Evidence map›Paper›PMID 38642094›Full record

ArticleAbdominal radiology (New York)2024

Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI.

Yuxin Wang, Dawei Yang, Lixue Xu, Siwei Yang, Wei Wang, Chao Zheng, Xiaolan Zhang, Botong Wu, Hongxia Yin, Zhenghan Yang and 1 more

Abstract read
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In one paragraph

Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 42% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. [Application and prospects of magnetic resonance imaging techniques in the diagnosis and evaluation of hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024
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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

11 authors at 2 institutions in 1 country.

Yuxin WangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China.ORCID 0000-0002-3136-7097
Dawei YangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China.ORCID 0000-0002-1868-2746
Lixue XuDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China.ORCID 0000-0002-7152-2741
Siwei YangDepartment of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.ORCID 0000-0003-3431-3999
Wei WangDepartment of Radiology, Zhuozhou Hospital, Zhuozhou, 072750, China.ORCID 0000-0002-0019-4840
Chao ZhengShukun (Beijing) Technology Co., Ltd., Beijing, 102200, China.
Xiaolan ZhangShukun (Beijing) Technology Co., Ltd., Beijing, 102200, China.
Botong WuShukun (Beijing) Technology Co., Ltd., Beijing, 102200, China.
Hongxia YinDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China.
Zhenghan YangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China. yangzhenghan@vip.163.com.ORCID 0000-0003-3986-1732
Hui XuDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China. mr_xuhui@163.com.ORCID 0000-0002-4236-3825
Capital Medical University · CNShuozhou Central Hospital · CN

Funding

Digestive Medical Coordinated Development Center of Beijing Hospitals Authority ZYLX202101National Key R&D Program of China 2021ZD0113205National Natural Science Foundation of China 52227814National Natural Science Foundation of China 62171298National Natural Science Foundation of China 82071876National Natural Science Foundation of China 82372043
6 · The paper itself

Abstract

purposeTo determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm.

methodsPatients diagnosed with HCC who underwent locoregional therapy were retrospectively collected. We constructed a deep learning-based subtraction model and automatically generated arterial subtraction images. Two radiologists evaluated LR-TR category on ordinary images and then evaluated again on ordinary images plus arterial subtraction images after a 2-month washout period. The reference standard for viability was tumor stain on the digital subtraction hepatic angiography within 1 month after MRI.

results286 observations of 105 patients were ultimately enrolled. 157 observations were viable and 129 observations were nonviable according to the reference standard. The sensitivity and accuracy of LR-TR algorithm for detecting viable HCC significantly increased with the application of arterial subtraction images (87.9% vs. 67.5%, p < 0.001; 86.4% vs. 75.9%, p < 0.001). And the specificity slightly decreased without significant difference when the arterial subtraction images were added (84.5% vs. 86.0%, p = 0.687). The AUC of LR-TR algorithm significantly increased with the addition of arterial subtraction images (0.862 vs. 0.768, p < 0.001). The arterial subtraction images also improved inter-reader agreement (0.857 vs. 0.727).

conclusionExtended application of deep learning-based arterial subtraction images on extracellular agents-enhanced MRI can increase the sensitivity of LR-TR algorithm for detecting viable HCC without significant change in specificity.

Indexed as

AlgorithmsCarcinoma, HepatocellularContrast MediaDeep LearningLiver NeoplasmsMagnetic Resonance ImagingAdultAgedAged, 80 and overAngiography, Digital SubtractionFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedMaleMiddle AgedContrast MediaHepatocellular carcinomaLI-RADSLocoregional therapyMagnetic resonance imagingSubtraction technique

Identifiers

PMID38642094
OpenAlexW4394989073

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.