Literature DB >> 21146484

A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer.

M Nielsen1, G Karemore, M Loog, J Raundahl, N Karssemeijer, J D M Otten, M A Karsdal, C M Vachon, C Christiansen.   

Abstract

OBJECTIVE: We investigated whether breast cancer is predicted by a breast cancer risk mammographic texture resemblance (MTR) marker.
METHODS: A previously published case-control study included 495 women of which 245 were diagnosed with breast cancer. In baseline mammograms, 2-4 years prior to diagnosis, the following mammographic parameters were analysed for relation to breast cancer risk: (C) categorical parenchymal pattern scores; (R) radiologist's percentage density, (P) computer-based percentage density; (H) computer-based breast cancer risk MTR marker; (E) computer-based hormone replacement treatment MTR marker; and (A) an aggregate of P and H.
RESULTS: Density scores, C, R, and P correlated (tau=0.3-0.6); no other pair of scores showed large (tau>0.2) correlation. For the parameters, the odds ratios of future incidence of breast cancer comparing highest to lowest categories (146 and 106 subject respectively) were C: 2.4(1.4-4.2), R: 2.4(1.4-4.1), P: 2.5(1.5-4.2), E: non-significant, H: 4.2(2.4-7.2), and A: 5.6(3.2-9.8). The AUC analysis showed a similarly increasing pattern (C: 0.58±0.02, R: 0.57±0.03, P: 0.60±0.03, H: 0.63±0.02, A: 0.66±0.02). The AUC of the aggregate marker (A) surpasses others significantly except H. HRT-MTR (E) did not significantly identify future cancers or correlate with any other marker.
CONCLUSIONS: Breast cancer risk MTR marker was independent of density scores and more predictive of risk. The hormone replacement treatment MTR marker did not identify patients at risk.
Copyright © 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 21146484     DOI: 10.1016/j.canep.2010.10.011

Source DB:  PubMed          Journal:  Cancer Epidemiol        ISSN: 1877-7821            Impact factor:   2.984


  19 in total

1.  Early detection of Alzheimer's disease using MRI hippocampal texture.

Authors:  Lauge Sørensen; Christian Igel; Naja Liv Hansen; Merete Osler; Martin Lauritzen; Egill Rostrup; Mads Nielsen
Journal:  Hum Brain Mapp       Date:  2015-12-21       Impact factor: 5.038

2.  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

3.  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

4.  Using multiscale texture and density features for near-term breast cancer risk analysis.

Authors:  Wenqing Sun; Tzu-Liang Bill Tseng; Wei Qian; Jianying Zhang; Edward C Saltzstein; Bin Zheng; Fleming Lure; Hui Yu; Shi Zhou
Journal:  Med Phys       Date:  2015-06       Impact factor: 4.071

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

Authors:  Shiju Yan; Yunzhi Wang; Faranak Aghaei; Yuchen Qiu; Bin Zheng
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-07-19       Impact factor: 2.924

6.  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

7.  Enhancement of mammographic density measures in breast cancer risk prediction.

Authors:  Abbas Cheddad; Kamila Czene; John A Shepherd; Jingmei Li; Per Hall; Keith Humphreys
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-04-10       Impact factor: 4.254

8.  The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006.

Authors:  Lin Chen; Shonket Ray; Brad M Keller; Said Pertuz; Elizabeth S McDonald; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2016-03-22       Impact factor: 11.105

9.  Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms.

Authors:  Marina Pollán; Rafael Llobet; Josefa Miranda-García; Joaquín Antón; María Casals; Inmaculada Martínez; Carmen Palop; Francisco Ruiz-Perales; Carmen Sánchez-Contador; Carmen Vidal; Beatriz Pérez-Gómez; Dolores Salas-Trejo
Journal:  Springerplus       Date:  2013-05-24

10.  High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer.

Authors:  Jingmei Li; Laszlo Szekely; Louise Eriksson; Boel Heddson; Ann Sundbom; Kamila Czene; Per Hall; Keith Humphreys
Journal:  Breast Cancer Res       Date:  2012-07-30       Impact factor: 6.466

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