Evidence map›Paper›PMID 35204423›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Differentiation of Benign and Malignant Breast Lesions Using ADC Values and ADC Ratio in Breast MRI.

Silvia Tsvetkova, Katya Doykova, Anna Vasilska, Katya Sapunarova, Daniel Doykov, Vladimir Andonov, Petar Uchikov

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

7 authors.

Silvia TsvetkovaDepartment of Diagnostic Imaging, Medical University Plovdiv, 15-A Vasil Aprilov Blvd, 4002 Plovdiv, Bulgaria.ORCID 0000-0002-0757-0140
Katya DoykovaDepartment of Diagnostic Imaging, Medical University Plovdiv, 15-A Vasil Aprilov Blvd, 4002 Plovdiv, Bulgaria.ORCID 0000-0001-6433-7970
Anna VasilskaDepartment of Diagnostic Imaging, Medical University Plovdiv, 15-A Vasil Aprilov Blvd, 4002 Plovdiv, Bulgaria.
Katya SapunarovaFirst Department of Internal Diseases, Medical University Plovdiv, 15-A Vasil Aprilov Blvd, 4002 Plovdiv, Bulgaria.
Daniel DoykovSecond Department of Internal Diseases, Medical University Plovdiv, 15-A Vasil Aprilov Blvd, 4002 Plovdiv, Bulgaria.
Vladimir AndonovSecond Department of Internal Diseases, Medical University Plovdiv, 15-A Vasil Aprilov Blvd, 4002 Plovdiv, Bulgaria.
Petar UchikovDepartment of Special Surgery, Medical University Plovdiv, 15-A Vasil Aprilov Blvd, 4002 Plovdiv, Bulgaria.ORCID 0000-0003-2064-173X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) of the breast has been increasingly used for the detailed evaluation of breast lesions. Diffusion-weighted imaging (DWI) gives additional information for the lesions based on tissue cellularity. The aim of our study was to evaluate the possibilities of DWI, apparent diffusion coefficient (ADC) value and ADC ratio (the ratio between the ADC of the lesion and the ADC of normal glandular tissue) to differentiate benign from malignant breast lesions. MATERIALS AND

methodsEighty-seven patients with solid breast lesions (52 malignant and 35 benign) were examined on a 1.5 T MR scanner before histopathological evaluation. ADC values and ADC ratios were calculated.

resultsThe ADC values in the group with malignant tumors were significantly lower (mean 0.88 ± 0.15 × 10

conclusionAccording to the results from our study DWI, ADC values and ADC ratio proved to be valuable additional techniques with high sensitivity and specificity for distinguishing benign from malignant breast lesions.

Indexed as

ADC ratioADC valuebreast lesionsbreast MRIDWI

Identifiers

PMID35204423
PMCPMC8871288

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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.