Personalized breast cancer screening strategies: A systematic review and quality assessment
Autoři:
Marta Román aff001; Maria Sala aff001; Laia Domingo aff001; Margarita Posso aff001; Javier Louro aff001; Xavier Castells aff001
Působiště autorů:
Department of Epidemiology and Evaluation, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
aff001; Network on Health Services in Chronic Diseases (REDISSEC), Spain
aff002
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0226352
Souhrn
Background
The effectiveness of breast cancer screening is still under debate. Our objective was to systematically review studies assessing personalized breast cancer screening strategies based on women’s individual risk and to conduct a risk of bias assessment.
Methods
We followed the standard methods of The Cochrane Collaboration and PRISMA declaration and searched the MEDLINE, EMBASE and Clinical Trials databases for studies published in English. The quality of the studies was assessed using the ISPOR-AMCP-NPC Questionnaire and The Cochrane Risk of Bias Tool. Two independent reviewers screened full texts and evaluated the risk of bias.
Results
Out of the 1533 initially retrieved citations, we included 13 studies. Three studies were randomized controlled trials, while nine were mathematical modeling studies, and one was an observational pilot study. The trials are in the recruitment phase and have not yet reported their results. All three trials used breast density and age to define risk groups, and two of them included family history, previous biopsies, and genetic information. Among the mathematical modeling studies, the main risk factors used to define risk groups were breast density, age, family history, and previous biopsies. Six studies used genetic information to define risk groups. The most common outcome measures were the gain in quality-adjusted life years (QALY), absolute costs, and incremental cost-effectiveness ratio (ICER), while the main outcome in the observational study was the detection rate. In all models, personalized screening strategies were shown to be effective. The randomized trials were of good quality. The modeling studies showed moderate risk of bias but there was wide variability across studies. The observational study showed a low risk of bias but its utility was moderate due to its pilot design and its relatively small scale.
Conclusions
There is some evidence of the effectiveness of screening personalization in terms of QUALYs and ICER from the modeling studies and the observational study. However, evidence is lacking on feasibility and acceptance by the target population.
Review registration
PROSPERO: CRD42018110483
Klíčová slova:
Biopsy – Breast cancer – Cancer risk factors – Cancer screening – Database searching – Mammography – Randomized controlled trials – Mathematical modeling
Zdroje
1. Marmot MG, Altman DG, Cameron DA, Dewar JA, Thompson SG, Wilcox M. The benefits and harms of breast cancer screening: an independent review. Br J Cancer. 2013 Jun 11;108(11):2205–40. doi: 10.1038/bjc.2013.177 23744281
2. Perry N, Broeders M, de Wolf C, T”rnberg S, Holland R, von Karsa L. European guidelines for quality assurance in breast cancer screening and diagnosis. 2006; Available from: http://screening.iarc.fr/doc/ND7306954ENC_002.pdf
3. Bevers TB, Helvie M, Bonaccio E, Calhoun KE, Daly MB, Farrar WB, et al. Breast Cancer Screening and Diagnosis, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Netw JNCCN. 2018 Nov;16(11):1362–89.
4. Siu AL. Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med. 2016 Feb 16;164(4):279–96. doi: 10.7326/M15-2886 26757170
5. Hofvind S, Sagstad S, Sebuodegard S, Chen Y, Roman M, Lee CI. Interval Breast Cancer Rates and Histopathologic Tumor Characteristics after False-Positive Findings at Mammography in a Population-based Screening Program. Radiology. 2018 Apr;287(1):58–67. doi: 10.1148/radiol.2017162159 29239711
6. Hofvind S, Ponti A, Patnick J, Ascunce N, Njor S, Broeders M, et al. False-positive results in mammographic screening for breast cancer in Europe: a literature review and survey of service screening programmes. J Med Screen. 2012;19 Suppl 1:57–66.
7. Roman R, Sala M, Salas D, Ascunce N, Zubizarreta R, Castells X. Effect of protocol-related variables and women’s characteristics on the cumulative false-positive risk in breast cancer screening. Ann Oncol. 2012;23(1):104–11. doi: 10.1093/annonc/mdr032 21430183
8. Roman M, Skaane P, Hofvind S. The cumulative risk of false-positive screening results across screening centres in the Norwegian breast cancer screening program. Eur J Radiol. 2014;
9. 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 Jun;118(12):1559–70. doi: 10.1038/s41416-018-0080-3 29736009
10. Evans DG, Kesavan N, Lim Y, Gadde S, Hurley E, Massat NJ, et al. MRI breast screening in high-risk women: cancer detection and survival analysis. Breast Cancer Res Treat. 2014 Jun;145(3):663–72. doi: 10.1007/s10549-014-2931-9 24687378
11. 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 Apr 4;307(13):1394–404. doi: 10.1001/jama.2012.388 22474203
12. Owens DK, Davidson KW, Krist AH, Barry MJ, Cabana M, Caughey AB, et al. Risk Assessment, Genetic Counseling, and Genetic Testing for BRCA-Related Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2019 Aug 20;322(7):652–65. doi: 10.1001/jama.2019.10987 31429903
13. Pashayan N, Morris S, Gilbert FJ, Pharoah PDP. Cost-effectiveness and Benefit-to-Harm Ratio of Risk-Stratified Screening for Breast Cancer: A Life-Table Model. JAMA Oncol. 2018 Nov 1;4(11):1504–10. doi: 10.1001/jamaoncol.2018.1901 29978189
14. Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [Internet]. updated March 2011. The Cochrane Collaboration; 2011. Available from: Available from http://handbook.cochrane.org
15. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009 Jul 21;6(7):e1000097. doi: 10.1371/journal.pmed.1000097 19621072
16. Jaime Caro J, Eddy DM, Kan H, Kaltz C, Patel B, Eldessouki R, et al. Questionnaire to assess relevance and credibility of modeling studies for informing health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report. Value Health J Int Soc Pharmacoeconomics Outcomes Res. 2014 Mar;17(2):174–82.
17. Venturini E, Losio C, Panizza P, Rodighiero MG, Fedele I, Tacchini S, et al. Tailored breast cancer screening program with microdose mammography, us, and mr imaging: Short-term results of a pilot study in 40-49-year-old wome. Radiology. 2013;268(2):347–55. doi: 10.1148/radiol.13122278 23579052
18. Esserman LJ. The WISDOM Study: breaking the deadlock in the breast cancer screening debate. NPJ Breast Cancer. 2017;3:34. doi: 10.1038/s41523-017-0035-5 28944288
19. Ahern CH, Shih YC, Dong W, Parmigiani G, Shen Y. Cost-effectiveness of alternative strategies for integrating MRI into breast cancer screening for women at high risk. Br J Cancer. 2014;111(8):1542–51. doi: 10.1038/bjc.2014.458 25137022
20. O’Mahony JF, van RJ, Mushkudiani NA, Goudsmit FW, Eijkemans MJ, Heijnsdijk EA, et al. The influence of disease risk on the optimal time interval between screens for the early detection of cancer: a mathematical approach. Med Decis Mak. 2015;35(2):183–95.
21. Pashayan N, Duffy SW, Chowdhury S, Dent T, Burton H, Neal DE, et al. Polygenic susceptibility to prostate and breast cancer: implications for personalised screening. Br J Cancer. 2011;104(10):1656–63. doi: 10.1038/bjc.2011.118 21468051
22. Schousboe JT, Kerlikowske K, Loh A, Cummings SR. Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med. 2011;155(1):10–20. doi: 10.7326/0003-4819-155-1-201107050-00003 21727289
23. Trentham-Dietz A, Kerlikowske K, Stout NK, Miglioretti DL, Schechter CB, Ergun MA, et al. Tailoring Breast Cancer Screening Intervals by Breast Density and Risk for Women Aged 50 Years or Older: Collaborative Modeling of Screening Outcomes. Ann Intern Med. 2016;165(10):700–12. doi: 10.7326/M16-0476 27548583
24. Van Dyck W, Gassull D, Vértes G, Jain P, Palaniappan M, Schulthess D, et al. Unlocking the value of personalised healthcare in Europe—breast cancer stratification. Health Policy Technol. 2012 Jun 1;1(2):63–8.
25. Vilaprinyo E, Forne C, Carles M, Sala M, Pla R, Castells X, et al. Cost-effectiveness and harm-benefit analyses of risk-based screening strategies for breast cancer. PLoS One. 2014;9(2).
26. Wu YY, Yen MF, Yu CP, Chen HH. Individually tailored screening of breast cancer with genes, tumour phenotypes, clinical attributes, and conventional risk factors. Br J Cancer. 2013;108(11):2241–9. doi: 10.1038/bjc.2013.202 23674086
27. Gray E, Donten A, Karssemeijer N, van Gils C, Evans DG, Astley S, et al. Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis. Value Health. 2017 Sep;20(8):1100–9. doi: 10.1016/j.jval.2017.04.012 28964442
28. TBST study group. Tailored Screening for Breast Cancer in Premenopausal Women (TBST) [Internet]. Bethesda (MD): National Library of Medicine (US): ClinicalTrials.gov; 2015. Report No.: NCT02619123. Available from: https://clinicaltrials.gov/ct2/show/NCT02619123
29. MyPeBS. Randomized Comparison Of Risk-Stratified versus Standard Breast Cancer Screening In European Women Aged 40–70 (MyPeBS) [Internet]. 2017. Available from: www.brumammo.be/…/bmm-my-pebs-clinical-trial-protocol.pdf
30. Tice JA, Miglioretti DL, Li C-S, Vachon CM, Gard CC, Kerlikowske K. Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer. J Clin Oncol Off J Am Soc Clin Oncol. 2015 Oct 1;33(28):3137–43.
31. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004 Apr 15;23(7):1111–30. doi: 10.1002/sim.1668 15057881
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PLOS One
2019 Číslo 12
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