Evidence map›Paper›PMID 33358230›Full record

ReviewInternational journal of radiation oncology, biology, physics2021

A Primer on Dose-Response Data Modeling in Radiation Therapy.

Vitali Moiseenko, Lawrence B Marks, Jimm Grimm, Andrew Jackson, Michael T Milano, Jona A Hattangadi-Gluth, Minh-Phuong Huynh-Le, Niclas Pettersson, Ellen Yorke, Issam El Naqa

Abstract readReview
In one paragraph

Review in International journal of radiation oncology, biology, physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. VPrecision radiation oncology · 2025
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Vitali MoiseenkoDepartment of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California. Electronic address: vmoiseenko@health.ucsd.edu.
Lawrence B MarksDepartment of Radiation Oncology and the Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Jimm GrimmDepartment of Radiation Oncology, Geisinger Health System, Danville, Pennsylvania.
Andrew JacksonDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Michael T MilanoDepartment of Radiation Oncology, University of Rochester, Rochester, New York.
Jona A Hattangadi-GluthDepartment of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California.
Minh-Phuong Huynh-LeDepartment of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California.
Niclas PetterssonDepartment of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden.
Ellen YorkeDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Issam El NaqaDepartment of Machine Learning, Moffitt Cancer Center, Tampa, Florida.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
Optimal Decision Making in Radiotherapy Using Panomics AnalyticsR01CA233487 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2019 to 2023
$2.4M
Image-Guided Cognitive-Sparing Radiosurgery for Brain Metastases: Avoidance of Eloquent White Matter and Hippocampal RegionsR01CA238783 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HATTANGADI-GLUTH, JONA A · 2019 to 2021
$737k
NCI NIH HHS P30 CA008748NCI NIH HHS R01 CA233487NCI NIH HHS R01 CA238783
6 · The paper itself

Abstract

An overview of common approaches used to assess a dose response for radiation therapy-associated endpoints is presented, using lung toxicity data sets analyzed as a part of the High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic effort as an example. Each component presented (eg, data-driven analysis, dose-response analysis, and calculating uncertainties on model prediction) is addressed using established approaches. Specifically, the maximum likelihood method was used to calculate best parameter values of the commonly used logistic model, the profile-likelihood to calculate confidence intervals on model parameters, and the likelihood ratio to determine whether the observed data fit is statistically significant. The bootstrap method was used to calculate confidence intervals for model predictions. Correlated behavior of model parameters and implication for interpreting dose response are discussed.

Indexed as

Data AnalysisDose-Response Relationship, RadiationRadiation Dose HypofractionationConfidence IntervalsGoalsHumansLikelihood FunctionsLogistic ModelsLungOrgans at RiskRadiation PneumonitisRadiotherapyUncertainty

Identifiers

PMID33358230
PMCPMC9339232

What OpenQuestion holds

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Read underepoch 390

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.