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Population cancer registries can benefit from Deep Learning (DL) to automatically extract cancer characteristics from the high volume of unstructured pathology text reports they process annually. The success of DL to tackle this and other real-world problems is proportional to the availability of large labeled datasets for model training. Although collaboration among cancer registries is essential to fully exploit the promise of DL, privacy and confidentiality concerns are main obstacles for data sharing across cancer registries. Moreover, DL for natural language processing (NLP) requires sharing a vocabulary dictionary for the embedding layer which may contain patient identifiers. Thus, even distributing the trained models across cancer registries causes a privacy violation issue. In this article, we propose DL NLP model distribution via privacy-preserving transfer learning approaches without sharing sensitive data. These approaches are used to distribute a multitask convolutional neural network (MT-CNN) NLP model among cancer registries. The model is trained to extract six key cancer characteristics – tumor site, subsite, laterality, behavior, histology, and grade – from cancer pathology reports. Using 410,064 pathology documents from two cancer registries, we compare our proposed approach to conventional transfer learning without privacy-preserving, single-registry models, and a model trained on centrally hosted data. The results show that transfer learning approaches including data sharing and model distribution outperform significantly the single-registry model. In addition, the best performing privacy-preserving model distribution approach achieves statistically indistinguishable average micro- and macro-F1 scores across all extraction tasks (0.823,0.580) as compared to the centralized model (0.827,0.585).

Background Cancer recurrence is an important measure of the impact of cancer treatment. However, no population-based data on recurrence are available. Pathology reports could potentially identify cancer recurrences. Their utility to capture recurrences is unknown. Objective This analysis assesses the sensitivity of pathology reports to identify patients with cancer recurrence and the stage at recurrence. Subjects The study includes patients with recurrent breast (n=214) or colorectal (n=203) cancers. Research design This retrospective analysis included patients from a population-based cancer registry who were part of the Patient-Centered Outcomes Research (PCOR) Study, a project that followed cancer patients in-depth for 5 years after diagnosis to identify recurrences. Measures Information abstracted from pathology reports for patients with recurrence was compared with their PCOR data (gold standard) to determine what percent had a pathology report at the time of recurrence, the sensitivity of text in the report to identify recurrence, and if the stage at recurrence could be determined from the pathology report. Results One half of cancer patients had a pathology report near the time of recurrence. For patients with a pathology report, the report's sensitivity to identify recurrence was 98.1% for breast cancer cases and 95.7% for colorectal cancer cases. The specific stage at recurrence from the pathology report had a moderate agreement with gold-standard data. Conclusions Pathology reports alone cannot measure population-based recurrence of solid cancers but can identify specific cohorts of recurrent cancer patients. As electronic submission of pathology reports increases, these reports may identify specific recurrent patients in near real-time.

The coronavirus disease 2019 (COVID-19) pandemic led to delayed medical care in the United States. We examined changes in patterns of cancer diagnosis and surgical treatment between January 1 and December 31 in 2020 and 2019 with real-time electronic pathology report data from population-based Surveillance, Epidemiology, and End Results cancer registries from Georgia and Louisiana. During 2020, there were 29 905 fewer pathology reports than in 2019, representing a 10.2% decline. Declines were observed in all age groups, including children and adolescents younger than 18 years. The nadir was early April 2020, with 42.8% fewer reports than in April 2019. Numbers of reports through December 2020 never consistently exceeded those in 2019 after first declines. Patterns were similar by age group and cancer site. Findings suggest substantial delays in diagnosis and treatment services for cancers during the pandemic. Ongoing evaluation can inform public health efforts to minimize any lasting adverse effects of the pandemic on cancer diagnosis, stage, treatment, and survival.