Comprehensive Quantitative Evaluation of Variability in Magnetic Resonance-Guided Delineation of Oropharyngeal Gross Tumor Volumes and High-Risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study
Cardenas, Carlos E.; Blinde, Sanne E.; Mohamed, Abdallah S.R.; Ng, Sweet Ping; Raaijmakers, Cornelis; Philippens, Marielle; Kotte, Alexis; Al-Mamgani, Abrahim A.; Karam, Irene; Thomson, David J.; Robbins, Jared; Newbold, Kate; Fuller, Clifton D.; Terhaard, Chris
(2022) International Journal of Radiation Oncology Biology Physics, volume 113, issue 2, pp. 426 - 436
(Article)
Abstract
Purpose: Tumor and target volume manual delineation remains a challenging task in head and neck cancer radiation therapy. The purpose of this study was to conduct a multi-institutional evaluation of manual delineations of gross tumor volume (GTV), high-risk clinical target volume (CTV), parotids, and submandibular glands on treatment simulation magnetic
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resonance scans of patients with oropharyngeal cancer. Methods and Materials: We retrospectively collected pretreatment T1-weighted, T1-weighted with gadolinium contrast, and T2-weighted magnetic resonance imaging scans for 4 patients with oropharyngeal cancer under an institution review board–approved protocol. We provided the scans to 26 radiation oncologists from 7 international cancer centers that participated in this delineation study. We also provide the patients’ clinical history and physical examination findings, along with a medical photographic image and radiologic results. We used both the Simultaneous Truth and Performance Level Estimation algorithm and pair-wise comparisons of the contours, using overlap/distance metrics. Lastly, to assess experience and CTV delineation institutional practices, we had participants complete a brief questionnaire. Results: Large variability was measured between observers’ delineations for GTVs and CTVs. The mean Dice similarity coefficient values across all physicians’ delineations for GTVp, GTVn, CTVp, and CTVn were 0.77, 0.67, 0.77, and 0.69, respectively, for Simultaneous Truth and Performance Level Estimation algorithm comparison, and 0.67, 0.60, 0.67, and 0.58, respectively, for pair-wise analysis. Normal tissue contours were defined more consistently when considering overlap/distance metrics. The median radiation oncology clinical experience was 7 years. The median experience delineating on magnetic resonance imaging was 3.5 years. The GTV-to-CTV margin used was 10 mm for 6 of 7 participant institutions. One institution used 8 mm, and 3 participants (from 3 different institutions) used a margin of 5 mm. Conclusions: The data from this study suggests that appropriate guidelines, contouring quality assurance sessions, and training are still needed for the adoption of magnetic resonance–based treatment planning for head and neck cancers. Such efforts should play a critical role in reducing delineation variation and ensure standardization of target design across clinical practices.
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Keywords: Head and Neck Neoplasms, Humans, Magnetic Resonance Imaging/methods, Magnetic Resonance Spectroscopy, Observer Variation, Oropharyngeal Neoplasms/diagnostic imaging, Prospective Studies, Radiotherapy Planning, Computer-Assisted/methods, Retrospective Studies, Tumor Burden, Radiation, Oncology, Radiology Nuclear Medicine and imaging, Cancer Research, Research Support, Non-U.S. Gov't, Multicenter Study, Journal Article, Research Support, N.I.H., Extramural
ISSN: 0360-3016
Publisher: Elsevier Inc.
Note: Funding Information: Disclosures: C.D.F. was a 2017-2019 Sabin Family Foundation Fellow. C.D.F. reports funding and salary support during study execution interval from the National Institutes of Health (NIH), including the National Institute for Dental and Craniofacial Research Award (1R01DE025248/R56DE025248); a National Science Foundation, Division of Mathematical Sciences, Joint NIH/National Science Foundation Initiative on Quantitative Approaches to Biomedical Big Data (NSF 1557679); a National Institute of Biomedical Imaging and Bioengineering Research Education Programs for Residents and Clinical Fellows Grant (R25EB025787-01); the NIH Big Data to Knowledge Program of the National Cancer Institute (NCI) Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825); the NCI Early Phase Clinical Trials in Imaging and Image Guided Interventions Program (1R01CA218148); an NIH/NCI Cancer Center Support Grant (Pilot Research Program Award from the University of Texas MD Anderson Cancer Center CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672); and an NIH/NCI Head and Neck Specialized Programs of Research Excellence Developmental Research Program Award (P50 CA097007. C.D.F. also reports direct industry grant support and travel funding from Elekta AB. B.A.M. reports grants from the NIH/National Institute of Dental and Craniofacial Research (1F31DE029093-01A1) and research support from a Dr John J. Kopchick Fellowship via the University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences. Funding Information: This work was supported by infrastructure support from the MR-Linac Consortium and the Andrew Sabin Family Foundation. Publisher Copyright: © 2022
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