AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Cancer Biology & Medicine Article
PDF (379.2 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Review | Open Access

Breast cancer screening and early diagnosis in Chinese women

Rui Ding1,2,*, Yi Xiao1,2,*, Miao Mo3, Ying Zheng3, Yi-Zhou Jiang1,2 ( ), Zhi-Ming Shao1,2,4 ( )
Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, China

*These authors contributed equally to this work.

Show Author Information

Abstract

Breast cancer is the most common malignant tumor in Chinese women, and its incidence is increasing. Regular screening is an effective method for early tumor detection and improving patient prognosis. In this review, we analyze the epidemiological changes and risk factors associated with breast cancer in China and describe the establishment of a screening strategy suitable for Chinese women. Chinese patients with breast cancer tend to be younger than Western patients and to have denser breasts. Therefore, the age of initial screening in Chinese women should be earlier, and the importance of screening with a combination of ultrasound and mammography is stressed. Moreover, Chinese patients with breast cancers have several ancestry-specific genetic features, and aiding in the determination of genetic screening strategies for identifying high-risk populations. On the basis of current studies, we summarize the development of risk-stratified breast cancer screening guidelines for Chinese women and describe the significant improvement in the prognosis of patients with breast cancer in China.

Keywords

Breast cancerscreeningChineseimaging screeninggenetic test

References

1

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021; 71: 209-49.
Crossref Google Scholar
2

Zhang S, Sun K, Zheng R, Zeng H, Wang S, Chen R, et al. Cancer incidence and mortality in China, 2015. J Natl Cancer Center. 2021; 1: 2-11.
Crossref Google Scholar
3

Moss SM, Cuckle H, Evans A, Johns L, Waller M, Bobrow L. Effect of mammographic screening from age 40 years on breast cancer mortality at 10 years’ follow-up: a randomised controlled trial. Lancet. 2006; 368: 2053-60.
Crossref Google Scholar
4

Pace LE, Keating NL. A systematic assessment of benefits and risks to guide breast cancer screening decisions. JAMA. 2014; 311: 1327-35.
Crossref Google Scholar
5

Breen N, Gentleman JF, Schiller JS. Update on mammography trends: comparisons of rates in 2000, 2005, and 2008. Cancer. 2011; 117: 2209-18.
Crossref Google Scholar
6

Expert Panel on Breast Imaging, Mainiero MB, Moy L, Baron P, Didwania AD, diFlorio RM, et al. Acr appropriateness criteria® breast cancer screening. J Am Coll Radiol. 2017; 14: S383-90.
Crossref Google Scholar
7

Lauby-Secretan B, Scoccianti C, Loomis D, Benbrahim-Tallaa L, Bouvard V, Bianchini F, et al. Breast-cancer screening – viewpoint of the IARC Working Group. N Engl J Med. 2015; 372: 2353-8.
Crossref Google Scholar
8

Qaseem A, Lin JS, Mustafa RA, Horwitch CA, Wilt TJ, Clinical Guidelines Committee of the American College of Physicians; Forciea MA, et al. Screening for breast cancer in average-risk women: a guidance statement from the American college of physicians. Ann Intern Med. 2019; 170: 547-60.
Crossref Google Scholar
9
World Health Organization. Who position paper on mammography screening. https://www.Who.Int/publications/i/item/who-position-paper-on-mammography-screening. Published February 16, 2014.
10

Oeffinger KC, Fontham ET, Etzioni R, Herzig A, Michaelson JS, Shih YC, et al. Breast cancer screening for women at average risk: 2015 guideline update from the American cancer society. JAMA. 2015; 314: 1599-614.
Crossref Google Scholar
11

Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, et al. American cancer society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin. 2007; 57: 75-89.
Crossref Google Scholar
12

Schunemann HJ, Lerda D, Quinn C, Follmann M, Alonso-Coello P, Rossi PG, et al. Breast cancer screening and diagnosis: a synopsis of the European breast guidelines. Ann Intern Med. 2020; 172: 46-56.
Crossref Google Scholar
13
Siu AL, U.S. Preventive Services Task Force. Screening for breast cancer: U.S. Preventive services task force recommendation statement. Ann Intern Med. 2016; 164: 279-96.
Crossref
14

Bao Y, Kwok C, Lee CF. Breast cancer screening behaviors among chinese women in mainland China. Nurs Health Sci. 2018; 20: 445-51.
Crossref Google Scholar
15

Ellis L, Canchola AJ, Spiegel D, Ladabaum U, Haile R, Gomez SL. Racial and ethnic disparities in cancer survival: the contribution of tumor, sociodemographic, institutional, and neighborhood characteristics. J Clin Oncol. 2018; 36: 25-33.
Crossref Google Scholar
16

Fan L, Strasser-Weippl K, Li J-J, St Louis J, Finkelstein DM, Yu K-D, et al. Breast cancer in china. Lancet Oncol. 2014; 15: e279-89.
Crossref Google Scholar
17

Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the global burden of disease study. JAMA Oncol. 2019; 5: 1749-68.
Crossref Google Scholar
18
International agency for research on cancer. The global cancer observatory 2020 china fact sheets. https://gco.Iarc.Fr/today/data/factsheets/populations/160-china-fact-sheets.Pdf. Published March, 2021.
19

Chen W, Sun K, Zheng R, Zeng H, Zhang S, Xia C, et al. Cancer incidence and mortality in China, 2014. Chin J Cancer Res. 2018; 30: 1-12.
Crossref Google Scholar
20

Chen W, Zheng R, Zeng H, Zhang S, He J. Annual report on status of cancer in china, 2011. Chin J Cancer Res. 2015; 27: 2-12.
Crossref Google Scholar
21

Chen W, Zheng R, Zhang S, Zhao P, Zeng H, Zou X, et al. Annual report on status of cancer in China, 2010. Chin J Cancer Res. 2014; 26: 48-58.
Google Scholar
22

Chen W, Zheng R, Zuo T, Zeng H, Zhang S, He J. National cancer incidence and mortality in China, 2012. Chin J Cancer Res. 2016; 28: 1-11.
Crossref Google Scholar
23

Yu XQ, Baade P. Re: Cancer incidence and mortality in China, 2013 by Chen et al. Cancer Lett. 2017; 401: 72-3.
Crossref Google Scholar
24
Ervik M, Lam F, Laversanne M, Ferlay J, Bray F. Global cancer observatory: cancer over time. Lyon, France: international Agency for Research on Cancer. 2021. Available from: https://gco.Iarc.Fr/overtime. Accessed 05 February 2022.
25

Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women’s Health Initiative randomized controlled trial. JAMA. 2002; 288: 321-33.
Crossref Google Scholar
26

Fan L, Zheng Y, Yu KD, Liu GY, Wu J, Lu JS, et al. Breast cancer in a transitional society over 18 years: trends and present status in Shanghai, China. Breast Cancer Res Treat. 2009; 117: 409-16.
Crossref Google Scholar
27
Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, et al. Global cancer observatory: cancer today. Lyon, France: international agency for research on cancer. 2020. Available from: https://gco.Iarc.Fr/today. Accessed 05 February 2022.
28
National Bureau of Statistics PRC. Tabulation on the 2010 population census of the people’s republic of China. China Statistics Press. 2012.04 http://www.Stats.Gov.Cn/tjsj/pcsj/rkpc/6rp/indexch.htm.
29
National Bureau of Statistics PRC. A press conference on the results of the seventh national population census. http://www.Stats.Gov.Cn/ztjc/zdtjgz/zgrkpc/dqcrkpc/ggl/202105/t20210519_1817702.html. Published May 11, 2021.
30

Jiang Q, Yang S, Li S, Feldman MW. The decline in China’s fertility level: a decomposition analysis. J Biosoc Sci. 2019; 51: 785-98.
Crossref Google Scholar
31

Gao Y-T, Shu X-O, Dai Q, Potter JD, Brinton LA, Wen W, et al. Association of menstrual and reproductive factors with breast cancer risk: results from the Shanghai breast cancer study. Int J Cancer. 2000; 87: 295-300.
Crossref Google Scholar
32

Huang Z, Beeghly-Fadiel A, Gao YT, Zheng Y, Dai Q, Lu W, et al. Associations of reproductive time events and intervals with breast cancer risk: a report from the Shanghai breast cancer study. Int J Cancer. 2014; 135: 186-95.
Crossref Google Scholar
33

Zhang JY, Wang MX, Wang X, Li YL, Liang ZZ, Lin Y, et al. Associations of reproductive factors with breast cancer prognosis and the modifying effects of menopausal status. Cancer Med. 2020; 9: 385-93.
Crossref Google Scholar
34

Li L, Ji J, Wang JB, Niyazi M, Qiao YL, Boffetta P. Attributable causes of breast cancer and ovarian cancer in china: reproductive factors, oral contraceptives and hormone replacement therapy. Chin J Cancer Res. 2012; 24: 9-17.
Crossref Google Scholar
35

Porter P. “Westernizing” women’s risks? Breast cancer in lowerincome countries. N Engl J Med. 2008; 358: 213-6.
Crossref Google Scholar
36

Cui X, Dai Q, Tseng M, Shu XO, Gao YT, Zheng W. Dietary patterns and breast cancer risk in the shanghai breast cancer study. Cancer Epidemiol Biomarkers Prev. 2007; 16: 1443-8.
Crossref Google Scholar
37

Mi YJ, Zhang B, Wang HJ, Yan J, Han W, Zhao J, et al. Prevalence and secular trends in obesity among chinese adults, 1991-2011. Am J Prev Med. 2015; 49: 661-9.
Crossref Google Scholar
38

Pattacini P, Nitrosi A, Giorgi Rossi P, Iotti V, Ginocchi V, Ravaioli S, et al. Digital mammography versus digital mammography plus tomosynthesis for breast cancer screening: The Reggio Emilia Tomosynthesis randomized trial. Radiology. 2018; 288: 375-85.
Crossref Google Scholar
39

Hofvind S, Holen ÅS, Aase HS, Houssami N, Sebuødegård S, Moger TA, et al. Two-view digital breast tomosynthesis versus digital mammography in a population-based breast cancer screening programme (To-Be): a randomised, controlled trial. Lancet Oncol. 2019; 20: 795-805.
Crossref Google Scholar
40

Hofvind S, Moshina N, Holen ÅS, Danielsen AS, Lee CI, Houssami N, et al. Interval and subsequent round breast cancer in a randomized controlled trial comparing digital breast tomosynthesis and digital mammography screening. Radiology. 2021; 300: 66-76.
Crossref Google Scholar
41

Phi XA, Tagliafico A, Houssami N, Greuter MJW, de Bock GH. Digital breast tomosynthesis for breast cancer screening and diagnosis in women with dense breasts – a systematic review and meta-analysis. BMC Cancer. 2018; 18: 380.
Crossref Google Scholar
42

Marinovich ML, Hunter KE, Macaskill P, Houssami N. Breast cancer screening using tomosynthesis or mammography: a metaanalysis of cancer detection and recall. J Natl Cancer Inst. 2018; 110: 942-9.
Crossref Google Scholar
43

Sung JS, Lebron L, Keating D, D’Alessio D, Comstock CE, Lee CH, et al. Performance of dual-energy contrast-enhanced digital mammography for screening women at increased risk of breast cancer. Radiology. 2019; 293: 81-8.
Crossref Google Scholar
44

Sorin V, Yagil Y, Yosepovich A, Shalmon A, Gotlieb M, Neiman OH, et al. Contrast-enhanced spectral mammography in women with intermediate breast cancer risk and dense breasts. AJR Am J Roentgenol. 2018; 211: W267-74.
Crossref Google Scholar
45

Cheung YC, Lin YC, Wan YL, Yeow KM, Huang PC, Lo YF, et al. Diagnostic performance of dual-energy contrast-enhanced subtracted mammography in dense breasts compared to mammography alone: interobserver blind-reading analysis. Eur Radiol. 2014; 24: 2394-403.
Crossref Google Scholar
46

Hadadi I, Rae W, Clarke J, McEntee M, Ekpo E. Diagnostic performance of adjunctive imaging modalities compared to mammography alone in women with non-dense and dense breasts: a systematic review and meta-analysis. Clin Breast Cancer. 2021; 21: 278-91.
Crossref Google Scholar
47

Melnikow J, Fenton JJ, Whitlock EP, Miglioretti DL, Weyrich MS, Thompson JH, et al. Supplemental screening for breast cancer in women with dense breasts: a systematic review for the U.S. Preventive Service Task Force, Rockville (MD). Ann Intern Med. 2016;164:268-78.
Crossref Google Scholar
48

Wang L, Qi ZH. Automatic breast volume scanner versus handheld ultrasound in differentiation of benign and malignant breast lesions: a systematic review and meta-analysis. Ultrasound Med Biol. 2019; 45: 1874-81.
Crossref Google Scholar
49

Comstock CE, Gatsonis C, Newstead GM, Snyder BS, Gareen IF, Bergin JT, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening. JAMA. 2020; 323: 746-56.
Crossref Google Scholar
50

Bakker MF, de Lange SV, Pijnappel RM, Mann RM, Peeters PHM, Monninkhof EM, et al. Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med. 2019; 381: 2091-102.
Crossref Google Scholar
51

Saadatmand S, Geuzinge HA, Rutgers EJT, Mann RM, de Roy van Zuidewijn DBW, Zonderland HM, et al. MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial. Lancet Oncol. 2019; 20: 1136-47.
Crossref Google Scholar
52

Miller AB. Digital mammography. J Natl Cancer Inst. 2014; 106: dju125.
Crossref Google Scholar
53

Pisano ED, Yaffe MJ. Digital mammography. Radiology. 2005; 234: 353-62.
Crossref Google Scholar
54

Nelson HD, Fu R, Cantor A, Pappas M, Daeges M, Humphrey L. Effectiveness of breast cancer screening: systematic review and meta-analysis to update the 2009 U.S. Preventive Services Task Force Recommendation. Ann Intern Med. 2016; 164: 244-55.
Crossref Google Scholar
55

Otto SJ, Fracheboud J, Looman CWN, Broeders MJM, Boer R, Hendriks JHCL, et al. Initiation of population-based mammography screening in Dutch municipalities and effect on breast-cancer mortality: a systematic review. Lancet. 2003; 361: 1411-17.
Crossref Google Scholar
56

Jorgensen KJ, Gotzsche PC. Overdiagnosis in publicly organised mammography screening programmes: systematic review of incidence trends. BMJ. 2009; 339: b2587.
Crossref Google Scholar
57

Chong A, Weinstein SP, McDonald ES, Conant EF. Digital breast tomosynthesis: concepts and clinical practice. Radiology. 2019; 292: 1-14.
Crossref Google Scholar
58

Gastounioti A, McCarthy AM, Pantalone L, Synnestvedt M, Kontos D, Conant EF. Effect of mammographic screening modality on breast density assessment: digital mammography versus digital breast tomosynthesis. Radiology. 2019; 291: 320-7.
Crossref Google Scholar
59

Conant EF, Barlow WE, Herschorn SD, Weaver DL, Beaber EF, Tosteson ANA, et al. Association of digital breast tomosynthesis vs digital mammography with cancer detection and recall rates by age and breast density. JAMA Oncol. 2019; 5: 635-42.
Crossref Google Scholar
60

Rafferty EA, Durand MA, Conant EF, Copit DS, Friedewald SM, Plecha DM, et al. Breast cancer screening using tomosynthesis and digital mammography in dense and nondense breasts. JAMA. 2016; 315: 1784-6.
Crossref Google Scholar
61

Skaane P, Bandos AI, Niklason LT, Sebuødegård S, Østerås BH, Gullien R, et al. Digital mammography versus digital mammography plus tomosynthesis in breast cancer screening: the oslo tomosynthesis screening trial. Radiology. 2019; 291: 23-30.
Crossref Google Scholar
62

Pisano ED, Yaffe MJ. Breast cancer screening: should tomosynthesis replace digital mammography? JAMA. 2014; 311: 2488-9.
Crossref Google Scholar
63

Jochelson MS, Lobbes MBI. Contrast-enhanced mammography: state of the art. Radiology. 2021; 299: 36-48.
Crossref Google Scholar
64

Jochelson MS, Pinker K, Dershaw DD, Hughes M, Gibbons GF, Rahbar K, et al. Comparison of screening CEDM and MRI for women at increased risk for breast cancer: a pilot study. Eur J Radiol. 2017; 97: 37-43.
Crossref Google Scholar
65

Lee-Felker SA, Tekchandani L, Thomas M, Gupta E, Andrews-Tang D, Roth A, et al. Newly diagnosed breast cancer: comparison of contrast-enhanced spectral mammography and breast MR imaging in the evaluation of extent of disease. Radiology. 2017; 285: 389-400.
Crossref Google Scholar
66

Sumkin JH, Berg WA, Carter GJ, Bandos AI, Chough DM, Ganott MA, et al. Diagnostic performance of MRI, molecular breast imaging, and contrast-enhanced mammography in women with newly diagnosed breast cancer. Radiology. 2019; 293: 531-40.
Crossref Google Scholar
67

Phillips J, Mihai G, Hassonjee SE, Raj SD, Palmer MR, Brook A, et al. Comparative dose of contrast-enhanced spectral mammography (CESM), digital mammography, and digital breast tomosynthesis. AJR Am J Roentgenol. 2018; 211: 839-46.
Crossref Google Scholar
68

Zanardo M, Cozzi A, Trimboli RM, Labaj O, Monti CB, Schiaffino S, et al. Technique, protocols and adverse reactions for contrast-enhanced spectral mammography (CESM): a systematic review. Insights Imaging. 2019; 10: 76.
Crossref Google Scholar
69

Hooley RJ, Scoutt LM, Philpotts LE. Breast ultrasonography: state of the art. Radiology. 2013; 268: 642-59.
Crossref Google Scholar
70

Lee SH, Chung J, Choi HY, Choi SH, Ryu EB, Ko KH, et al. Evaluation of screening US-detected breast masses by combined use of elastography and color doppler US with B-mode US in women with dense breasts: a multicenter prospective study. Radiology. 2017; 285: 660-9.
Crossref Google Scholar
71

Yang WT, Metreweli C, Lam PK, Chang J. Benign and malignant breast masses and axillary nodes: evaluation with echo-enhanced color power Doppler US. Radiology. 2001; 220: 795-802.
Crossref Google Scholar
72

Sigrist RMS, Liau J, Kaffas AE, Chammas MC, Willmann JK. Ultrasound elastography: review of techniques and clinical applications. Theranostics. 2017; 7: 1303-29.
Crossref Google Scholar
73

Harada-Shoji N, Suzuki A, Ishida T, Zheng YF, Narikawa-Shiono Y, Sato-Tadano A, et al. Evaluation of adjunctive ultrasonography for breast cancer detection among women aged 40-49 years with varying breast density undergoing screening mammography: a secondary analysis of a randomized clinical trial. JAMA Netw Open. 2021; 4: e2121505.
Crossref Google Scholar
74

Sprague BL, Stout NK, Schechter C, van Ravesteyn NT, Cevik M, Alagoz O, et al. Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts. Ann Intern Med. 2015; 162: 157-66.
Crossref Google Scholar
75

Berg WA, Mendelson EB. Technologist-performed handheld screening breast US imaging: how is it performed and what are the outcomes to date? Radiology. 2014; 272: 12-27.
Crossref Google Scholar
76

Rella R, Belli P, Giuliani M, Bufi E, Carlino G, Rinaldi P, et al. Automated breast ultrasonography (ABUS) in the screening and diagnostic setting: Indications and practical use. Acad Radiol. 2018; 25: 1457-70.
Crossref Google Scholar
77

Vourtsis A. Three-dimensional automated breast ultrasound: technical aspects and first results. Diagn Interv Imaging. 2019; 100: 579-92.
Crossref Google Scholar
78

Jia M, Lin X, Zhou X, Yan H, Chen Y, Liu P, et al. Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts. Breast Cancer Res Treat. 2020; 181: 589-97.
Crossref Google Scholar
79

Lin X, Jia M, Zhou X, Bao L, Chen Y, Liu P, et al. The diagnostic performance of automated versus handheld breast ultrasound and mammography in symptomatic outpatient women: a multicenter, cross-sectional study in China. Eur Radiol. 2021; 31: 947-57.
Crossref Google Scholar
80

Vourtsis A, Kachulis A. The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 1,886 women. Eur Radiol. 2018; 28: 592-601.
Crossref Google Scholar
81

Xin Y, Zhang X, Yang Y, Chen Y, Wang Y, Zhou X, et al. A multicenter, hospital-based and non-inferiority study for diagnostic efficacy of automated whole breast ultrasound for breast cancer in China. Sci Rep. 2021; 11: 13902.
Crossref Google Scholar
82

Zhang X, Lin X, Tan Y, Zhu Y, Wang H, Feng R, et al. A multicenter hospital-based diagnosis study of automated breast ultrasound system in detecting breast cancer among Chinese women. Chin J Cancer Res. 2018; 30: 231-9.
Crossref Google Scholar
83

Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology. 2019; 292: 520-36.
Crossref Google Scholar
84

Mann RM, Kuhl CK, Moy L. Contrast-enhanced MRI for breast cancer screening. J Magn Reson Imaging. 2019; 50: 377-90.
Crossref Google Scholar
85

Onishi N, Sadinski M, Gibbs P, Gallagher KM, Hughes MC, Ko ES, et al. Differentiation between subcentimeter carcinomas and benign lesions using kinetic parameters derived from ultrafast dynamic contrast-enhanced breast MRI. Eur Radiol. 2020; 30: 756-66.
Crossref Google Scholar
86

Gao Y, Heller SL. Abbreviated and ultrafast breast MRI in clinical practice. Radiographics. 2020; 40: 1507-27.
Crossref Google Scholar
87

Cheon H, Kim HJ, Kim TH, Ryeom HK, Lee J, Kim GC, et al. Invasive breast cancer: prognostic value of peritumoral edema identified at preoperative MR imaging. Radiology. 2018; 287: 68-75.
Crossref Google Scholar
88

Amornsiripanitch N, Bickelhaupt S, Shin HJ, Dang M, Rahbar H, Pinker K, et al. Diffusion-weighted MRI for unenhanced breast cancer screening. Radiology. 2019; 293: 504-20.
Crossref Google Scholar
89

Berg WA, Zhang Z, Lehrer D, Jong RA, Pisano ED, Barr RG, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA. 2012; 307: 1394-404.
Crossref Google Scholar
90

Smith RA. The evolving role of MRI in the detection and evaluation of breast cancer. N Engl J Med. 2007; 356: 1362-4.
Crossref Google Scholar
91

Kuhl CK. Abbreviated magnetic resonance imaging (MRI) for breast cancer screening: rationale, concept, and transfer to clinical practice. Annu Rev Med. 2019; 70: 501-19.
Crossref Google Scholar
92

US Preventive Services Task Force. Screening for breast cancer: U.S. Preventive services task force recommendation statement. Ann Intern Med. 2009; 151: 716-26.
Crossref Google Scholar
93

Harkness EF, Astley SM, Evans DG. Risk-based breast cancer screening strategies in women. Best Pract Res Clin Obstet Gynaecol. 2020; 65: 3-17.
Crossref Google Scholar
94

Mulder RL, Kremer LCM, Hudson MM, Bhatia S, Landier W, Levitt G, et al. Recommendations for breast cancer surveillance for female survivors of childhood, adolescent, and young adult cancer given chest radiation: a report from the International Late Effects of Childhood Cancer Guideline Harmonization Group. Lancet Oncol. 2013; 14: e621-9.
Crossref Google Scholar
95

Smith RA, Andrews KS, Brooks D, Fedewa SA, Manassaram-Baptiste D, Saslow D, et al. Cancer screening in the United States, 2019: a review of current American Cancer Society guidelines and current issues in cancer screening. CA Cancer J Clin. 2019; 69: 184-210.
Crossref Google Scholar
96

Dai H, Yan Y, Wang P, Liu P, Cao Y, Xiong L, et al. Distribution of mammographic density and its influential factors among Chinese women. Int J Epidemiol. 2014; 43: 1240-51.
Crossref Google Scholar
97

Maskarinec G, Meng L, Ursin G. Ethnic differences in mammographic densities. Int J Epidemiol. 2001; 30: 959-65.
Crossref Google Scholar
98

Flobbe K, Nelemans PJ, Kessels AGH, Beets GL, von Meyenfeldt MF, van Engelshoven JMA. The role of ultrasonography as an adjunct to mammography in the detection of breast cancer. Eur J Cancer. 2002; 38: 1044-50.
Crossref Google Scholar
99

Rebolj M, Assi V, Brentnall A, Parmar D, Duffy SW. Addition of ultrasound to mammography in the case of dense breast tissue: systematic review and meta-analysis. Br J Cancer. 2018; 118: 1559-70.
Crossref Google Scholar
100

Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology. 2002; 225: 165-75.
Crossref Google Scholar
101

Zhao H, Zou L, Geng X, Zheng S. Limitations of mammography in the diagnosis of breast diseases compared with ultrasonography: a single-center retrospective analysis of 274 cases. Eur J Med Res. 2015; 20: 49.
Crossref Google Scholar
102

Wang FL, Chen F, Yin H, Xu N, Wu XX, Ma JJ, et al. Effects of age, breast density and volume on breast cancer diagnosis: a retrospective comparison of sensitivity of mammography and ultrasonography in China’s rural areas. Asian Pac J Cancer Prev. 2013; 14: 2277-82.
Crossref Google Scholar
103

Pu H, Peng J, Xu F, Liu N, Wang F, Huang X, et al. Ultrasound and clinical characteristics of false-negative results in mammography screening of dense breasts. Clin Breast Cancer. 2020; 20: 317-25.
Crossref Google Scholar
104

Berg WA, Blume JD, Cormack JB, Mendelson EB, Lehrer D, Bohm-Velez M, et al. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA. 2008; 299: 2151-63.
Crossref Google Scholar
105

Brem RF, Tabar L, Duffy SW, Inciardi MF, Guingrich JA, Hashimoto BE, et al. Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the SomoInsight Study. Radiology. 2015; 274: 663-73.
Crossref Google Scholar
106

Lee JM, Arao RF, Sprague BL, Kerlikowske K, Lehman CD, Smith RA, et al. Performance of screening ultrasonography as an adjunct to screening mammography in women across the spectrum of breast cancer risk. JAMA Intern Med. 2019; 179: 658-67.
Crossref Google Scholar
107

Ohuchi N, Suzuki A, Sobue T, Kawai M, Yamamoto S, Zheng Y-F, et al. Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial. Lancet. 2016; 387: 341-8.
Crossref Google Scholar
108

Tagliafico AS, Calabrese M, Mariscotti G, Durando M, Tosto S, Monetti F, et al. Adjunct screening with tomosynthesis or ultrasound in women with mammography-negative dense breasts: interim report of a prospective comparative trial. J Clin Oncol. 2016; 34: 1882-8.
Crossref Google Scholar
109

Mo M, Liu GY, Zheng Y, Di LF, Ji YJ, Lv LL, et al. Performance of breast cancer screening methods and modality among Chinese women: a report from a society-based breast screening program (SBSP) in Shanghai. Springerplus. 2013; 2: 276.
Crossref Google Scholar
110
Wu F, Mo M, Qin XX, Fang H, Zhao GM, Liu GY, et al.[cost-effectiveness of multiple screening modalities on breast cancer in Chinese women from Shanghai]. Zhonghua Liu Xing Bing Xue Za Zhi. 2017; 38: 1665-71.
111

Ya-jie J, Wei-jun P, Cai C, Jian-hui D, Wei Z, Min C, et al. Application of breast ultrasound in a mammography-based Chinese breast screening study. Cell Biochem Biophys. 2012; 65: 37-41.
Crossref Google Scholar
112

Dong H, Huang Y, Song F, Dai H, Liu P, Zhu Y, et al. Improved performance of adjunctive ultrasonography after mammography screening for breast cancer among Chinese females. Clin Breast Cancer. 2018; 18: e353-61.
Crossref Google Scholar
113

Shen S, Zhou Y, Xu Y, Zhang B, Duan X, Huang R, et al. A multicentre randomised trial comparing ultrasound vs mammography for screening breast cancer in high-risk Chinese women. Br J Cancer. 2015; 112: 998-1004.
Crossref Google Scholar
114

Li J, Xing P, Feng L, Dong H, Jin F, Wu Y, et al. The value of substratified combined imaging assessment with mammography and ultrasonography for Chinese women with palpable breast masses. Breast Cancer Res Treat. 2014; 144: 391-6.
Crossref Google Scholar
115

Yang X, Wu J, Lu J, Liu G, Di G, Chen C, et al. Identification of a comprehensive spectrum of genetic factors for hereditary breast cancer in a Chinese population by next-generation sequencing. PLoS One. 2015; 10: e0125571.
Crossref Google Scholar
116

Yao L, Sun J, Zhang J, He Y, Ouyang T, Li J, et al. Breast cancer risk in Chinese women with BRCA1 or BRCA2 mutations. Breast Cancer Res Treat. 2016; 156: 441-5.
Crossref Google Scholar
117

Couch FJ, Shimelis H, Hu C, Hart SN, Polley EC, Na J, et al. Associations between cancer predisposition testing panel genes and breast cancer. JAMA Oncol. 2017; 3: 1190-6.
Crossref Google Scholar
118

Deng M, Chen HH, Zhu X, Luo M, Zhang K, Xu CJ, 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.
Crossref Google Scholar
119

Kurian AW, Ward KC, Howlader N, Deapen D, Hamilton AS, Mariotto A, et al. Genetic testing and results in a population-based cohort of breast cancer patients and ovarian cancer patients. J Clin Oncol. 2019; 37: 1305-15.
Crossref Google Scholar
120

Sun J, Meng H, Yao L, Lv M, Bai J, Zhang J, et al. Germline mutations in cancer susceptibility genes in a large series of unselected breast cancer patients. Clin Cancer Res. 2017; 23: 6113-9.
Crossref Google Scholar
121

Tung N, Lin NU, Kidd J, Allen BA, Singh N, Wenstrup RJ, et al. Frequency of germline mutations in 25 cancer susceptibility genes in a sequential series of patients with breast cancer. J Clin Oncol. 2016; 34: 1460-8.
Crossref Google Scholar
122

Zeng C, Guo X, Wen W, Shi J, Long J, Cai Q, et al. Evaluation of pathogenetic mutations in breast cancer predisposition genes in population-based studies conducted among Chinese women. Breast Cancer Res Treat. 2020; 181: 465-73.
Crossref Google Scholar
123

Xiao W, Zhang G, Chen B, Chen X, Wen L, Lai J, et al. Characterization of frequently mutated cancer genes and tumor mutation burden in Chinese breast cancer. Front Oncol. 2021; 11: 618767.
Crossref Google Scholar
124

Sheng S, Xu Y, Guo Y, Yao L, Hu L, Ouyang T, et al. Prevalence and clinical impact of TP53 germline mutations in Chinese women with breast cancer. Int J Cancer. 2020; 146: 487-95.
Crossref Google Scholar
125

Wu Y, Huang D, Zhang H, Weng X, Wang H, Zhou Q, et al. The frequency of PTEN germline mutations in Chinese breast cancer patients: the PTEN gene may not be closely associated with breast cancer in the Chinese population. Gene. 2020; 744: 144630.
Crossref Google Scholar
126

Lang GT, Shi JX, Hu X, Zhang CH, Shan L, Song CG, 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.
Crossref Google Scholar
127

Ma D, Chen SY, Ren JX, Pei YC, Jiang CW, Zhao S, et al. Molecular features and functional implications of germline variants in triplenegative breast cancer. J Natl Cancer Inst. 2021; 113: 884-92.
Crossref Google Scholar
128

Couch FJ, Hart SN, Sharma P, Toland AE, Wang X, Miron P, 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: 304-11.
Crossref Google Scholar
129

Chen X, Li Y, Ouyang T, Li J, Wang T, Fan Z, et al. Associations between RAD51D germline mutations and breast cancer risk and survival in BRCA1/2-negative breast cancers. Ann Oncol. 2018; 29: 2046-51.
Crossref Google Scholar
130

Yang X, Song H, Leslie G, Engel C, Hahnen E, Auber B, et al. Ovarian and breast cancer risks associated with pathogenic variants in RAD51C and RAD51D. J Natl Cancer Inst. 2020; 112: 1242-50.
Crossref Google Scholar
131

Lang GT, Jiang YZ, Shi JX, Yang F, Li XG, Pei YC, et al. Characterization of the genomic landscape and actionable mutations in Chinese breast cancers by clinical sequencing. Nat Commun. 2020; 11: 5679.
Crossref Google Scholar
132

Wang X, Huang Y, Li L, Dai H, Song F, Chen K. Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis. Breast Cancer Res. 2018; 20: 18.
Crossref Google Scholar
133

Li XQ, Mo M, Wu F, Liu GY, Xu WH, Shao ZM. Artificial neural network models based on questionnaire survey for prediction of breast cancer risk among chinese women in shanghai. Tumor. 2018; 38: 883-93.
Google Scholar
134

Han Y, Lv J, Yu C, Guo Y, Bian Z, Hu Y, et al. Development and external validation of a breast cancer absolute risk prediction model in Chinese population. Breast Cancer Res. 2021; 23: 62.
Crossref Google Scholar
135

Wang L, Liu L, Lou Z, Ding L, Guan H, Wang F, et al. Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model. BMC Cancer. 2019; 19: 128.
Crossref Google Scholar
136

China Anti-Cancer Association, National Clinical Research Center for Cancer. Breast cancer screening guideline for Chinese women. Cancer Biol Med. 2019; 16: 822-4.
Crossref Google Scholar
137

Chinese Anti-Cancer Association, Committee of Breast Cancer Society. Chinese Anti-Cancer Association breast cancer diagnosis and treatment guidelines. China Oncology. 2021; 31: 954-1040.
Google Scholar
138

Huang Y, Tong Z, Chen K, Wang Y, Liu P, Gu L, et al. Interpretation of breast cancer screening guideline for chinese women. Cancer Biol Med. 2019; 16: 825-35.
Crossref Google Scholar
139

Speicher MR, Geigl JB, Tomlinson IP. Effect of genome-wide association studies, direct-to-consumer genetic testing, and highspeed sequencing technologies on predictive genetic counselling for cancer risk. Lancet Oncol. 2010; 11: 890-8.
Crossref Google Scholar
140

Aboutalib SS, Mohamed AA, Berg WA, Zuley ML, Sumkin JH, Wu S. Deep learning to distinguish recalled but benign mammography images in breast cancer screening. Clin Cancer Res. 2018; 24: 5902-9.
Crossref Google Scholar
141

Kim H-E, Kim HH, Han B-K, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health. 2020; 2: e138-48.
Crossref Google Scholar
142

Lotter W, Diab AR, Haslam B, Kim JG, Grisot G, Wu E, et al. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat Med. 2021; 27: 244-9.
Crossref Google Scholar
143

McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020; 577: 89-94.
Crossref Google Scholar
144

Rodriguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Kobrunner SH, Sechopoulos I, et al. Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology. 2019; 290: 305-14.
Crossref Google Scholar
145

Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst. 2019; 111: 916-22.
Crossref Google Scholar
146

Dembrower K, Liu Y, Azizpour H, Eklund M, Smith K, Lindholm P, et al. Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction. Radiology. 2020; 294: 265-72.
Crossref Google Scholar
147

Kakileti ST, Madhu HJ, Manjunath G, Wee L, Dekker A, Sampangi S. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics. Artif Intell Med. 2020; 105: 101854.
Crossref Google Scholar
148

Ming C, Viassolo V, Probst-Hensch N, Dinov ID, Chappuis PO, Katapodi MC. Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations. Br J Cancer. 2020; 123: 860-7.
Crossref Google Scholar
149

Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 2019; 292: 60-6.
Crossref Google Scholar
150

Zubor P, Kubatka P, Kajo K, Dankova Z, Polacek H, Bielik T, et al. Why the gold standard approach by mammography demands extension by multiomics? Application of liquid biopsy mirna profiles to breast cancer disease management. Int J Mol Sci. 2019; 20: 2878.
Crossref Google Scholar
151

Zhang X, Zhao D, Yin Y, Yang T, You Z, Li D, et al. Circulating cellfree DNA-based methylation patterns for breast cancer diagnosis. NPJ Breast Cancer. 2021; 7: 106.
Crossref Google Scholar
Cancer Biology & Medicine
Volume 19 Issue 4,
April 2022
Pages 450-467
DOI: 10.20892/j.issn.2095-3941.2021.0676
Cite this article:
Ding R, Xiao Y, Mo M, et al. Breast cancer screening and early diagnosis in Chinese women. Cancer Biology & Medicine, 2022, 19(4): 450-467. https://doi.org/10.20892/j.issn.2095-3941.2021.0676

332

Views

8

Downloads

47

Crossref

46

Web of Science

49

Scopus

Altmetrics
Received: 17 December 2021
Accepted: 21 February 2022
Published: 01 April 2022
©2022 Cancer Biology & Medicine.

Creative Commons Attribution-NonCommercial 4.0 International License
Return