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Published in: Breast Cancer Research 1/2016

Open Access 01-12-2016 | Review

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

Authors: Aimilia Gastounioti, Emily F. Conant, Despina Kontos

Published in: Breast Cancer Research | Issue 1/2016

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Abstract

Background

The assessment of a woman’s risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.

Main text

The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.

Conclusions

The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.
Literature
1.
go back to reference Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, et al. GLOBOCAN 2012 v1.0, cancer incidence and mortality worldwide: IARC CancerBase No. 11 Lyon. International Agency for Research on Cancer: France; 2013. http://globocan.iarc.fr. Accessed 8 Mar 2016. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, et al. GLOBOCAN 2012 v1.0, cancer incidence and mortality worldwide: IARC CancerBase No. 11 Lyon. International Agency for Research on Cancer: France; 2013. http://​globocan.​iarc.​fr. Accessed 8 Mar 2016.
4.
go back to reference Howell A, Astley S, Warwick J, Stavrinos P, Sahin S, Ingham S, et al. Prevention of breast cancer in the context of a national breast screening programme. J Int Med. 2012;271(4):321–30.CrossRef Howell A, Astley S, Warwick J, Stavrinos P, Sahin S, Ingham S, et al. Prevention of breast cancer in the context of a national breast screening programme. J Int Med. 2012;271(4):321–30.CrossRef
5.
go back to reference Amir E, Freedman OC, Seruga B, Evans DG. Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010;102(10):680–91.CrossRefPubMed Amir E, Freedman OC, Seruga B, Evans DG. Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010;102(10):680–91.CrossRefPubMed
6.
go back to reference Gail MH, Mai PL. Comparing breast cancer risk assessment models. J Natl Cancer Inst. 2010;102(10):665–8.CrossRefPubMed Gail MH, Mai PL. Comparing breast cancer risk assessment models. J Natl Cancer Inst. 2010;102(10):665–8.CrossRefPubMed
7.
go back to reference Onega T, Beaber EF, Sprague BL, Barlow WE, Haas JS, Tosteson AN, et al. Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for risk-based and preference-based approaches at a population level. Cancer. 2014;120(19):2955–64.CrossRefPubMedPubMedCentral Onega T, Beaber EF, Sprague BL, Barlow WE, Haas JS, Tosteson AN, et al. Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for risk-based and preference-based approaches at a population level. Cancer. 2014;120(19):2955–64.CrossRefPubMedPubMedCentral
8.
go back to reference McDonald ES, Clark AS, Tchou J, Zhang P, Freedman GM. Clinical diagnosis and management of breast cancer. J Nucl Med. 2016;57(Supplement 1):9S–16S.CrossRefPubMed McDonald ES, Clark AS, Tchou J, Zhang P, Freedman GM. Clinical diagnosis and management of breast cancer. J Nucl Med. 2016;57(Supplement 1):9S–16S.CrossRefPubMed
10.
go back to reference Ng K-H, Lau S. Vision 20/20: Mammographic breast density and its clinical applications. Med Phys. 2015;42(12):7059–77.CrossRefPubMed Ng K-H, Lau S. Vision 20/20: Mammographic breast density and its clinical applications. Med Phys. 2015;42(12):7059–77.CrossRefPubMed
11.
go back to reference Sherratt MJ, McConnell JC, Streuli CH. Raised mammographic density: causative mechanisms and biological consequences. Breast Cancer Res. 2016;18(1):1.CrossRef Sherratt MJ, McConnell JC, Streuli CH. Raised mammographic density: causative mechanisms and biological consequences. Breast Cancer Res. 2016;18(1):1.CrossRef
12.
go back to reference McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1159–69.CrossRefPubMed McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1159–69.CrossRefPubMed
13.
go back to reference Brentnall AR, Harkness EF, Astley SM, Donnelly LS, Stavrinos P, Sampson S, et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res. 2015;17(1):1–10.CrossRef Brentnall AR, Harkness EF, Astley SM, Donnelly LS, Stavrinos P, Sampson S, et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res. 2015;17(1):1–10.CrossRef
14.
go back to reference Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 2008;148(5):337–47.CrossRefPubMedPubMedCentral Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 2008;148(5):337–47.CrossRefPubMedPubMedCentral
16.
go back to reference Abdolell M, Tsuruda K, Lightfoot CB, Payne JI, Caines J, Iles SE. Utility of relative and absolute measures of mammographic density versus clinical risk factors in evaluating breast cancer risk at time of screening mammography. Br J Radiol. 2016;89(1059):20150522.CrossRefPubMed Abdolell M, Tsuruda K, Lightfoot CB, Payne JI, Caines J, Iles SE. Utility of relative and absolute measures of mammographic density versus clinical risk factors in evaluating breast cancer risk at time of screening mammography. Br J Radiol. 2016;89(1059):20150522.CrossRefPubMed
17.
go back to reference Tan M, Zheng B, Ramalingam P, Gur D. Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry. Acad Radiol. 2013;20(12):1542–50.CrossRefPubMed Tan M, Zheng B, Ramalingam P, Gur D. Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry. Acad Radiol. 2013;20(12):1542–50.CrossRefPubMed
18.
go back to reference Tan M, Zheng B, Leader J, Gur D. Association between changes in mammographic image features and risk for near-term breast cancer development. IEEE Trans Med Imaging. 2016;35(7):1719–28.CrossRefPubMed Tan M, Zheng B, Leader J, Gur D. Association between changes in mammographic image features and risk for near-term breast cancer development. IEEE Trans Med Imaging. 2016;35(7):1719–28.CrossRefPubMed
19.
go back to reference Wang X, Lederman D, Tan J, Wang XH, Zheng B. Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification. Acad Radiol. 2010;17(10):1234–41.CrossRefPubMedPubMedCentral Wang X, Lederman D, Tan J, Wang XH, Zheng B. Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification. Acad Radiol. 2010;17(10):1234–41.CrossRefPubMedPubMedCentral
20.
go back to reference Holm J, Humphreys K, Li J, Ploner A, Cheddad A, Eriksson M, et al. Risk factors and tumor characteristics of interval cancers by mammographic density. J Clin Oncol. 2015;33(9):1030–7.CrossRefPubMed Holm J, Humphreys K, Li J, Ploner A, Cheddad A, Eriksson M, et al. Risk factors and tumor characteristics of interval cancers by mammographic density. J Clin Oncol. 2015;33(9):1030–7.CrossRefPubMed
21.
go back to reference Bae MS, Moon H-G, Han W, Noh D-Y, Ryu HS, Park I-A, et al. Early stage triple-negative breast cancer: imaging and clinical-pathologic factors associated with recurrence. Radiology. 2015;278(2):356–64.CrossRefPubMed Bae MS, Moon H-G, Han W, Noh D-Y, Ryu HS, Park I-A, et al. Early stage triple-negative breast cancer: imaging and clinical-pathologic factors associated with recurrence. Radiology. 2015;278(2):356–64.CrossRefPubMed
22.
go back to reference Sala E, Solomon L, Warren R, McCann J, Duffy S, Luben R, et al. Size, node status and grade of breast tumours: association with mammographic parenchymal patterns. Eur Radiol. 2000;10(1):157–61.CrossRefPubMed Sala E, Solomon L, Warren R, McCann J, Duffy S, Luben R, et al. Size, node status and grade of breast tumours: association with mammographic parenchymal patterns. Eur Radiol. 2000;10(1):157–61.CrossRefPubMed
23.
go back to reference Oza AM, Boyd NF. Mammographic parenchymal patterns: a marker of breast cancer risk. Epidemiol Rev. 1992;15(1):196–208. Oza AM, Boyd NF. Mammographic parenchymal patterns: a marker of breast cancer risk. Epidemiol Rev. 1992;15(1):196–208.
25.
go back to reference Saftlas AF, Szklo M. Mammographic parenchymal patterns and breast cancer risk. Epidemiol Rev. 1987;9(1):146–74.PubMed Saftlas AF, Szklo M. Mammographic parenchymal patterns and breast cancer risk. Epidemiol Rev. 1987;9(1):146–74.PubMed
26.
go back to reference Wolfe JN. Breast patterns as an index for developing breast cancer. Am J Roentgenol. 1976;126:1130–7.CrossRef Wolfe JN. Breast patterns as an index for developing breast cancer. Am J Roentgenol. 1976;126:1130–7.CrossRef
27.
go back to reference Wolfe JN. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer. 1976;37(5):2486–92.CrossRefPubMed Wolfe JN. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer. 1976;37(5):2486–92.CrossRefPubMed
28.
go back to reference Boyd N, O'Sullivan B, Campbell J, Fishell E, Simor I, Cooke G, et al. Mammographic signs as risk factors for breast cancer. Br J Cancer. 1982;45(2):185.CrossRefPubMedPubMedCentral Boyd N, O'Sullivan B, Campbell J, Fishell E, Simor I, Cooke G, et al. Mammographic signs as risk factors for breast cancer. Br J Cancer. 1982;45(2):185.CrossRefPubMedPubMedCentral
29.
go back to reference Boyd N, Byng J, Jong R, Fishell E, Little L, Miller A, et al. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst. 1995;87(9):670–5.CrossRefPubMed Boyd N, Byng J, Jong R, Fishell E, Little L, Miller A, et al. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst. 1995;87(9):670–5.CrossRefPubMed
30.
go back to reference Boyd N, Jensen H, Cooke G, Han HL. Relationship between mammographic and histological risk factors for breast cancer. J Natl Cancer Inst. 1992;84(15):1170–9.CrossRefPubMed Boyd N, Jensen H, Cooke G, Han HL. Relationship between mammographic and histological risk factors for breast cancer. J Natl Cancer Inst. 1992;84(15):1170–9.CrossRefPubMed
31.
go back to reference Gram IT, Funkhouser E, Tabár L. The Tabar classification of mammographic parenchymal patterns. Eur J Radiol. 1997;24(2):131–6.CrossRefPubMed Gram IT, Funkhouser E, Tabár L. The Tabar classification of mammographic parenchymal patterns. Eur J Radiol. 1997;24(2):131–6.CrossRefPubMed
32.
go back to reference Brisson J, Merletti F, Sadowsky NL, Twaddle JA, Morrison AS, Cole P. Mammographic features of the breast and breast cancer risk. Am J Epidemiol. 1982;115(3):428–37.PubMed Brisson J, Merletti F, Sadowsky NL, Twaddle JA, Morrison AS, Cole P. Mammographic features of the breast and breast cancer risk. Am J Epidemiol. 1982;115(3):428–37.PubMed
33.
34.
go back to reference Krook PM, Carlile T, Bush W, Hall MH. Mammographic parenchymal patterns as a risk indicator for prevalent and incident cancer. Cancer. 1978;41(3):1093–7.CrossRefPubMed Krook PM, Carlile T, Bush W, Hall MH. Mammographic parenchymal patterns as a risk indicator for prevalent and incident cancer. Cancer. 1978;41(3):1093–7.CrossRefPubMed
35.
go back to reference Threatt B, Norbeck JM, Ullman NS, Kummer R, Roselle P. Association between mammographic parenchymal pattern classification and incidence of breast cancer. Cancer. 1980;45(10):2550–6.CrossRefPubMed Threatt B, Norbeck JM, Ullman NS, Kummer R, Roselle P. Association between mammographic parenchymal pattern classification and incidence of breast cancer. Cancer. 1980;45(10):2550–6.CrossRefPubMed
36.
go back to reference Tabár L, Dean PB. Mammographic parenchymal patterns: risk indicator for breast cancer? JAMA. 1982;247(2):185–9.CrossRefPubMed Tabár L, Dean PB. Mammographic parenchymal patterns: risk indicator for breast cancer? JAMA. 1982;247(2):185–9.CrossRefPubMed
37.
go back to reference Wolfe JN, Saftlas AF, Salane M. Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case–control study. Am J Roentgenol. 1987;148(6):1087–92.CrossRef Wolfe JN, Saftlas AF, Salane M. Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case–control study. Am J Roentgenol. 1987;148(6):1087–92.CrossRef
38.
go back to reference Saftlas AF, Wolfe JN, Hoover RN, Brinton LA, Schairer C, Salane M, et al. Mammographic parenchymal patterns as indicators of breast cancer risk. Am J Epidemiol. 1989;129(3):518–26.PubMed Saftlas AF, Wolfe JN, Hoover RN, Brinton LA, Schairer C, Salane M, et al. Mammographic parenchymal patterns as indicators of breast cancer risk. Am J Epidemiol. 1989;129(3):518–26.PubMed
39.
go back to reference Myers L, McLelland R, Stricker C, Feig S, Martin J, Moskowitz M, et al. Reproducibility of mammographic classifications. Am J Roentgenol. 1983;141(3):445–50.CrossRef Myers L, McLelland R, Stricker C, Feig S, Martin J, Moskowitz M, et al. Reproducibility of mammographic classifications. Am J Roentgenol. 1983;141(3):445–50.CrossRef
40.
go back to reference Toniolo P, Bleich AR, Beinart C, Koenig KL. Reproducibility of Wolfe's classification of mammographic parenchymal patterns. Prev Med. 1992;21(1):1–7.CrossRefPubMed Toniolo P, Bleich AR, Beinart C, Koenig KL. Reproducibility of Wolfe's classification of mammographic parenchymal patterns. Prev Med. 1992;21(1):1–7.CrossRefPubMed
41.
go back to reference Goodwin PJ, Boyd NF. Mammographic parenchymal pattern and breast cancer risk: a critical appraisal of the evidence. Am J Epidemiol. 1988;127(6):1097–108.PubMed Goodwin PJ, Boyd NF. Mammographic parenchymal pattern and breast cancer risk: a critical appraisal of the evidence. Am J Epidemiol. 1988;127(6):1097–108.PubMed
42.
go back to reference Witt I, Hansen HS, Brünner S. The risk of developing breast cancer in relation to mammography findings. Eur J Radiol. 1984;4(1):65–7.PubMed Witt I, Hansen HS, Brünner S. The risk of developing breast cancer in relation to mammography findings. Eur J Radiol. 1984;4(1):65–7.PubMed
43.
go back to reference Warner E, Lockwood G, Tritchler D, Boyd N. The risk of breast cancer associated with mammographic parenchymal patterns: a meta-analysis of the published literature to examine the effect of method of classification. Cancer Detect Prev. 1991;16(1):67–72. Warner E, Lockwood G, Tritchler D, Boyd N. The risk of breast cancer associated with mammographic parenchymal patterns: a meta-analysis of the published literature to examine the effect of method of classification. Cancer Detect Prev. 1991;16(1):67–72.
44.
go back to reference Muhimmah I, Oliver A, Denton ER, Pont J, Pérez E, Zwiggelaar R. Comparison between Wolfe, Boyd, BI-RADS and Tabár based mammographic risk assessment. Lect Notes Comput Sci. 2006;4046:407.CrossRef Muhimmah I, Oliver A, Denton ER, Pont J, Pérez E, Zwiggelaar R. Comparison between Wolfe, Boyd, BI-RADS and Tabár based mammographic risk assessment. Lect Notes Comput Sci. 2006;4046:407.CrossRef
45.
go back to reference Gram IT, Bremnes Y, Ursin G, Maskarinec G, Bjurstam N, Lund E. Percentage density, Wolfe's and Tabar's mammographic patterns: agreement and association with risk factors for breast cancer. Breast Cancer Res. 2005;7(5):R854–61.CrossRefPubMedPubMedCentral Gram IT, Bremnes Y, Ursin G, Maskarinec G, Bjurstam N, Lund E. Percentage density, Wolfe's and Tabar's mammographic patterns: agreement and association with risk factors for breast cancer. Breast Cancer Res. 2005;7(5):R854–61.CrossRefPubMedPubMedCentral
46.
go back to reference Byng J, Boyd N, Fishell E, Jong R, Yaffe M. Automated analysis of mammographic densities. Phys Med Biol. 1996;41(5):909.CrossRefPubMed Byng J, Boyd N, Fishell E, Jong R, Yaffe M. Automated analysis of mammographic densities. Phys Med Biol. 1996;41(5):909.CrossRefPubMed
47.
go back to reference Caldwell CB, Stapleton SJ, Holdsworth DW, Jong RA, Weiser WJ, Cooke G, et al. Characterisation of mammographic parenchymal pattern by fractal dimension. Phys Med Biol. 1990;35(2):235–47.CrossRefPubMed Caldwell CB, Stapleton SJ, Holdsworth DW, Jong RA, Weiser WJ, Cooke G, et al. Characterisation of mammographic parenchymal pattern by fractal dimension. Phys Med Biol. 1990;35(2):235–47.CrossRefPubMed
48.
go back to reference Magnin IE, Cluzeau F, Odet CL, Bremond A. Mammographic texture analysis: an evaluation of risk for developing breast cancer. Opt Eng. 1986;25(6):156780.CrossRef Magnin IE, Cluzeau F, Odet CL, Bremond A. Mammographic texture analysis: an evaluation of risk for developing breast cancer. Opt Eng. 1986;25(6):156780.CrossRef
49.
go back to reference Tahoces P, Correa J, Soutos M, Gomez L, Vidal J. Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns. Phys Med Biol. 1995;40(1):103.CrossRefPubMed Tahoces P, Correa J, Soutos M, Gomez L, Vidal J. Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns. Phys Med Biol. 1995;40(1):103.CrossRefPubMed
50.
go back to reference Taylor P, Hajnal S, Dilhuydy M-H, Barreau B. Measuring image texture to separate “difficult” from “easy” mammograms. Br J Radiol. 1994;67(797):456–63.CrossRefPubMed Taylor P, Hajnal S, Dilhuydy M-H, Barreau B. Measuring image texture to separate “difficult” from “easy” mammograms. Br J Radiol. 1994;67(797):456–63.CrossRefPubMed
52.
go back to reference Zheng Y, Wang Y, Keller BM, Conant E, Gee JC, Kontos D, editors. A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case–control breast cancer risk assessment study. Orlando: SPIE Medical Imaging; International Society for Optics and Photonics; 2013. Zheng Y, Wang Y, Keller BM, Conant E, Gee JC, Kontos D, editors. A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case–control breast cancer risk assessment study. Orlando: SPIE Medical Imaging; International Society for Optics and Photonics; 2013.
53.
go back to reference Sun W, Tseng T-LB, Qian W, Zhang J, Saltzstein EC, Zheng B, et al. Using multiscale texture and density features for near-term breast cancer risk analysis. Med Phys. 2015;42(6):2853–62.CrossRefPubMedPubMedCentral Sun W, Tseng T-LB, Qian W, Zhang J, Saltzstein EC, Zheng B, et al. Using multiscale texture and density features for near-term breast cancer risk analysis. Med Phys. 2015;42(6):2853–62.CrossRefPubMedPubMedCentral
54.
go back to reference Li H, Giger ML, Huo Z, Olopade OI, Lan L, Weber BL, et al. Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. Med Phys. 2004;31(3):549–55.CrossRefPubMed Li H, Giger ML, Huo Z, Olopade OI, Lan L, Weber BL, et al. Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. Med Phys. 2004;31(3):549–55.CrossRefPubMed
55.
go back to reference Huo Z, Giger ML, Olopade OI, Wolverton DE, Weber BL, Metz CE, et al. Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. Radiology. 2002;225(2):519–26.CrossRefPubMed Huo Z, Giger ML, Olopade OI, Wolverton DE, Weber BL, Metz CE, et al. Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. Radiology. 2002;225(2):519–26.CrossRefPubMed
56.
go back to reference Häberle L, Wagner F, Fasching PA, Jud SM, Heusinger K, Loehberg CR, et al. Characterizing mammographic images by using generic texture features. Breast Cancer Res. 2012;14(2):R59.CrossRefPubMedPubMedCentral Häberle L, Wagner F, Fasching PA, Jud SM, Heusinger K, Loehberg CR, et al. Characterizing mammographic images by using generic texture features. Breast Cancer Res. 2012;14(2):R59.CrossRefPubMedPubMedCentral
57.
go back to reference Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on. 1973;SMC-3(6):610–21.CrossRef Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on. 1973;SMC-3(6):610–21.CrossRef
58.
go back to reference Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process. 1975;4(2):172–9.CrossRef Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process. 1975;4(2):172–9.CrossRef
59.
go back to reference Chu A, Sehgal CM, Greenleaf JF. Use of gray value distribution of run lengths for texture analysis. Pattern Recogn Lett. 1990;11(6):415–9.CrossRef Chu A, Sehgal CM, Greenleaf JF. Use of gray value distribution of run lengths for texture analysis. Pattern Recogn Lett. 1990;11(6):415–9.CrossRef
60.
go back to reference Byng JW, Yaffe MJ, Lockwood GA, Little LE, Tritchler DL, Boyd NF. Automated analysis of mammographic densities and breast carcinoma risk. Cancer. 1997;80(1):66–74.CrossRefPubMed Byng JW, Yaffe MJ, Lockwood GA, Little LE, Tritchler DL, Boyd NF. Automated analysis of mammographic densities and breast carcinoma risk. Cancer. 1997;80(1):66–74.CrossRefPubMed
63.
go back to reference Choi JY, Ro YM. Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms. Phys Med Biol. 2012;57(21):7029–52. doi:10.1088/0031-9155/57/21/7029.CrossRefPubMed Choi JY, Ro YM. Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms. Phys Med Biol. 2012;57(21):7029–52. doi:10.​1088/​0031-9155/​57/​21/​7029.CrossRefPubMed
64.
go back to reference Nielsen M, Vachon CM, Scott CG, Chernoff K, Karemore G, Karssemeijer N, et al. Mammographic texture resemblance generalizes as an independent risk factor for breast cancer. Breast Cancer Res. 2014;16:R37.CrossRefPubMedPubMedCentral Nielsen M, Vachon CM, Scott CG, Chernoff K, Karemore G, Karssemeijer N, et al. Mammographic texture resemblance generalizes as an independent risk factor for breast cancer. Breast Cancer Res. 2014;16:R37.CrossRefPubMedPubMedCentral
65.
go back to reference Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Anal Mach Intell, IEEE Trans. 2002;24(7):971–87.CrossRef Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Anal Mach Intell, IEEE Trans. 2002;24(7):971–87.CrossRef
66.
go back to reference Manduca A, Carston MJ, Heine JJ, Scott CG, Pankratz VS, Brandt KR, et al. Texture features from mammographic images and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2009;18(3):837–45.CrossRefPubMedPubMedCentral Manduca A, Carston MJ, Heine JJ, Scott CG, Pankratz VS, Brandt KR, et al. Texture features from mammographic images and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2009;18(3):837–45.CrossRefPubMedPubMedCentral
67.
go back to reference Zyout I, Czajkowska J, Grzegorzek M. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography. Comput Med Imaging Graph. 2015;46:95–107.CrossRefPubMed Zyout I, Czajkowska J, Grzegorzek M. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography. Comput Med Imaging Graph. 2015;46:95–107.CrossRefPubMed
68.
go back to reference Ford D, Easton DF, Bishop DT, Narod SA, Goldgar DE. Risks of cancer in BRCA1-mutation carriers. Lancet. 1994;343(8899):692–5.CrossRefPubMed Ford D, Easton DF, Bishop DT, Narod SA, Goldgar DE. Risks of cancer in BRCA1-mutation carriers. Lancet. 1994;343(8899):692–5.CrossRefPubMed
69.
go back to reference Mitchell G, Antoniou AC, Warren R, Peock S, Brown J, Davies R, et al. Mammographic density and breast cancer risk in BRCA1 and BRCA2 mutation carriers. Cancer Res. 2006;66(3):1866–72.CrossRefPubMed Mitchell G, Antoniou AC, Warren R, Peock S, Brown J, Davies R, et al. Mammographic density and breast cancer risk in BRCA1 and BRCA2 mutation carriers. Cancer Res. 2006;66(3):1866–72.CrossRefPubMed
70.
go back to reference Gierach GL, Loud JT, Chow CK, Prindiville SA, Eng-Wong J, Soballe PW, et al. Mammographic density does not differ between unaffected BRCA1/2 mutation carriers and women at low-to-average risk of breast cancer. Breast Cancer Res Treat. 2010;123(1):245–55.CrossRefPubMedPubMedCentral Gierach GL, Loud JT, Chow CK, Prindiville SA, Eng-Wong J, Soballe PW, et al. Mammographic density does not differ between unaffected BRCA1/2 mutation carriers and women at low-to-average risk of breast cancer. Breast Cancer Res Treat. 2010;123(1):245–55.CrossRefPubMedPubMedCentral
71.
go back to reference Li H, Giger ML, Lan L, Janardanan J, Sennett CA. Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls. J Med Imaging. 2014;1(3):031009.CrossRef Li H, Giger ML, Lan L, Janardanan J, Sennett CA. Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls. J Med Imaging. 2014;1(3):031009.CrossRef
72.
go back to reference Torres-Mejia G, De Stavola B, Allen DS, Perez-Gavilan JJ, Ferreira JM, Fentiman IS, et al. Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies. Cancer Epidemiol Biomarkers Prev. 2005;14(5):1052–9.CrossRefPubMed Torres-Mejia G, De Stavola B, Allen DS, Perez-Gavilan JJ, Ferreira JM, Fentiman IS, et al. Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies. Cancer Epidemiol Biomarkers Prev. 2005;14(5):1052–9.CrossRefPubMed
74.
go back to reference Brandt SS, Karemore G, Karssemeijer N, Nielsen M. An anatomically oriented breast coordinate system for mammogram analysis. Med Imaging, IEEE Trans. 2011;30(10):1841–51.CrossRef Brandt SS, Karemore G, Karssemeijer N, Nielsen M. An anatomically oriented breast coordinate system for mammogram analysis. Med Imaging, IEEE Trans. 2011;30(10):1841–51.CrossRef
75.
go back to reference Chen X, Moschidis E, Taylor C, Astley S. Breast cancer risk analysis based on a novel segmentation framework for digital mammograms. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Boston: Springer; 2014. p. 536–43. Chen X, Moschidis E, Taylor C, Astley S. Breast cancer risk analysis based on a novel segmentation framework for digital mammograms. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Boston: Springer; 2014. p. 536–43.
76.
go back to reference Wu Y-T, Sahiner B, Chan H-P, Wei J, Hadjiiski LM, Helvie MA, et al., editors. Comparison of mammographic parenchymal patterns of normal subjects and breast cancer patients. San Diego: Medical Imaging: International Society for Optics and Photonics; 2008 Wu Y-T, Sahiner B, Chan H-P, Wei J, Hadjiiski LM, Helvie MA, et al., editors. Comparison of mammographic parenchymal patterns of normal subjects and breast cancer patients. San Diego: Medical Imaging: International Society for Optics and Photonics; 2008
77.
go back to reference Tan M, Qian W, Pu J, Liu H, Zheng B. A new approach to develop computer-aided detection schemes of digital mammograms. Phys Med Biol. 2015;60(11):4413.CrossRefPubMedPubMedCentral Tan M, Qian W, Pu J, Liu H, Zheng B. A new approach to develop computer-aided detection schemes of digital mammograms. Phys Med Biol. 2015;60(11):4413.CrossRefPubMedPubMedCentral
78.
go back to reference Tan M, Pu J, Cheng S, Liu H, Zheng B. Assessment of a four-view mammographic image feature based fusion model to predict near-term breast cancer risk. Ann Biomed Eng. 2015;43(10):2416–28.CrossRefPubMedPubMedCentral Tan M, Pu J, Cheng S, Liu H, Zheng B. Assessment of a four-view mammographic image feature based fusion model to predict near-term breast cancer risk. Ann Biomed Eng. 2015;43(10):2416–28.CrossRefPubMedPubMedCentral
79.
go back to reference Keller BM, Chen J, Conant EF, Kontos D, editors. Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer. San Diego: SPIE Medical Imaging: International Society for Optics and Photonics; 2014 Keller BM, Chen J, Conant EF, Kontos D, editors. Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer. San Diego: SPIE Medical Imaging: International Society for Optics and Photonics; 2014
80.
go back to reference Huo Z, Giger ML, Wolverton DE, Zhong W, Cumming S, Olopade OI. Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. Med Phys. 2000;27(1):4–12.CrossRefPubMed Huo Z, Giger ML, Wolverton DE, Zhong W, Cumming S, Olopade OI. Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. Med Phys. 2000;27(1):4–12.CrossRefPubMed
81.
go back to reference Li H, Giger ML, Olopade OI, Margolis A, Lan L, Chinander MR. Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad Radiol. 2005;12:863–73.CrossRefPubMed Li H, Giger ML, Olopade OI, Margolis A, Lan L, Chinander MR. Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad Radiol. 2005;12:863–73.CrossRefPubMed
82.
go back to reference Li H, Giger ML, Olopade OI, Lan L. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol. 2007;14(5):513–21.CrossRefPubMed Li H, Giger ML, Olopade OI, Lan L. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol. 2007;14(5):513–21.CrossRefPubMed
83.
go back to reference Li H, Giger ML, Olopade OI, Chinander MR. Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment. J Digit Imaging. 2008;21(2):145–52. doi:10.1007/s10278-007-9093-9. Li H, Giger ML, Olopade OI, Chinander MR. Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment. J Digit Imaging. 2008;21(2):145–52. doi:10.​1007/​s10278-007-9093-9.
84.
go back to reference Li H, Giger ML, Lan L, Brown JB, MacMahon A, Mussman M, et al. Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets. J Digit Imaging. 2012;25(5):591–8.CrossRefPubMedPubMedCentral Li H, Giger ML, Lan L, Brown JB, MacMahon A, Mussman M, et al. Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets. J Digit Imaging. 2012;25(5):591–8.CrossRefPubMedPubMedCentral
85.
go back to reference Gierach GL, Li H, Loud JT, Greene MH, Chow CK, Lan L, et al. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res. 2014;16(4):424.PubMedPubMedCentral Gierach GL, Li H, Loud JT, Greene MH, Chow CK, Lan L, et al. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res. 2014;16(4):424.PubMedPubMedCentral
86.
go back to reference Kontos D, Bakic PR, Carton AK, Troxel AB, Conant EF, Maidment ADA. Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study. Acad Radiol. 2009;16(3):283–98.CrossRefPubMedPubMedCentral Kontos D, Bakic PR, Carton AK, Troxel AB, Conant EF, Maidment ADA. Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study. Acad Radiol. 2009;16(3):283–98.CrossRefPubMedPubMedCentral
87.
go back to reference Kontos D, Ikejimba L, Bakic PR, Troxel AB, Conant EF, Maidment ADA. Digital breast tomosynthesis parenchymal texture analysis: comparison with digital mammography and implications for cancer risk assessment. Radiology. 2011;261(1):80–91.CrossRefPubMedPubMedCentral Kontos D, Ikejimba L, Bakic PR, Troxel AB, Conant EF, Maidment ADA. Digital breast tomosynthesis parenchymal texture analysis: comparison with digital mammography and implications for cancer risk assessment. Radiology. 2011;261(1):80–91.CrossRefPubMedPubMedCentral
88.
go back to reference Karemore G, Brand S, Sporring J, Nielsen M, editors. Anisotropic diffusion tensor applied to temporal mammograms: an application to breast cancer risk assessment. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 2010: IEEE. Karemore G, Brand S, Sporring J, Nielsen M, editors. Anisotropic diffusion tensor applied to temporal mammograms: an application to breast cancer risk assessment. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 2010: IEEE.
89.
go back to reference Karemore G, Nielsen M, Karssemeijer N, Brandt SS. A method to determine the mammographic regions that show early changes due to the development of breast cancer. Phys Med Biol. 2014;59(22):6759.CrossRefPubMed Karemore G, Nielsen M, Karssemeijer N, Brandt SS. A method to determine the mammographic regions that show early changes due to the development of breast cancer. Phys Med Biol. 2014;59(22):6759.CrossRefPubMed
90.
go back to reference Gastounioti A, Keller BM, Hsieh M-K, Conant EF, Kontos D, editors. Towards a breast-anatomy-weighted parenchymal texture signature for breast cancer risk assessment. Munich: Breast Image Analysis (BIA) Workshop, Medical Image Computing and Computer Assisted Intervention (MICCAI) Annual Meeting; 2015. Gastounioti A, Keller BM, Hsieh M-K, Conant EF, Kontos D, editors. Towards a breast-anatomy-weighted parenchymal texture signature for breast cancer risk assessment. Munich: Breast Image Analysis (BIA) Workshop, Medical Image Computing and Computer Assisted Intervention (MICCAI) Annual Meeting; 2015.
91.
go back to reference Gastounioti A, Keller BM, Hsieh M-K, Conant EF, Kontos D, editors. Parenchymal texture measures weighted by breast anatomy: preliminary optimization in a case–control study. Munich: SPIE Medical Imaging: Computer-aided diagnosis; 2016. Gastounioti A, Keller BM, Hsieh M-K, Conant EF, Kontos D, editors. Parenchymal texture measures weighted by breast anatomy: preliminary optimization in a case–control study. Munich: SPIE Medical Imaging: Computer-aided diagnosis; 2016.
92.
go back to reference Gastounioti A, Oustimov A, Keller BM, Pantalone L, Hsieh M-K, Conant EF, et al., editors. Associations of dense and fatty breast-tissue heterogeneity with breast cancer risk: Preliminary evaluation using parenchymal texture measurements driven by breast anatomy. San Diego: Radiological Society of North America (RSNA) Annual Meeting; 2015. Gastounioti A, Oustimov A, Keller BM, Pantalone L, Hsieh M-K, Conant EF, et al., editors. Associations of dense and fatty breast-tissue heterogeneity with breast cancer risk: Preliminary evaluation using parenchymal texture measurements driven by breast anatomy. San Diego: Radiological Society of North America (RSNA) Annual Meeting; 2015.
94.
go back to reference Kallenberg M, Petersen K, Nielsen M, Ng A, Diao P, Igel C, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging. 2016;35(5):1322–31.CrossRefPubMed Kallenberg M, Petersen K, Nielsen M, Ng A, Diao P, Igel C, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging. 2016;35(5):1322–31.CrossRefPubMed
95.
go back to reference Petersen K, Nielsen M, Diao P, Karssemeijer N, Lillholm M. Breast tissue segmentation and mammographic risk scoring using deep learning. Breast Imaging: Springer; 2014. p. 88–94 Petersen K, Nielsen M, Diao P, Karssemeijer N, Lillholm M. Breast tissue segmentation and mammographic risk scoring using deep learning. Breast Imaging: Springer; 2014. p. 88–94
96.
go back to reference Qiu Y, Wang Y, Yan S, Tan M, Cheng S, Liu H, et al., editors. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology. Gifu City: SPIE Medical Imaging: Computer-aided diagnosis; 2016 Qiu Y, Wang Y, Yan S, Tan M, Cheng S, Liu H, et al., editors. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology. Gifu City: SPIE Medical Imaging: Computer-aided diagnosis; 2016
97.
100.
go back to reference Houssami N, Miglioretti DL. Digital breast tomosynthesis: a brave new world of mammography screening. JAMA Oncol. 2016;2(6):725–7.CrossRefPubMed Houssami N, Miglioretti DL. Digital breast tomosynthesis: a brave new world of mammography screening. JAMA Oncol. 2016;2(6):725–7.CrossRefPubMed
101.
go back to reference Keller BM, Oustimov A, Wang Y, Chen J, Acciavatti RJ, Zheng Y, et al. Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices. J Med Imaging. 2015;2(2):024501.CrossRef Keller BM, Oustimov A, Wang Y, Chen J, Acciavatti RJ, Zheng Y, et al. Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices. J Med Imaging. 2015;2(2):024501.CrossRef
102.
go back to reference Heine JJ, Malhotra P. Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors. Acad Radiol. 2002;9(3):298–316.CrossRefPubMed Heine JJ, Malhotra P. Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors. Acad Radiol. 2002;9(3):298–316.CrossRefPubMed
103.
go back to reference Heine JJ, Malhotra P. Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography: Part 2. Serial breast tissue change and related temporal influences. Acad Radiol. 2002;9(3):317–35.CrossRefPubMed Heine JJ, Malhotra P. Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography: Part 2. Serial breast tissue change and related temporal influences. Acad Radiol. 2002;9(3):317–35.CrossRefPubMed
104.
go back to reference Li H, Giger ML, Sun C, Ponsukcharoen U, Huo D, Lan L, et al. Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys. 2014;41(3):031917.CrossRefPubMedPubMedCentral Li H, Giger ML, Sun C, Ponsukcharoen U, Huo D, Lan L, et al. Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys. 2014;41(3):031917.CrossRefPubMedPubMedCentral
105.
go back to reference Russo J, Lynch H, Russo IH. Mammary gland architecture as a determining factor in the susceptibility of the human breast to cancer. Breast J. 2001;7(5):278–91.CrossRefPubMed Russo J, Lynch H, Russo IH. Mammary gland architecture as a determining factor in the susceptibility of the human breast to cancer. Breast J. 2001;7(5):278–91.CrossRefPubMed
106.
go back to reference Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.CrossRefPubMedPubMedCentral Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.CrossRefPubMedPubMedCentral
107.
go back to reference Kuo MD, Jamshidi N. Behind the numbers: decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations. Radiology. 2014;270(2):320–5.CrossRefPubMed Kuo MD, Jamshidi N. Behind the numbers: decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations. Radiology. 2014;270(2):320–5.CrossRefPubMed
108.
go back to reference Yamamoto S, Maki DD, Korn RL, Kuo MD. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. Am J Roentgenol. 2012;199(3):654–63.CrossRef Yamamoto S, Maki DD, Korn RL, Kuo MD. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. Am J Roentgenol. 2012;199(3):654–63.CrossRef
109.
go back to reference Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology. 2014;273(2):365–72.CrossRefPubMed Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology. 2014;273(2):365–72.CrossRefPubMed
110.
go back to reference Mendel KR, Li H, Giger ML, editors. Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations. San Diego: SPIE Medical Imaging: International Society for Optics and Photonics; 2016 Mendel KR, Li H, Giger ML, editors. Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations. San Diego: SPIE Medical Imaging: International Society for Optics and Photonics; 2016
111.
go back to reference Guo W, Li H, Zhu Y, Lan L, Yang S, Drukker K, et al. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging. 2015;2(4):041007.CrossRef Guo W, Li H, Zhu Y, Lan L, Yang S, Drukker K, et al. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging. 2015;2(4):041007.CrossRef
Metadata
Title
Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment
Authors
Aimilia Gastounioti
Emily F. Conant
Despina Kontos
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2016
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-016-0755-8

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