Hartman M, Czene K, Reilly M, et al. Incidence and prognosis of synchronous and metachronous bilateral breast cancer. J Clin Oncol. 2007;25:4210–6.
Jobsen JJ, van der Palen J, Ong F, Riemersma S, Struikmans H. Bilateral breast cancer, synchronous and metachronous; differences and outcome. Breast Cancer Res Treat. 2015;153:277–83.
Alkner S, Bendahl PO, Ferno M, Manjer J, Ryden L. Prediction of outcome after diagnosis of metachronous contralateral breast cancer. BMC Cancer. 2011;11:114.
Schwentner L, Wolters R, Wischnewsky M, Kreienberg R, Wockel A. Survival of patients with bilateral versus unilateral breast cancer and impact of guideline adherent adjuvant treatment: a multi-centre cohort study of 5292 patients. Breast. 2012;21:171–7.
Kheirelseid EA, Jumustafa H, Miller N, et al. Bilateral breast cancer: analysis of incidence, outcome, survival and disease characteristics. Breast Cancer Res Treat. 2011;126:131–40.
Kuo WH, Yen AM, Lee PH, et al. Cumulative survival in early-onset unilateral and bilateral breast cancer: an analysis of 1907 Taiwanese women. Br J Cancer. 2009;100:563–70.
Nichol AM, Yerushalmi R, Tyldesley S, et al. A case-match study comparing unilateral with synchronous bilateral breast cancer outcomes. J Clin Oncol. 2011;29:4763–8.
Rogozinska-Szczepka J, Utracka-Hutka B, Grzybowska E, et al. BRCA1 and BRCA2 mutations as prognostic factors in bilateral breast cancer patients. Ann Oncol. 2004;15:1373–6.
Deng M, Chen HH, Zhu X, et al. Prevalence and clinical outcomes of germline mutations in BRCA1/2 and PALB2 genes in 2769 unselected breast cancer patients in China. Int J Cancer. 2019;145:1517–28.
www.nccn.org/NCCN guidelines insights: Genetic/Familial high-risk assessment: breast, ovarian and pancreatic, version 2.2022.
Pohl-Rescigno E, Hauke J, Loibl S, et al. Association of Germline Variant Status With Therapy Response in High-risk Early-Stage Breast Cancer: A Secondary Analysis of the GeparOcto Randomized Clinical Trial. JAMA Oncol. 2020.
Hahnen E, Lederer B, Hauke J, et al. Germline Mutation Status, Pathological Complete Response, and Disease-Free Survival in Triple-Negative Breast Cancer: Secondary Analysis of the GeparSixto Randomized Clinical Trial. JAMA Oncol. 2017;3:1378–85.
Kurian AW, Gong GD, Chun NM, et al. Performance of BRCA1/2 mutation prediction models in Asian Americans. J Clin Oncol. 2008;26:4752–8.
Ang BH, Ho WK, Wijaya E, et al. Predicting the Likelihood of Carrying a BRCA1 or BRCA2 Mutation in Asian Patients With Breast Cancer. J Clin Oncol. 2022;40:1542–51.
Hung FH, Wang YA, Jian JW, et al. Evaluating BRCA mutation risk predictive models in a Chinese cohort in Taiwan. Sci Rep. 2019;9:10229.
Liu J, Zhao H, Zheng Y, et al. DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data. Genome Med. 2022;14:21.
Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–24.
Chen L, Fu F, Huang M, Lv J, Zhang W, Wang C. The spectrum of BRCA1 and BRCA2 mutations and clinicopathological characteristics in Chinese women with early-onset breast cancer. Breast Cancer Res Treat. 2020;180:759–66.
Bergmeir C, Benítez JM. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. ournal of Statistical Software. 2012 46(7):1–26.
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.
IBM SPSS. http://www-01.ibm.com/software/uk/analytics/spss.
The R Project for Statistical Computing. https://www.r-project.org.
Landrum MJ, Lee JM, Riley GR, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42:D980–5.
Sherry ST1, Ward MH, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–11.
Guan Y, Hu H, Peng Y, et al. Detection of inherited mutations for hereditary cancer using target enrichment and next generation sequencing. Fam Cancer. 2015;14:9–18.
Zhang J, Sun J, Chen J, et al. Comprehensive analysis of BRCA1 and BRCA2 germline mutations in a large cohort of 5931 Chinese women with breast cancer. Breast Cancer Res Treat. 2016;158:455–62.
Lang GT, Shi JX, Hu X, et al. The spectrum of BRCA mutations and characteristics of BRCA-associated breast cancers in China: Screening of 2,991 patients and 1,043 controls by next-generation sequencing. Int J Cancer. 2017;141:129–42.
Kang E, Seong MW, Park SK, et al. The prevalence and spectrum of BRCA1 and BRCA2 mutations in Korean population: recent update of the Korean Hereditary Breast Cancer (KOHBRA) study. Breast Cancer Res Treat. 2015;151:157–68.
Dodova RI, Mitkova AV, Dacheva DR, et al. Spectrum and frequencies of BRCA1/2 mutations in Bulgarian high risk breast cancer patients. BMC Cancer. 2015;15:523.
Sun J, Meng H, Yao L, et al. Germline Mutations in Cancer Susceptibility Genes in a Large Series of Unselected Breast Cancer Patients. Clin Cancer Res. 2017;23:6113–9.
Hall MJ, Reid JE, Burbidge LA, et al. BRCA1andBRCA2mutations in women of different ethnicities undergoing testing for hereditary breast-ovarian cancer. Cancer. 2009;115:2222–33.
Bayraktar S, Jackson M, Gutierrez-Barrera AM, et al. Genotype-Phenotype Correlations by Ethnicity and Mutation Location in BRCA Mutation Carriers. Breast J. 2015;21:260–7.
Meng H, Yao L, Yuan H, et al. BRCA1 c.5470_5477del, a founder mutation in Chinese Han breast cancer patients. Int J Cancer. 2020;146:3044–52.
Parmigiani G, Berry D, Aguilar O. Determining carrier probabilities for breast cancersusceptibility genes BRCA1 and BRCA2. Am J Hum Genet. 1998;62:145–58.
Frank TS, Deffenbaugh AM, Reid JE, et al. Clinical characteristics of individuals with germline mutations in BRCA1 and BRCA2: analysis of 10,000 individuals. J Clin Oncol. 2002;20:1480–90.
Antoniou AC, Pharoah PP, Smith P, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004;91:1580–90.
Ready KJ, Vogel KJ, Atchley DP, et al. Accuracy of the BRCAPRO model among women with bilateral breast cancer. Cancer. 2009;115:725–30.
Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer. 1997;79:857–62.
Cucchetti A, Piscaglia F, Grigioni AD, et al. Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study. J Hepatol. 2010;52:880–8.
Ziada AM, Lisle TC, Snow PB, Levine RF, Miller G, Crawford ED. Impact of different variables on the outcome of patients with clinically confined prostate carcinoma: prediction of pathologic stage and biochemical failure using an artificial neural network. Cancer. 2001;91:1653–60.
Qin X, Wang H, Hu X, Gu X, Zhou W. Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information. J Cancer Res Clin Oncol. 2020;146:767–75.
Zhang X, Yang Y, Wang Y, Fan Q. Detection of the BRAF V600E Mutation in Colorectal Cancer by NIR Spectroscopy in Conjunction with Counter Propagation Artificial Neural Network. Molecules. 2019;24.