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Purpose Previous studies of ethnic disparities in colorectal cancer (CRC) have focused mainly on patients of Caucasian and African-American descent. We aimed to evaluate outcomes for a range of races, representing a broader demographic of the US population. Methods The Surveillance, Epidemiology, and End Results database was queried to identify patients with CRC diagnosed between 1994 and 2014. We performed unadjusted Kaplan-Meier test and multivariable covariate-adjusted Cox models to calculate the overall and CRC-specific survival of patients according to their race. Results We identified 401,723 patients diagnosed with CRC between 1994 and 2014. Overall survival (OS) and CRC-specific survival were compared across different races stratified by age, sex, marital status, disease stage and grade, and undergoing surgery as a treatment. Overall, Asian/Pacific Islanders and Hispanics had improved CRC-specific survival compared to Whites (HR = 0.873, 95%CI 0.853–0.893, P  < .001, and HR = 0.958, 95%CI 0.937–0.979, P  < .001, respectively). Blacks had the worst CRC-specific survival outcomes when compared to Whites (HR = 1.215, 95%CI 1.192–1.238, P  < .001). Racial disparity persisted when looking at two different time periods (1994–2003 and 2004–2014). Conclusions Asians/Pacific Islanders have improved outcomes from CRC compared to other races. Multifactorial, including genetic, environmental, and socioeconomic factors appear to influence outcomes and need to be addressed separately in order to reduce racial disparities among patients with CRC.

High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology. Deep learning algorithms can provide accurate mappings given large numbers of labeled instances for training and validation. Generating adequate volume of quality labels has emerged as a critical barrier in computational pathology given the time and effort required from pathologists. In this paper we describe an approach for engaging crowds of medical students and pathologists that was used to produce a dataset of over 220,000 annotations of cell nuclei in breast cancers. We show how suggested annotations generated by a weak algorithm can improve the accuracy of annotations generated by non-experts and can yield useful data for training segmentation algorithms without laborious manual tracing. We systematically examine interrater agreement and describe modifications to the MaskRCNN model to improve cell mapping. We also describe a technique we call Decision Tree Approximation of Learned Embeddings (DTALE) that leverages nucleus segmentations and morphologic features to improve the transparency of nucleus classification models. The annotation data produced in this study are freely available for algorithm development and benchmarking at: https://sites.google.com/view/nucls.