Evidence map›Paper›PMID 33449180›Full record

ArticleEuropean radiology2021

CNN color-coded difference maps accurately display longitudinal changes in liver MRI-PDFF.

Kyle Hasenstab, Guilherme Moura Cunha, Shintaro Ichikawa, Soudabeh Fazeli Dehkordy, Min Hee Lee, Soo Jin Kim, Alexandra Schlein, Yesenia Covarrubias, Claude B Sirlin, Kathryn J Fowler

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In one paragraph

Article in European radiology, 2021. 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
–field-weighted citation impact
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. Article
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

10 authors.

Kyle Hasenstab *Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA. kylehasenstab@gmail.com.ORCID http://orcid.org/0000-0002-4687-960X
Guilherme Moura Cunha *Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA.
Shintaro IchikawaDepartment of Radiology, University of Yamanashi, Yamanashi, Japan.
Soudabeh Fazeli DehkordyLiver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA.
Min Hee LeeSoonchunhyang University Bucheon Hospital, Gyeonggi-do, South Korea.
Soo Jin KimNational Cancer Center, Republic of Korea, Gyeonggi-do, South Korea.
Alexandra SchleinLiver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA.
Yesenia CovarrubiasLiver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA.
Claude B SirlinLiver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA.
Kathryn J FowlerLiver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA.

Funding

Traning Clinical Scientists in Radiological ImagingT32EB005970 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Eric Y Chang, Rebecca Ann Rakow-Penner · 2007 to 2026
$3.2M
NIBIB NIH HHS T32 EB005970
6 · The paper itself

Abstract

objectivesTo assess the feasibility of a CNN-based liver registration algorithm to generate difference maps for visual display of spatiotemporal changes in liver PDFF, without needing manual annotations.

methodsThis retrospective exploratory study included 25 patients with suspected or confirmed NAFLD, who underwent PDFF-MRI at two time points at our institution. PDFF difference maps were generated by applying a CNN-based liver registration algorithm, then subtracting follow-up from baseline PDFF maps. The difference maps were post-processed by smoothing (5 cm

resultsThe mean patient age was 49 years (12 males). Baseline and follow-up PDFF ranged from 2.0 to 35.3% and 3.5 to 32.0%, respectively. PDFF changes ranged from - 20.4 to 14.1%. ICCs against the manual reference exceeded 0.95 for each reader, except for segment 2 (2 readers ICC = 0.86-0.91) and segment 4a (reader 3 ICC = 0.94). Bland-Altman limits of agreement were within 5% across all three readers. Inter-reader agreement for visually assessed PDFF change (whole liver and segmental) was excellent (ICCs > 0.96), except for segment 2 (ICC = 0.93).

conclusionsVisual assessment of liver segmental PDFF changes using a CNN-generated difference map strongly agreed with manual estimates performed by an expert reader and yielded high inter-reader agreement. KEY POINTS: • Visual assessment of longitudinal changes in quantitative liver MRI can be performed using a CNN-generated difference map and yields strong agreement with manual estimates performed by expert readers.

Indexed as

Image Interpretation, Computer-AssistedNon-alcoholic Fatty Liver DiseaseHumansLiverMagnetic Resonance ImagingMaleMiddle AgedProspective StudiesReproducibility of ResultsRetrospective StudiesImage interpretation, computer-assistedLiverMagnetic resonance imagingNeural networks, computer

Identifiers

PMID33449180
PMCPMC8906007

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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.