Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names

Khajamoinuddin Syed; William Sleeman Iv; Kevin Ivey; Michael Hagan; Jatinder Palta; Rishabh Kapoor; Preetam Ghosh

doi:10.3390/healthcare8020120

Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names

Healthcare (Basel). 2020 Apr 30;8(2):120. doi: 10.3390/healthcare8020120.

Authors

Khajamoinuddin Syed¹, William Sleeman Iv^{1

2}, Kevin Ivey³, Michael Hagan^{2

4}, Jatinder Palta^{2

4}, Rishabh Kapoor^{2

4}, Preetam Ghosh¹

Affiliations

¹ Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.
² Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23298, USA.
³ Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.
⁴ National Radiation Oncology Program, Department of Veteran Affairs Richmond, VA 23249, USA.

Abstract

The lack of standardized structure names in radiotherapy (RT) data limits interoperability, data sharing, and the ability to perform big data analysis. To standardize radiotherapy structure names, we developed an integrated natural language processing (NLP) and machine learning (ML) based system that can map the physician-given structure names to American Association of Physicists in Medicine (AAPM) Task Group 263 (TG-263) standard names. The dataset consist of 794 prostate and 754 lung cancer patients across the 40 different radiation therapy centers managed by the Veterans Health Administration (VA). Additionally, data from the Radiation Oncology department at Virginia Commonwealth University (VCU) was collected to serve as a test set. Domain experts identified as anatomically significant nine prostate and ten lung organs-at-risk (OAR) structures and manually labeled them according to the TG-263 standards, and remaining structures were labeled as Non_OAR. We experimented with six different classification algorithms and three feature vector methods, and the final model was built with fastText algorithm. Multiple validation techniques are used to assess the robustness of the proposed methodology. The macro-averaged F 1 score was used as the main evaluation metric. The model achieved an F 1 score of 0.97 on prostate structures and 0.99 for lung structures from the VA dataset. The model also performed well on the test (VCU) dataset, achieving an F 1 score of 0.93 for prostate structures and 0.95 on lung structures. In this work, we demonstrate that NLP and ML based approaches can used to standardize the physician-given RT structure names with high fidelity. This standardization can help with big data analytics in the radiation therapy domain using population-derived datasets, including standardization of the treatment planning process, clinical decision support systems, treatment quality improvement programs, and hypothesis-driven clinical research.

Keywords: TG-263; machine learning; natural language processing; nomenclature standardization; quality assurance; radiotherapy structure names; text categorization.