ArticleMedical physics2018
Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach.
Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Artificial intelligence (AI)-based multi-organ contour quality assurance with uncertainty estimation for online adaptive radiotherapy (oART).Machine Learning. Health · 2026Article
- The impact of neck tilt on the accuracy of deep learning generated contours for CT images of the head and neck.Journal of applied clinical medical physics · 2025Article
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy.Physics and imaging in radiation oncology · 2024Article
- Performance analysis and knowledge-based quality assurance of critical organ auto-segmentation for pediatric craniospinal irradiation.Scientific reports · 2024Article
- Knowledge-based quality assurance of a comprehensive set of organ at risk contours for head and neck radiotherapy.Frontiers in oncology · 2024Article
- Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation.Medical physics · 2023Article
- Predictive modeling using shape statistics for interpretable and robust quality assurance of automated contours in radiation treatment planning.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Quality assurance for automatically generated contours with additional deep learning.Insights into imaging · 2022Article
- Knowledge-based quality control of organ delineations in radiation therapy.Medical physics · 2022Article
- Auto-segmentation for total marrow irradiation.Frontiers in oncology · 2022Article
- Challenges in the target volume definition of lung cancer radiotherapy.Translational lung cancer research · 2021Review
- Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment.Technology in cancer research & treatmentArticle
- An Automated Workflow to Improve Efficiency in Radiation Therapy Treatment Planning by Prioritizing Organs at Risk.Advances in radiation oncologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeTo develop a quality assurance (QA) tool that identifies inaccurate organ at risk (OAR) delineations.
methodsThe QA tool computed volumetric features from prior OAR delineation data from 73 thoracic patients to construct a reference database. All volumetric features of the OAR delineation are computed in three-dimensional space. Volumetric features of a new OAR are compared with respect to those in the reference database to discern delineation outliers. A multicriteria outlier detection system warns users of specific delineation outliers based on combinations of deviant features. Fifteen independent experimental sets including automatic, propagated, and clinically approved manual delineation sets were used for verification. The verification OARs included manipulations to mimic common errors. Three experts reviewed the experimental sets to identify and classify errors, first without; and then 1 week after with the QA tool.
resultsIn the cohort of manual delineations with manual manipulations, the QA tool detected 94% of the mimicked errors. Overall, it detected 37% of the minor and 85% of the major errors. The QA tool improved reviewer error detection sensitivity from 61% to 68% for minor errors (P = 0.17), and from 78% to 87% for major errors (P = 0.02).
conclusionsThe QA tool assists users to detect potential delineation errors. QA tool integration into clinical procedures may reduce the frequency of inaccurate OAR delineation, and potentially improve safety and quality of radiation treatment planning.
Indexed as
Identifiers
What OpenQuestion holds
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.