Breast cancer molecular subtype classifier that incorporates MRI features Journal Article
| Authors: | Sutton, E. J.; Dashevsky, B. Z.; Oh, J. H.; Veeraraghavan, H.; Apte, A. P.; Thakur, S. B.; Morris, E. A.; Deasy, J. O. |
| Article Title: | Breast cancer molecular subtype classifier that incorporates MRI features |
| Abstract: | Purpose: To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. Materials and Methods: This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006–2011 with: 1) ERPR + (n = 95, 53.4%), ERPR–/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal–Wallis test. Results: Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), 63.6% (ERPR–/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR–/HER2+), and 81.0% (TN). Conclusion: We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122–129. © 2016 Wiley Periodicals, Inc. |
| Keywords: | breast cancer; molecular subtypes; machine-learning; mri texture |
| Journal Title: | Journal of Magnetic Resonance Imaging |
| Volume: | 44 |
| Issue: | 1 |
| ISSN: | 1053-1807 |
| Publisher: | Wiley Blackwell |
| Date Published: | 2016-07-01 |
| Start Page: | 122 |
| End Page: | 129 |
| Language: | English |
| DOI: | 10.1002/jmri.25119 |
| PUBMED: | 26756416 |
| PROVIDER: | scopus |
| PMCID: | PMC5532744 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 August 2016 -- Source: Scopus |
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