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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).

Objective We implement 2 different multitask learning (MTL) techniques, hard parameter sharing and cross-stitch, to train a word-level convolutional neural network (CNN) specifically designed for automatic extraction of cancer data from unstructured text in pathology reports. We show the importance of learning related information extraction (IE) tasks leveraging shared representations across the tasks to achieve state-of-the-art performance in classification accuracy and computational efficiency. Materials and Methods Multitask CNN (MTCNN) attempts to tackle document information extraction by learning to extract multiple key cancer characteristics simultaneously. We trained our MTCNN to perform 5 information extraction tasks: (1) primary cancer site (65 classes), (2) laterality (4 classes), (3) behavior (3 classes), (4) histological type (63 classes), and (5) histological grade (5 classes). We evaluated the performance on a corpus of 95 231 pathology documents (71 223 unique tumors) obtained from the Louisiana Tumor Registry. We compared the performance of the MTCNN models against single-task CNN models and 2 traditional machine learning approaches, namely support vector machine (SVM) and random forest classifier (RFC). Results MTCNNs offered superior performance across all 5 tasks in terms of classification accuracy as compared with the other machine learning models. Based on retrospective evaluation, the hard parameter sharing and cross-stitch MTCNN models correctly classified 59.04% and 57.93% of the pathology reports respectively across all 5 tasks. The baseline models achieved 53.68% (CNN), 46.37% (RFC), and 36.75% (SVM). Based on prospective evaluation, the percentages of correctly classified cases across the 5 tasks were 60.11% (hard parameter sharing), 58.13% (cross-stitch), 51.30% (single-task CNN), 42.07% (RFC), and 35.16% (SVM). Moreover, hard parameter sharing MTCNNs outperformed the other models in computational efficiency by using about the same number of trainable parameters as a single-task CNN. Conclusions The hard parameter sharing MTCNN offers superior classification accuracy for automated coding support of pathology documents across a wide range of cancers and multiple information extraction tasks while maintaining similar training and inference time as those of a single task–specific model.

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.

1514 Background: The COVID-19 pandemic led to delays in medical care in the United States. We examined changes in patterns of cancer diagnosis and surgical treatment in 2020 using real-time electronic pathology report data from population-based SEER cancer registries in Georgia and Louisiana. Methods: Bi-weekly numbers, distributions, and patterns of pathology reports were compared between January 1st and December 31st in 2020 and the same period in 2019 by age group and cancer site. Results: During 2020, there were 29,905 fewer pathology reports than in 2019, representing a 10.2% decline. Absolute declines were greatest among adults aged ≥50 years (N=23,065); percentage declines were greatest among children and young adults ≤18 years (38.3%). By cancer site, percentage declines were greatest for lung cancer (17.4%), followed by colorectal (12.0%), breast (9.0%) and prostate (5.8%) cancers. Biweekly reports were statistically significantly lower in 2020 than in 2019 from late March through the end of December in most biweekly periods. The nadir was the month of April 2020 – the number of reports was at least 40% lower than in April 2019. The number of reports in 2020 compared with 2019 also declined sharply in early November (26.8%) and late December (32.0%). Numbers of reports in 2020 never consistently exceeded those in 2019 after the first decline. Patterns were similar by cancer site, with variation in magnitude and duration of declines. Conclusions: Significant declines in cancer pathology reports from population-based registries during 2020 suggest substantial delays in screening, evaluation of signs and symptoms, diagnosis, and treatment services for cancers with effective screening tests as well as in cancer sites and age groups without effective screening tests as an indirect result of the COVID-19 pandemic. Ongoing evaluation will be critical for informing public health efforts to minimize any lasting adverse effects of the pandemic on cancer screening, diagnosis, treatment, and survival.[Table: see text]