Evidence map›Paper›PMID 37675222›Full record

ArticleFrontiers in oncology2023

Liver Imaging Reporting and Data System (LI-RADS) v2018: differential diagnostic value of ADC values for benign and malignant nodules with moderate probability (LR-3).

Xue Chen, Quanyu Cai, Jinju Xia, Huan Huang, Zhaoxing Li, Kairong Song, Ningyang Jia, Wanmin Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. 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
–field-weighted citation impact, top 72% 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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 0 citations in OpenAlex.

  1. Review
  2. Correlation of 3T Diffusion-weighted MRI andMolecular imaging and radionuclide therapy · 2025
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4 · The record

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5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Xue ChenDepartment of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Quanyu CaiDepartment of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Jinju XiaDepartment of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Huan HuangDepartment of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Zhaoxing LiDepartment of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Kairong SongDepartment of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Ningyang JiaDepartment of Radiology, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Wanmin LiuDepartment of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Tongji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the usefulness of the apparent diffusion coefficient (ADC) in differentiating between benign and malignant LR-3 lesions classified by Liver Imaging Reporting and Data System 2018 (LI-RADS v2018). Methods: Retrospectively analyzed 88 patients with liver nodules confirmed by pathology and classified as LR-3 by LI-RADS. All patients underwent preoperative contrast-enhanced MR examination, and the following patient-related imaging features were collected: tumor size,nonrim APHE, nonperipheral "washout", enhancing "capsule", mild-moderate T2 hyperintensity, fat in mass, restricted diffusion, and nodule-in-nodule architecture. We performed ROC analysis and calculated the sensitivity and specificity. Results: A total of 122 lesions were found in 88 patients, with 68 benign and 54 malignant lesions. The mean ADC value for malignant and benign lesions were 1.01 ± 0.15 × 10 Conclusions: ADC values are useful for differentiating between benign and malignant liver nodules in LR-3 classification, it improves the sensitivity of LI-RADS in the diagnosis of HCC while maintaining high specificity, and we recommend including ADC values in the standard interpretation of LI-RADSv2018.

Indexed as

apparent diffusion coefficientliver cancerliver imaging reporting and data systemLR-3MRI

Identifiers

PMID37675222
PMCPMC10478080
OpenAlexW4386069889

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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 OpenQuestion graph.