Evidence map›Paper›PMID 39186105›Full record

ArticleEuropean radiology2025

Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT.

Takashi Ota, Hiromitsu Onishi, Hideyuki Fukui, Takahiro Tsuboyama, Atsushi Nakamoto, Toru Honda, Shohei Matsumoto, Mitsuaki Tatsumi, Noriyuki Tomiyama

Abstract read
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Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Takashi OtaDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan. t-ota@radiol.med.osaka-u.ac.jp.ORCID http://orcid.org/0000-0003-3902-8230
Hiromitsu OnishiDepartment of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan.
Hideyuki FukuiDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Takahiro TsuboyamaDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Atsushi NakamotoDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Toru HondaDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Shohei MatsumotoDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Mitsuaki TatsumiDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Noriyuki TomiyamaDepartment of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents. MATERIALS AND

methodsThis retrospective study included patients with liver lesions who underwent DECT, including non-contrast-enhanced scans. Benign lesions included hepatic hemangioma, whereas malignant lesions included hepatocellular carcinoma, metastatic liver cancer, and intrahepatic cholangiocellular carcinoma. Patients were divided into derivation and validation groups. In the derivation group, two radiologists calculated ten multiparametric data using univariate and multivariate logistic regression to generate PMs. In the validation group, two additional radiologists measured the parameters to assess the diagnostic performance of PMs.

resultsThe study included 121 consecutive patients (mean age 67.4 ± 13.8 years, 80 males), with 97 in the derivation group (25 benign and 72 malignant) and 24 in the validation group (7 benign and 17 malignant). Oversampling increased the benign lesion sample to 75, equalizing the malignant group for building PMs. All parameters were statistically significant in univariate analysis (all p < 0.05), leading to the creation of five PMs in multivariate analysis. The area under the curve for the five PMs of two observers was as follows: PM1 (slope K, blood) = 0.76, 0.74; PM2 (slope K, fat) = 0.55, 0.51; PM3 (effective-Z difference, blood) = 0.75, 0.72; PM4 (slope K, blood, fat) = 0.82, 0.78; and PM5 (slope K, effective-Z difference, blood) = 0.90, 0.87. PM5 yielded the best diagnostic performance.

conclusionMultiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions. CLINICAL RELEVANCE STATEMENT: The utilization of non-contrast-enhanced DECT is extremely useful for distinguishing between benign and malignant liver lesions. This approach enables physicians to plan better treatment strategies, alleviating concerns associated with contrast allergy, contrast-induced nephropathy, radiation exposure, and excessive medical expenses. KEY POINTS: Distinguishing benign from malignant liver lesions with non-contrast-enhanced CT would be desirable. This model, incorporating slope K, effective Z, and blood quantification, distinguished benign from malignant liver lesions. Non-contrast-enhanced DECT has benefits, particularly in patients with an iodine allergy, renal failure, or asthma.

Indexed as

Liver NeoplasmsRadiography, Dual-Energy Scanned ProjectionTomography, X-Ray ComputedAgedAged, 80 and overContrast MediaDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesSensitivity and SpecificityContrast MediaDifferential diagnosisLiver neoplasmsLogistic modelsLogistic regressionX-ray computed tomography

Identifiers

PMID39186105
PMCPMC11836082

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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.