切换至 "中华医学电子期刊资源库"

中华乳腺病杂志(电子版) ›› 2024, Vol. 18 ›› Issue (05) : 287 -291. doi: 10.3877/cma.j.issn.1674-0807.2024.05.006

综 述

乳腺癌风险预测模型的研究现状
明昊1,2, 肖迎聪2, 巨艳1, 宋宏萍1,()   
  1. 1.710032 西安,空军军医大学第一附属医院(西京医院)超声医学科
    2.712046 咸阳,陕西中医药大学医学技术学院
  • 收稿日期:2024-03-14 出版日期:2024-10-01
  • 通信作者: 宋宏萍
  • 基金资助:
    国家自然科学基金面上资助项目(82471991、82071934)陕西省国际科技合作与交流计划重点资助项目(2020KWZ-022)陕西省高等教育教学改革研究重点项目(21JZ009)空军军医大学临床研究资助项目(2021LC2210)

Breast cancer risk prediction models

Hao Ming, Yingcong Xiao, Yan Ju, Hongping Song()   

  • Received:2024-03-14 Published:2024-10-01
  • Corresponding author: Hongping Song
引用本文:

明昊, 肖迎聪, 巨艳, 宋宏萍. 乳腺癌风险预测模型的研究现状[J]. 中华乳腺病杂志(电子版), 2024, 18(05): 287-291.

Hao Ming, Yingcong Xiao, Yan Ju, Hongping Song. Breast cancer risk prediction models[J]. Chinese Journal of Breast Disease(Electronic Edition), 2024, 18(05): 287-291.

乳腺癌的高发病率和高死亡率严重威胁女性健康。 有效的筛查是提高乳腺癌治愈率和延长患者生存期的关键措施。 近年来,乳腺癌风险预测模型作为一种实现个性化筛查的工具逐渐受到关注,其核心在于通过评估个体未来患乳腺癌的风险,帮助医生制定有针对性的筛查计划和干预措施。传统的乳腺癌风险预测模型,如Gail 模型、Claus 模型和BRCAPRO 模型,主要基于家族史、遗传因素及其他非遗传性风险因素进行预测。 这些模型尽管在临床中被广泛应用,但也存在一定的局限性。 随着人工智能和深度学习技术的快速发展,基于乳腺影像的深度学习模型逐渐崭露头角。 结合影像学特征与传统临床风险因素的模型在预测性能上表现更优。 因此,本文系统回顾了乳腺癌的主要风险因素以及现有乳腺癌风险预测模型的应用与发展。

图1 乳腺癌个性化筛查方法
表1 乳腺癌风险预测模型的特点
模型 年份 风险因素 性能 优点 不足 网址
Gail模型[29] 1989 年龄、初潮年龄和首次生育年龄、既往乳腺活组织检查次数、一级乳腺癌亲属人数 E/O=0.73~0.85;AUC=0.58~0.62 简单、便捷、可用于评估是否适用化疗 不能用于35岁以下女性;对非白人女性的使用有限;只考虑一级家族史数据 https://bcrisktool.cancer.gov/
BCSC模型[49] 2008 年龄、种族、BMI、一级乳腺癌家族史、乳腺良性疾病史、乳房密度 E/O=0.90~0.98;AUC=0.61~0.67 简单、便捷 不能用于35岁以下、有乳腺手术史和乳腺癌史女性;需乳腺X线检查得到乳腺密度 https://tools.bcscscc.org/
Tyrer-Cuzick模型[29] 2004 初潮与绝经年龄、首次生育年龄、身高、BMI、激素替代疗法、乳腺良性疾病史、乳腺癌和卵巢癌家族史、乳腺密度、多基因风险评分 E/O=0.96~1.12;AUC=0.69~0.73 风险因素全面,可用于35岁以下女性 无法预测曾接受过胸部放射治疗患者的风险;高估非典型增生或高乳腺密度人群的风险 https://ibis-riskcalculator.magview.com/
BRCAPRO模型[29] 1997 患乳腺癌和卵巢癌的一级、二级家族史、种族、肿瘤标记物 E/O=0.55~0.64;AUC=0.65~0.70 侧重于有乳腺癌家族史的患者 并未考虑除BRCA基因以外的遗传因素以及非遗传因素 https://brcatool.stanford.edu/
Claus模型[50] 1994 年龄、患乳腺癌的亲属数量及发病年龄(包括一级和二级亲属) E/O=0.67~1.38;AUC=0.72~0.75 包括了父系家族史 不包括非遗传性风险因素 https://www.princetonradiology.com/
BOADICEA模型[29] 2004 个人因素、癌症家族史、8种乳腺癌易感基因、肿瘤分子标记物(ER、PR、HER-2) E/O=0.97~1.14;AUC=0.68~0.72 适用于有家族史女性 需要详细的家族史 https://www.canrisk.org/
[1]
Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263.
[2]
中国抗癌协会乳腺癌专业委员会. 中国乳腺癌筛查与早期诊断指南[J]. 中国癌症杂志, 2022, 32(4): 363-372.
[3]
Xia C, Basu P, KramerBS, et al. Cancer screening in China: a steep road from evidence to implementation[J]. Lancet Public Health,2023,8(12): e996-e1005.
[4]
中国抗癌协会乳腺癌专业委员会, 中华医学会肿瘤学分会乳腺肿瘤学组. 中国抗癌协会乳腺癌诊治指南与规范(2024 年版)[J]. 中国癌症杂志, 2023, 33(12): 1092-1187.
[5]
Dembrower K, Liu Y, Azizpour H, et al. Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction[J]. Radiology, 2020, 294(2): 265-272.
[6]
Pashayan N, Antoniou AC, Ivanus U, et al. Personalized early detection and prevention of breast cancer: ENVISION consensus statement[J]. Nat Rev Clin Oncol, 2020, 17(11): 687-705.
[7]
Gastounioti A, Desai S, Ahluwalia VS, et al. Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review[J]. Breast Cancer Res, 2022, 24(1): 14.
[8]
Monticciolo DL, Newell MS, Moy L, et al. Breast cancer screening in women at higher-than-average risk: recommendations from the ACR[J]. J Am Coll Radiol, 2018, 15(3): 408-414.
[9]
SmithRA, Andrews KS, Brooks D, et al. Cancer screening in the United States, 2019: a review of current American Cancer Society guidelines and current issues in cancer screening[J]. CA Cancer J Clin, 2019, 69(3): 184-210.
[10]
BrentnallAR, Cohn WF, Knaus WA,et al. A case-control study to add volumetric or clinical mammographic density into the Tyrer-Cuzick breast cancer risk model[J]. J Breast Imaging, 2019, 1(2): 99-106.
[11]
Ahn JS, Shin S,Yang SA,et al. Artificial intelligence in breast cancer diagnosis and personalized medicine [J]. J Breast Cancer, 2023,26(5): 405-435.
[12]
Bahl M. Harnessing the power of deep learning to assess breast cancer risk[J]. Radiology, 2020, 294(2): 273-274.
[13]
Huang S, Xu JT, Yang M. Review: predictive approaches to breast cancer risk[J]. Heliyon, 2023, 9(11): e21344.
[14]
EngmannNJ, Golmakani MK, Miglioretti DL, et al. Populationattributable risk proportion of cinical risk factors for breast cancer[J].JAMA Oncol, 2017, 3(9): 1228-1236.
[15]
Zheng Y, Dong X, Li J, et al. Use of breast cancer risk factors to identify risk-adapted starting age of screening in China[J]. JAMA Netw Open, 2022, 5(11): e2241441.
[16]
张晓辉, 孙强, 李炎, 等. 中国女性乳腺癌预防专家共识[J]. 中国研究型医院, 2022, 9(4): 5-13.
[17]
Cuzick J, Sestak I, ThoratMA. Impact of preventive therapy on the risk of breast cancer among women with benign breast disease[J]. Breast,2015, 24 (2): S51-S55.
[18]
BrandtKR, Scott CG, Ma L, et al. Comparison of clinical and automated breast density measurements: implications for risk prediction and supplemental screening[J]. Radiology, 2016, 279(3): 710-719.
[19]
BaeJM, Kim EH. Breast density and risk of breast cancer in Asian women: a meta-analysis of observational studies[J]. J Prev Med Public Health, 2016, 49(6): 367-375.
[20]
BaeMS, Moon WK, Chang JM, et al. Breast cancer detected with screening US: reasons for nondetection at mammography [ J].Radiology, 2014, 270(2): 369-377.
[21]
WandersJO, Holland K,Veldhuis WB,et al. Volumetric breast density affects performance of digital screening mammography [J]. Breast Cancer Res Treat, 2017, 162(1): 95-103.
[22]
VisscherDW, Frost MH, Hartmann LC, et al. Clinicopathologic features of breast cancers that develop in women with previous benign breast disease[J]. Cancer, 2016, 122(3): 378-385.
[23]
SalamatFM, Niakan BM,Keshtkar AP,et al. Subtypes of benign breast disease as a risk factor of breast cancer: a systematic review and meta analyses[J]. Iran J Med Sci, 2018, 43(4): 355-364.
[24]
ShiyanbolaOO, Arao RF, Miglioretti DL, et al. Emerging trends in family history of breast cancer and associated risk [J]. Cancer Epidemiol Biomarkers Prev, 2017, 26(12): 1753-1760.
[25]
Mahdavi M, Nassiri M,Kooshyar MM,et al. Hereditary breast cancer;Genetic penetrance and current status with BRCA[J]. J Cell Physiol,2019, 234(5): 5741-5750.
[26]
Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually[J]. J Natl Cancer Inst,1989,81(24):1879-1886.
[27]
Crispo A, D'aiuto G, De Marco M, et al. Gail model risk factors:impact of adding an extended family history for breast cancer[J]. Breast J, 2008, 14(3): 221-227.
[28]
Rockhill B, Spiegelman D,Byrne C,et al. Validation of the Gail et al.model of breast cancer risk prediction and implications for chemoprevention[J]. J Natl Cancer Inst, 2001, 93(5): 358-366.
[29]
Terry MB, Liao Y, Whittemore AS, et al. 10-year performance of four models of breast cancer risk: a validation study[J]. Lancet Oncol,2019, 20(4): 504-517.
[30]
Tice JA, Cummings SR,Smith-Bindman R,et al. Using clinical factors and mammographic breast density to estimate breast cancer risk:development and validation of a new predictive model[J]. Ann Intern Med, 2008, 148(5): 337-347.
[31]
Gard CC, Tice JA, Miglioretti DL, et al. Extending the breast cancer surveillance consortium model of invasive breast cancer[J]. J Clin Oncol, 2024,42(7):779-789.
[32]
Vilmun BM, Vejborg I,Lynge E,et al. Impact of adding breast density to breast cancer risk models: a systematic review[J]. Eur J Radiol,2020, 127: 109019.
[33]
BrentnallAR, Cuzick J,Buist DSM,et al. Long-term accuracy of breast cancer risk assessment combining classic risk factors and breast density[J]. JAMA Oncol, 2018, 4(9): e180174.
[34]
Wooster R, Bignell G, Lancaster J, et al. Identification of the breast cancer susceptibility gene BRCA2[J]. Nature, 1995, 378(6559):789-792.
[35]
BerryDA, Parmigiani G, Sanchez J, et al. Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history[J]. J Natl Cancer Inst, 1997, 89(3): 227-238.
[36]
Parmigiani G, Berry D,Aguilar O. Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2[J]. Am J Hum Genet, 1998, 62(1): 145-158.
[37]
Claus EB, Risch N,Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction[J]. Cancer,1994, 73(3): 643-651.
[38]
Antoniou AC, Pharoah PP, Smith P, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancer[J]. Br J Cancer,2004, 91(8): 1580-1590.
[39]
Lee A, Mavaddat N,Cunningham A,et al. Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C,RAD51D, BARD1 updates to tumour pathology and cancer incidence[J]. J Med Genet, 2022, 59(12): 1206-1218.
[40]
Lee A, Mavaddat N, Wilcox AN, et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors[J]. Genet Med, 2019, 21(8): 1708-1718.
[41]
Jin Z, Zhang S, Zhang L, et al. Artificial intelligence risk model(Mirai) delivers robust generalization and outperforms Tyrer-Cuzick guidelines in breast cancer screening[J]. J Clin Oncol, 2022, 40(20): 2280-2281.
[42]
Eriksson M, Czene K, Vachon C, et al. Long-term performance of an image-based short-term risk model for breast cancer[J]. J Clin Oncol,2023, 41(14): 2536-2545.
[43]
ArasuVA, Habel LA, Achacoso NS, et al. Comparison of mammography AI algorithms with a clinical risk model for 5-year breast cancer risk prediction: an observational study[J]. Radiology, 2023,307(5): e222733.
[44]
Yala A, Mikhael PG, Strand F, et al. Toward robust mammographybased models for breast cancer risk [J]. Sci Transl Med, 2021,13(578): eaba4373.
[45]
Yala A, Mikhael PG,Strand F,et al. Multi-Institutional validation of a mammography-based breast cancer risk model[J]. J Clin Oncol,2022,40(16): 1732-1740.
[46]
Wang X,Tan T, Gao Y, et al. Predicting up to 10 year breast cancer risk using longitudinal mammographic screening history[J]. medRxiv,2023: 2023.06. 28.23291994.
[47]
Dadsetan S, Arefan D, Berg WA, et al. Deep learning of longitudinal mammogram examinations for breast cancer risk prediction[EB/OL].[2024-01-15].https:/ /www.medrxiv.org/content/10.1101/2023.06.28.23291994v1.
[48]
Kretz T, Mueller KR, Schaeffter T, et al. Mammography image quality assurance using deep learning[J]. IEEE Trans Biomed Eng, 2020,67(12): 3317-3326.
[49]
Tice JA, Bissell MCS, Miglioretti DL, et al. Validation of the breast cancer surveillance consortium model of breast cancer risk[J]. Breast Cancer Res Treat, 2019, 175(2): 519-523.
[50]
Fischer C, Kuchenbäcker K, Engel C, et al. Evaluating the performance of the breast cancer genetic risk models BOADICEA,IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium[J]. J Med Genet,2013, 50(6): 360-367.
[1] 顾莉莉, 姜凡. 安徽省超声产前筛查切面图像质量现状调查情况及分析[J]. 中华医学超声杂志(电子版), 2024, 21(07): 671-674.
[2] 李洋, 蔡金玉, 党晓智, 常婉英, 巨艳, 高毅, 宋宏萍. 基于深度学习的乳腺超声应变弹性图像生成模型的应用研究[J]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[3] 洪玮, 叶细容, 刘枝红, 杨银凤, 吕志红. 超声影像组学联合临床病理特征预测乳腺癌新辅助化疗完全病理缓解的价值[J]. 中华医学超声杂志(电子版), 2024, 21(06): 571-579.
[4] 杨敬武, 周美君, 陈雨凡, 李素淑, 何燕妮, 崔楠, 刘红梅. 人工智能超声结合品管圈活动对低年资超声医师甲状腺结节风险评估能力的作用[J]. 中华医学超声杂志(电子版), 2024, 21(05): 522-526.
[5] 于桐, 孙姗姗, 刘扬. 乳腺导管原位癌的浸润转化机制及临床病理特征[J]. 中华乳腺病杂志(电子版), 2024, 18(05): 304-307.
[6] 潘荔生, 刘忠强, 周莹莹, 陈勃, 李晏宁, 徐金锋, 蔡隆梅, 王宏梅. 乳腺癌内乳淋巴结的诊断和治疗[J]. 中华乳腺病杂志(电子版), 2024, 18(05): 308-314.
[7] 罗文斌, 韩玮. 胰腺癌患者首次化疗后中重度骨髓抑制的相关危险因素分析及预测模型构建[J]. 中华普通外科学文献(电子版), 2024, 18(05): 357-362.
[8] 张志兆, 王睿, 郜苹苹, 王成方, 王成, 齐晓伟. DNMT3B与乳腺癌预后的关系及其生物学机制[J]. 中华普外科手术学杂志(电子版), 2024, 18(06): 624-629.
[9] 王玲艳, 高春晖, 冯雪园, 崔鑫淼, 刘欢, 赵文明, 张金库. 循环肿瘤细胞在乳腺癌新辅助及术后辅助治疗中的应用[J]. 中华普外科手术学杂志(电子版), 2024, 18(06): 630-633.
[10] 赵林娟, 吕婕, 王文胜, 马德茂, 侯涛. 超声引导下染色剂标记切缘的梭柱型和圆柱型保乳区段切除术的效果研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(06): 634-637.
[11] 贺斌, 马晋峰. 胃癌脾门淋巴结转移危险因素[J]. 中华普外科手术学杂志(电子版), 2024, 18(06): 694-699.
[12] 莫淇舟, 苏劲, 黄健, 李健维, 李思宁, 柳建军. 智能控压输尿管软镜碎石吸引取石术在直径10~25 mm上尿路结石中的应用[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(05): 497-502.
[13] 李义亮, 苏拉依曼·牙库甫, 麦麦提艾力·麦麦提明, 克力木·阿不都热依木. 机器人与腹腔镜食管裂孔疝修补术联合Nissen 胃底折叠术短期疗效分析[J]. 中华疝和腹壁外科杂志(电子版), 2024, 18(05): 512-517.
[14] 周艳, 李盈, 周小兵, 程发辉, 何恒正. 不同类型补片联合Nissen 胃底折叠术修补食管裂孔疝的疗效及复发潜在危险因素[J]. 中华疝和腹壁外科杂志(电子版), 2024, 18(05): 528-533.
[15] 董晟, 郎胜坤, 葛新, 孙少君, 薛明宇. 反向休克指数乘以格拉斯哥昏迷评分对老年严重创伤患者发生急性创伤性凝血功能障碍的预测价值[J]. 中华临床医师杂志(电子版), 2024, 18(06): 541-547.
阅读次数
全文


摘要