ArticleEuropean radiology2025
Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT.
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.
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5 citing papers in PubMed.
- Spectral CT-based Nomogram for Noninvasive Assessment and Prognostic Prediction of MSI/dMMR in Esophagogastric Junction Adenocarcinoma.Radiology. Imaging cancer · 2026Article
- Quantifying whole lung iron oxide deposition with dual-energy CT for diagnosis of arc-welders' pneumoconiosis.European radiology · 2026Article
- Research Progress of Dual-energy CT in Diagnosis and Evaluation of Curative Effect of Liver Cancer: A ReviewCurrent medical imaging · 2025Review
- Global research hotspots and trends of iodinated contrast agents in medical imaging: a bibliometric and visualization analysis.Frontiers in medicine · 2024Article
- An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models.Technology in cancer research & treatmentReview
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9 authors.
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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.
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