemeshow goodness-of-fit test showed that there was no significant difference between the prediction of pCR status by the combined model and radiomics model and the actual results in the training set and validation set, and the fitting was good ( P>0.05). Conclusion:The MRI-based radiomics model can be used to predict pCR status in HER-2 positive breast cancer and outperforms the visual qualitative assessments of radiologists.
