Evidence map›Paper›PMID 24674363›Full record

ArticlePractical radiation oncology

Prospective assessment of an atlas-based intervention combined with real-time software feedback in contouring lymph node levels and organs-at-risk in the head and neck: Quantitative assessment of conformance to expert delineation.

Musaddiq Awan, Jayashree Kalpathy-Cramer, G Brandon Gunn, Beth M Beadle, Adam S Garden, Jack Phan, Emma Holliday, William E Jones, Elizabeth Maani, Abhilasha Patel and 5 more

Open access · greenAbstract read
In one paragraph

Article in Practical radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Journal of medical imaging (Bellingham, Wash.) · 2023
    Article
  4. Article
  5. Article
  6. AnClinical and translational radiation oncology · 2019
    Article
  7. Article
  8. Article
  9. A dose based approach for evaluation of inter-observer variations in target delineation.Technical innovations & patient support in radiation oncology
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 3 institutions in 1 country.

Musaddiq AwanDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Jayashree Kalpathy-CramerAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Charlestown, Massachusetts; Department of Radiology and Neuroscience, Massachusetts General Hospital, Charlestown, Massachusetts; Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon.
G Brandon GunnDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Beth M BeadleDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Adam S GardenDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Jack PhanDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Emma HollidayDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
William E JonesSouth Texas Veterans Affairs Health Care System, San Antonio, Texas; Department of Radiation Oncology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas.
Elizabeth MaaniSouth Texas Veterans Affairs Health Care System, San Antonio, Texas; Department of Radiation Oncology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas.
Abhilasha PatelDepartment of Radiation Oncology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas.
Jehee ChoiDepartment of Radiation Oncology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas.
Virginia ClyburnDepartment of Radiation Oncology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas.
Bundhit TantiwongkosiDepartment of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas.
David I RosenthalDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Clifton D FullerDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon. Electronic address: cdfuller@mdanderson.org.
The University of Texas MD Anderson Cancer Center · USThe University of Texas Health Science Center at San Antonio · USAthinoula A. Martinos Center for Biomedical Imaging · US

Funding

Quantitative MRI of Glioblastoma ResponseU01CA154601 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI KALPATHY-CRAMER, JAYASHREE · 2011 to 2016
$3.4M
Clinical Image Retrieval: User needs assessment toolbox development & evaluationR00LM009889 · NLM · MASSACHUSETTS GENERAL HOSPITAL · PI KALPATHY-CRAMER, JAYASHREE · 2011 to 2013
$690k
Clinical Image Retrieval: User needs assessment, toolbox development & evaluationK99LM009889 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI KALPATHY-CRAMER, JAYASHREE · 2009 to 2010
$216k
NCI NIH HHS L30 CA136381NCI NIH HHS U01 CA154601NLM NIH HHS K99 LM009889NLM NIH HHS R00 LM009889
6 · The paper itself

Abstract

purposeA number of studies have previously assessed the role of teaching interventions to improve organ-at-risk (OAR) delineation. We present a preliminary study demonstrating the benefit of a combined atlas and real time software-based feedback intervention to aid in contouring of OARs in the head and neck. METHODS AND MATERIALS: The study consisted of a baseline evaluation, a real-time feedback intervention, atlas presentation, and a follow-up evaluation. At baseline evaluation, 8 resident observers contoured 26 OARs on a computed tomography scan without intervention or aid. They then received feedback comparing their contours both statistically and graphically to a set of atlas-based expert contours. Additionally, they received access to an atlas to contour these structures. The resident observers were then asked to contour the same 26 OARs on a separate computed tomography scan with atlas access. In addition, 6 experts (5 radiation oncologists specializing in the head and neck, and 1 neuroradiologist) contoured the 26 OARs on both scans. A simultaneous truth and performance level estimation (STAPLE) composite of the expert contours was used as a gold-standard set for analysis of OAR contouring.

resultsOf the 8 resident observers who initially participated in the study, 7 completed both phases of the study. Dice similarity coefficients were calculated for each user-drawn structure relative to the expert STAPLE composite for each structure. Mean dice similarity coefficients across all structures increased between phase 1 and phase 2 for each resident observer, demonstrating a statistically significant improvement in overall OAR-contouring ability (P < .01). Additionally, intervention improved contouring in 16/26 delineated organs-at-risk across resident observers at a statistically significant level (P ≤ .05) including all otic structures and suprahyoid lymph node levels of the head and neck.

conclusionsOur data suggest that a combined atlas and real-time feedback-based educational intervention detectably improves contouring of OARs in the head and neck.

Identifiers

PMID24674363
PMCPMC3706296
OpenAlexW1994146150

What OpenQuestion holds

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Registered trials

None linked

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.