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Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

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

Khajamoinuddin Syed¹, William Sleeman Iv^1,2, Kevin Ivey³

¹Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.

Healthcare (Basel, Switzerland)

|May 6, 2020

Summary

This summary is machine-generated.

We developed a natural language processing (NLP) and machine learning (ML) system to standardize radiotherapy (RT) structure names, improving data sharing and analysis. This system achieved high accuracy in mapping physician-given names to AAPM TG-263 standards.

Keywords:

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

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Area of Science:

Medical Informatics
Computational Oncology
Natural Language Processing

Background:

Inconsistent radiotherapy (RT) structure naming hinders data interoperability, sharing, and big data analysis.
Standardization is crucial for advancing radiation oncology research and clinical practice.

Purpose of the Study:

To develop and validate an integrated natural language processing (NLP) and machine learning (ML) system for standardizing physician-given RT structure names.
To map existing structure names to the American Association of Physicists in Medicine (AAPM) Task Group 263 (TG-263) standards.

Main Methods:

Utilized a dataset from 794 prostate and 754 lung cancer patients across 40 Veterans Health Administration (VA) centers, plus data from Virginia Commonwealth University (VCU) as a test set.
Domain experts identified and manually labeled key organs-at-risk (OAR) structures according to TG-263 standards.
Experimented with various classification algorithms and feature vector methods, selecting fastText as the final model, and employed multiple validation techniques.

Main Results:

The NLP/ML system achieved high macro-averaged F1 scores: 0.97 for prostate and 0.99 for lung structures on the VA dataset.
Performance on the VCU test set was also strong, with F1 scores of 0.93 for prostate and 0.95 for lung structures.
Demonstrated high fidelity in standardizing physician-given RT structure names.

Conclusions:

NLP and ML-based approaches are effective for standardizing radiotherapy structure names with high accuracy.
This standardization facilitates big data analytics, treatment planning, clinical decision support, quality improvement, and hypothesis-driven research in radiation oncology.