Published April 2021
| Version v1
Journal article
Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617
- 1. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York (United States)
Description
Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses were higher than reported due to inconsistent and insufficient heart segmentation. We tested our hypothesis by comparing doses between deep-learning (DL) segmented hearts and trial hearts.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.ijrobp.2020.11.011Additional details
Identifiers
- DOI
- 10.1016/j.ijrobp.2020.11.011;
- PII
- S0360301620344990;
Publishing Information
- Journal Title
- International Journal of Radiation Oncology, Biology and Physics
- Journal Volume
- 109
- Journal Issue
- 5
- Journal Page Range
- p. 1619-1626
- ISSN
- 0360-3016
- CODEN
- IOBPD3
INIS
- Country of Publication
- United States
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 53124538
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- CLINICAL TRIALS; HEART; MACHINE LEARNING; RADIATION DOSES; RADIOTHERAPY; SIMULATION
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; BODY; CARDIOVASCULAR SYSTEM; DOSES; LEARNING; MATHEMATICAL LOGIC; MEDICINE; NUCLEAR MEDICINE; ORGANS; RADIOLOGY; TESTING; THERAPY
Optional Information
- Copyright
- Copyright (c) 2020 Elsevier Inc. All rights reserved.