A Fusion NLP Model for the Inference of Standardized Thyroid Nodule Malignancy Scores from Radiology Report Text

• Type of Material: Article
• Authors: Thiago Santos, Emory University; Omar N Kallas, Emory University School of Medicine; Janice Newsome, Emory University; Daniel Rubin, Stanford University; Judy Gichoya, Emory University; Imon Banerjee, Emory University
• Language: English
• Date: 2021-01-01
• Publisher: American Medical Informatics Association
• Publication Version: Final Published Version
• Copyright Statement: ©2021 AMIA - All rights reserved.
• Title of Journal or Parent Work: AMIA ... Annual Symposium proceedings. AMIA Symposium
• Volume: 2021
• Start Page: 1079
• End Page: 1088
• Keywords: Radiology; Data Systems; Humans; Retrospective Studies; Thyroid Neoplasms; Thyroid Nodule
• Research Categories: Engineering, Biomedical; Computer Science; Health Sciences, Radiology

Abstract

Radiology reports are a rich resource for advancing deep learning applications for medical images, facilitating the generation of large-scale annotated image databases. Although the ambiguity and subtlety of natural language poses a significant challenge to information extraction from radiology reports. Thyroid Imaging Reporting and Data Systems (TI-RADS) has been proposed as a system to standardize ultrasound imaging reports for thyroid cancer screening and diagnosis, through the implementation of structured templates and a standardized thyroid nodule malignancy risk scoring system; however there remains significant variation in radiologist practice when it comes to diagnostic thyroid ultrasound interpretation and reporting. In this work, we propose a computerized approach using a contextual embedding and fusion strategy for the large-scale inference of TI-RADS final assessment categories from narrative ultrasound (US) reports. The proposed model has achieved high accuracy on an internal data set, and high performance scores on an external validation dataset.