Abstract P4-02-04: Quantitative MRI features analysis for differentiation of triple negative and HER2 positive subtypes of breast cancer
Authors: GM Rauch, H Li, H Zhu, BE Adrada, L Santiago, RP Candelaria, H Wang, J Leung, A Thompson, J Litton, Y Wu, B Lim, S Moulder, EA Mittendorf, W Yang
Journal: Cancer Research (2018)

Abstract Objective: The aim of this study was to evaluate ability of quantitative analysis of MRI features to distinguish triple negative (TN) and HER2 positive (HER2+) subtypes of breast cancer, which have different biological characteristics, exhibiting different growth patterns and response to treatment. Materials and Method: Breast cancer patients, who had MRI exam of the breast in our institution at the time of staging for breast carcinoma and who subsequently had surgery (segmentectomy or mastectomy) from January 1, 2008 through December 31, 2015 were identified. All lesions were evaluated by radiologists in accordance with the BI-RADS lexicon. The patient's age, breast cancer histology, multifocality/multicentricity (MF/MC), lesion size, axillary lymphadenopathy (LAN), MRI morphologic and enhancement characteristics were documented. Quantitative MRI feature analysis was performed using shape, texture, and histogram based features. Machine-learning-based (Xgboost) models were used to predict subtypes, and Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Student's t-test. Results: Total of 105 patients, 51 patients with TN and 54 patients with HER2+ breast cancer were included in analysis. Mean age for TN was 50 (range 29-79)) years old and for HER2+ was 49 (range 25-70) years old. Axillary LAN and MF/MC disease was seen more commonly in HER2+ patients when compared to TN patients, but didn't reach statistical significance (13 vs 7, p=0.9; and 31 vs 20, p=0.06, respectively). Mass rim enhancement was associated with TN subtype and irregular mass enhancement was associated with HER2+ subtype of breast cancer (p&amp;lt;0.05). Quantitative analysis showed that six out of the top 10 ranked MRI features: surface to volume ratio, difference variance, difference entropy, inverse difference moment, 75 percentile in histogram and sum average, were significantly different between these 2 subtypes with p&amp;lt;0.05. When the significant features were incorporated to distinguish TN and HER+ subtypes, use of the top 2 features achieved the best accuracy on LOOCV of 0.69. Conclusion: The quantitative MRI features show promise in distinguishing TN and HER2+ breast cancer subtypes reflecting their underlying biological characteristics and may be used as predictive quantitative biological markers. Further studies in a larger cohort evaluating associations with treatment response are underway. FeatureIndexP-valueSurface to volume ratioShape30.005Difference VarianceGLCM110.005Difference EntropyGLCM100.009Inverse Difference MomentGLCM50.01875 percentile in histogramHistogram50.043Sum AverageGLCM60.044Median in histogramHistogram 30.08025 percentile in histogramHistogram 40.095VolumeShape10.104Max in histogramHistogram 10.105 Citation Format: Rauch GM, Li H, Zhu H, Adrada BE, Santiago L, Candelaria RP, Wang H, Leung J, Thompson A, Litton J, Wu Y, Lim B, Moulder S, Mittendorf EA, Yang W. Quantitative MRI features analysis for differentiation of triple negative and HER2 positive subtypes of breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-04.