DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction

Abstract

Purpose: Manual extraction of case details from patient records for cancer surveillance is a resource-intensive task. Natural Language Processing (NLP) techniques have been proposed for automating the identification of key details in clinical notes. Our goal was to develop NLP application programming interfaces (APIs) for integration into cancer registry data abstraction tools in a computer-assisted abstraction setting.

Methods: We used cancer registry manual abstraction processes to guide the design of DeepPhe-CR, a web-based NLP service API. The coding of key variables was performed through NLP methods validated using established workflows. A container-based implementation of the NLP methods and the supporting infrastructure was developed. Existing registry data abstraction software was modified to include results from DeepPhe-CR. An initial usability study with data registrars provided early validation of the feasibility of the DeepPhe-CR tools.

Results: API calls support submission of single documents and summarization of cases across one or more documents. The container-based implementation uses a REST router to handle requests and support a graph database for storing results. NLP modules extract topography, histology, behavior, laterality, and grade at 0.79-1.00 F1 across multiple cancer types (breast, prostate, lung, colorectal, ovary, and pediatric brain) from data of two population-based cancer registries. Usability study participants were able to use the tool effectively and expressed interest in the tool.

Conclusion: The DeepPhe-CR system provides an architecture for building cancer-specific NLP tools directly into registrar workflows in a computer-assisted abstraction setting. Improved user interactions in client tools may be needed to realize the potential of these approaches.

FIG 1.

The original data abstraction screen from the Cancer Patient Data Management System, augmented to support suggested data items as extracted from clinical text by DeepPhe-CR: (A) document-level topography, histology, behavior, laterality, and grade values are shown color-coded and labeled with appropriate ICD-O/NAACCR codes; (B) suggested items and demographics can be copied to the in-progress abstract with a single click; (C) the clinical text is highlighted with color codes, indicating text spans associated with the five summary values displayed in the suggestion area above (A). Thus, “Right breast cancer” is associated with topography C50.9, “breast” with histology 8500, and “right” with laterality 1. ICD, International Classification of Diseases; NAACCR, North American Association of Central Cancer Registries.