Automated Information Extraction on Treatment and Prognosis for Non-Small Cell Lung Cancer Radiotherapy Patients: Clinical Study

Shuai Zheng; Salma K Jabbour; Shannon E O'Reilly; James J Lu; Lihua Dong; Lijuan Ding; Ying Xiao; Ning Yue; Fusheng Wang; Wei Zou

doi:10.2196/medinform.8662

Automated Information Extraction on Treatment and Prognosis for Non-Small Cell Lung Cancer Radiotherapy Patients: Clinical Study

JMIR Med Inform. 2018 Feb 1;6(1):e8. doi: 10.2196/medinform.8662.

Authors

Shuai Zheng¹, Salma K Jabbour², Shannon E O'Reilly³, James J Lu⁴, Lihua Dong⁵, Lijuan Ding⁵, Ying Xiao³, Ning Yue², Fusheng Wang^#^{6

7}, Wei Zou^#³

Affiliations

¹ Department of Biomedical Informatics, Emory University, Atlanta, GA, United States.
² Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States.
³ Penn Medicine, Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States.
⁴ Department of Mathematics and Computer Science, Emory University, Atlanta, GA, United States.
⁵ Department of Radiation Oncology, The First Hospital, Changchun, China.
⁶ Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States.
⁷ Department of Computer Science, Stony Brook University, Stony Brook, NY, United States.

^# Contributed equally.

Abstract

Background: In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correlate the treatment regimen with the prognosis to develop evidence-based radiation therapy paradigms. These data are available mainly in forms of narrative texts or table formats with heterogeneous vocabularies. Manual extraction of the related information from these data can be time consuming and labor intensive, which is not ideal for large studies.

Objective: The objective of this study was to adapt the interactive information extraction platform Information and Data Extraction using Adaptive Learning (IDEAL-X) to extract treatment and prognosis data for patients with locally advanced or inoperable non-small cell lung cancer (NSCLC).

Methods: We transformed patient treatment and prognosis documents into normalized structured forms using the IDEAL-X system for easy data navigation. The adaptive learning and user-customized controlled toxicity vocabularies were applied to extract categorized treatment and prognosis data, so as to generate structured output.

Results: In total, we extracted data from 261 treatment and prognosis documents relating to 50 patients, with overall precision and recall more than 93% and 83%, respectively. For toxicity information extractions, which are important to study patient posttreatment side effects and quality of life, the precision and recall achieved 95.7% and 94.5% respectively.

Conclusions: The IDEAL-X system is capable of extracting study data regarding NSCLC chemoradiation patients with significant accuracy and effectiveness, and therefore can be used in large-scale radiotherapy clinical data studies.

Keywords: chemoradiation treatment; information extraction; information storage and retrieval; natural language processing; non–small cell lung; oncology; prognosis.

©Shuai Zheng, Salma K Jabbour, Shannon E O'Reilly, James J Lu, Lihua Dong, Lijuan Ding, Ying Xiao, Ning Yue, Fusheng Wang, Wei Zou. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 01.02.2018.