Evidence map›Paper›PMID 40844688›Full record

ArticleJapanese journal of radiology2026

Novel silicon-based material decomposition images in diagnosis of pancreatic ductal adenocarcinoma: comparison with iodine-based and 50-keV virtual monoenergetic images.

Yoshifumi Noda, Mayu Hattori, Nobuyuki Kawai, Tetsuro Kaga, Akio Ito, Takuma Ishihara, Toshiharu Miyoshi, Yukiko Takai, Masashi Asano, Hiroki Kato and 3 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Japanese journal of radiology, 2026. 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Yoshifumi NodaDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan. noda.yoshifumi.g9@f.gifu-u.ac.jp.ORCID http://orcid.org/0000-0003-3611-4790
Mayu HattoriDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Nobuyuki KawaiDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Tetsuro KagaDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Akio ItoDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Takuma IshiharaInnovative and Clinical Research Promotion Center, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan.
Toshiharu MiyoshiDepartment of Radiology Services, Gifu University Hospital, Gifu 501-1194, Japan.
Yukiko TakaiDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Masashi AsanoDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Hiroki KatoDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Fuminori HyodoDepartment of Pharmacology, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
Avinash R KambadakoneDepartment of Radiology, Massachusetts General Hospital Harvard Medical School, 55 Fruit Street, White 270, Boston, MA 02114, USA.
Masayuki MatsuoDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo identify the optimal material decomposition (MD) images for diagnosis of pancreatic ductal adenocarcinoma (PDAC) and evaluate the added value of the MD image to 50-keV virtual monoenergetic images (VMIs) by comparing with iodine-based images and 50-keV VMIs.

methodsThis retrospective study included patients who underwent pancreatic protocol dual-energy CT (DECT) between February 2019 and May 2023. First, a radiologist evaluated 702 image datasets generated using 27 different materials to identify the top three MD images which provided maximum contrast difference between normal pancreas and PDAC, and subsequently, the best MD image was selected based on z value and image quality by four radiologists. Then, another four radiologists independently interpreted the conventional image dataset (iodine-based images and 50-keV VMIs) and new optimal image dataset (optimal MD images and 50-keV VMIs), and graded the presence or absence of PDAC. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were compared between the two image datasets using generalized estimating equations.

resultsOverall, 110 patients (median age, 73 years; 63 men) were included. Among them, 67 patients (61%) had pathologically proven PDAC, and the optimal MD image selected was Silicon/Struvite. The optimal image dataset had higher specificity (88% vs. 81%; P = 0.006), PPV (93% vs. 89%; P < 0.001), and accuracy (94% vs. 92%; P = 0.01) than the conventional image dataset. No difference was found in the sensitivity (P = 0.34) and NPV (P = 0.33) between the two image datasets.

conclusionSilicon/Struvite images provided high contrast difference between normal pancreas and PDAC and higher diagnostic performance for diagnosis of PDAC in combination of 50-keV VMIs compared to iodine-based images and 50-keV VMIs.

Indexed as

Carcinoma, Pancreatic DuctalIodinePancreatic NeoplasmsRadiography, Dual-Energy Scanned ProjectionSiliconTomography, X-Ray ComputedAgedAged, 80 and overContrast MediaFemaleHumansMaleMiddle AgedPancreasPredictive Value of TestsRadiographic Image Interpretation, Computer-AssistedContrast MediaIodineSiliconImage reconstructionMultidetector computed tomographyPancreatic cancer

Identifiers

PMID40844688
PMCPMC12769992

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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.