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Predicting response to neoadjuvant therapy is a vexing challenge in breastcancer. In this study, we evaluate the ability of deep learning to predictresponse to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatmentdynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In aretrospective study encompassing DCE-MRI data from a total of 157 HER2+ breastcancer patients from 5 institutions, we developed and validated a deep learningapproach for predicting pathological complete response (pCR) to HER2-targetedNAC prior to treatment. 100 patients who received HER2-targeted neoadjuvantchemotherapy at a single institution were used to train (n=85) and tune (n=15)a convolutional neural network (CNN) to predict pCR. A multi-input CNNleveraging both pre-contrast and late post-contrast DCE-MRI acquisitions wasidentified to achieve optimal response prediction within the validation set(AUC=0.93). This model was then tested on two independent testing cohorts withpre-treatment DCE-MRI data. It achieved strong performance in a 28 patienttesting set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) anda 29 patient multicenter trial including data from 3 additional institutions(AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response predictionmodel was found to exceed a multivariable model incorporating predictiveclinical variables (AUC < .65 in testing cohorts) and a model ofsemi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testingcohorts). The results presented in this work across multiple sites suggest thatwith further validation deep learning could provide an effective and reliabletool to guide targeted therapy in breast cancer, thus reducing overtreatmentamong HER2+ patients.

Deep learning-based prediction of response to HER2-targeted neoadjuvant  chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional  validation study