Evidence map›Paper›PMID 27358688›Full record

ArticleWorld journal of radiology2016

Predictive model for contrast-enhanced ultrasound of the breast: Is it feasible in malignant risk assessment of breast imaging reporting and data system 4 lesions?

Jun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Yi Li, Chi-Hua Wu, Jing-Qiao Lu

Open access · hybridAbstract read
In one paragraph

Article in World journal of radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 11% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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  9. Review
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  11. Contrast-Enhanced Ultrasound Imaging of Breast Masses: Adjunct Tool to Decrease the Number of False-Positive Biopsy Results.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2019
    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 2 institutions in 2 countries.

Jun LuoJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Ji-Dong ChenJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Qing ChenJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Lin-Xian YueJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Guo ZhouJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Cheng LanJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Yi LiJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Chi-Hua WuJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Jing-Qiao LuJun Luo, Ji-Dong Chen, Qing Chen, Lin-Xian Yue, Guo Zhou, Cheng Lan, Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China.
Fourth People's Hospital of Sichuan Province · CNEmory University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo build and evaluate predictive models for contrast-enhanced ultrasound (CEUS) of the breast to distinguish between benign and malignant lesions.

methodsA total of 235 breast imaging reporting and data system (BI-RADS) 4 solid breast lesions were imaged via CEUS before core needle biopsy or surgical resection. CEUS results were analyzed on 10 enhancing patterns to evaluate diagnostic performance of three benign and three malignant CEUS models, with pathological results used as the gold standard. A logistic regression model was developed basing on the CEUS results, and then evaluated with receiver operating curve (ROC).

resultsExcept in cases of enhanced homogeneity, the rest of the 9 enhancement appearances were statistically significant (P < 0.05). These 9 enhancement patterns were selected in the final step of the logistic regression analysis, with diagnostic sensitivity and specificity of 84.4% and 82.7%, respectively, and the area under the ROC curve of 0.911. Diagnostic sensitivity, specificity, and accuracy of the malignant vs benign CEUS models were 84.38%, 87.77%, 86.38% and 86.46%, 81.29% and 83.40%, respectively.

conclusionThe breast CEUS models can predict risk of malignant breast lesions more accurately, decrease false-positive biopsy, and provide accurate BI-RADS classification.

Indexed as

BreastBreast imaging reporting and data systemContrast-enhanced ultrasoundPredictive modelQualitative analysis

Identifiers

PMID27358688
PMCPMC4919760
OpenAlexW2467941891

What OpenQuestion holds

Textmetadata
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.