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.

Background: Our aim is to study if mammographic density (MD) prior to neoadjuvant chemotherapy is a predictive factor in accomplishing a pathological complete response (pCR) in neoadjuvant-treated breast cancer patients.Methods: Data on all neoadjuvant treated breast cancer patients in Southern Sweden (2005–2016) were retrospectively identified, with patient and tumor characteristics retrieved from their medical charts. Diagnostic mammograms were used to evaluate and score MD as categorized by breast composition with the Breast Imaging-Reporting and Data System (BI-RADS) 5th edition. Logistic regression was used in complete cases to assess the odds ratios (OR) for pCR compared to BI-RADS categories (avsb-d), adjusting for patient and pre-treatment tumor characteristics.Results: A total of 302 patients were included in the study population, of which 57 (18.9%) patients accomplished pCR following neoadjuvant chemotherapy. The number of patients in the BI-RADS categorya, b, c, anddwere separately 16, 120, 140, and 26, respectively. In comparison to patients with BI-RADS breast compositiona, patients with denser breasts had a lower OR of accomplishing pCR: BI-RADSb0.32 (95%CI 0.07–0.1.5), BI-RADSc0.30 (95%CI 0.06–1.45), and BI-RADSd0.06 (95%CI 0.01–0.56). These associations were measured with lower point estimates, but wider confidence interval, in premenopausal patients; OR of accomplishing pCR for BI-RADS d in comparison to BI-RADS a: 0.03 (95%CI 0.00–0.76).Conclusions: The likelihood of accomplishing pCR is indicated to be lower in breast cancer patients with higher MD, which need to be analysed in future studies for improved clinical decision-making regarding neoadjuvant treatment.

Purpose: High-performing imaging and predictive markers are warranted to minimize surgical overtreatment of the axilla in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NACT). Here we have investigated whether axillary ultrasound (AUS) could identify axillary lymph node (ALN) metastasis (ALNM) pre-NACT and post-NACT for BC. The association of tumor, AUS features and mammographic density (MD) with axillary-pathological complete response (axillary-pCR) post-NACT was also assessed. Methods: The NeoDense-study cohort (N = 202, NACT during 2014–2019), constituted a pre-NACT cohort, whereas patients whom had a cytology verified ALNM pre-NACT and an axillary dissection performed (N = 114) defined a post-NACT cohort. AUS characteristics were prospectively collected pre- and post-NACT. The diagnostic accuracy of AUS was evaluated and stratified by histological subtype and body mass index (BMI). Predictors of axillary-pCR were analyzed, including MD, using simple and multivariable logistic regression models. Results: AUS demonstrated superior performance for prediction of ALNM pre-NACT in comparison to post-NACT, as reflected by the positive predictive value (PPV) 0.94 (95% CI 0.89–0.97) and PPV 0.76 (95% CI 0.62–0.87), respectively. We found no difference in AUS performance according to neither BMI nor histological subtype. Independent predictors of axillary-pCR were: premenopausal status, ER-negativity, HER2-overexpression, and high MD. Conclusion: Baseline AUS could, to a large extent, identify ALNM; however, post-NACT, AUS was insufficient to determine remaining ALNM. Thus, our results support the surgical staging of the axilla post-NACT. Baseline tumor biomarkers and patient characteristics were predictive of axillary-pCR. Larger, multicenter studies are needed to evaluate the performance of AUS post-NACT.

Importance: In patients with clinically node-negative (cN0) breast cancer and 1 or 2 sentinel lymph node (SLN) macrometastases, omitting completion axillary lymph node dissection (CALND) is standard. High nodal burden (≥4 axillary nodal metastases) is an indication for intensified treatment in luminal breast cancer; hence, abstaining from CALND may result in undertreatment.Objective: To develop a prediction model for high nodal burden in luminal ERBB2-negative breast cancer (all histologic types and lobular breast cancer separately) without CALND.Design, Setting, and Participants: The prospective Sentinel Node Biopsy in Breast Cancer: Omission of Axillary Clearance After Macrometastases (SENOMAC) trial randomized patients 1:1 to CALND or its omission from January 2015 to December 2021 among adult patients with cN0 T1-T3 breast cancer and 1 or 2 SLN macrometastases across 5 European countries. The cohort was randomly split into training (80%) and test (20%) sets, with equal proportions of high nodal burden. Prediction models were developed by multivariable logistic regression in the complete luminal ERBB2-negative cohort and a lobular breast cancer subgroup. Nomograms were constructed. The present diagnostic/prognostic study presents the results of a prespecified secondary analysis of the SENOMAC trial. Herein, only patients with luminal ERBB2-negative tumors assigned to CALND were selected. Data analysis for this article took place from June 2023 to April 2024.Exposure: Predictors of high nodal burden.Main Outcomes and Measures: High nodal burden was defined as ≥4 axillary nodal metastases. The luminal prediction model was evaluated regarding discrimination and calibration.Results: Of 1010 patients (median [range] age, 61 [34-90] years; 1006 [99.6%] female and 4 [0.4%] male), 138 (13.7%) had a high nodal burden and 212 (21.0%) had lobular breast cancer. The model in the training set (n = 804) included number of SLN macrometastases, presence of SLN micrometastases, SLN ratio, presence of SLN extracapsular extension, and tumor size (not included in lobular subgroup). Upon validation in the test set (n = 201), the area under the receiver operating characteristic curve (AUC) was 0.74 (95% CI, 0.62-0.85) and the calibration was satisfactory. At a sensitivity threshold of ≥80%, all but 5 low-risk patients were correctly classified corresponding to a negative predictive value of 94%. The prediction model for the lobular subgroup reached an AUC of 0.74 (95% CI, 0.66-0.83).Conclusions and Relevance: The predictive models and nomograms may facilitate systemic treatment decisions without exposing patients to the risk of arm morbidity due to CALND. External validation is needed.Trial Registration: ClinicalTrials.gov Identifier: NCT02240472

To assess if mammographic density (MD) changes during neoadjuvant breast cancer treatment and is predictive of a pathological complete response (pCR). We prospectively included 200 breast cancer patients assigned to neoadjuvant chemotherapy (NACT) in the NeoDense study (2014-2019). Raw data mammograms were used to assess MD with a fully automated volumetric method and radiologists categorized MD using the Breast Imaging-Reporting and Data System (BI-RADS), 5th Edition. Logistic regression was used to calculate odds ratios (OR) for pCR comparing BI-RADS categories c vs. a, b, and d as well as with a 0.5% change in percent dense volume adjusting for baseline characteristics. The overall median age was 53.1 years, and 48% of study participants were premenopausal pre-NACT. A total of 23% (N = 45) of the patients accomplished pCR following NACT. Patients with very dense breasts (BI-RADS d) were more likely to have a positive axillary lymph node status at diagnosis: 89% of the patients with very dense breasts compared to 72% in the entire cohort. A total of 74% of patients decreased their absolute dense volume during NACT. The likelihood of accomplishing pCR following NACT was independent of volumetric MD at diagnosis and change in volumetric MD during treatment. No trend was observed between decreasing density according to BI-RADS and the likelihood of accomplishing pCR following NACT. The majority of patients decreased their MD during NACT. We found no evidence of MD as a predictive marker of pCR in the neoadjuvant setting.

Neoadjuvant chemotherapy (NACT) is offered to an increasing number of breast cancer (BC) patients, and comprehensive monitoring of treatment response is of utmost importance. Several imaging modali...