Abstract 579: Magnetic resonance imaging of the breast and radiomics analysis for an improved early prediction of the response to neoadjuvant chemotherapy in breast cancer patients

Abstract

Abstract Background and Aim In patients undergoing neoadjuvant chemotherapy for breast cancer the achievement of a pathological complete response (pCR) is associated with a significantly improved disease-free and overall survival. Therefore, accurate means to predict treatment response as early as possible are desirable to identify women who don't benefit from this cytotoxic therapy. Several studies have demonstrated that dynamic contrast-enhanced MRI is the most sensitive method for the assessment and prediction of treatment response. In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support. This emerging field in medical research is termed radiomics. The aim of this study was to assess radiomics with dynamic contrast-enhanced (DCE) MRI for the early prediction of pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy. Methods and materials In this IRB approved prospective study 41 women with breast cancer scheduled for NAC were included and underwent MRI of the breast at 3T with DCE and T2-weighted imaging prior to and after two cycles of NAC. For each lesion a total of 14 features were extracted ranging from morphological and kinetic MRI ADC parameters. A recursive feature elimination method along with five different classifiers was performed including: linear support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), and stochastic gradient descent (SGD) was employed to rank the features. Histopathology using the Residual Cancer Burden (RCB) score and class calculated from post-treatment surgical specimen and patient outcomes were used as the standard of reference. Results Classification accuracy was assessed for pCR as defined by the RCB score, metastases and disease specific death. Radiomics analysis of MRI data achieved AUCs for RCB score (AUC 0.85), metastases (AUC 0.87) based on RF and death (AUC 0.92) based on SVM. The most relevant parameters for prediction of RCB score were mass internal enhancement characteristics, shape and margins with DCE, for metastasis peri-tumoral edema on T2-weighted imaging, mass margins and internal enhancement characteristics, and for death high signal intensity on T2-weighted imaging, mass margins, and internal enhancement characteristics. Conclusion Radiomics with MRI of the breast using DCE and T2-weighted imaging enables prediction of response to NAC with high accuracy and thus can provide predictive information to guide treatment decisions. Citation Format: Katja Pinker-Domenig, Amirhessam Tahmassebi, Georg Wengert, Thomas H. Helbich, Zsuzsanna Bago-Horvath, Elizabeth A. Morris, Anke Meyer-Baese. Magnetic resonance imaging of the breast and radiomics analysis for an improved early prediction of the response to neoadjuvant chemotherapy in breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 579.