Evidence map›Paper›PMID 42226141›Full record

ArticleBMC medical imaging2026

Improving image quality and diagnostic confidence for PRETEXT staging in pediatric hepatoblastoma using thin-slice and low-energy virtual monochromatic images in dual-energy CT with deep learning image reconstruction algorithm.

Guangheng Yin, Tingting Guo, Jun Feng, Haoyan Li, Yaoyao Song, Yunxian Zhang, Yun Peng, Jihang Sun

Abstract read
In one paragraph

Article in BMC medical imaging, 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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1 · What the graph read from it

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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

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Guangheng Yin *Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China.
Tingting Guo *Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China.
Jun FengDepartment of Surgical Oncology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
Haoyan LiDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China.
Yaoyao SongDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China.
Yunxian ZhangMedical Science Center, Yangtze University, No.1 Xueyuan road, Jingzhou, Hubei, 434023, China.
Yun PengDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China. Ppengyun@hotmail.com.
Jihang SunDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China. jihangsuns@163.com.

Funding

Beijing Research Ward Excellence Program, BRWEP BRWEP2024W102090105State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University YGSKL-SHTech-2025-KF01
6 · The paper itself

Abstract

backgroundHepatoblastoma is the most common pediatric hepatic tumor, for which surgery is the primary treatment option. The PRETEXT (Pretreatment Extent of Disease) staging system, based on CT images, is a crucial basis for surgical planning. Therefore, improving the image quality and diagnostic confidence of PRETEXT staging impacts the overall therapeutic outcomes in pediatric hepatoblastoma.

objectiveTo investigate whether thin-slice 40 keV dual-energy CT (DECT) images combined with a deep learning image reconstruction (DLIR) algorithm can improve image quality and diagnostic confidence for the evaluation of PRETEXT staging for pediatric hepatoblastoma.

methodsThis single-center retrospective study included 53 pediatric patients (mean age, 3.54 ± 2.26 years) with pathologically confirmed hepatoblastoma who underwent contrast-enhanced abdominal DECT. From the raw data, three distinct image series were reconstructed with a slice thickness of 0.625 mm for comparison: (A) standard-energy 68 keV VMI (virtual monoenergetic image) with 50% adaptive statistical iterative reconstruction-V (ASIR-V50%); (B) 68 keV VMI with high-level DLIR (DLIR-H); and (C) low-energy 40 keV VMI with DLIR-H. Objective image quality was quantified by the contrast-to-noise ratio (CNR) and Edge Rise Slope (ERS) of hepatic veins. Two independent radiologists performed PRETEXT staging and subjectively assessed image noise, hepatic vein visualization, and diagnostic confidence using a 5-point Likert scale.

resultsThe final PRETEXT staging results showed no statistically significant difference among the three image groups. However, objectively, the 40 keV DLIR-H images demonstrated significantly superior ERS (83.73 ± 46.50), indicating the sharpest vessel boundaries (p < 0.001), and CNR values for the hepatic veins. Subjectively, the 40 keV DLIR-H images received the highest scores for hepatic vein visualization and diagnostic confidence (p < 0.001), and was the only group consistently deemed sufficient to meet all diagnostic requirements for staging.

conclusionThe 0.625 mm thin-slice 40 keV VMI in DECT combined with DLIR-H reconstruction provides superior image quality and significantly enhances the diagnostic confidence for PRETEXT staging, and may be considered for routine clinical use.

Indexed as

Deep LearningHepatoblastomaLiver NeoplasmsRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAlgorithmsChildChild, PreschoolContrast MediaFemaleHumansImage Processing, Computer-AssistedInfantMaleNeoplasm StagingRadiography, Dual-Energy Scanned ProjectionContrast MediaComputed tomography angiographyComputer-assistedDeep learningDual-energy scanned projectionHepatoblastomaImage processingRadiography

Identifiers

PMID42226141
PMCPMC13435389

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.