Evidence map›Paper›PMID 28816147›Full record

ArticleInternational journal of radiation oncology, biology, physics2017

Evaluating Which Dose-Function Metrics Are Most Critical for Functional-Guided Radiation Therapy.

Austin M Faught, Tokihiro Yamamoto, Richard Castillo, Edward Castillo, Jingjing Zhang, Moyed Miften, Yevgeniy Vinogradskiy

Abstract read
In one paragraph

Article in International journal of radiation oncology, biology, physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing 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

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

  1. Pooled it
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  4. Evaluation of variables predicting PFT changes for lung cancer patients treated on a prospective 4DCT-ventilation functional avoidance clinical trial.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023
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  20. Lung functional imaging.Breathe (Sheffield, England) · 2023
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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

7 authors.

Austin M FaughtDepartment of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado. Electronic address: austin.faught@ucdenver.edu.
Tokihiro YamamotoDepartment of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California.
Richard CastilloDepartment of Radiation Oncology, University of Texas Medical Branch of Galveston, League City, Texas.
Edward CastilloDepartment of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan.
Jingjing ZhangDepartment of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan.
Moyed MiftenDepartment of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.
Yevgeniy VinogradskiyDepartment of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.

Funding

Clinical trial personalizing radiation therapy through a novel lung function imaging modalityR01CA200817 · NCI · UNIVERSITY OF COLORADO DENVER · PI VINOGRADSKIY, YEVGENIY · 2016 to 2018
$618k
A Bayesian Framework for Inter-Modality Deformable Image Registration EvaluationK01CA181292 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CASTILLO, RICHARD · 2013 to 2016
$478k
NCI NIH HHS K01 CA181292NCI NIH HHS R01 CA200817
6 · The paper itself

Abstract

purposeFour-dimensional (4D) computed tomography (CT) ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiation therapy in clinical trials. There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP). METHODS AND MATERIALS: Seventy lung cancer patients who underwent 4D CT imaging and pneumonitis grading were assessed. Pretreatment 4D CT scans of each patient were used to calculate ventilation images. We evaluated 3 types of dose-function metrics that combined the patient's 4D CT ventilation image and treatment planning dose distribution: (1) structure-based approaches; (2) image-based approaches using the dose-function histogram; and (3) nonlinear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability models predicting grade 3 or higher (ie, grade 3+) pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared with normal tissue complication probability models based on traditional, total lung dose metrics.

resultsThe most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20 Gy (AUC, 0.70). Probabilities of grade 3+ RP of 20% and 10% correspond to V20 (percentage of volume receiving ≥20 Gy) to the functional subvolumes of 26.8% and 9.3%, respectively. Imaging-based analysis with the dose-function histogram and nonlinear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when we evaluated the percentage of functionality receiving ≥20 Gy. All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC of 0.55).

conclusionsA full range of dose-function metrics and functional thresholds was examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66-0.70), and all showed notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+ RP provides valuable data to inform the functional-guided radiation therapy process.

Indexed as

Four-Dimensional Computed TomographyRespirationAlgorithmsArea Under CurveCarcinoma, Non-Small-Cell LungDose-Response Relationship, RadiationHumansLungLung NeoplasmsOrgans at RiskProbabilityRadiation DosageRadiation PneumonitisRadiotherapy Planning, Computer-AssistedRetrospective Studies

Identifiers

PMID28816147
PMCPMC5603256

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.