NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.

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.

Suicidal death within a year of a cancer diagnosis: A population-based study.

BACKGROUND: The suicide risk after a new cancer diagnosis remains a controversial issue. This study examines the suicide risk within the year after a cancer diagnosis. This is the largest study to assess recent trends in suicide risk after a cancer diagnosis. METHODS: Data were obtained from the Surveillance, Epidemiology, and End Results Program. All patients diagnosed with cancer between 2000 and 2014 were selected. The event was defined as death due to suicide within the first year after a cancer diagnosis, and patients who experienced the event after their diagnosis were observed. The observed/expected (O/E) ratio was assessed as well as the excess risk per 10,000 person-years to determine the suicide risk change after the diagnosis in comparison with the general population. RESULTS: A total of 4,671,989 patients with cancer were included; 1585 committed suicide within 1 year of their diagnosis. The risk of suicide increased significantly with an O/E ratio of 2.52 and with an excess risk of 2.51 per 10,000 person-years. When the risk of suicide was studied according to the cancer site, the highest increases in the O/E ratio came after diagnoses of pancreatic cancer (8.01) and lung cancer (6.05). The risk of suicide also increased significantly after a diagnosis of colorectal cancer with an O/E ratio of 2.08. However, the risk of suicidal death did not increase significantly after breast and prostate cancer diagnoses. CONCLUSIONS: The risk of suicide increases significantly in the first year after a diagnosis of cancer in comparison with the general population, and this increase varies with the type and prognosis of cancer. Close observation and referral to mental health services, when indicated, are important for mitigating such risk.

Outcome disparities in colorectal cancer: a SEER-based comparative analysis of racial subgroups.

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.