Evidence map›Paper›PMID 32350660›Full record

ArticleEuropean radiology2020

Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging-based differential diagnoses of benign and malignant breast lesions.

Xiaoping Yang, Mengshi Dong, Shu Li, Ruimei Chai, Zheng Zhang, Nan Li, Lina Zhang

Abstract read
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In one paragraph

Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 12% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

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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 2 institutions in 1 country.

Xiaoping YangDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Mengshi DongDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Shu LiDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Ruimei ChaiDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Zheng ZhangDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Nan LiDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Lina ZhangDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China. zhanglnda@163.com.
China Medical University · CNFirst Hospital of China Medical University · CN

Funding

National Key Research and Development Program of China 2017YFC1309100
6 · The paper itself

Abstract

objectiveTo compare the diagnostic performance of models based on a combination of contrast-enhanced (CE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) or time-intensity curves (TIC) in diagnosing malignancies of breast lesions.

methodsA double-blind retrospective study was conducted in 328 patients (254 for training and the following 74 for validation) who underwent dynamic contrast-enhanced MRI (DCE-MRI) of the breast with pathological results. Two score models, the DWI model (apparent diffusion coefficient (ADC) + morphology + enhanced information) and the TIC model (TIC + morphology + enhanced information), were established with binary logistic regression for mass and non-mass enhancements (NMEs) in the training set. The sensitivity, specificity, and area under the curve (AUC) were compared between the two models (DWI model vs. TIC model); p < 0.05 was considered as statistically different. External validation was used.

resultsIn the training set, the sensitivities, specificities, and AUCs of the DWI/TIC model were 95.2%/95.8%, 70.8%/47.9%, and 0.932/0.891 for masses, and 94.2%/90.4%, 47.4%/47.4%, and 0.798 (95% CI, 0.686-0.884)/0.802 (95% CI, 0.691-0.887) for NMEs, respectively. The AUC of the DWI model was significantly higher than that of the TIC model (p < 0.05) for masses. In the validation set, the AUCs of the DWI/TIC model were 0.896/0.861 for masses (p < 0.05) and 0.936/0.836 for NMEs (p > 0.05).

conclusionsCombined with CE MRI, the DWI model was superior or equal to the TIC model in differentiating benign and malignant breast lesions. KEY POINTS: • Diffusion magnetic resonance imaging played an important role in the diagnosis of breast neoplasms. • On the basis of contrast-enhanced MRI, the DWI model had significantly higher diagnostic ability than the TIC model in distinguishing benign and malignant masses. • It would be reasonable to replace the time-consuming TIC with DWI for less scan time and similar diagnostic efficiency.

Indexed as

AdultBreastBreast NeoplasmsContrast MediaDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingDouble-Blind MethodFemaleHumansMiddle AgedRetrospective StudiesContrast MediaBreast neoplasmsDiffusion magnetic resonance imagingMagnetic resonance imaging

Identifiers

PMID32350660
OpenAlexW3023376904

What OpenQuestion holds

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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.