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Epidemiology

Fatal and non-fatal breast cancers in women targeted by BreastScreen Norway: a cohort study

Abstract

Background

Many breast cancer survivors experience anxiety related to dying from their disease even if it is detected at an early stage. We aimed to increase knowledge about fatal and non-fatal breast cancer by describing how histopathological tumour profiles and detection modes were associated with 10-year breast cancer-specific survival.

Methods

This cohort study included data from women targeted by BreastScreen Norway (aged 50–69) and diagnosed with invasive breast cancer during 1996–2011. Breast cancer was classified as fatal if causing death within 10 years after diagnosis and non-fatal otherwise. We described histopathologic characteristics of fatal and non-fatal cancers, stratified by mode of detection. Recursive partitioning identified subgroups with differing survival profiles.

Results

In total, 6.3% of 9954 screen-detected cancers (SDC) were fatal, as were 17.4% of 3205 interval cancers (IC) and 20.9% of 3237 cancers detected outside BreastScreen Norway. Four to five subgroups with differing survival profiles were identified within each detection mode. Women with lymph node-negative SDC or Grade 1–2, node-negative IC without distant metastases had the highest 10-year survival (95–96%).

Conclusions

Two subgroups representing 53% of the cohort had excellent (95–96%) 10-year breast cancer-specific survival. Most women with SDC had excellent survival, as did nearly 40% of women diagnosed with IC.

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Fig. 1: Number of women included and excluded in this study.
Fig. 2: Survival profiles for women diagnosed with screen-detected cancer through BreastScreen Norway.
Fig. 3: Survival profiles for women diagnosed with interval cancer through BreastScreen Norway.
Fig. 4: Survival profiles for women diagnosed outside of BreastScreen Norway.

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Data availability

The data underlying this article cannot be shared publicly due to patient privacy. The data can be shared for research purposes on request to the Cancer Registry of Norway’s data delivery unit via Helsedata.no (https://helsedata.no/).

References

  1. International Agency for Research on Cancer (IARC). Estimated age-standardized incidence and mortality rates (World) in 2020, World, females, ages 20-84 (excl. NMSC). 2020. https://gco.iarc.fr/today/online-analysis-multi-bars?v=2020&mode=cancer&mode_population=countries&population=900&populations=900&key=asr&sex=2&cancer=39&type=0&statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=4&ages_group%5B%5D=16&nb_items=10&group_cancer=0&include_nmsc=0&include_nmsc_other=1&type_multiple=%257B%2522inc%2522%253Atrue%252C%2522mort%2522%253Atrue%252C%2522prev%2522%253Afalse%257D&orientation=horizontal&type_sort=0&type_nb_items=%257B%2522top%2522%253Atrue%252C%2522bottom%2522%253Afalse%257D. Accessed February 1, 2023.

  2. Breast Cancer Screening. IARC handbook of cancer prevention. Vol. 15. Lyon, France: International Agency for Research on Cancer; 2016.

  3. Dibden A, Offman J, Duffy SW, Gabe R. Worldwide review and meta-analysis of cohort studies measuring the effect of mammography screening programmes on incidence-based breast cancer mortality. Cancers. 2020;12:976.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Badve SS, Beitsch PD, Bose S, Byrd D, Chen VW, Connolly JL, et al. Part XI, breast. In: Amin MB, et al., editors. AJCC Cancer Staging Manual, 8th edn. New York, USA: Springer International Publishing; 2017. p 587–636.

  5. Tabar L, Duffy SW, Vitak B, Chen H-H, Prevost TC. The natural history of breast carcinoma. Cancer. 1999;86:449–62.

    Article  CAS  PubMed  Google Scholar 

  6. Johansson ALV, Trewin CB, Fredriksson I, Reinertsen KV, Russnes H, Ursin G. In modern times, how important are breast cancer stage, grade and receptor subtype for survival: a population-based cohort study. Breast Cancer Res. 2021;23:17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Broeders M, Moss S, Nystrom L, Njor S, Jonsson H, Paap E, et al. The impact of mammographic screening on breast cancer mortality in Europe: a review of observational studies. J Med Screen. 2012;19:14–25.

    Article  PubMed  Google Scholar 

  8. Duffy SW, Tabar L, Yen AM, Dean PB, Smith RA, Jonsson H, et al. Beneficial effect of consecutive screening mammography examinations on mortality from breast cancer: a prospective study. Radiology. 2021;299:541–7.

    Article  PubMed  Google Scholar 

  9. Shen Y, Yang Y, Inoue LY, Munsell MF, Miller AB, Berry DA. Role of detection method in predicting breast cancer survival: analysis of randomized screening trials. J Natl Cancer Inst. 2005;97:1195–203.

    Article  PubMed  Google Scholar 

  10. Fortin J, Leblanc M, Elgbeili G, Cordova MJ, Marin MF, Brunet A. The mental health impacts of receiving a breast cancer diagnosis: a meta-analysis. Br J Cancer. 2021;125:1582–92.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Cancer Registry of Norway. Kvalitetsmanualen i Mammografiprogrammet [Quality manual for BreastScreen Norway]. Oslo, Norway: Cancer Registry of Norway; 2003.

  12. European Commission Initiative on Breast Cancer. European guidelines on breast cancer screening and diagnosis. 2022. https://healthcare-quality.jrc.ec.europa.eu/ecibc/european-breast-cancer-guidelines. Accessed November 8, 2022.

  13. 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.

    Article  CAS  PubMed  Google Scholar 

  14. Hofvind S, Tsuruda K, Mangerud G, Ertzaas AK, Holen Å, Pedersen K, et al. The Norwegian Breast Cancer Screening Program, 1996-2016: celebrating 20 years of organised screening in Norway. Oslo, Norway: Cancer Registry of Norway; 2017.

  15. Skaane P, Skjennald A. Screen-film mammography versus full-field digital mammography with soft-copy reading: randomized trial in a population-based screening program—the Oslo II Study. Radiology. 2004;232:197–204.

    Article  PubMed  Google Scholar 

  16. Larsen IK, Smastuen M, Johannesen TB, Langmark F, Parkin DM, Bray F, et al. Data quality at the Cancer Registry of Norway: an overview of comparability, completeness, validity and timeliness. Eur J Cancer. 2009;45:1218–31.

    Article  PubMed  Google Scholar 

  17. Lydersen S, Fagerland MW, Laake P. Categorical data and contingency tables. In: Veierød MB, Lydersen S, Laake P, editors. Medical statistics in clinical and epidemiological research, 1st edn. Oslo, Norway: Gylendal Norsk Forlag; 2012. p 48–89.

  18. LeBlanc M, Crowley J. Relative risk trees for censored survival data. Biometrics. 1992;48:411–25.

    Article  CAS  PubMed  Google Scholar 

  19. Therneau T, Atkinson E. An introduction to recursive partitioning using the RPART Routines. 2022. https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf.

  20. Zhang H, Singer BH. Recursive partitioning and applications. 2nd edn. New York NY, USA: Springer; 2010.

  21. R Core Team. R: a language and environment for statistical computing v. 4.1.3. Vienna, Austria: R Foundation for Statistical Computing; 2022.

  22. Therneau T, Atkinson B. rpart: Recursive partitioning and regression trees. R package version 4.1.19 (2022). https://cran.r-project.org/package=rpart.

  23. Milborrow S. rpart.plot: Plot ‘rpart’ models: an enhanced version of ‘plot.rpart’. R package version 3.1.1 (2022). https://cran.r-project.org/package=rpart.plot.

  24. Coviello V, Boggess M. Cumulative incidence estimation in the presence of competing risks. Stata J. 2004;4:103–12.

    Article  Google Scholar 

  25. Haybittle JL, Blamey RW, Elston CW, Johnson J, Doyle PJ, Campbell FC, et al. A prognostic index in primary breast cancer. Br J Cancer. 1982;45:361–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hastie T, Tibshirani R, Friedman J. Chapter 9: additive models, trees, and related methods. In: Hastie T, Tibshirani R, Friedman J. The elements of statistical learning, 2nd edn. New York, NY, USA: Springer; 2009. p 295–336.

  27. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24:2206–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Debien V, De Caluwe A, Wang X, Piccart-Gebhart M, Tuohy VK, Romano E, et al. Immunotherapy in breast cancer: an overview of current strategies and perspectives. NPJ Breast Cancer. 2023;9:7.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Larønningen S, Ferlay J, Beydogan H, Bray F, Engholm G, Ervik M, et al. NORDCAN: period, age-specific rate per 100 000, mortality, females, age [40-74] (Norway, Breast). Association of the Nordic Cancer Registries, Cancer Registry of Norway. 2023. https://nordcan.iarc.fr/en/dataviz/cohorts?cancers=180&sexes=2&populations=578&age_start=8&years_available=1943_2020&types=1&cohort=period (Data version 9.2 - June 23, 2022). Accessed August 26, 2023.

  30. Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol. 2012;41:861–70.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Satagopan JM, Ben-Porat L, Berwick M, Robson M, Kutler D, Auerbach AD. A note on competing risks in survival data analysis. Br J Cancer. 2004;91:1229–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation. 2016;133:601–9.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Gooley TA, Leisenring W, Crowley J, Storer BE. Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Stat Med. 1999;18:695–706.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank Luca Pestarino for sharing his insights on recursive partitioning.

Funding

The author(s) received no specific funding for this work.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: SH, SRH and LAA. Methodology: SH, SRH, LAA and KMT. Software: KMT. Validation: KMT, SH, SRH and LAA. Formal analysis: KMT. Investigation: all authors. Resources: SH. Data curation: KMT and SH. Writing—original draft preparation: KMT, SH and SRH. Writing—review and editing: KMT, SH, SRH and LAA. Visualization: KMT. Supervision: not relevant. Project administration: SH. Funding acquisition: not relevant.

Corresponding author

Correspondence to Solveig Hofvind.

Ethics declarations

Competing interests

SH is the head of BreastScreen Norway. The remaining authors declare no competing interests.

Ethics approval and consent to participate

This study has been reviewed by the privacy ombudsman at the Oslo University Hospital (PVO 20/12601) and was performed in accordance with the Declaration of Helsinki. It has a legal basis in accordance with Articles 6 (1) (e) and 9 (2) (j) of the GDPR. The data were disclosed with legal basis in the Cancer Registry Regulations section 3-1 and the Personal Health Data Filing System Act section 19 a to 19 h.

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Not applicable.

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Tsuruda, K.M., Hoff, S.R., Akslen, L.A. et al. Fatal and non-fatal breast cancers in women targeted by BreastScreen Norway: a cohort study. Br J Cancer 130, 99–107 (2024). https://doi.org/10.1038/s41416-023-02512-7

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  • DOI: https://doi.org/10.1038/s41416-023-02512-7

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