Evidence map›Paper›PMID 38544996›Full record

ArticlePakistan journal of medical sciences

Diagnostic accuracy of diffusion weighted MRI in differentiating malignant from benign liver lesions taking histopathology as gold standard.

Jia, Sumera Tabassum, Shahbaz Haider, Haania

Open access · goldAbstract read
In one paragraph

Article in Pakistan journal of medical sciences. 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
0.8field-weighted citation impact, top 30% 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, 2 citations in OpenAlex.

  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

4 authors at 1 institution in 2 countries.

JiaJia Resident, Department of Radiology JPMC, Karachi.
Sumera TabassumSumera Tabassum Associate Professor, Department of Radiology JPMC Karachi.
Shahbaz HaiderShahbaz Haider Professor, Medical Unit-I JPMC Karachi.
HaaniaHaania Research Fellow, Clinical Attachment, JPMC Karachi.
Jinnah Postgraduate Medical Center · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To determine the diagnostic accuracy of Diffusion Weighted MRI in differentiating malignant from benign liver lesions taking histopathology as gold standard. Methods: This Cross-sectional study was conducted at Departments of Radiology and Medicine, JPMC, Karachi from February 23, 2019 till September 25, 2019. Data was prospectively collected from patients after taking consent. One hundred twenty five patients presenting with hepatic mass who met the inclusion criteria were included. Quantitative data was presented as simple descriptive statistics giving mean and standard deviation and qualitative variables as frequency and percentages. Sensitivity, specificity, positive and negative predictive values and diagnostic accuracy were calculated. P-value of ≤0.05 was considered as significant. Results: Mean age in our study was 59.75±8.57 years. Total 71 (56.8%) were male and 54 (43.2%) were female. Out of 125 patients, sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of DW MRI for the diagnosis of malignant focal liver lesion by taking histopathology as gold standard was found to be 92.3%, 93.6%, 96%, 88% and 92.8% respectively. Conclusion: DW MRI scan has high diagnostic accuracy and being accurate in making a diagnosis and differentiation of benign from malignant focal liver lesion would decrease need of invasive modality of histopathology.

Indexed as

BenignDiffusion Weighted Imaging (DWI)Focal liver lesionHistopathologyMagnetic Resonance Imaging (MRI)Malignant

Identifiers

PMID38544996
PMCPMC10963981
OpenAlexW4391592861

What OpenQuestion holds

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LicenceCC BY
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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.