A prospective cohort study identifying radiologic and tumor related factors of importance for breast conserving surgery after neoadjuvant chemotherapy.

INTRODUCTION: Neoadjuvant chemotherapy (NAC) is an established treatment option for early breast cancer, potentially downstaging the tumor and increasing the eligibility for breast-conserving surgery (BCS). The primary aim of this study was to assess the rate of BCS after NAC, and the secondary aim was to identify predictors of application of BCS after NAC. MATERIALS AND METHODS: This was an observational prospective cohort study of 226 patients in the SCAN-B (Clinical Trials NCT02306096) neoadjuvant cohort during 2014-2019. Eligibility for BCS was assessed at baseline and after NAC. Uni- and multivariable logistic regression analyses were performed using covariates with clinical relevance and/or those associated with outcome (BCS versus mastectomy), including tumor subtype, by gene expression analysis. RESULTS: The overall BCS rate was 52%, and this rate increased during the study period (from 37% to 52%). Pathological complete response was achieved in 69 patients (30%). Predictors for BCS were smaller tumor size on mammography, visibility on ultrasound, histological subtype other than lobular, benign axillary status, and a diagnosis of triple-negative or HER2-positive subtype, with a similar trend for gene expression subtypes. Mammographic density was negatively related to BCS in a dose-response pattern. In the multivariable logistic regression model, tumor stage at diagnosis and mammographic density showed the strongest association with BCS. CONCLUSION: The rate of BCS after NAC increased during the study period to 52%. With modern treatment options for NAC the potential for tumor response and BCS eligibility might further increase.

Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept.

OBJECTIVES: In this proof of concept study, a deep learning-based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined. METHODS: Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients. RESULTS: A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7-62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53-0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%. CONCLUSIONS: Our study describes an AI platform using baseline DM to predict BC patients' responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted. KEY POINTS: • We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? • DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. • By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.