Evidence map›Paper›PMID 30767773›Full record

ArticleBMC medical imaging2019

Computed tomography volumetry of esophageal cancer - the role of semiautomatic assessment.

Yi-Hua Zhang, Michael A Fischer, Henrik Lehmann, Åse Johnsson, Ioannis Rouvelas, Gunnar Herlin, Lars Lundell, Torkel B Brismar

Abstract readComparative Study
In one paragraph

Article in BMC medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

8 authors.

Yi-Hua ZhangDepartment of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden. yi-hua.zhang@ki.se.ORCID 0000-0001-6088-1776
Michael A FischerDepartment of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden.
Henrik LehmannDepartment of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden.
Åse JohnssonDepartment of Radiology, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
Ioannis RouvelasDepartment of Surgery, Centre for Digestive Diseases and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden.
Gunnar HerlinDepartment of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden.
Lars LundellDepartment of Surgery, Centre for Digestive Diseases and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden.
Torkel B BrismarDepartment of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe clinical and research value of Computed Tomography (CT) volumetry of esophageal cancer tumor size remains controversial. Development in CT technique and image analysis has made CT volumetry less cumbersome and it has gained renewed attention. The aim of this study was to assess esophageal tumor volume by semi-automatic measurements as compared to manual.

methodsA total of 23 esophageal cancer patients (median age 65, range 51-71), undergoing CT in the portal-venous phase for tumor staging, were retrospectively included between 2007 and 2012. One radiology resident and one consultant radiologist measured the tumor volume by semiautomatic segmentation and manual segmentation. Reproducibility of the respective measurements was assessed by intraclass correlation coefficients (ICC) and by average deviation from mean.

resultsMean tumor volume was 46 ml (range 5-137 ml) using manual segmentation and 42 ml (range 3-111 ml) using semiautomatic segmentation. Semiautomatic measurement provided better inter-observer agreement than traditional manual segmentation. The ICC was significantly higher for semiautomatic segmentation in comparison to manual segmentation (0.86, 0.56, p < 0.01). The average absolute percentage difference from mean was reduced from 24 to 14% (p < 0.001) when using semiautomatic segmentation.

conclusionsSemiautomatic analysis outperforms manual analysis for assessment of esophageal tumor volume, improving reproducibility.

Indexed as

AgedCone-Beam Computed TomographyEsophageal NeoplasmsFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedMulticenter Studies as TopicNeoplasm StagingRandomized Controlled Trials as TopicRetrospective StudiesComputed tomographyEsophageal cancerEsophageal resectionTumor volume

Identifiers

PMID30767773
PMCPMC6377716

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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.