Evidence map›Paper›PMID 31518437›Full record

ArticleMedical physics2019

Development and prospective in-patient proof-of-concept validation of a surface photogrammetry + CT-based volumetric motion model for lung radiotherapy.

M Ranjbar, P Sabouri, S Mossahebi, D Leiser, M Foote, J Zhang, G Lasio, S Joshi, A Sawant

Open access · greenAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-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

7 citing papers in PubMed, 8 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

9 authors at 2 institutions in 1 country.

M RanjbarDepartment of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA.
P SabouriDepartment of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA.
S MossahebiDepartment of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA.
D LeiserDepartment of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA.
M FooteDepartment of Biomedical Engineering, Scientific Computing and Imaging Institute, University of Utah, 72 South Central Campus Drive, Room 3750, Salt Lake City, UT, 84112, USA.
J ZhangDepartment of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA.
G LasioDepartment of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA.
S JoshiDepartment of Biomedical Engineering, Scientific Computing and Imaging Institute, University of Utah, 72 South Central Campus Drive, Room 3750, Salt Lake City, UT, 84112, USA.
A SawantDepartment of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA.
University of Maryland, Baltimore · USUniversity of Utah · US

Funding

Personalized Motion Management for truly 4D Lung Stereotactic Body RadiotherapyR01CA169102 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI SAWANT, AMIT · 2013 to 2018
$2.8M
Hunstman Cancer InstituteNCI NIH HHS R01 CA169102NIH HHS R01 CA169102Varian Medical SystemsVisionRT Ltd
6 · The paper itself

Abstract

purposeWe develop and validate a motion model that uses real-time surface photogrammetry acquired concurrently with four-dimensional computed tomography (4DCT) to estimate respiration-induced changes within the entire irradiated volume, over arbitrarily many respiratory cycles.

methodsA research, couch-mounted, VisionRT (VRT) system was used to acquire optical surface data (15 Hz, ROI = 15 × 20 cm

resultsFor five of the six fluoroscopic acquisition sessions, the model out-performed 4DCT in predicting contour Dice coefficient with respect to fluoroscopy-derived contours. Similarly, the model exhibited a marked improvement over 4DCT for patch positions on the diaphragm. Model patch position errors varied from 5 to -15 mm while 4DCT errors ranged between 5 and -22.4 mm. For one fluoroscopic acquisition, a marked change in the a priori internal-external correlation resulted in model errors comparable to those of 4DCT.

conclusionsWe described the development and a proof-of-concept validation for a volumetric motion model that uses surface photogrammetry to correlate the time-varying thoraco-abdominal surface to the time-varying internal thoraco-abdominal volume. These early results indicate that the proposed approach can result in a marked improvement over 4DCT. While limited by the duration of the fluoroscopic acquisitions as well as the resolution of the acquired images, the DRF-based proof-of-concept technique developed here is model-agnostic, and therefore, has the potential to be used as an in-patient validation tool for other volumetric motion models.

Indexed as

Four-Dimensional Computed TomographyModels, BiologicalMovementPhotogrammetryAgedDose Fractionation, RadiationHumansLung NeoplasmsMaleRespirationTime Factorsin-patient validationlung radiotherapymotion managementreal-timesurface-guidedvolumetric motion model

Identifiers

PMID31518437
PMCPMC6941204
OpenAlexW2973101515

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.