Abstract
Objective: This study aimed to determine the utility of a radiomic nomogram combined with clinical imaging and radiomic features based on MRI for the diagnosis of triple-negative breast cancer.
Methods: Multi-parametric MRI images of 136 breast cancer patients were retrospectively analyzed, 95 cases were stratified into the training cohort, and 41 cases were selected for the test group. According to the pathological molecular typing, the patients were divided into 23 cases of triple-negative breast cancer and 113 cases of non-triple-negative breast cancer. ITK software was used to manually delineate the lesion volume region of interest (VOI), and the Pyradiomics package was used to extract radiomic features for screening and model building. The platform was then used to analyze the clinical and imaging risk factors of breast cancer to build a characteristic model separately. Finally, a radiomic nomogram was constructed by integrating the radiomic and independent clinical image features. The diagnostic performance of the model was assessed using ROC curves.
Results: Univariate and multivariate analyses showed that the menstrual cycle, glandular density, and skin thickening were risk factors for clinical imaging characteristics of triple-negative breast cancer. The Area Under the Curve (AUC) was 0.839 and 0.826 for univariate and multivariate analysis, respectively. After screening, 11 radiomic features participated in the calculation of the radiomic score, and its AUC in the test set was 0.803. Combining it further with clinical models, the AUC improved to 0.899.
Conclusion: The radiomic nomogram developed in this study has great value in the diagnosis of triple-negative breast cancer.
