Literature DB >> 21406634

Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study.

Jun Wei1, Heang-Ping Chan, Yi-Ta Wu, Chuan Zhou, Mark A Helvie, Alexander Tsodikov, Lubomir M Hadjiiski, Berkman Sahiner.   

Abstract

PURPOSE: To develop a computerized mammographic parenchymal pattern (MPP) measure and investigate its association with breast cancer risk.
MATERIALS AND METHODS: A pilot case-control study was conducted by collecting mammograms from 382 subjects retrospectively. The study was institutional review board approved and HIPAA compliant. Informed consent was waived. The cases included the contralateral mammograms of cancer patients (n = 136) obtained at least 1 year before diagnosis. The controls included mammograms of healthy subjects (n = 246) who had cancer-free follow-up for at least 5 years. The data set was historically divided into a training set and an independent test set. An MPP measure was designed to analyze the texture patterns of fibroglandular tissue in the retroareolar region. Odds ratios (ORs) were used to assess the association between breast cancer risk and MPP. To test the trend in ORs, we divided the MPP measure into three categories (C1, C2, and C3) on the basis of its values from low to high, with C1 as the baseline. The confounding factors in this study included patient age, body mass index, first-degree relatives with history of breast cancer, number of previous breast biopsies, and percentage density (PD).
RESULTS: Among all of the subjects from the training and test data sets, the Pearson product-moment correlation coefficient between MPP and PD was 0.13. With logistic regression to adjust the confounding, the adjusted ORs for C2 and C3 relative to C1 in the test set were 2.82 (P = .041) and 13.89 (P < .001), respectively.
CONCLUSION: The proposed MPP measure demonstrated a strong association with breast cancer risk and has the potential to serve as an independent factor for risk prediction.

Entities:  

Mesh:

Year:  2011        PMID: 21406634      PMCID: PMC3135878          DOI: 10.1148/radiol.11101266

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  28 in total

Review 1.  The reliability of clinical methods, data and judgments (first of two parts).

Authors:  L M Koran
Journal:  N Engl J Med       Date:  1975-09-25       Impact factor: 91.245

2.  Computerized image analysis: estimation of breast density on mammograms.

Authors:  C Zhou; H P Chan; N Petrick; M A Helvie; M M Goodsitt; B Sahiner; L M Hadjiiski
Journal:  Med Phys       Date:  2001-06       Impact factor: 4.071

3.  The reliability of clinical methods, data and judgments (second of two parts).

Authors:  L M Koran
Journal:  N Engl J Med       Date:  1975-10-02       Impact factor: 91.245

4.  Experiments in the visual perception of texture.

Authors:  B Julesz
Journal:  Sci Am       Date:  1975-04       Impact factor: 2.142

5.  Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention.

Authors:  B Rockhill; D Spiegelman; C Byrne; D J Hunter; G A Colditz
Journal:  J Natl Cancer Inst       Date:  2001-03-07       Impact factor: 13.506

Review 6.  Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer.

Authors:  M H Gail; J P Costantino; J Bryant; R Croyle; L Freedman; K Helzlsouer; V Vogel
Journal:  J Natl Cancer Inst       Date:  1999-11-03       Impact factor: 13.506

Review 7.  Assessing women at high risk of breast cancer: a review of risk assessment models.

Authors:  Eitan Amir; Orit C Freedman; Bostjan Seruga; D Gareth Evans
Journal:  J Natl Cancer Inst       Date:  2010-04-28       Impact factor: 13.506

8.  Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality.

Authors:  L Tabár; B Vitak; H H Chen; M F Yen; S W Duffy; R A Smith
Journal:  Cancer       Date:  2001-05-01       Impact factor: 6.860

9.  Breast cancer screening: a summary of the evidence for the U.S. Preventive Services Task Force.

Authors:  Linda L Humphrey; Mark Helfand; Benjamin K S Chan; Steven H Woolf
Journal:  Ann Intern Med       Date:  2002-09-03       Impact factor: 25.391

10.  Wolfe's parenchymal pattern and percentage of the breast with mammographic densities: redundant or complementary classifications?

Authors:  Jacques Brisson; Caroline Diorio; Benoît Mâsse
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2003-08       Impact factor: 4.254

View more
  30 in total

1.  Breast density estimation from high spectral and spatial resolution MRI.

Authors:  Hui Li; William A Weiss; Milica Medved; Hiroyuki Abe; Gillian M Newstead; Gregory S Karczmar; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2016-12-28

2.  Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.

Authors:  Maxine Tan; Bin Zheng; Pandiyarajan Ramalingam; David Gur
Journal:  Acad Radiol       Date:  2013-12       Impact factor: 3.173

3.  Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls.

Authors:  Hui Li; Maryellen L Giger; Li Lan; Jyothi Janardanan; Charlene A Sennett
Journal:  J Med Imaging (Bellingham)       Date:  2014-11-13

4.  Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.

Authors:  Maxine Tan; Jiantao Pu; Bin Zheng
Journal:  Phys Med Biol       Date:  2014-07-17       Impact factor: 3.609

5.  Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

Authors:  Yuanjie Zheng; Brad M Keller; Shonket Ray; Yan Wang; Emily F Conant; James C Gee; Despina Kontos
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

6.  Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method.

Authors:  Xingwei Wang; Lihua Li; Weidong Xu; Wei Liu; Dror Lederman; Bin Zheng
Journal:  Phys Med Biol       Date:  2012-01-21       Impact factor: 3.609

7.  Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.

Authors:  Hui Li; Maryellen L Giger; Li Lan; Jeremy Bancroft Brown; Aoife MacMahon; Mary Mussman; Olufunmilayo I Olopade; Charlene Sennett
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

8.  Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.

Authors:  Maxine Tan; Jiantao Pu; Samuel Cheng; Hong Liu; Bin Zheng
Journal:  Ann Biomed Eng       Date:  2015-04-08       Impact factor: 3.934

9.  Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.

Authors:  Richard Ha; Peter Chang; Jenika Karcich; Simukayi Mutasa; Eduardo Pascual Van Sant; Michael Z Liu; Sachin Jambawalikar
Journal:  Acad Radiol       Date:  2018-07-31       Impact factor: 3.173

10.  Response of bilateral breasts to the endogenous hormonal fluctuation in a menstrual cycle evaluated using 3D MRI.

Authors:  Jeon-Hor Chen; Siwa Chan; Dah-Cherng Yeh; Peter T Fwu; Muqing Lin; Min-Ying Su
Journal:  Magn Reson Imaging       Date:  2012-12-05       Impact factor: 2.546

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.