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- Title
Multi-View Data Integration Methods for Radiotherapy Structure Name Standardization.
- Authors
Syed, Khajamoinuddin; Sleeman IV, William C.; Hagan, Michael; Palta, Jatinder; Kapoor, Rishabh; Ghosh, Preetam; Matuschek, Christiane
- Abstract
Simple Summary: Structure names associated with radiotherapy treatments need standardization to develop data pipelines enabling personalized treatment plans. Automatic classification of structure names based on the currently available TG-263 nomenclature can help with data aggregation from both retrospective and future data sources. The aim of our proposed machine learning-based data integration methods is to achieve highly accurate structure name classification to automate the data aggregation process. Our multi-view models can overcome the challenges of integrating different data types associated with radiotherapy structures, such as the physician-given text labels and geometric or image data. The models exhibited high accuracy when tested on multi-center and multi-institutional lung and prostate cancer patients data and outperformed the models built on any single data type. This highlights the importance of combining different types of data in building generalizable models for structure name standardization. Standardization of radiotherapy structure names is essential for developing data-driven personalized radiotherapy treatment plans. Different types of data are associated with radiotherapy structures, such as the physician-given text labels, geometric (image) data, and Dose-Volume Histograms (DVH). Prior work on structure name standardization used just one type of data. We present novel approaches to integrate complementary types (views) of structure data to build better-performing machine learning models. We present two methods, namely (a) intermediate integration and (b) late integration, to combine physician-given textual structure name features and geometric information of structures. The dataset consisted of 709 prostate cancer and 752 lung cancer patients across 40 radiotherapy centers administered by the U.S. Veterans Health Administration (VA) and the Department of Radiation Oncology, Virginia Commonwealth University (VCU). We used randomly selected data from 30 centers for training and ten centers for testing. We also used the VCU data for testing. We observed that the intermediate integration approach outperformed the models with a single view of the dataset, while late integration showed comparable performance with single-view results. Thus, we demonstrate that combining different views (types of data) helps build better models for structure name standardization to enable big data analytics in radiation oncology.
- Subjects
MACHINE learning; LUNG tumors; DATABASE management; CANCER patients; RADIOTHERAPY; DATA analytics; PROSTATE tumors; ONCOLOGY
- Publication
Cancers, 2021, Vol 13, Issue 8, p1796
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers13081796