Evidence map›Paper›PMID 25889380›Full record

ArticleWorld journal of surgical oncology2015

Differential diagnosis of benign and malignant breast masses using diffusion-weighted magnetic resonance imaging.

Qinghua Min, Kangwei Shao, Lulan Zhai, Wei Liu, Caisong Zhu, Lixin Yuan, Jun Yang

Open access · goldAbstract readComparative Study
In one paragraph

Article in World journal of surgical oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 4% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.

  1. Pooled it
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  4. Multimodal apparent diffusion MRI model in noninvasive evaluation of breast cancer and Ki-67 expression.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
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  12. Ability ofContrast media & molecular imaging · 2019
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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 at 1 institution in 1 country.

Qinghua MinDepartment of Radiology, Shanghai Tongren Hospital, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, PR China. qinghuamin1@163.com.
Kangwei ShaoDepartment of Radiology, Shanghai Tongren Hospital, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, PR China. kangwei_shao@163.com.
Lulan ZhaiDepartment of Radiology, Shanghai Tongren Hospital, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, PR China. lulanzhai22@163.com.
Wei LiuDepartment of Radiology, Shanghai Tongren Hospital, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, PR China. weiludr@163.com.
Caisong ZhuDepartment of Radiology, Shanghai Tongren Hospital, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, PR China. caisongzhuu@163.com.
Lixin YuanDepartment of Radiology, Shanghai Tongren Hospital, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, PR China. lixin_yuan1@163.com.
Jun YangDepartment of Radiology, Shanghai Tongren Hospital, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, PR China. jun_yangjy@163.com.
Tongren Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiffusion-weighted magnetic resonance imaging (DW-MRI) is different from conventional diagnostic methods and has the potential to delineate the microscopic anatomy of a target tissue or organ. The purpose of our study was to evaluate the value of DW-MRI in the diagnosis of benign and malignant breast masses, which would help the clinical surgeon to decide the scope and pattern of operation.

methodsA total of 52 female patients with palpable solid breast masses received breast MRI scans using routine sequences, dynamic contrast-enhanced imaging, and diffusion-weighted echo-planar imaging at b values of 400, 600, and 800 s/mm(2), respectively. Two regions of interest (ROIs) were plotted, with a smaller ROI for the highest signal and a larger ROI for the overall lesion. Apparent diffusion coefficient (ADC) values were calculated at three different b values for all detectable lesions and from two different ROIs. The sensitivity, specificity, positive predictive value, and positive likelihood ratio of DW-MRI were determined for comparison with histological results.

resultsA total of 49 (49/52, 94.2%) lesions were detected using DW-MRI, including 20 benign lesions (two lesions detected in the same patient) and 29 malignant lesions. Benign lesion had a higher mean ADC value than their malignant counterparts, regardless of b value. According to the receiver operating characteristic (ROC) curve, the smaller-range ROI was more effective in differentiation between benign and malignant lesions. The area under the ROC curve was the largest at a b value of 800 s/mm(2). With a threshold ADC value at 1.23 × 10(-3) mm(2)/s, DW-MRI achieved a sensitivity of 82.8%, specificity of 90.0%, positive predictive value of 92.3%, and positive likelihood ratio of 8.3 for differentiating benign and malignant lesions.

conclusionsDW-MRI is an accurate diagnostic tool for differentiation between benign and malignant breast lesions, with an optimal b value of 800 s/mm(2). A smaller-range ROI focusing on the highest signal has a better differential value.

Indexed as

AdultAgedAged, 80 and overBreastBreast NeoplasmsCarcinoma, Ductal, BreastCarcinoma, LobularDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingFemaleFollow-Up StudiesHumansImage EnhancementImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMiddle Aged

Identifiers

PMID25889380
PMCPMC4328303
OpenAlexW2144198870

What OpenQuestion holds

Textmetadata
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.