Cancer Res Treat.  2023 Jan;55(1):136-144. 10.4143/crt.2021.962.

Estimating Age-Specific Mean Sojourn Time of Breast Cancer and Sensitivity of Mammographic Screening by Breast Density among Korean Women

• Choi E
• Suh M
• Jung SY
• Jung KW
• Park S
• Jun JK
• Choi KS

• KMID: 2537999
• DOI: http://doi.org/10.4143/crt.2021.962

Abstract

Purpose
High breast cancer incidence and dense breast prevalence among women in forties are specific to Asian. This study examined the natural history of breast cancer among Korean women.
Materials and Methods
We applied a three-state Markov model (i.e., healthy, preclinical, and clinical state) to fit the natural history of breast cancer to data in the Korean National Cancer Screening Program. Breast cancer was ascertained by linkage to the Korean Central Cancer Registry. Disease-progression rates (i.e., transition rates between three states), mean sojourn time (MST) and mammographic sensitivity were estimated across 10-year age groups and levels of breast density determined by the Breast Imaging, Reporting and Data System.
Results
Overall prevalence of dense breast was 53.9%. Transition rate from healthy to preclinical state, indicating the preclinical incidence of breast cancer, was higher among women in forties (0.0019; 95% confidence interval [CI], 0.0017 to 0.0021) and fifties (0.0020; 95% CI, 0.0017 to 0.0022), than women in sixties (0.0014; 95% CI, 0.0012 to 0.0017). The MSTs, in which the tumor is asymptomatic but detectable by screening, were also fastest among younger age groups, estimated as 1.98 years (95% CI, 1.67 to 2.33), 2.49 years (95% CI, 1.92 to 3.22), and 3.07 years (95% CI, 2.11 to 4.46) for women in forties, fifties, and sixties, respectively. Having dense breasts increased the likelihood of the preclinical cancer risk (1.96 to 2.35 times) and decreased the duration of MST (1.53 to 2.02 times).
Conclusion
This study estimated Korean-specific natural history parameters of breast cancer that would be utilized for establishing optimal screening strategies in countries with higher dense breast prevalence.

Keyword

Breast density; Mammography; Natural history; Mean sojourn time; Screening sensitivity

Figure

• 

  Fig. 1 A three-state Markov model of natural history of breast cancer and effects of breast density.

• 

  Fig. 2 Cumulative incidence of breast cancer by breast density levels. Cumulative incidence of invasive breast cancer (A) and in situ breast cancer (B) from the time of cohort enrollment.

Reference

References

1. Choi E, Lee YY, Yoon HJ, Lee S, Suh M, Park B, et al. Relationship between cancer worry and stages of adoption for breast cancer screening among Korean women. PLoS One. 2015; 10:e0132351.

2. 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; 15:1159–69.

3. Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007; 356:227–36.

4. Park B, Cho HM, Lee EH, Song S, Suh M, Choi KS, et al. Does breast density measured through population-based screening independently increase breast cancer risk in Asian females? Clin Epidemiol. 2018; 10:61–70.

5. Burton A, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, et al. Mammographic density and ageing: a collaborative pooled analysis of cross-sectional data from 22 countries worldwide. PLoS Med. 2017; 14:e1002335.

6. 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.

7. Japanese Research Group for the Development of Breast Cancer Screening Guidelines. Hamashima C, Hamashima CC, Hattori M, Honjo S, Kasahara Y, et al. The Japanese guidelines for breast cancer screening. Jpn J Clin Oncol. 2016; 46:482–92.

8. Yen AM, Tsau HS, Fann JC, Chen SL, Chiu SY, Lee YC, et al. Population-based breast cancer screening with risk-based and universal mammography screening compared with clinical breast examination: a propensity score analysis of 1429890 Taiwanese women. JAMA Oncol. 2016; 2:915–21.

9. Choi E, Jun JK, Suh M, Jung KW, Park B, Lee K, et al. Effectiveness of the Korean National Cancer Screening Program in reducing breast cancer mortality. NPJ Breast Cancer. 2021; 7:83.

10. Day NE, Walter SD. Simplified models of screening for chronic disease: estimation procedures from mass screening programmes. Biometrics. 1984; 40:1–14.

11. Paci E, Duffy SW. Modelling the analysis of breast cancer screening programmes: sensitivity, lead time and predictive value in the Florence District Programme (1975–1986). Int J Epidemiol. 1991; 20:852–8.

12. Duffy SW, Chen HH, Tabar L, Day NE. Estimation of mean sojourn time in breast cancer screening using a Markov chain model of both entry to and exit from the preclinical detectable phase. Stat Med. 1995; 14:1531–43.

13. Taghipour S, Banjevic D, Miller AB, Montgomery N, Jardine AK, Harvey BJ. Parameter estimates for invasive breast cancer progression in the Canadian National Breast Screening Study. Br J Cancer. 2013; 108:542–8.

14. Tan KH, Simonella L, Wee HL, Roellin A, Lim YW, Lim WY, et al. Quantifying the natural history of breast cancer. Br J Cancer. 2013; 109:2035–43.

15. Jackson CH. Multi-state models for panel data: the msm package for R. J Stat Sofw. 2011; 38:1–29.

16. Aarts A, Duffy SW, Geurts S, Vulkan DP, Otten J, Hsu CY, et al. Test sensitivity of mammography and mean sojourn time over 40 years of breast cancer screening in Nijmegen (The Netherlands). J Med Screen. 2019; 26:147–53.

17. Lee EH, Kim KW, Kim YJ, Shin DR, Park YM, Lim HS, et al. Performance of screening mammography: a report of the alliance for breast cancer screening in Korea. Korean J Radiol. 2016; 17:489–96.

18. Ryu JE, Choi E, Lee K, Jun JK, Suh M, Jung KW, et al. Trends in the performance of the Korean National Cancer Screening Program for gastric cancer from 2007 to 2016. Cancer Res Treat. 2022; 54:842–9.

19. Chiu SY, Duffy S, Yen AM, Tabar L, Smith RA, Chen HH. Effect of baseline breast density on breast cancer incidence, stage, mortality, and screening parameters: 25-year follow-up of a Swedish mammographic screening. Cancer Epidemiol Biomarkers Prev. 2010; 19:1219–28.

20. Gray RJ. A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat. 1988; 16:1141–54.

21. Hong S, Won YJ, Park YR, Jung KW, Kong HJ, Lee ES, et al. Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2017. Cancer Res Treat. 2020; 52:335–50.

22. 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 Services Task Force. Ann Intern Med. 2016; 164:268–78.

23. Duffy SW, Chen HH, Tabar L, Fagerberg G, Paci E. Sojourn time, sensitivity and positive predictive value of mammography screening for breast cancer in women aged 40–49. Int J Epidemiol. 1996; 25:1139–45.

24. Duffy SW, Day NE, Tabar L, Chen HH, Smith TC. Markov models of breast tumor progression: some age-specific results. J Natl Cancer Inst Monogr. 1997; (22):93–7.

25. Walter SD, Day NE. Estimation of the duration of a pre-clinical disease state using screening data. Am J Epidemiol. 1983; 118:865–86.

26. Chen TH, Kuo HS, Yen MF, Lai MS, Tabar L, Duffy SW. Estimation of sojourn time in chronic disease screening without data on interval cases. Biometrics. 2000; 56:167–72.

27. Jiang H, Walter SD, Brown PE, Chiarelli AM. Estimation of screening sensitivity and sojourn time from an organized screening program. Cancer Epidemiol. 2016; 44:178–85.

28. Wu JC, Hakama M, Anttila A, Yen AM, Malila N, Sarkeala T, et al. Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer. Breast Cancer Res Treat. 2010; 122:553–66.

29. Bae JM, Kim EH. Breast density and risk of breast cancer in Asian women: a meta-analysis of observational studies. J Prev Med Public Health. 2016; 49:367–75.

30. Wanders JO, Holland K, Veldhuis WB, Mann RM, Pijnappel RM, Peeters PH, et al. Volumetric breast density affects performance of digital screening mammography. Breast Cancer Res Treat. 2017; 162:95–103.

31. Lee SY, Jeong SH, Kim J, Jung SH, Song KB, Nam CM. Scheduling mammography screening for the early detection of breast cancer in Korean women. J Med Screen. 2007; 14:205–9.