Evidence map›Paper›PMID 36017515›Full record

ReviewFrontiers in surgery2022

Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis.

Yirong Chen, Qijia Han, Zhiwei Huang, Mo Lyu, Zhu Ai, Yuying Liang, Haowen Yan, Mengzhu Wang, Zhiming Xiang

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

10 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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  3. Review
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  5. Article
  6. Article
  7. Amide proton transfer-weighted imaging and stretch-exponential model DWI basedCancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article
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  9. Article
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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

9 authors at 3 institutions in 1 country.

Yirong ChenGraduate School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Qijia HanDepartment of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Zhiwei HuangGraduate School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Mo LyuDepartment of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Zhu AiDepartment of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Yuying LiangDepartment of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Haowen YanDepartment of Oncology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Mengzhu WangDepartment of MR Scientific Marketing, Siemens Healthineers, Guangzhou, China.
Zhiming XiangDepartment of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Panyu District Central Hospital · CNSiemens (China) · CNSouth China Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses. Methods: Studies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity ( Results: A total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, Conclusions: The parameters derived from IVIM-DWI, especially the

Indexed as

differential diagnosisIVIM-DWIlung nodulesmagnetic resonance imagingmeta-analysis

Identifiers

PMID36017515
PMCPMC9396547
OpenAlexW4281654016

What OpenQuestion holds

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LicenceCC BY
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.