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Grant Details

Grant Number: 5R01CA160205-05 Interpret this number
Primary Investigator: Zheng, Bin
Organization: University Of Oklahoma Norman
Project Title: Mammographic Density and Tissue Asymmetry Based Breast Cancer Risk Stratification
Fiscal Year: 2015
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Abstract

DESCRIPTION (provided by applicant): Despite being one of the leading cancers in women, breast cancer detection rates in a repeat screened population are quite low (i.e., 3 to 5 cancers detected per 1000 examinations). Screening for the early detection of breast cancer has been controversial from the start, but recent events highlight the need to develop and optimize individualized screening regimens by identifying women who are at higher than average risk of developing breast cancer in the near future, namely within five years. Establishing optimal individualized screening regimens that facilitate women to be screened at different intervals and/or with different imaging methods based on their assigned risk group will not only increase sensitivity, resulting in the detection of earlier cancers, but also reduce overall cost and anxiet associated with screening programs. Breast cancer risk assessment has been studied for many years; however, due to reasonably low positive predictive values there are no existing risk models that are universally accepted in routine clinical practice, in particular as related to screening and diagnosis. There is no doubt that a breast cancer risk model with high discriminatory power will enable an increase in efficiency, efficacy, and cost effectiveness of screening paradigms. We propose to develop and test an innovative risk predictor that is based primarily on computed image features representing bilateral mammographic tissue density asymmetry between left and right breasts. As important, we will develop and test this predictor using mammograms acquired prior to any depiction of a highly suspicious abnormality leading to a biopsy and/or a verification of cancer. To achieve our objectives we will assemble a large and diverse image database of full-field digital mammography (FFDM) images with sequentially available images and related clinical information. The database will include three groups of cases, namely (1) positive cases that were verified to have cancer one to six years after the first available negative FFDM examination, (2) screening negative cases that have not been recalled during the period of interest, and (3) recalled and/or biopsied cases due to suspicious mammographic findings, but later proven to be negative or benign. Computed bilateral mammographic tissue asymmetry features will be used to develop the new risk predictor. In addition to evaluating the overall classification performance on the entire database, we will investigate the reproducibility of the predictor's results and the relationship between predictor's classification performance and the time lag between a negative FFDM in question and the first recall due to the actual detection of a highly suspicious finding leading to a biopsy and/or a confirmed cancer. We will also assess the impact, if any, of several other commonly used risk factors (i.e., age, family history, and breast density BIRADS) on predictor's performance. A bootstrapping method will be used to compute predictor's performance levels and 95% confidence intervals. By incorporating this risk predictor with other existing risk models, we will investigate the feasibility of improving discriminatory power in predicting risk of individual women developing breast cancer in near-term (<5 years).

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Publications

A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms.
Authors: Yin K. , Yan S. , Song C. , Zheng B. .
Source: International journal of computer assisted radiology and surgery, 2019 Feb; 14(2), p. 237-248.
EPub date: 2018-10-04.
PMID: 30288698
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Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk.
Authors: Mirniaharikandehei S. , Hollingsworth A.B. , Patel B. , Heidari M. , Liu H. , Zheng B. .
Source: Physics in medicine and biology, 2018-05-15; 63(10), p. 105005.
EPub date: 2018-05-15.
PMID: 29667606
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Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.
Authors: Heidari M. , Khuzani A.Z. , Hollingsworth A.B. , Danala G. , Mirniaharikandehei S. , Qiu Y. , Liu H. , Zheng B. .
Source: Physics in medicine and biology, 2018-01-30; 63(3), p. 035020.
EPub date: 2018-01-30.
PMID: 29239858
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Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis: An Assessment.
Authors: Yan S. , Wang Y. , Aghaei F. , Qiu Y. , Zheng B. .
Source: Academic radiology, 2017-10-03; , .
EPub date: 2017-10-03.
PMID: 28985925
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Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction.
Authors: Yan S. , Wang Y. , Aghaei F. , Qiu Y. , Zheng B. .
Source: International journal of computer assisted radiology and surgery, 2017 Oct; 12(10), p. 1819-1828.
EPub date: 2017-07-19.
PMID: 28726117
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Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions.
Authors: Tan M. , Aghaei F. , Wang Y. , Zheng B. .
Source: Physics in medicine and biology, 2017-01-21; 62(2), p. 358-376.
EPub date: 2016-12-20.
PMID: 27997380
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A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.
Authors: Qiu Y. , Yan S. , Gundreddy R.R. , Wang Y. , Cheng S. , Liu H. , Zheng B. .
Source: Journal of X-ray science and technology, 2017; 25(5), p. 751-763.
PMID: 28436410
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Computer-aided classification of mammographic masses using visually sensitive image features.
Authors: Wang Y. , Aghaei F. , Zarafshani A. , Qiu Y. , Qian W. , Zheng B. .
Source: Journal of X-ray science and technology, 2017; 25(1), p. 171-186.
PMID: 27911353
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Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.
Authors: Aghaei F. , Tan M. , Hollingsworth A.B. , Zheng B. .
Source: Journal of magnetic resonance imaging : JMRI, 2016 11; 44(5), p. 1099-1106.
EPub date: 2016-04-15.
PMID: 27080203
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Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development.
Authors: Tan M. , Zheng B. , Leader J.K. , Gur D. .
Source: IEEE transactions on medical imaging, 2016 07; 35(7), p. 1719-28.
EPub date: 2016-02-11.
PMID: 26886970
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Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy.
Authors: Aghaei F. , Tan M. , Hollingsworth A.B. , Qian W. , Liu H. , Zheng B. .
Source: Medical physics, 2015 Nov; 42(11), p. 6520-8.
PMID: 26520742
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Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.
Authors: Tan M. , Pu J. , Cheng S. , Liu H. , Zheng B. .
Source: Annals of biomedical engineering, 2015 Oct; 43(10), p. 2416-28.
EPub date: 2015-04-08.
PMID: 25851469
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Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions.
Authors: Gundreddy R.R. , Tan M. , Qiu Y. , Cheng S. , Liu H. , Zheng B. .
Source: Medical physics, 2015 Jul; 42(7), p. 4241-9.
PMID: 26133622
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A new approach to develop computer-aided detection schemes of digital mammograms.
Authors: Tan M. , Qian W. , Pu J. , Liu H. , Zheng B. .
Source: Physics in medicine and biology, 2015-06-07; 60(11), p. 4413-27.
EPub date: 2015-05-18.
PMID: 25984710
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A new quantitative image analysis method for improving breast cancer diagnosis using DCE-MRI examinations.
Authors: Yang Q. , Li L. , Zhang J. , Shao G. , Zheng B. .
Source: Medical physics, 2015 Jan; 42(1), p. 103-9.
PMID: 25563251
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Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model.
Authors: Tan M. , Pu J. , Zheng B. .
Source: International journal of computer assisted radiology and surgery, 2014 Nov; 9(6), p. 1005-20.
EPub date: 2014-03-25.
PMID: 24664267
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Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.
Authors: Tan M. , Pu J. , Zheng B. .
Source: Physics in medicine and biology, 2014-08-07; 59(15), p. 4357-73.
EPub date: 2014-07-17.
PMID: 25029964
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A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.
Authors: Tan M. , Pu J. , Zheng B. .
Source: Medical physics, 2014 Aug; 41(8), p. 081906.
PMID: 25086537
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A computerized global MR image feature analysis scheme to assist diagnosis of breast cancer: a preliminary assessment.
Authors: Yang Q. , Li L. , Zhang J. , Shao G. , Zheng B. .
Source: European journal of radiology, 2014 Jul; 83(7), p. 1086-1091.
EPub date: 2014-03-22.
PMID: 24743001
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Association between computed tissue density asymmetry in bilateral mammograms and near-term breast cancer risk.
Authors: Zheng B. , Tan M. , Ramalingam P. , Gur D. .
Source: The breast journal, 2014 May-Jun; 20(3), p. 249-57.
EPub date: 2014-03-27.
PMID: 24673749
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Computer-aided diagnosis of breast DCE-MRI images using bilateral asymmetry of contrast enhancement between two breasts.
Authors: Yang Q. , Li L. , Zhang J. , Shao G. , Zhang C. , Zheng B. .
Source: Journal of digital imaging, 2014 Feb; 27(1), p. 152-60.
PMID: 24043592
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Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework.
Authors: Tan M. , Pu J. , Zheng B. .
Source: Cancer informatics, 2014; 13(Suppl 1), p. 17-27.
EPub date: 2014-10-13.
PMID: 25392680
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Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.
Authors: Tan M. , Zheng B. , Ramalingam P. , Gur D. .
Source: Academic radiology, 2013 Dec; 20(12), p. 1542-50.
PMID: 24200481
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Bilateral mammographic density asymmetry and breast cancer risk: a preliminary assessment.
Authors: Zheng B. , Sumkin J.H. , Zuley M.L. , Wang X. , Klym A.H. , Gur D. .
Source: European journal of radiology, 2012 Nov; 81(11), p. 3222-8.
EPub date: 2012-05-12.
PMID: 22579527
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