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

Predicting response to neoadjuvant therapy is a vexing challenge in breast cancer. In this study, we evaluate the ability of deep learning to predict response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast cancer patients from 5 institutions, we developed and validated a deep learning approach for predicting pathological complete response (pCR) to HER2-targeted NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant chemotherapy 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 CNN leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was identified to achieve optimal response prediction within the validation set (AUC=0.93). This model was then tested on two independent testing cohorts with pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and a 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 prediction model was found to exceed a multivariable model incorporating predictive clinical variables (AUC < .65 in testing cohorts) and a model of semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing cohorts). The results presented in this work across multiple sites suggest that with further validation deep learning could provide an effective and reliable tool to guide targeted therapy in breast cancer, thus reducing overtreatment among HER2+ patients.Comment: Braman and El Adoui contributed equally to this work. 33 pages, 3 figures in main tex

Additional file 1: of Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Supplementary methods. (DOCX 12.4 kb

False-negative rates of breast cancer screening with and without digital breast tomosynthesis

Background Screening with digital breast tomosynthesis (DBT) improves breast cancer detection and recall rates compared with those obtained with digital mammography (DM); however, the impact of DBT on patient survival has not been established. False-negative (FN) screening examinations can be a surrogate for long-term outcomes, such as breast cancer morbidity and mortality. Purpose To determine if screening with DBT is associated with lower FN rates, detection of cancers with more favorable prognoses, and improved performance outcomes versus DM. Materials and Methods This retrospective study involved 10 academic and community practices. DM screening examinations 1 year prior to DBT implementation and DBT screening examinations from the start date until June 30, 2013, were linked to cancers through June 30, 2014, with data collection in 2016 and analysis in 2018-2019. Cancers after FN examinations were characterized by presentation, either symptomatic or asymptomatic. FN rates, sensitivity, specificity, cancer detection and recall rates, positive predictive values, tumor size, histologic features, and receptor profile were compared. Results A total of 380 641 screening examinations were included. There were 183 989 DBT and 196 652 DM examinations. With DBT, rates trended lower for overall FN examinations (DBT, 0.6 per 1000 screens; DM, 0.7 per 1000 screens; DM: 0.07 per 1000 screens; P = .07). With DBT, improved sensitivity (DBT, 89.8% [966 of 1076 cancers]; DM, 85.6% [789 of 922 cancers]; P = .004) and specificity (DBT, 90.7% [165 830 of 182 913 examinations]; DM, 89.1% [174 480 of 195 730 examinations]; P \u3c .001) were observed. Overall, cancers identified with DBT were more frequently invasive (P \u3c .001), had fewer positive lymph nodes (P = .04) and distant metastases (P = .01), and had lower odds of an FN finding of advanced cancer (odds ratio, 0.9 [95% CI: 0.5, 1.5]). Conclusion Screening with digital breast tomosynthesis improves sensitivity and specificity and reveals more invasive cancers with fewer nodal or distant metastases