Evidence map›Paper›PMID 42638336›Full record

ArticleJournal of applied clinical medical physics2026

Prospective estimation of water equivalent diameter and SSDE using 3D landmark CT scans.

Andrew M Hernandez, Sarah Yoon, Michael T Corwin, Eric S Diaz, Ahmadreza Ghasemiesfe, Sarah E McKenney, John M Boone

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Andrew M HernandezDepartment of Radiology, University of California Davis Health, Sacramento, California, USA.
Sarah YoonDepartment of Radiology, University of California Davis Health, Sacramento, California, USA.
Michael T CorwinDepartment of Radiology, University of California Davis Health, Sacramento, California, USA.
Eric S DiazDepartment of Radiology, University of California Davis Health, Sacramento, California, USA.
Ahmadreza GhasemiesfeDepartment of Radiology, University of California Davis Health, Sacramento, California, USA.
Sarah E McKenneyDepartment of Radiology, University of California Davis Health, Sacramento, California, USA.
John M BooneDepartment of Radiology, University of California Davis Health, Sacramento, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background3D Landmark (3DLM) scans have recently been introduced in some new CT scanners for automatic scan planning instead of paired 2D localizers, with potential additional utility. PURPOSE: The purpose of this work was to measure the accuracy and precision of 3DLM scans for determining water equivalent diameter (

methodsA cohort of 223 diagnostic CT series comprised of 82 Head [52 non-contrast (NC); 30 contrast-enhanced (CE)], 66 Chest (36 NC; 30 CE), and 75 Abdomen-pelvis (45 NC; 30 CE) series, acquired on a CT scanner equipped with 3DLM capabilities, were retrospectively collected. All series were acquired for patient standard-of-care, and 3DLM series were generated according to the manufacturer's default protocol.

resultsThe mean [range] patient age was 45.9 years old [0.1-91.5]. The range of

conclusion3DLM-based measurements of

Indexed as

Imaging, Three-DimensionalPhantoms, ImagingRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedWaterAdultAgedFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedPelvisProspective StudiesRadiation DosageRadiotherapy DosageWater3D landmarkradiation dosesize‐specific dose estimatewater equivalent diameter

Identifiers

PMID42638336
PMCPMC13504162

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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.