TY - JOUR
T1 - Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data
T2 - Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium
AU - Caissie, Amanda
AU - Mierzwa, Michelle
AU - Fuller, Clifton David
AU - Rajaraman, Murali
AU - Lin, Alex
AU - MacDonald, Andrew
AU - Popple, Richard
AU - Xiao, Ying
AU - VanDijk, Lisanne
AU - Balter, Peter
AU - Fong, Helen
AU - Xu, Heping
AU - Kovoor, Matthew
AU - Lee, Joonsang
AU - Rao, Arvind
AU - Martel, Mary
AU - Thompson, Reid
AU - Merz, Brandon
AU - Yao, John
AU - Mayo, Charles
N1 - Funding Information:
Disclosures: Dr Mayo reports receiving a research grant from Varian Medical Systems . Dr Xiao reports receiving grant NCI 2U24CA180803-06 from the Imaging and Radiation Oncology Core and grant 2U10CA180868-06 from the National Research Group . Dr MacDonald reports receiving a research grant from Varian Medical Systems . All other authors have no disclosures to declare.
Funding Information:
Disclosures: Dr Mayo reports receiving a research grant from Varian Medical Systems. Dr Xiao reports receiving grant NCI 2U24CA180803-06 from the Imaging and Radiation Oncology Core and grant 2U10CA180868-06 from the National Research Group. Dr MacDonald reports receiving a research grant from Varian Medical Systems. All other authors have no disclosures to declare. Sources of support: This work had no specific funding.
Publisher Copyright:
© 2022
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Purpose: Outside of randomized clinical trials, it is difficult to develop clinically relevant evidence-based recommendations for radiation therapy (RT) practice guidelines owing to lack of comprehensive real-world data. To address this knowledge gap, we formed the Learning from Analysis of Multicenter Big Data Aggregation consortium to cooperatively implement RT data standardization, develop software solutions for data analysis, and recommend clinical practice change based on real-world data analyzed. The first phase of this “Big Data” study aimed at characterizing variability in clinical practice patterns of dosimetric data for organs at risk (OARs) that would undermine subsequent use of large-scale, electronically aggregated data to characterize associations with outcomes. Evidence from this study was used as the basis for practical recommendations to improve data quality. Methods and Materials: Dosimetric details of patients with head and neck cancer treated with radiation therapy between 2014 and 2019 were analyzed. Institutional patterns of practice were characterized, including structure nomenclature, volumes, and frequency of contouring. Dose volume histogram (DVH) distributions were characterized and compared with institutional constraints and literature values. Results: Plans for 4664 patients treated to a mean plan dose of 64.4 ± 13.2 Gy in 32 ± 4 fractions were aggregated. Before implementation of TG-263 guidelines in each institution, there was variability in OAR nomenclature across institutions and structures. With evidence from this study, we identified a targeted and practical set of recommendations aimed at improving the quality of real-world data. Conclusions: Quantifying similarities and differences among institutions for OAR structures and DVH metrics is the launching point for next steps to investigate potential relationships between DVH parameters and patient outcomes.
AB - Purpose: Outside of randomized clinical trials, it is difficult to develop clinically relevant evidence-based recommendations for radiation therapy (RT) practice guidelines owing to lack of comprehensive real-world data. To address this knowledge gap, we formed the Learning from Analysis of Multicenter Big Data Aggregation consortium to cooperatively implement RT data standardization, develop software solutions for data analysis, and recommend clinical practice change based on real-world data analyzed. The first phase of this “Big Data” study aimed at characterizing variability in clinical practice patterns of dosimetric data for organs at risk (OARs) that would undermine subsequent use of large-scale, electronically aggregated data to characterize associations with outcomes. Evidence from this study was used as the basis for practical recommendations to improve data quality. Methods and Materials: Dosimetric details of patients with head and neck cancer treated with radiation therapy between 2014 and 2019 were analyzed. Institutional patterns of practice were characterized, including structure nomenclature, volumes, and frequency of contouring. Dose volume histogram (DVH) distributions were characterized and compared with institutional constraints and literature values. Results: Plans for 4664 patients treated to a mean plan dose of 64.4 ± 13.2 Gy in 32 ± 4 fractions were aggregated. Before implementation of TG-263 guidelines in each institution, there was variability in OAR nomenclature across institutions and structures. With evidence from this study, we identified a targeted and practical set of recommendations aimed at improving the quality of real-world data. Conclusions: Quantifying similarities and differences among institutions for OAR structures and DVH metrics is the launching point for next steps to investigate potential relationships between DVH parameters and patient outcomes.
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U2 - 10.1016/j.adro.2022.100925
DO - 10.1016/j.adro.2022.100925
M3 - Article
AN - SCOPUS:85146035782
SN - 2452-1094
VL - 8
JO - Advances in Radiation Oncology
JF - Advances in Radiation Oncology
IS - 1
M1 - 100925
ER -