PURPOSE Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological complete response (pCR) has prognostic value depending on BC subtype. Rates of pCR, however, can be variable. Predictive modeling is desirable to help identify patients early who may have suboptimal NAC response. Here, we test and compare the predictive performances of machine learning (ML) prediction models to a standard statistical model, using clinical and pathological data. METHODS Clinical and pathological variables were collected in 431 patients, including tumor size, patient demographics, histological characteristics, molecular status, and staging information. A standard multivariable logistic regression (MLR) was developed and compared with five ML models: k-nearest neighbor classifier, random forest (RF) classifier, naive Bayes algorithm, support vector machine, and multilayer perceptron model. Model performances were measured using a receiver operating characteristic (ROC) analysis and statistically compared. RESULTS MLR predictors of NAC response included: estrogen receptor (ER) status, human epidermal growth factor-2 (HER2) status, tumor size, and Nottingham grade. The strongest MLR predictors of pCR included HER2+ versus HER2− BC (odds ratio [OR], 0.13; 95% CI, 0.07 to 0.23; P < .001) and Nottingham grade G3 versus G1-2 (G1-2: OR, 0.36; 95% CI, 0.20 to 0.65; P < .001). The area under the curve (AUC) for the MLR was AUC = 0.64. Among the various ML models, an RF classifier performed best, with an AUC = 0.88, sensitivity of 70.7%, and specificity of 84.6%, and included the following variables: menopausal status, ER status, HER2 status, Nottingham grade, tumor size, nodal status, and presence of inflammatory BC. CONCLUSION Modeling performances varied between standard versus ML classification methods. RF ML classifiers demonstrated the best predictive performance among all models.

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Objective: Atherosclerotic plaques have a complex composition, consisting of inflammation, fibrosis, cholesterol crystals, hemorrhage, and/or calcification. The segmentation and quantification of plaque features in histopathology images form the foundation for studies evaluating plaque instability and the mechanisms that underlie the atherosclerotic process. Manual segmentation of plaque features from histology images is a tedious, time-consuming, and subjective visual recognition task. Herein, we present a fully automatic approach using state-of-the-art deep learning techniques to identify three major features of the atherosclerotic plaque: calcification, lipid core, and fibrosis. Methods: Plaques (n=70) were collected from patients who underwent a carotid endarterectomy at McGill University-affiliated hospitals. Hematoxylin and Eosin-stained sections were obtained from the region with the largest plaque burden. The “ground truth annotations” for lipid core, calcification, and fibrosis were performed manually by three blinded cardiovascular pathologists, using Sedeen Viewer. A total of 23,000 patches with 512x512 pixel size were extracted from our image dataset, and divided into train, validate, and test sets. Using Transfer Learning, multi-class U-Net models for semantic segmentation were trained on the patches to extract fibrosis, lipid, and calcification plaque features. Evaluation of model performance was based on the mean value of the Intersection over Union (Mean-IOU) between the prediction results and the “ground truth annotations”. Results: Our models resulted in an overall performance of 77% for test images, and a per-class performance for the three plaque features: fibrosis = 0.77±0.2, lipid core = 0.80±0.3, calcification = 0.75±0.25. However, a qualitative evaluation by the pathologists confirmed that the prediction results in fact outperformed the “ground truth annotations”, and detected non-annotated regions. Conclusion: To our knowledge, this is a first attempt at developing a fully automatic approach for atherosclerotic plaque feature segmentation from histology images. Our models can accelerate atherosclerosis research, by improving the speed, quality, and reproducibility of plaque analysis.