Evidence map›Paper›PMID 38525156›Full record

ArticleJournal of hepatocellular carcinoma2024

Enhancement Pattern Mapping for Early Detection of Hepatocellular Carcinoma in Patients with Cirrhosis.

Newsha Nikzad, David Thomas Fuentes, Millicent Roach, Tasadduk Chowdhury, Matthew Cagley, Mohamed Badawy, Ahmed Elkhesen, Manal Hassan, Khaled M Elsayes, Laura Beretta and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of hepatocellular carcinoma, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.5field-weighted citation impact, top 19% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
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

12 authors at 3 institutions in 1 country.

Newsha Nikzad *Department of Medicine and Surgery, Baylor College of Medicine, Houston, TX, USA.ORCID 0000-0001-8447-8527
David Thomas Fuentes *Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Millicent RoachDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Tasadduk ChowdhuryDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Matthew CagleyDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0002-1595-1699
Mohamed BadawyDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ahmed ElkhesenDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA.
Manal HassanDepartment of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Khaled M ElsayesDepartment of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0003-0184-0717
Laura BerettaDepartment of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Eugene Jon KoayDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0001-7675-3461
Prasun Kumar JalalDepartment of Medicine and Surgery, Baylor College of Medicine, Houston, TX, USA.
The University of Texas MD Anderson Cancer Center · USBaylor College of Medicine · USTexas Tech University · US

Funding

Early Detection of Hepatocellular CarcinomaR01CA195524 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LAURA BERETTA, David Fuentes · 2016 to 2026
$6.5M
NCI NIH HHS R01 CA195524
6 · The paper itself

Abstract

Background and Aims: Limited methods exist to accurately characterize the risk of malignant progression of liver lesions. Enhancement pattern mapping (EPM) measures voxel-based root mean square deviation (RMSD) of parenchyma and the contrast-to-noise (CNR) ratio enhances in malignant lesions. This study investigates the utilization of EPM to differentiate between HCC versus cirrhotic parenchyma with and without benign lesions. Methods: Patients with cirrhosis undergoing MRI surveillance were studied prospectively. Cases (n=48) were defined as patients with LI-RADS 3 and 4 lesions who developed HCC during surveillance. Controls (n=99) were patients with and without LI-RADS 3 and 4 lesions who did not develop HCC. Manual and automated EPM signals of liver parenchyma between cases and controls were quantitatively validated on an independent patient set using cross validation with manual methods avoiding parenchyma with artifacts or blood vessels. Results: With manual EPM, RMSD of 0.37 was identified as a cutoff for distinguishing lesions that progress to HCC from background parenchyma with and without lesions on pre-diagnostic scans (median time interval 6.8 months) with an area under the curve (AUC) of 0.83 (CI: 0.73-0.94) and a sensitivity, specificity, and accuracy of 0.65, 0.97, and 0.89, respectively. At the time of diagnostic scans, a sensitivity, specificity, and accuracy of 0.79, 0.93, and 0.88 were achieved with manual EPM with an AUC of 0.89 (CI: 0.82-0.96). EPM RMSD signals of background parenchyma that did not progress to HCC in cases and controls were similar (case EPM: 0.22 ± 0.08, control EPM: 0.22 ± 0.09, p=0.8). Automated EPM produced similar quantitative results and performance. Conclusion: With manual EPM, a cutoff of 0.37 identifies quantifiable differences between HCC cases and controls approximately six months prior to diagnosis of HCC with an accuracy of 89%.

Indexed as

artificial intelligenceLI-RADSliver cancermachine learningMRIradiomics

Identifiers

PMID38525156
PMCPMC10961013
OpenAlexW4392956399

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.