PREDICTION OF BREAST CANCER MOLECULAR SUBTYPES BASED ON MULTI-PARAMETER MRI
- 12 March 2025
- journal article
- Published by World Scientific Pub Co Pte Ltd in Journal of Mechanics in Medicine and Biology
- Vol. 25 (02)
Abstract
This study aims to develop a safe and effective multi-parameter MRI-based molecular subtype prediction model for breast cancer, emphasizing the advantages of this multi-parameter approach over single-parameter models. This study retrospectively collected and organized MRI data from 318 breast cancer patients at Liaoning Provincial Cancer Hospital, including dynamic contrast-enhanced MRI (DCE-MRI, abbreviated as DCE), diffusion weighted MRI (DWI-MRI, abbreviated as DWI), T1-weighted MRI (T1WI-MRI, abbreviated as T1WI), and T2-weighted MRI (T2WI-MRI, abbreviated as T2WI). The dataset includes 57 cases of Luminal A type, 162 cases of Luminal B type, 46 cases of human epidermal growth factor receptor-2 (HER-2) overexpression type, and 53 cases of triple-negative type. Predictive models were established using four single-parameter MRI methods and seven multi-parametric MRI methods, employing quantitative feature extraction. Model performance was evaluated through the area under the curve (AUC) and balanced accuracy (BA). In the single-parameter MRI models, the T2WI-MRI model demonstrated the best predictive performance for four-class classification, with average AUC and BA values of 0.794 and 0.518, respectively. In contrast, the multi-parameter model combining DWI+T2WI exhibited even better performance, with these metrics reaching 0.823 and 0.565, respectively. The multi-parameter feature fusion model for breast cancer molecular subtypes prediction, utilizing DWI+T2WI, exhibited superior BA and AUC values compared to models based solely on single-parameter MRI. It showed enhanced predictive capabilities for Luminal A, Luminal B, HER-2 overexpression, and triple-negative subtypes. Therefore, the multi-parameter MRI-based model offers improved predictive performance over single-parameter models.Keywords
Funding Information
- National Natural Science Foundation of China (82260505)
This publication has 25 references indexed in Scilit:
- Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast‐Enhanced MRIJournal of Magnetic Resonance Imaging, 2021
- Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centersEuropean Radiology, 2020
- Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammogramsBiocybernetics and Biomedical Engineering, 2020
- Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transformMeasurement, 2019
- Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network AlgorithmJournal of Digital Imaging, 2019
- Breast cancer molecular subtype classifier that incorporates MRI featuresJournal of Magnetic Resonance Imaging, 2016
- Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithmsJournal of Magnetic Resonance Imaging, 2015
- Correlation of perfusion parameters on dynamic contrast‐enhanced MRI with prognostic factors and subtypes of breast cancersJournal of Magnetic Resonance Imaging, 2012
- Characteristics and outcomes according to molecular subtypes of breast cancer as classified by a panel of four biomarkers using immunohistochemistryThe Breast, 2012
- Image characterizations based on joint gray level—run length distributionsPattern Recognition Letters, 1991