Evidence map›Paper›PMID 36934202›Full record

Trial reportEuropean radiology2023

Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.

Hyo-Jin Kang, Jeong Min Lee, Chulkyun Ahn, Jae Seok Bae, Seungchul Han, Se Woo Kim, Jeong Hee Yoon, Joon Koo Han

Registry-linked trialAbstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04722120 (Low Contrast Agent and Radiation Dose Protocol Liver CT With Deep-learning-based Contrast Boosting Model in Patients at High Risk for HCC), which is not on this map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04722120 nacompletednot on this map

Low Contrast Agent and Radiation Dose Protocol Liver CT With Deep-learning-based Contrast Boosting Model in Patients at High Risk for HCC: Prospective, Randomized, Single Blinded - Preliminary Study

TypeinterventionalSponsorSeoul National University HospitalRan2016 to 2021Enrolled68ConditionsHepatocellular CarcinomaArmsLow radiation dose CT, Conventional CT (radiation), Low contrast dose CT, Conventional CT (contrast agent), Deep-learning based contrast boosting algorithms
3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 26 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 3 countries.

Hyo-Jin KangDepartment of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-Gu, Seoul, 03080, Korea.
Jeong Min LeeDepartment of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-Gu, Seoul, 03080, Korea. jmsh@snu.ac.kr.ORCID http://orcid.org/0000-0003-0561-8777
Chulkyun AhnDepartment of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.
Jae Seok BaeDepartment of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-Gu, Seoul, 03080, Korea.
Seungchul HanDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Se Woo KimDepartment of Radiology, Armed Forces Daejeon Hospital, Daejeon, Korea.
Jeong Hee YoonDepartment of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-Gu, Seoul, 03080, Korea.
Joon Koo HanDepartment of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-Gu, Seoul, 03080, Korea.
Seoul National University · KRArmed Forces Capital Hospital · KRNew Generation University College · ETSamsung Medical Center · KR

Funding

Ministry of Education, Science and Technology 1711174549Ministry of Education, Science and Technology RS-2020-KD000226
6 · The paper itself

Abstract

objectiveTo investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hepatocellular carcinoma (HCC).

methodsParticipants were recruited and underwent four-phase dynamic CT (NCT04722120). They were randomly assigned to either standard-dose (SD) or DLD protocol. All CT images were initially reconstructed using iterative reconstruction, and the images of the DLD protocol were further processed using the DL-CB algorithm (DLD-DL). The primary endpoint was the contrast-to-noise ratio (CNR), the secondary endpoint was qualitative image quality (noise, hepatic lesion, and vessel conspicuity), and the tertiary endpoint was lesion detection rate. The t-test or repeated measures analysis of variance was used for analysis.

resultsSixty-eight participants with 57 focal liver lesions were enrolled (20 with HCC and 37 with benign findings). The DLD protocol had a 19.8% lower radiation dose (DLP, 855.1 ± 254.8 mGy·cm vs. 713.3 ± 94.6 mGy·cm, p = .003) and 27% lower contrast dose (106.9 ± 15.0 mL vs. 77.9 ± 9.4 mL, p < .001) than the SD protocol. The comparative analysis demonstrated that CNR (p < .001) and portal vein conspicuity (p = .002) were significantly higher in the DLD-DL than in the SD protocol. There was no significant difference in lesion detection rate for all lesions (82.7% vs. 73.3%, p = .140) and HCCs (75.7% vs. 70.4%, p = .644) between the SD protocol and DLD-DL.

conclusionsDL-CB on double-low-dose CT provided improved CNR of the aorta and portal vein without significant impairment of the detection rate of HCC compared to the standard-dose acquisition, even in participants at high risk for HCC. KEY POINTS: • Deep-learning-based contrast-boosting algorithm on double-low-dose CT provided an improved contrast-to-noise ratio compared to standard-dose CT. • The detection rate of focal liver lesions was not significantly differed between standard-dose CT and a deep-learning-based contrast-boosting algorithm on double-low-dose CT. • Double-low-dose CT without a deep-learning algorithm presented lower CNR and worse image quality.

Indexed as

Carcinoma, HepatocellularDeep LearningLiver NeoplasmsAlgorithmsContrast MediaDouble-Blind MethodHumansProspective StudiesRadiation DosageRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedContrast MediaContrast mediaDeep learningHepatocellular carcinomaProspective studyTomography, X-ray computed

Identifiers

PMID36934202
OpenAlexW4327813555

What OpenQuestion holds

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Read underepoch 390

Registered trials

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.