ArticleAdvances in radiation oncology
Big Data Readiness in Radiation Oncology: An Efficient Approach for Relabeling Radiation Therapy Structures With Their TG-263 Standard Name in Real-World Data Sets.
Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Harmonizing organ-at-risk structure names using open-source large language models.Physics and imaging in radiation oncology · 2025Article
- Perceptions on and roadblocks to implementation of standardized nomenclature in radiation oncology: A survey from TG-263U1.Journal of applied clinical medical physics · 2024Article
- Artificial general intelligence for radiation oncology.Meta-radiology · 2023Article
- Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.Journal of applied clinical medical physics · 2023Article
- Educative Impact of Automatic Delineation Applied to Head and Neck Cancer Patients on Radiation Oncology Residents.Journal of cancer education : the official journal of the American Association for Cancer Education · 2023Article
- Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach.Cancers · 2023Article
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.Journal of applied clinical medical physics · 2021Article
- Article
- Introduction to machine and deep learning for medical physicists.Medical physics · 2020Article
- Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names.Healthcare (Basel, Switzerland) · 2020Article
- Technical Note: An open source solution for improving TG-263 compliance.Journal of applied clinical medical physics · 2019Article
- Adapting training for medical physicists to match future trends in radiation oncology.Physics and imaging in radiation oncology · 2019Article
- Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium.Advances in radiation oncologyArticle
- Automated Plan Checking Software Demonstrates Continuous and Sustained Improvements in Safety and Quality: A 3-year Longitudinal Analysis.Practical radiation oncologyReview
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo prepare for big data analyses on radiation therapy data, we developed METHODS AND MATERIALS: The clinically relevant structures in the radiation therapy plans were identified by reference to randomized controlled trials. The Stature approach was used by clinicians to identify the synonyms for each relevant structure, which was then mapped to the corresponding TG-263 name. We applied Stature to standardize the structure names for 654 patients with prostate cancer (PCa) and 224 patients with head and neck squamous cell carcinoma (HNSCC) who received curative radiation therapy at our institution between 2007 and 2017. The accuracy of the Stature process was manually validated in a random sample from each cohort. For the HNSCC cohort we measured the resource requirements for Stature, and for the PCa cohort we demonstrated its impact on an example clinical analytics scenario.
resultsAll but 1 synonym group ("Hydrogel") was mapped to the corresponding TG-263 name, resulting in a TG-263 relabel rate of 99% (8837 of 8925 structures). For the PCa cohort, Stature matched a total of 5969 structures. Of these, 5682 structures were exact matches (ie, following local naming convention), 284 were matched via a synonym, and 3 required manual matching. This original radiation therapy structure names therefore had a naming inconsistency rate of 4.81%. For the HNSCC cohort, Stature mapped a total of 2956 structures (2638 exact, 304 synonym, 14 manual; 10.76% inconsistency rate) and required 7.5 clinician hours. The clinician hours required were one-fifth of those that would be required for manual relabeling. The accuracy of Stature was 99.97% (PCa) and 99.61% (HNSCC).
conclusionsThe Stature approach was highly accurate and had significant resource efficiencies compared with manual curation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.