Evidence map›Paper›PMID 33533481›Full record

ArticleMedical physics2021

Assessment of pleural invasion and adhesion of lung tumors with dynamic chest radiography: A virtual clinical imaging study.

Rie Tanaka, Ehsan Samei, William Paul Segars, Ehsan Abadi, Isao Matsumoto, Masaya Tamura, Munehisa Takata, Tsuneo Yamashiro

Open access · greenAbstract read
In one paragraph

Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

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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

8 authors at 4 institutions in 2 countries.

Rie TanakaCollege of Medical, Pharmaceutical & Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
Ehsan SameiCarl E Ravin Advanced Imaging Labs, Department of Radiology, Duke University, Durham, NC, 27705, United States.
William Paul SegarsCarl E Ravin Advanced Imaging Labs, Department of Radiology, Duke University, Durham, NC, 27705, United States.
Ehsan AbadiCarl E Ravin Advanced Imaging Labs, Department of Radiology, Duke University, Durham, NC, 27705, United States.
Isao MatsumotoDepartment of Thoracic Surgery, Kanazawa University, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan.
Masaya TamuraDepartment of Thoracic Surgery, Kanazawa University, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan.
Munehisa TakataDepartment of Thoracic Surgery, Kanazawa University, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan.
Tsuneo YamashiroDepartment of Diagnostic Radiology, Yokohama City University, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.
Kanazawa University · JPAdvanced Imaging Research (United States) · USDuke University · USYokohama City University · JP

Funding

TR&D Project 3: Virtual ReadersP41EB028744 · NIBIB · DUKE UNIVERSITY · PI Ehsan Samei · 2021 to 2026
$7.8M
mext kakenhi 19K12841NIBIB NIH HHS P41 EB028744
6 · The paper itself

Abstract

purposeAccurate preoperative assessment of tumor invasion/adhesion is crucial for planning appropriate operative procedures. Recent advances in digital radiography allow a motion analysis of lung tumors with dynamic chest radiography (DCR) with total exposure dose comparable to that of conventional chest radiography. The aim of this study was to investigate the feasibility of preoperative evaluation of pleural invasion/adhesion of lung tumors with DCR through a virtual clinical imaging study, using a four-dimensional (4D) extended cardiac-torso (XCAT) computational phantom.

methodsAn XCAT phantom of an adult man (50th percentile in height and weight) with simulated respiratory and cardiac motions was generated to use as a virtual patient. To simulate lung tumors with and without pleural invasion, a 30-mm diameter tumor sphere was inserted into each lobe of the phantom. The virtual patient during respiration was virtually projected using an x-ray simulator in posteroanterior (PA) and oblique directions, and sequential bone suppression (BS) images were created. The measurement points (tumor, rib, and diaphragm) were automatically tracked on simulated images by a template matching technique. We calculated five quantitative metrics related to the movement distance and directions of the targeted tumor and evaluated whether DCR could distinguish between tumors with and without pleural invasion/adhesion.

resultsPrecise tracking of the targeted tumor was achieved on the simulated BS images without undue influence of rib shadows. There was a significant difference in all five quantitative metrics between the lung tumors with and without pleural invasion both on the oblique and PA projection views (P < 0.05). Quantitative metrics related to the movement distance were effective for tumors in the middle and lower lobes, while, those related to the movement directions were effective for tumors close to the frontal chest wall on the oblique projection view. The oblique views were useful for the evaluation of the space between the chest wall and a moving tumor.

conclusionDCR could help distinguish between tumors with and without pleural invasion/adhesion based on the two-dimensional movement distance and direction using oblique and PA projection views. With anticipated improved image: processing to evaluate the respiratory displacement of lung tumors in the upper lobe or behind the heart, DCR holds promise for clinical assessment of tumor invasion/adhesion in the parietal pleura.

Indexed as

Lung NeoplasmsPleuraAdultFour-Dimensional Computed TomographyHumansMalePhantoms, ImagingRespirationdynamic chest radiographyflat-panel detectortumor invasion/adhesionvirtual clinical trialXCAT phantom

Identifiers

PMID33533481
PMCPMC8587535
OpenAlexW3126861447

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.