Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.

Despite the important role of preclinical experiments to characterize tumor biology and molecular pathways, there are ongoing challenges to model the tumor microenvironment, specifically the dynamic interactions between tumor cells and immune infiltrates. Comprehensive models of host-tumor immune interactions will enhance the development of emerging treatment strategies, such as immunotherapies. Although in vitro and murine models are important for the early modelling of cancer and treatment-response mechanisms, comparative research studies involving veterinary oncology may bridge the translational pathway to human studies. The natural progression of several malignancies in animals exhibits similar pathogenesis to human cancers, and previous studies have shown a relevant and evaluable immune system. Veterinary oncologists working alongside oncologists and cancer researchers have the potential to advance discovery. Understanding the host-tumor-immune interactions can accelerate drug and biomarker discovery in a clinically relevant setting. This review presents discoveries in comparative immuno-oncology and implications to cancer therapy.

Context.—: Physicians face a high rate of burnout, especially during the residency training period when trainees often experience a rapid increase in professional responsibilities and expectations. Effective burnout prevention programs for resident physicians are needed to address this significant issue. Objective.—: To examine the content, format, and effectiveness of resident burnout interventions published in the last 10 years. Design.—: The literature search was conducted on the MEDLINE database with the following keywords: internship, residency, health promotion, wellness, occupational stress, burnout, program evaluation, and program. Only studies published in English between 2010 and 2020 were included. Exclusion criteria were studies on interventions related to the COVID-19 pandemic, studies on duty hour restrictions, and studies without assessment of resident well-being postintervention. Results.—: Thirty studies were included, with 2 randomized controlled trials, 3 case-control studies, 20 pretest and posttest studies, and 5 case reports. Of the 23 studies that used a validated well-being assessment tool, 10 reported improvements postintervention. These effective burnout interventions were longitudinal and included wellness training (7 of 10), physical activities (4 of 10), healthy dietary habits (2 of 10), social activities (1 of 10), formal mentorship programs (1 of 10), and health checkups (1 of 10). Combinations of burnout interventions, low numbers of program participants with high dropout rates, lack of a control group, and lack of standardized well-being assessment are the limitations identified. Conclusions.—: Longitudinal wellness training and other interventions appear effective in reducing resident burnout. However, the validity and generalizability of the results are limited by the study designs.

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

Self-assessment, a personal evaluation of one’s professional attributes and abilities against a perceived norm, has frequently been cited as a necessary component of self-directed learning and the maintenance of competency within regulated health professions, including the medical professions. However, education research literature has consistently shown uninformed personal global assessment of performance to be inaccurate in a variety of contexts, and have limited value in a workplace-based curriculum. Incorporating known standards of performance with internal and external data on the performance improves a learner’s ability to accurately self-assess. Selecting content suitable for self-assessment, providing explicit assessment standards, encouraging feedback-seeking behaviors, supporting a growth mindset, and providing quality feedback in a supportive context are all strategies that can support learner self-assessment, learner engagement in reflection, and action on feedback in Anatomical Pathology graduate medical education.