Evidence map›Paper›PMID 35693252›Full record

ArticleAdvances in radiation oncology

Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment.

Daniel R Owen, Yilun Sun, Jim C Irrer, Matthew J Schipper, Caitlin A Schonewolf, Stefanie Galbán, Shruti Jolly, Randall K Ten Haken, C J Galbán, M M Matuszak

Open access · goldAbstract read
In one paragraph

Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 52% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

10 authors at 1 institution in 1 country.

Daniel R OwenDepartments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Yilun SunDepartments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Jim C IrrerDepartments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Matthew J SchipperDepartments of Biostatistics, University of Michigan, Ann Arbor, Michigan.
Caitlin A SchonewolfDepartments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Stefanie GalbánDepartments of Radiology, University of Michigan, Ann Arbor, Michigan.
Shruti JollyDepartments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Randall K Ten HakenDepartments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
C J GalbánDepartments of Radiology, University of Michigan, Ann Arbor, Michigan.
M M MatuszakDepartments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
University of Michigan · US

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI THEODORE S LAWRENCE · 1988 to 2026
$178.2M
TREATMENT VERIFICATION AND TREATMENT PLAN REFINEMENTP01CA059827 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KESSLER, MARC LEWIS, LAWRENCE, THEODORE S · 1993 to 2018
$37.9M
Prediction of COPD Progression by PRMR01HL150023 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GALBAN, CRAIG J, HAN, MEILAN K · 2020 to 2023
$2.8M
An Early Imaging Marker of EmphysemaR01HL139690 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GALBAN, CRAIG J · 2019 to 2022
$1.9M
Commercialization of a CT-based Technique for BOS AssessmentR44HL140894 · NHLBI · IMBIO, LLC · PI GALBAN, CRAIG J, LUDWIG, KAI DAVID · 2018 to 2021
$1.7M
NCI NIH HHS P01 CA059827NCI NIH HHS P30 CA046592NHLBI NIH HHS R01 HL139690NHLBI NIH HHS R01 HL150023NHLBI NIH HHS R44 HL140894
6 · The paper itself

Abstract

Purpose: Parametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary pathologies on a patient-specific basis. As one of the first studies to implement such a technique in the radiation oncology clinic, the goal of this work was to assess the feasibility for PRM analysis to identify pulmonary abnormalities in patients with lung cancer before radiation therapy (RT). Methods and Materials: High-resolution, paired inspiration and expiration CT scans were acquired from 23 patients with lung cancer as part of routine treatment planning CT acquisition. When applied to the paired CT scans, PRM analysis classifies lung parenchyma, on a voxel-wise basis, as normal, small airways disease (SAD), emphysema, or parenchymal disease (PD). PRM classifications were quantified as a percent of total lung volume and were evaluated globally and regionally within the lung. Results: PRM analysis of pre-RT CT scans was successfully implemented using a workflow that produced patient-specific maps and quantified specific phenotypes of pulmonary abnormalities. Through this study, a large prevalence of SAD and PD was demonstrated in this lung cancer patient population, with global averages of 10% and 17%, respectively. Moreover, PRM-classified normal and SAD in the region with primary tumor involvement were found to be significantly different from global lung values. When present, elevated levels of PD and SAD abnormalities tended to be pervasive in multiple regions of the lung, indicating a large burden of underlying disease. Conclusions: Pulmonary abnormalities, as detected by PRM, were characterized in patients with lung cancer scheduled for RT. Although further study is needed, PRM is a highly accessible CT-based imaging technique that has the potential to identify local lung abnormalities associated with chronic obstructive pulmonary disease and interstitial lung disease. Further investigation in the radiation oncology setting may provide strategies for tailoring RT planning and risk assessment based on pre-existing PRM-based pathology.

Identifiers

PMID35693252
PMCPMC9184868
OpenAlexW4224881496

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.