Evidence map›Paper›PMID 38916617›Full record

ArticleAbdominal radiology (New York)2024

Material density dual-energy CT images: value added in early diagnosis of peritoneal carcinomatosis : Original research.

Nisanard Pisuchpen, Simon Lennartz, Anushri Parakh, Sasiprang Kongboonvijit, Shravya Srinivas Rao, Theodore T Pierce, Mark A Anderson, Peter F Hahn, Nathaniel D Mercaldo, Avinash Kambadakone

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Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

10 authors.

Nisanard PisuchpenAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Simon LennartzAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Anushri ParakhAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Sasiprang KongboonvijitAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Shravya Srinivas RaoAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Theodore T PierceAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Mark A AndersonAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Peter F HahnAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Nathaniel D MercaldoAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Avinash KambadakoneAbdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA. akambadakone@mgh.harvard.edu.ORCID 0000-0001-6714-9190

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the value of material density (MD) images generated from a rapid kilovoltage-switching dual-energy CT (rsDECT) in early detection of peritoneal carcinomatosis (PC). MATERIALS AND

methodsThirty patients (60 ± 13 years; 24 women) with PC detected on multiple abdominal DECT scans were included. Four separate DECTs with varying findings of PC from each patient were used for qualitative/quantitative analysis, resulting in a total of 120 DECT scans (n = 30 × 4). Three radiologists independently reviewed DECT images (65 keV alone and 65 keV + MD) for diagnosis of PC (diagnostic confidence, lesion conspicuity, sharpness/delineation and image quality) using a 5-point Likert scale. Quantitative estimation of contrast-to-noise ratio (CNR) was done. Wilcoxon signed-rank test and Odds ratio calculation were used to compare between the two protocols. Inter-observer agreement was evaluated using Kappa coefficient analysis. P values < 0.05 were considered statistically significant.

results65 keV + MD images showed a slightly higher sensitivity (89%[95%CI:84,92]) for PC detection compared with 65 keV images alone without statistical significance (84%[95%CI:78,88], p = 0.11) with the experienced reader showing significant improvement (98%[95%CI:93,100] vs. 90%[95%CI:83,94], p = 0.02). On a per-patient basis, use of MD images allowed earlier diagnosis for PC in an additional 13-23% of patients. On sub-group analysis, earlier diagnosis of PC was particularly beneficial in patients with BMI ≤ 29.9 kg/m

conclusionMD images allow earlier and improved detection of PC with the degree of benefit varying based on reader experience and patient body habitus.

Indexed as

Peritoneal NeoplasmsRadiography, Dual-Energy Scanned ProjectionTomography, X-Ray ComputedAgedContrast MediaEarly Detection of CancerFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesContrast MediaCT, material density images, iodine imagesDual-energy CTPeritoneal carcinomatosisPeritoneal neoplasms

Identifiers

PMID38916617

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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.