Evidence map›Paper›PMID 28726117›Full record

ArticleInternational journal of computer assisted radiology and surgery2017

Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction.

Shiju Yan, Yunzhi Wang, Faranak Aghaei, Yuchen Qiu, Bin Zheng

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Shiju YanSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai, 200093, China. yanshj99@aliyun.com.
Yunzhi WangSchool of Electrical and Computer Engineering, University of Oklahoma,Stephenson Research and Technology Center, Suite 1001 University of Oklahoma 101 David L. Boren Blvd, Norman, OK, 73019, USA.
Faranak AghaeiSchool of Electrical and Computer Engineering, University of Oklahoma,Stephenson Research and Technology Center, Suite 1001 University of Oklahoma 101 David L. Boren Blvd, Norman, OK, 73019, USA.
Yuchen QiuSchool of Electrical and Computer Engineering, University of Oklahoma,Stephenson Research and Technology Center, Suite 1001 University of Oklahoma 101 David L. Boren Blvd, Norman, OK, 73019, USA.
Bin ZhengSchool of Electrical and Computer Engineering, University of Oklahoma,Stephenson Research and Technology Center, Suite 1001 University of Oklahoma 101 David L. Boren Blvd, Norman, OK, 73019, USA.

Funding

Increasing Cancer Detection Yield Using Breast MRI Screening ModalityR01CA197150 · NCI · UNIVERSITY OF OKLAHOMA · PI HOLLINGSWORTH, ALAN B., LIU, HONG · 2015 to 2019
$2.5M
Mammographic Density and Tissue Asymmetry Based Breast Cancer Risk StratificationR01CA160205 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ZHENG, BIN · 2012 to 2015
$895k
National Cancer Institute, National Institutes of Health, USA R01 CA160205NCI NIH HHS R01 CA160205NCI NIH HHS R01 CA197150the National Cancer Institute, National Institutes of Health, USA. R01 CA197150
6 · The paper itself

Abstract

purposeHow to optimally detect bilateral mammographic asymmetry and improve risk prediction accuracy remains a difficult and unsolved issue. Our aim was to find an effective mammographic density segmentation method to improve accuracy of breast cancer risk prediction.

methodsA dataset including 168 negative mammography screening cases was used. We applied a mutual threshold to bilateral mammograms of left and right breasts to segment the dense breast regions. The mutual threshold was determined by the median grayscale value of all pixels in both left and right breast regions. For each case, we then computed three types of image features representing asymmetry, mean and the maximum of the image features, respectively. A two-stage classification scheme was developed to fuse the three types of features. The risk prediction performance was tested using a leave-one-case-out cross-validation method.

resultsBy using the new density segmentation method, the computed area under the receiver operating characteristic curve was 0.830 ± 0.033 and overall prediction accuracy was 81.0%, significantly higher than those of 0.633 ± 0.043 and 57.1% achieved by using the previous density segmentation method ([Formula: see text], t-test).

conclusionsA new mammographic density segmentation method based on a bilateral mutual threshold can be used to more effectively detect bilateral mammographic density asymmetry and help significantly improve accuracy of near-term breast cancer risk prediction.

Indexed as

Breast DensityDiagnosis, Computer-AssistedBreastBreast NeoplasmsFemaleHumansMammographyRadiographic Image Interpretation, Computer-AssistedROC CurveBreastCancerComputer-aided detectionMammographic density segmentationRisk stratification

Identifiers

PMID28726117
PMCPMC5711603

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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.