Title:The Diagnostic Value of a Nomogram Based on Clinical Imaging and MRIBased
Radiomic Features in Triple-Negative Breast Cancer
Volume: 20
Author(s): Liu Meng Xin, Ge Min*, Wang Shi Wei, Lu Huan, Pan Zhi Yong and Ding Xue Wei
Affiliation:
- Department of Medical Imaging, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
Keywords:
Magnetic resonance imaging, Radiomics, Triple-negative breast cancer, AUC, Clinical models, ROC.
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.
