Publications from the MyPeBS team


Wij zijn er trots op deze recente artikelen te kunnen delen die zijn gepubliceerd door de teams die betrokken zijn bij het MyPeBS-consortium, op het gebied van risicobeoordeling en preventie van borstkanker.

Personalized early detection and prevention of breast cancer: ENVISION consensus statement. Pashayan N et al. Nat Rev Clin Oncol. 2020 Nov;17(11):687-705. doi: 10.1038/s41571-020-0388-9. Epub 2020 Jun 18.

Ovarian and breast cancer risks associated with pathogenic variants in RAD51C and RAD51D. Yang X, Song H, et al.  J Natl Cancer Inst. 2020 Dec 14;112(12):1242-1250. doi: 10.1093/jnci/djaa030.

A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density. Brentnall AR, et al. Int J Cancer. 2020 Apr 15;146(8):2122-2129. doi: 10.1002/ijc.32541. Epub 2019 Jul 13.

Evidence for reducing cancer-specific mortality due to screening for breast cancer in Europe: A systematic review. Zielonke N, et al.  Eur J Cancer. 2020 Mar;127:191-206. doi: 10.1016/j.ejca.2019.12.010. Epub 2020 Jan 10.

Cancer Risks Associated With Germline PALB2 Pathogenic Variants: An International Study of 524 Families. Yang X, et al. J Clin Oncol. 2020 Mar 1;38(7):674-685. doi: 10.1200/JCO.19.01907. Epub 2019 Dec 16

Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. De Koning HJ, et al.  N Engl J Med. 2020 Feb 6;382(6):503-513. doi: 10.1056/NEJMoa1911793. Epub 2020 Jan 29

Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium. Kapoor PM, et al. Int J Epidemiol. 2020 Feb 1;49(1):216-232. doi: 10.1093/ije/dyz193.

International evaluation of an AI system for breast cancer screening. McKinney SM, et al.  Nature. 2020 Jan;577(7788):89-94. doi: 10.1038/s41586-019-1799-6. Epub 2020 Jan 1

Use of anastrozole for breast cancer prevention (IBIS-II): long-term results of a randomised controlled trial. Cuzick J, et al. Lancet. 2020 Jan 11;395(10218):117-122. doi: 10.1016/S0140-6736(19)32955-1. Epub 2019 Dec 12

Improving Workflow Efficiency for Mammography Using Machine Learning. Kyono T, Gilbert FJ, van der Schaar M. J Am Coll Radiol. 2020 Jan;17(1 Pt A):56-63. doi: 10.1016/j.jacr.2019.05.012. Epub 2019 May 30.

New evidence confirms that reproductive risk factors can be used to stratify breast cancer risks: Implications for a new population screening paradigm. Evans DG, Howell SJ, Howell A. Eur J Cancer. 2020 Jan;124:204-206. doi: 10.1016/j.ejca.2019.10.012. Epub 2019 Nov 26

Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Papadimitriou, et al.  Nat Commun. 2020 Jan 30;11(1):597. doi: 10.1038/s41467-020-14389-8.

Breast cancer risk status influences uptake, retention and efficacy of a weight loss programme amongst breast cancer screening attendees: two randomised controlled feasibility trials. Harvie M, et al. BMC Cancer. 2019 Dec 4;19(1):1089. doi: 10.1186/s12885-019-6279-8

Artificial intelligence and breast screening: French Radiology Community position paper. Thomassin-Naggara I, Balleyguier C, Ceugnart L, et al. Diagn Interv Imaging. 2019 Oct;100(10):553-566. doi: 10.1016/j.diii.2019.08.005. Epub 2019 Sep 12.

All-cause mortality versus cancer-specific mortality as outcome in cancer screening trials: A review and modeling study. Heijnsdijk EAM, et al. Cancer Med. 2019 Oct;8(13):6127-6138. doi: 10.1002/cam4.2476. Epub 2019 Aug 18.

Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms. Akselrod-Ballin A, et al. Radiology. 2019 Aug;292(2):331-342. doi: 10.1148/radiol.2019182622. Epub 2019 Jun 18

Methods for Development of the European Commission Initiative on Breast Cancer Guidelines: Recommendations in the Era of Guideline Transparency. Schünemann HJ et al. Ann Intern Med. 2019 Aug 20;171(4):273-280. doi: 10.7326/M18-3445. Epub 2019 Jul 23.

Effect of screening mammography on breast cancer mortality: Quasi-experimental evidence from rollout of the Dutch population-based program with 17-year follow-up of a cohort. Van Ourti T, et al. Int J Cancer. 2020 Apr 15;146(8):2201-2208. doi: 10.1002/ijc.32584. Epub 2019 Aug 7.

The influence of health systems on breast, cervical and colorectal cancer screening: an overview of systematic reviews using health systems and implementation research frameworks.
Priaulx J, et al. J Health Serv Res Policy. 2019 Jul 8:1355819619842314. doi: 10.1177/1355819619842314. [Epub ahead of print] No abstract available.

MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial.
Saadatmand S, et al. FaMRIsc study group. Lancet Oncol. 2019 Aug;20(8):1136-1147. doi: 10.1016/S1470-2045(19)30275-X. Epub 2019 Jun 17.

The WISDOM Personalized Breast Cancer Screening Trial: Simulation Study to Assess Potential Bias and Analytic Approaches.
Eklund M, et al. JNCI Cancer Spectr. 2019 Jan 8;2(4):pky067.

Attitudes towards risk-stratified breast cancer screening among women in England: A cross-sectional survey.
Ghanouni A, et al. J Med Screen. 2019 Nov 8. doi: 10.1177/0969141319883662.

Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants.
Evans DGR, et al. Breast Cancer Res Treat. 2019 Apr 2. doi: 10.1007/s10549-019-05210-2. [Epub ahead of print]

Women’s perceptions of personalized risk-based breast cancer screening and prevention: An international focus group study.
Rainey L, Jervaeus A, Donnelly LS, Evans DG, Hammarström M, Hall P, Wengström Y, Broeders MJM, van der Waal D.Psychooncology. 2019 Mar 7. doi: 10.1002/pon.5051. [Epub ahead of print]

Modifiable risk factors for advanced versus early breast cancer in the French E3N cohort.
Veron L, et al. Int J Cancer. 2019 Apr 19. doi: 10.1002/ijc.32354.

A response to « Personalised medicine and population health: breast and ovarian cancer ».
Antoniou A, et al. Hum Genet. 2019 Mar;138(3):287-289. doi: 10.1007/s00439-019-01984-z. Epub 2019 Feb 27.

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.
Mavaddat N, et al.  Am J Hum Genet. 2019 Jan 3;104(1):21-34. doi: 10.1016/j.ajhg.2018.11.002. Epub 2018 Dec 13.

Genome-wide association study of germline variants and breast cancer-specific mortality.
Escala-Garcia M, et al. Br J Cancer. 2019 Mar;120(6):647-657. doi: 10.1038/s41416-019-0393-x. Epub 2019 Feb 21.

BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors.
Lee A, at al.Genet Med. 2019 Jan 15. doi: 10.1038/s41436-018-0406-9.

Prediction of reader estimates of mammographic density using convolutional neural networks.
Ionescu GV, et al. J Med Imaging (Bellingham). 2019 Jul;6(3):031405. doi: 10.1117/1.JMI.6.3.031405. Epub 2019 Jan 31.

A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations.
Balleyguier C et al. Eur Radiol. 2019 Feb 15. doi: 10.1007/s00330-019-06016-y.

Annual mammography at age 45-49 years and biennial mammography at age 50-69 years: comparing performance measures in an organised screening setting.
Bucchi L, et al. Emilia-Romagna Region Workgroup for Mammography Screening Evaluation.Eur Radiol. 2019 Mar 18. doi: 10.1007/s00330-019-06050-w.

Comparing two visualization protocols for tomosynthesis in screening: specificity and sensitivity of slabs versus planes plus slabs.
Iotti V, et al. RETomo Working Group.Eur Radiol. 2019 Feb 8. do16.