Breast cancer molecular subtype classifier that incorporates MRI features. Issue 1 (12th January 2016)

• Record Type: Journal Article
• Authors: Sutton, Elizabeth J.; Dashevsky, Brittany Z.; Oh, Jung Hun; Veeraraghavan, Harini; Apte, Aditya P.; Thakur, Sunitha B.; Morris, Elizabeth A.; Deasy, Joseph O.
• Is Part Of: Journal of magnetic resonance imaging. Volume 44:Issue 1(2016)
• Page Start: 122
• Page End: 129
• Publication Date: 2016-01-12
• Subjects: breast cancer -- molecular subtypes -- machine‐learning -- MRI texture; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.25119
• Languages: English
• ISSNs: 1053-1807

Abstract

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.