What question did this study set out to answer?

To determine if thin-slice 40 keV dual-energy CT images with deep learning reconstruction improve PRETEXT staging accuracy in pediatric hepatoblastoma.

June 3, 2026Open Access

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

Key Points

To determine if thin-slice 40 keV dual-energy CT images with deep learning reconstruction improve PRETEXT staging accuracy in pediatric hepatoblastoma.
Retrospective single-center study with 53 pediatric hepatoblastoma patients
Evaluated three image series: standard-energy, high-level DLIR, and low-energy DLIR
Measured image quality via contrast-to-noise ratio and edge rise slope, with subjective assessments from radiologists.
40 keV DLIR-H images showed significantly higher edge rise slope (83.73 ± 46.50, p < 0.001) indicating improved vessel visualization.
40 keV DLIR-H images received the highest subjective scores for hepatic vein visualization and diagnostic confidence (p < 0.001).
All outcomes suggest DLIR-H reconstruction meets diagnostic requirements for PRETEXT staging.

Abstract

Abstract Background Hepatoblastoma 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. Objective To 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. Methods This 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. Results The 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. Conclusion The 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.

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