Background Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. Results This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes. Conclusions This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.

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Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.

The incidence of pancreatic neuroendocrine tumors (PNETs) has increased over the last decade. Black patients have worse survival outcomes. This study investigates whether oncologic outcomes are racially disparate at a single institution.Methods: Retrospective analysis was performed on 151 patients with resected PNETs between 2010 and 2019.Results: More White males and Black females presented with PNETs (P = 0.02). White patients were older (65 years vs 60 years; P = 0.03), more likely to be married (P < 0.01), and had higher median estimated yearly incomes ($28,973 vs $17,767; P < 0.01) than Black patients. Overall and disease-free survival were not different. Black patients had larger median tumor sizes (30 mm vs 23 mm; P = 0.02). Tumor size was predictive of recurrence only for White patients (hazard ratio, 1.02; P = 0.01). Collectively, tumors greater than 20 mm in size were more likely to have recurrence (P = 0.048), but this cutoff was not predictive in either racial cohort independently.Conclusions: Black patients undergoing curative resection of PNETs at our institution presented with larger tumors, but that increased size is not predictive of disease-free survival in this population.