Characterizing breast phenotype with a novel measure of fibroglandular structure

John H. Hipwell; Lewis D. Griffin; Patsy J. Whelehan; Wenlong Song; Xiying Zhang; Jan M. Lesniak; Sarah Vinnicombe; Andy Evans; Jonathan Berg; David J. Hawkes

doi:10.1007/978-3-642-31271-7_24

Characterizing breast phenotype with a novel measure of fibroglandular structure

John H. Hipwell, Lewis D. Griffin, Patsy J. Whelehan, Wenlong Song, Xiying Zhang, Jan M. Lesniak, Sarah Vinnicombe, Andy Evans, Jonathan Berg, David J. Hawkes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Understanding, and accurately being able to predict, breast cancer risk would greatly enhance the early detection, and hence treatment, of the disease. In this paper we describe a new metric for mammographic structure, "orientated mammographic entropy", via a comprehensive classification of image pixels into one of seven basic image feature (BIF) classes. These classes are flat (zero order), slope-like (first order), and maximum, minimum, light-lines, dark-lines and saddles (second order). By computing a reference breast orientation with respect to breast shape and nipple location, these classes are further subdivided into 23 orientated BIF classes. For a given mammogram a histogram is constructed from the proportion of pixels in each of the 23 classes, and the orientated mammographic entropy, H , computed from this histogram. H , shows good correlation between left and right breasts (r =0.76, N=478), and is independent of both mammographic breast area, a surrogate for breast size (r =0.07, N=974), and breast density, as estimated using Volpara software (r =0.11, N=385). We illustrate this metric by examining its relationship to familial breast cancer risk, for 118 subjects, using the BOADICEA genetic susceptibility to breast and ovarian cancer model.

Original language	English
Title of host publication	Breast Imaging
Subtitle of host publication	11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings
Editors	Andrew D. A. Maidment, Predrag R. Bakic, Sara Gavenonis
Place of Publication	Berlin
Publisher	Springer
Pages	181-188
Number of pages	8
ISBN (Electronic)	9783642312717
ISBN (Print)	9783642312700
DOIs	https://doi.org/10.1007/978-3-642-31271-7_24
Publication status	Published - 2012
Event	11th International Workshop on Breast Imaging - Houston Hall, University of Pennsylvania , Philadelphia, United States Duration: 8 Jul 2012 → 11 Jul 2012 http://www.iwdm2012.org/tiki-index.php

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	7361

Workshop

Workshop	11th International Workshop on Breast Imaging
Abbreviated title	IWDM 2012
Country/Territory	United States
City	Philadelphia
Period	8/07/12 → 11/07/12
Internet address	http://www.iwdm2012.org/tiki-index.php

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-31271-7_24

Cite this

Hipwell, J. H., Griffin, L. D., Whelehan, P. J., Song, W., Zhang, X., Lesniak, J. M., Vinnicombe, S., Evans, A., Berg, J., & Hawkes, D. J. (2012). Characterizing breast phenotype with a novel measure of fibroglandular structure. In A. D. A. Maidment, P. R. Bakic, & S. Gavenonis (Eds.), Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings (pp. 181-188). (Lecture notes in computer science; Vol. 7361). Springer . https://doi.org/10.1007/978-3-642-31271-7_24

Hipwell, John H. ; Griffin, Lewis D. ; Whelehan, Patsy J. et al. / Characterizing breast phenotype with a novel measure of fibroglandular structure. Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. editor / Andrew D. A. Maidment ; Predrag R. Bakic ; Sara Gavenonis. Berlin : Springer , 2012. pp. 181-188 (Lecture notes in computer science).

@inproceedings{69f3b40ec98d4534bf617544e9fe3dde,

title = "Characterizing breast phenotype with a novel measure of fibroglandular structure",

abstract = "Understanding, and accurately being able to predict, breast cancer risk would greatly enhance the early detection, and hence treatment, of the disease. In this paper we describe a new metric for mammographic structure, {"}orientated mammographic entropy{"}, via a comprehensive classification of image pixels into one of seven basic image feature (BIF) classes. These classes are flat (zero order), slope-like (first order), and maximum, minimum, light-lines, dark-lines and saddles (second order). By computing a reference breast orientation with respect to breast shape and nipple location, these classes are further subdivided into 23 orientated BIF classes. For a given mammogram a histogram is constructed from the proportion of pixels in each of the 23 classes, and the orientated mammographic entropy, H , computed from this histogram. H , shows good correlation between left and right breasts (r =0.76, N=478), and is independent of both mammographic breast area, a surrogate for breast size (r =0.07, N=974), and breast density, as estimated using Volpara software (r =0.11, N=385). We illustrate this metric by examining its relationship to familial breast cancer risk, for 118 subjects, using the BOADICEA genetic susceptibility to breast and ovarian cancer model.",

author = "Hipwell, {John H.} and Griffin, {Lewis D.} and Whelehan, {Patsy J.} and Wenlong Song and Xiying Zhang and Lesniak, {Jan M.} and Sarah Vinnicombe and Andy Evans and Jonathan Berg and Hawkes, {David J.}",

year = "2012",

doi = "10.1007/978-3-642-31271-7_24",

language = "English",

isbn = "9783642312700",

series = "Lecture notes in computer science",

publisher = "Springer ",

pages = "181--188",

editor = "Maidment, {Andrew D. A.} and Bakic, {Predrag R.} and Sara Gavenonis",

booktitle = "Breast Imaging",

note = "11th International Workshop on Breast Imaging, IWDM 2012 ; Conference date: 08-07-2012 Through 11-07-2012",

url = "http://www.iwdm2012.org/tiki-index.php",

}

Hipwell, JH, Griffin, LD, Whelehan, PJ, Song, W, Zhang, X, Lesniak, JM, Vinnicombe, S, Evans, A , Berg, J & Hawkes, DJ 2012, Characterizing breast phenotype with a novel measure of fibroglandular structure. in ADA Maidment, PR Bakic & S Gavenonis (eds), Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. Lecture notes in computer science, vol. 7361, Springer , Berlin, pp. 181-188, 11th International Workshop on Breast Imaging, Philadelphia, United States, 8/07/12. https://doi.org/10.1007/978-3-642-31271-7_24

Characterizing breast phenotype with a novel measure of fibroglandular structure. / Hipwell, John H.; Griffin, Lewis D.; Whelehan, Patsy J. et al.
Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. ed. / Andrew D. A. Maidment; Predrag R. Bakic; Sara Gavenonis. Berlin: Springer , 2012. p. 181-188 (Lecture notes in computer science; Vol. 7361).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Characterizing breast phenotype with a novel measure of fibroglandular structure

AU - Hipwell, John H.

AU - Griffin, Lewis D.

AU - Whelehan, Patsy J.

AU - Song, Wenlong

AU - Zhang, Xiying

AU - Lesniak, Jan M.

AU - Vinnicombe, Sarah

AU - Evans, Andy

AU - Berg, Jonathan

AU - Hawkes, David J.

PY - 2012

Y1 - 2012

N2 - Understanding, and accurately being able to predict, breast cancer risk would greatly enhance the early detection, and hence treatment, of the disease. In this paper we describe a new metric for mammographic structure, "orientated mammographic entropy", via a comprehensive classification of image pixels into one of seven basic image feature (BIF) classes. These classes are flat (zero order), slope-like (first order), and maximum, minimum, light-lines, dark-lines and saddles (second order). By computing a reference breast orientation with respect to breast shape and nipple location, these classes are further subdivided into 23 orientated BIF classes. For a given mammogram a histogram is constructed from the proportion of pixels in each of the 23 classes, and the orientated mammographic entropy, H , computed from this histogram. H , shows good correlation between left and right breasts (r =0.76, N=478), and is independent of both mammographic breast area, a surrogate for breast size (r =0.07, N=974), and breast density, as estimated using Volpara software (r =0.11, N=385). We illustrate this metric by examining its relationship to familial breast cancer risk, for 118 subjects, using the BOADICEA genetic susceptibility to breast and ovarian cancer model.

AB - Understanding, and accurately being able to predict, breast cancer risk would greatly enhance the early detection, and hence treatment, of the disease. In this paper we describe a new metric for mammographic structure, "orientated mammographic entropy", via a comprehensive classification of image pixels into one of seven basic image feature (BIF) classes. These classes are flat (zero order), slope-like (first order), and maximum, minimum, light-lines, dark-lines and saddles (second order). By computing a reference breast orientation with respect to breast shape and nipple location, these classes are further subdivided into 23 orientated BIF classes. For a given mammogram a histogram is constructed from the proportion of pixels in each of the 23 classes, and the orientated mammographic entropy, H , computed from this histogram. H , shows good correlation between left and right breasts (r =0.76, N=478), and is independent of both mammographic breast area, a surrogate for breast size (r =0.07, N=974), and breast density, as estimated using Volpara software (r =0.11, N=385). We illustrate this metric by examining its relationship to familial breast cancer risk, for 118 subjects, using the BOADICEA genetic susceptibility to breast and ovarian cancer model.

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U2 - 10.1007/978-3-642-31271-7_24

DO - 10.1007/978-3-642-31271-7_24

M3 - Conference contribution

AN - SCOPUS:84864832809

SN - 9783642312700

T3 - Lecture notes in computer science

SP - 181

EP - 188

BT - Breast Imaging

A2 - Maidment, Andrew D. A.

A2 - Bakic, Predrag R.

A2 - Gavenonis, Sara

PB - Springer

CY - Berlin

T2 - 11th International Workshop on Breast Imaging

Y2 - 8 July 2012 through 11 July 2012

ER -

Hipwell JH, Griffin LD, Whelehan PJ, Song W, Zhang X, Lesniak JM et al. Characterizing breast phenotype with a novel measure of fibroglandular structure. In Maidment ADA, Bakic PR, Gavenonis S, editors, Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. Berlin: Springer . 2012. p. 181-188. (Lecture notes in computer science). doi: 10.1007/978-3-642-31271-7_24

Characterizing breast phenotype with a novel measure of fibroglandular structure

Abstract

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Workshop

UN SDGs

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11th International Workshop on Breast Imaging

11th International Workshop on Breast Imaging

11th International Workshop on Breast Imaging

Cite this

Characterizing breast phenotype with a novel measure of fibroglandular structure

Abstract

Publication series

Workshop

UN SDGs

Access to Document

Other files and links

Fingerprint

Activities

11th International Workshop on Breast Imaging

11th International Workshop on Breast Imaging

11th International Workshop on Breast Imaging

Cite this