Evidence map›Paper›PMID 41062865›Full record

ArticleEuropean radiology experimental2025

Improved detection and quantification of peritoneal metastases using delayed contrast-enhanced dual-energy CT scans.

Giulio Bagnacci, Armando Perrella, Nunzia Di Meglio, Vito Di Martino, Letizia Sansotta, Francesco Gentili, Susanna Guerrini, Silvia Ruggeri, Cristina Intrieri, Stefania Piccioni and 2 more

Abstract read
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Article in European radiology experimental, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

12 authors.

Giulio BagnacciDepartment of Medical, Surgical and Neuro Sciences, University of Siena, Siena, Italy. giulio.bagnacci@unisi.it.ORCID http://orcid.org/0000-0002-5797-1129
Armando PerrellaDepartment of Medical, Surgical and Neuro Sciences, University of Siena, Siena, Italy. armando.perrella@unisi.it.
Nunzia Di MeglioDepartment of Medical, Surgical and Neuro Sciences, University of Siena, Siena, Italy. dimeglionunzia@gmail.com.
Vito Di MartinoDepartment of Medical, Surgical and Neuro Sciences, University of Siena, Siena, Italy.
Letizia SansottaUnit of Radiology, Azienda USL Toscana Nord Ovest, Pontedera, Pisa, Italy.
Francesco GentiliUnit of Diagnostic Imaging, Azienda Ospedaliero Universitaria Senese (AOUS), Siena, Italy.
Susanna GuerriniUnit of Diagnostic Imaging, Azienda Ospedaliero Universitaria Senese (AOUS), Siena, Italy.
Silvia RuggeriDepartment of Radiology, Azienda Ospedaliero Universitaria Careggi, Florence, Italy.
Cristina IntrieriDepartment of Medical, Surgical and Neuro Sciences, University of Siena, Siena, Italy.
Stefania PiccioniDepartment of Medical, Surgical and Neuro Sciences, University of Siena, Siena, Italy.
Daniele MarrelliDepartment of Medical, Surgical and Neuro Sciences, University of Siena, Siena, Italy.
Maria Antonietta MazzeiDepartment of Medical, Surgical and Neuro Sciences, University of Siena, Siena, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComputed tomography (CT) is widely used to diagnose peritoneal metastases (PM), with debated accuracy. Dual-energy CT (DECT) may improve accuracy, yet its diagnostic performance is still unknown. We explored the potential of DECT for PM detection and quantification. MATERIALS AND

methodsWe retrospectively included patients undergoing staging DECT for cancers with a high risk of peritoneal involvement, followed by staging laparoscopy/laparotomy, which served as the reference standard. Nine readers with varying experience levels (three expert, three intermediate, and three inexpert) reviewed two sets of images, separated by ≥ 60 days, considering the presence/absence of PM, abdominal region(s) involved, and calculated the radiological peritoneal cancer index (PCI). The first set included contrast-enhanced delayed-DECT scans reconstructed as virtual 120-kVp images; the second set also included virtual monoenergetic, 40-keV images and iodine maps. Performance metrics, receiver operating characteristic (ROC) analysis, McNemar, DeLong, and Wilcoxon tests were applied.

resultsTwenty patients (mean age 64.2 years; 12 females) were included, 10 with PM. At per-patient analysis, the addition of monoenergetic 40-keV images and iodine maps slightly increased the performance and improved inter-reader agreement, with significant benefit for inexperienced readers only (p = 0.010). Per-region analysis demonstrated a significant advantage with an area under the ROC curve ranging from 0.709 to 0.766 (p < 0.001), confirmed for each reader group; in addition, the inter-reader agreement significantly improved. Quantitative analysis showed a reduction in the differences between CT results and surgical PCI by DECT (4 ± 12 versus 2 ± 9, p < 0.001).

conclusionDECT-derived reconstructions in the delayed-phase enhanced PM detection and quantification. RELEVANCE STATEMENT: Delayed-phase DECT reconstruction showed superior accuracy over conventional CT in detecting and quantifying peritoneal metastases. These findings could help establish a new standard CT protocol for malignancies with peritoneal tropism. KEY POINTS: CT is the most widely used technique for assessing peritoneal metastases. The accuracy of CT for peritoneal metastases is debated; dual-energy CT shows promise. In our study, delayed-phase dual-energy CT provided significant advantages for all readers.

Indexed as

Contrast MediaPeritoneal NeoplasmsRadiography, Dual-Energy Scanned ProjectionTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedRetrospective StudiesContrast MediaAbdomenLaparoscopyLaparotomyPeritoneal neoplasmsTomography (x-ray computed)

Identifiers

PMID41062865
PMCPMC12508340

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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.