Couch FJ, Nathanson KL, Offit K. Two decades after BRCA: setting paradigms in personalized cancer care and prevention. Science. 2014;343(6178):1466–70.
CAS
PubMed
PubMed Central
Google Scholar
Meindl A, Ditsch N, Kast K, Rhiem K, Schmutzler RK. Hereditary breast and ovarian cancer: new genes, new treatments, new concepts. Dtsch Arztebl Int. 2011;108(19):323–30.
PubMed
PubMed Central
Google Scholar
Kast K, Rhiem K, Wappenschmidt B, Hahnen E, Hauke J, Bluemcke B, Zarghooni V, Herold N, Ditsch N, Kiechle M, et al. Prevalence of BRCA1/2 germline mutations in 21 401 families with breast and ovarian cancer. J Med Genet. 2016;53(7):465–71.
CAS
PubMed
Google Scholar
Kuchenbaecker KB, Hopper JL, Barnes DR, Phillips KA, Mooij TM, Roos-Blom MJ, Jervis S, van Leeuwen FE, Milne RL, Andrieu N, et al. Risks of breast, ovarian, and contralateral breast Cancer for BRCA1 and BRCA2 mutation carriers. JAMA. 2017;317(23):2402–16.
CAS
PubMed
Google Scholar
Meindl A, German Consortium for Hereditary B, Ovarian C. Comprehensive analysis of 989 patients with breast or ovarian cancer provides BRCA1 and BRCA2 mutation profiles and frequencies for the German population. Int J Cancer. 2002;97(4):472–80.
CAS
PubMed
Google Scholar
Couch FJ, Hart SN, Sharma P, Toland AE, Wang X, Miron P, Olson JE, Godwin AK, Pankratz VS, Olswold C, et al. Inherited mutations in 17 breast cancer susceptibility genes among a large triple-negative breast cancer cohort unselected for family history of breast cancer. J Clin Oncol. 2015;33(4):304–11.
CAS
PubMed
Google Scholar
Hoyer J, Vasileiou G, Uebe S, Wunderle M, Kraus C, Fasching PA, Thiel CT, Hartmann A, Beckmann MW, Lux MP, et al. Addition of triple negativity of breast cancer as an indicator for germline mutations in predisposing genes increases sensitivity of clinical selection criteria. BMC Cancer. 2018;18(1):926.
CAS
PubMed
PubMed Central
Google Scholar
Fasching PA, Loibl S, Hu C, Hart SN, Shimelis H, Moore R, Schem C, Tesch H, Untch M, Hilfrich J, et al. BRCA1/2 mutations and Bevacizumab in the Neoadjuvant treatment of breast Cancer: response and prognosis results in patients with triple-negative breast Cancer from the GeparQuinto study. J Clin Oncol. 2018;36(22):2281–7.
CAS
PubMed
PubMed Central
Google Scholar
Couch FJ, DeShano ML, Blackwood MA, Calzone K, Stopfer J, Campeau L, Ganguly A, Rebbeck T, Weber BL. BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. N Engl J Med. 1997;336(20):1409–15.
CAS
PubMed
Google Scholar
Evans DG, Lalloo F, Wallace A, Rahman N. Update on the Manchester scoring system for BRCA1 and BRCA2 testing. J Med Genet. 2005;42(7):e39.
CAS
PubMed
PubMed Central
Google Scholar
Kang HH, Williams R, Leary J, kConFab I, Ringland C, Kirk J, Ward R. Evaluation of models to predict BRCA germline mutations. Br J Cancer. 2006;95(7):914–20.
CAS
PubMed
PubMed Central
Google Scholar
Weitzel JN, Lagos VI, Cullinane CA, Gambol PJ, Culver JO, Blazer KR, Palomares MR, Lowstuter KJ, MacDonald DJ. Limited family structure and BRCA gene mutation status in single cases of breast cancer. JAMA. 2007;297(23):2587–95.
CAS
PubMed
Google Scholar
Zugazagoitia J, Perez-Segura P, Manzano A, Blanco I, Vega A, Custodio A, Teule A, Fachal L, Martinez B, Gonzalez-Sarmiento R, et al. Limited family structure and triple-negative breast cancer (TNBC) subtype as predictors of BRCA mutations in a genetic counseling cohort of early-onset sporadic breast cancers. Breast Cancer Res Treat. 2014;148(2):415–21.
CAS
PubMed
Google Scholar
Franca LKL, Bitencourt AGV, Paiva HLS, Silva CB, Pereira NP, Paludo J, Graziano L, Guatelli CS, de Souza JA, Marques EF. Role of magnetic resonance imaging in the planning of breast cancer treatment strategies: comparison with conventional imaging techniques. Radiol Bras. 2017;50(2):76–81.
PubMed
PubMed Central
Google Scholar
Kulkarni S, Singh N, Crystal P. Preoperative breast magnetic resonance imaging: applications in clinical practice. Can Assoc Radiol J. 2012;63(3):207–14.
PubMed
Google Scholar
Kuhl CK, Schmutzler RK, Leutner CC, Kempe A, Wardelmann E, Hocke A, Maringa M, Pfeifer U, Krebs D, Schild HH. Breast MR imaging screening in 192 women proved or suspected to be carriers of a breast cancer susceptibility gene: preliminary results. Radiology. 2000;215(1):267–79.
CAS
PubMed
Google Scholar
Schrading S, Kuhl CK. Mammographic, US, and MR imaging phenotypes of familial breast cancer. Radiology. 2008;246(1):58–70.
PubMed
Google Scholar
Tilanus-Linthorst M, Verhoog L, Obdeijn IM, Bartels K, Menke-Pluymers M, Eggermont A, Klijn J, Meijers-Heijboer H, van der Kwast T, Brekelmans C. A BRCA1/2 mutation, high breast density and prominent pushing margins of a tumor independently contribute to a frequent false-negative mammography. Int J Cancer. 2002;102(1):91–5.
CAS
PubMed
Google Scholar
Trecate G, Manoukian S, Suman L, Vergnaghi D, Marchesini M, Agresti R, Ferraris C, Peissel B, Scaramuzza D, Bergonzi S. Is there a specific magnetic resonance phenotype characteristic of hereditary breast cancer? Tumori. 2010;96(3):363–84.
PubMed
Google Scholar
Uematsu T, Kasami M, Yuen S. Triple-negative breast cancer: correlation between MR imaging and pathologic findings. Radiology. 2009;250(3):638–47.
PubMed
Google Scholar
Veltman J, Mann R, Kok T, Obdeijn IM, Hoogerbrugge N, Blickman JG, Boetes C. Breast tumor characteristics of BRCA1 and BRCA2 gene mutation carriers on MRI. Eur Radiol. 2008;18(5):931–8.
CAS
PubMed
PubMed Central
Google Scholar
Tilanus-Linthorst MM, Alves C, Seynaeve C, Menke-Pluymers MB, Eggermont AM, Brekelmans CT. Contralateral recurrence and prognostic factors in familial non-BRCA1/2-associated breast cancer. Br J Surg. 2006;93(8):961–8.
CAS
PubMed
Google Scholar
Ha SM, Chae EY, Cha JH, Kim HH, Shin HJ, Choi WJ. Association of BRCA mutation types, imaging features, and pathologic findings in patients with breast Cancer with BRCA1 and BRCA2 mutations. AJR Am J Roentgenol. 2017;209(4):920–8.
PubMed
Google Scholar
Noh JM, Han BK, Choi DH, Rhee SJ, Cho EY, Huh SJ, Park W, Park H, Nam SJ, Lee JE, et al. Association between BRCA mutation status, pathological findings, and magnetic resonance imaging features in patients with breast Cancer at risk for the mutation. J Breast Cancer. 2013;16(3):308–14.
PubMed
PubMed Central
Google Scholar
Gilbert FJ, Warren RM, Kwan-Lim G, Thompson DJ, Eeles RA, Evans DG, Leach MO, United Kingdom Magnetic Resonance Imaging in Breast Screening Study G. Cancers in BRCA1 and BRCA2 carriers and in women at high risk for breast cancer: MR imaging and mammographic features. Radiology. 2009;252(2):358–68.
PubMed
Google Scholar
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.
PubMed
PubMed Central
Google Scholar
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.
PubMed
PubMed Central
Google Scholar
Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev. 2016;1(2):207–26.
PubMed
PubMed Central
Google Scholar
Haberle L, Hack CC, Heusinger K, Wagner F, Jud SM, Uder M, Beckmann MW, Schulz-Wendtland R, Wittenberg T, Fasching PA. Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound. Eur J Med Res. 2017;22(1):30.
PubMed
PubMed Central
Google Scholar
Haberle L, Wagner F, Fasching PA, Jud SM, Heusinger K, Loehberg CR, Hein A, Bayer CM, Hack CC, Lux MP, et al. Characterizing mammographic images by using generic texture features. Breast Cancer Res. 2012;14(2):R59.
PubMed
PubMed Central
Google Scholar
Cai H, Liu L, Peng Y, Wu Y, Li L. Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols. BMC Cancer. 2014;14:366.
PubMed
PubMed Central
Google Scholar
Jiang X, Xie F, Liu L, Peng Y, Cai H, Li L. Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast-enhanced and diffusion-weighted MRI. Oncol Lett. 2018;16(2):1521–8.
PubMed
PubMed Central
Google Scholar
McLaren CE, Chen WP, Nie K, Su MY. Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. Acad Radiol. 2009;16(7):842–51.
PubMed
PubMed Central
Google Scholar
Nie K, Chen JH, Yu HJ, Chu Y, Nalcioglu O, Su MY. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol. 2008;15(12):1513–25.
PubMed
PubMed Central
Google Scholar
Wang TC, Huang YH, Huang CS, Chen JH, Huang GY, Chang YC, Chang RF. Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. Magn Reson Imaging. 2014;32(3):197–205.
CAS
PubMed
Google Scholar
Chang RF, Chen HH, Chang YC, Huang CS, Chen JH, Lo CM. Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI. Magn Reson Imaging. 2016;34(6):809–19.
CAS
PubMed
Google Scholar
Holli K, Laaperi AL, Harrison L, Luukkaala T, Toivonen T, Ryymin P, Dastidar P, Soimakallio S, Eskola H. Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol. 2010;17(2):135–41.
PubMed
Google Scholar
Holli-Helenius K, Salminen A, Rinta-Kiikka I, Koskivuo I, Bruck N, Bostrom P, Parkkola R. MRI texture analysis in differentiating luminal a and luminal B breast cancer molecular subtypes - a feasibility study. BMC Med Imaging. 2017;17(1):69.
PubMed
PubMed Central
Google Scholar
Sun X, He B, Luo X, Li Y, Cao J, Wang J, Dong J, Sun X, Zhang G. Preliminary study on molecular subtypes of breast Cancer based on magnetic resonance imaging texture analysis. J Comput Assist Tomogr. 2018;42(4):531–5.
PubMed
Google Scholar
Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: luminal a and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging. 2015;42(4):902–7.
PubMed
Google Scholar
Ahmed A, Gibbs P, Pickles M, Turnbull L. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging. 2013;38(1):89–101.
PubMed
Google Scholar
Kraus C, Hoyer J, Vasileiou G, Wunderle M, Lux MP, Fasching PA, Krumbiegel M, Uebe S, Reuter M, Beckmann MW, et al. Gene panel sequencing in familial breast/ovarian cancer patients identifies multiple novel mutations also in genes others than BRCA1/2. Int J Cancer. 2017;140(1):95–102.
CAS
PubMed
Google Scholar
LeFevre KDD, Ramarkrishnan R. Incognito: efficient full-domain K-anonymity; 2005. https://doi.org/10.1145/1066157.1066164.
Book
Google Scholar
Klemm M, Kirchner T, Grohl J, Cheray D, Nolden M, Seitel A, Hoppe H, Maier-Hein L, Franz AM. MITK-OpenIGTLink for combining open-source toolkits in real-time computer-assisted interventions. Int J Comput Assist Radiol Surg. 2017;12(3):351–61.
PubMed
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2018.
Google Scholar
Kuhn M. Caret package. J Stat Softw. 2008;28(5):1–26.
Google Scholar
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.
PubMed
PubMed Central
Google Scholar
Simon N, Tibshirani R. Standardization and the group lasso penalty. Stat Sin. 2012;22(3):983–1001.
PubMed
PubMed Central
Google Scholar
Ewald IP, Ribeiro PL, Palmero EI, Cossio SL, Giugliani R, Ashton-Prolla P. Genomic rearrangements in BRCA1 and BRCA2: a literature review. Genet Mol Biol. 2009;32(3):437–46.
CAS
PubMed
PubMed Central
Google Scholar
Palma MD, Domchek SM, Stopfer J, Erlichman J, Siegfried JD, Tigges-Cardwell J, Mason BA, Rebbeck TR, Nathanson KL. The relative contribution of point mutations and genomic rearrangements in BRCA1 and BRCA2 in high-risk breast cancer families. Cancer Res. 2008;68(17):7006–14.
PubMed
PubMed Central
Google Scholar
Foulkes WD, Smith IE, Reis-Filho JS. Triple-negative breast cancer. N Engl J Med. 2010;363(20):1938–48.
CAS
PubMed
Google Scholar
Domagala P, Huzarski T, Lubinski J, Gugala K, Domagala W. Immunophenotypic predictive profiling of BRCA1-associated breast cancer. Virchows Arch. 2011;458(1):55–64.
CAS
PubMed
Google Scholar
Rhiem K, Engel C, Graeser M, Zachariae S, Kast K, Kiechle M, Ditsch N, Janni W, Mundhenke C, Golatta M, et al. The risk of contralateral breast cancer in patients from BRCA1/2 negative high risk families as compared to patients from BRCA1 or BRCA2 positive families: a retrospective cohort study. Breast Cancer Res. 2012;14(6):R156.
PubMed
PubMed Central
Google Scholar
Rebbeck TR, Friebel T, Lynch HT, Neuhausen SL, van’t Veer L, Garber JE, Evans GR, Narod SA, Isaacs C, Matloff E, et al. Bilateral prophylactic mastectomy reduces breast cancer risk in BRCA1 and BRCA2 mutation carriers: the PROSE study group. J Clin Oncol. 2004;22(6):1055–62.
PubMed
Google Scholar
Rebbeck TR, Lynch HT, Neuhausen SL, Narod SA, Van't Veer L, Garber JE, Evans G, Isaacs C, Daly MB, Matloff E, et al. Prophylactic oophorectomy in carriers of BRCA1 or BRCA2 mutations. N Engl J Med. 2002;346(21):1616–22.
PubMed
Google Scholar
Tomao F, Musacchio L, Di Mauro F, Boccia SM, Di Donato V, Giancotti A, Perniola G, Palaia I, Muzii L, Benedetti Panici P. Is BRCA mutational status a predictor of platinum-based chemotherapy related hematologic toxicity in high-grade serous ovarian cancer patients? Gynecol Oncol. 2019;154:138–43.
CAS
PubMed
Google Scholar
Fong PC, Boss DS, Yap TA, Tutt A, Wu P, Mergui-Roelvink M, Mortimer P, Swaisland H, Lau A, O'Connor MJ, et al. Inhibition of poly (ADP-ribose) polymerase in tumors from BRCA mutation carriers. N Engl J Med. 2009;361(2):123–34.
CAS
PubMed
Google Scholar
Ma J, Deng H, Li J, Hu S, Yang Y, Liu S, Han X. Efficacy and safety of olaparib maintenance therapy in platinum-sensitive ovarian cancer patients with BRCA mutations: a meta-analysis on randomized controlled trials. Cancer Manag Res. 2019;11:3061–78.
CAS
PubMed
PubMed Central
Google Scholar
Smith S, Marino I, Schaller J, Arnell C, Moyes K, Manley S. Optimization of quality assurance to increase clinical utility and cost effectiveness of hereditary cancer testing. Perinat Med. 2017;14(3):213–20.
CAS
Google Scholar
Speiser D, Rebitschek FG, Feufel MA, Brand H, Besch L, Kendel F. Accuracy in risk understanding among BRCA1/2-mutation carriers. Patient Educ Couns. 2019;102:1925–31.
PubMed
Google Scholar
Glassey R, O'Connor M, Ives A, Saunders C, kConFab I, O'Sullivan S, Hardcastle SJ. Heightened perception of breast cancer risk in young women at risk of familial breast cancer. Familial Cancer. 2018;17(1):15–22.
PubMed
Google Scholar
Kemp Z, Turnbull A, Yost S, Seal S, Mahamdallie S, Poyastro-Pearson E, Warren-Perry M, Eccleston A, Tan MM, Teo SH, et al. Evaluation of Cancer-based criteria for use in mainstream BRCA1 and BRCA2 genetic testing in patients with breast Cancer. JAMA Netw Open. 2019;2(5):e194428.
PubMed
PubMed Central
Google Scholar
Lindor NM, Johnson KJ, Harvey H, Shane Pankratz V, Domchek SM, Hunt K, Wilson M, Cathie Smith M, Couch F. Predicting BRCA1 and BRCA2 gene mutation carriers: comparison of PENN II model to previous study. Familial Cancer. 2010;9(4):495–502.
CAS
PubMed
PubMed Central
Google Scholar
Li H, Giger ML, Huynh BQ, Antropova NO. Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. J Med Imaging (Bellingham). 2017;4(4):041304.
Google Scholar
Gierach GL, Li H, Loud JT, Greene MH, Chow CK, Lan L, Prindiville SA, Eng-Wong J, Soballe PW, Giambartolomei C, 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.
PubMed
PubMed Central
Google Scholar
Huo Z, Giger ML, Olopade OI, Wolverton DE, Weber BL, Metz CE, Zhong W, Cummings SA. Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. Radiology. 2002;225(2):519–26.
PubMed
Google Scholar
Li H, Giger ML, Huo Z, Olopade OI, Lan L, Weber BL, Bonta I. Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. Med Phys. 2004;31(3):549–55.
PubMed
Google Scholar
Berry DA, Parmigiani G, Sanchez J, Schildkraut J, Winer E. Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history. J Natl Cancer Inst. 1997;89(3):227–38.
CAS
PubMed
Google Scholar
Riahi A, Ghourabi ME, Fourati A, Chaabouni-Bouhamed H. Family history predictors of BRCA1/BRCA2 mutation status among Tunisian breast/ovarian cancer families. Breast Cancer. 2017;24(2):238–44.
PubMed
Google Scholar
Shattuck-Eidens D, Oliphant A, McClure M, McBride C, Gupte J, Rubano T, Pruss D, Tavtigian SV, Teng DH, Adey N, et al. BRCA1 sequence analysis in women at high risk for susceptibility mutations. Risk factor analysis and implications for genetic testing. JAMA. 1997;278(15):1242–50.
CAS
PubMed
Google Scholar
Boyle P. Triple-negative breast cancer: epidemiological considerations and recommendations. Ann Oncol. 2012;23(Suppl 6):vi7–12.
PubMed
Google Scholar
Loibl S, Untch M, Burchardi N, Huober J, Sinn BV, Blohmer JU, Grischke EM, Furlanetto J, Tesch H, Hanusch C, et al. A randomised phase II study investigating durvalumab in addition to an anthracycline taxane-based neoadjuvant therapy in early triple negative breast cancer - clinical results and biomarker analysis of GeparNuevo study. Ann Oncol. 2019;30:1279–88.
CAS
PubMed
Google Scholar
Warner E, Plewes DB, Hill KA, Causer PA, Zubovits JT, Jong RA, Cutrara MR, DeBoer G, Yaffe MJ, Messner SJ, et al. Surveillance of BRCA1 and BRCA2 mutation carriers with magnetic resonance imaging, ultrasound, mammography, and clinical breast examination. JAMA. 2004;292(11):1317–25.
CAS
PubMed
Google Scholar