Abstract ED2-2: Screening for high risk patients: Does everyone need annual MRI with mammogram?
Bibliographic record
Abstract
Abstract Introduction: To reduce mortality, all guidelines advice women with very high breast cancer (BC) risk, due to a pathogenic variant (PV) in genes like BRA1/2 or chest wall irradiation between age 10-30 yrs., annual screening with Magnetic Resonance Imaging (MRI) and 2 or 3D mammography (Mm).1-6 For MRI, starting age for this group is usually 25 years. However for Mm some guidelines advise annual from age 30 yrs.1,2, others 10 yr. younger than the youngest family member 4,5, or for BRCA1 biennial from age 40 yrs.7 USA and Canadian but not European guidelines advice MRI screening also for women with a ≥ 20% lifetime breast cancer (BC) risk, while the European Eusobi guideline, unlike the US and Canadian, now advises to screen women with extremely dense breasts with MRI although not yearly.8 Considerations and evidence: We need to balance the possible benefit with the disadvantages of screening, like false positive rate, possible overdiagnosis and cost. We therefore have to use the optimal frequency of screening, depending on the expected tumor growth rate, which varies with a woman’s age and the cause of the increased risk.9 Two recent randomized trials one in women with familial risk the other for extremely dense breasts showed how much MRI advances BC detection compared to mammography, at which side effects.10,11 Observational and modelling studies show varying additional value of Mm to MRI-screening for different risk – and age groups.12-17 Conclusion: Screening for women at high risk can be better tailored to the age and risk-group. References: 1. American College of Radiology (ACR) Appropriateness Criteria. Breast Cancer Screening: High risk women. www. cancer.org. Assessed 2022-9-17 2. American Cancer Society Guideline High risk. Last Revised Jan. 2022. www.cancer.org 3. Mulder RL, Hudson MM, Bathia S et al. J Clin Oncol. 2020 Dec 10;38(35):4194-4207.Updated Breast Cancer Surveillance Recommendations for Female Survivors of Childhood, Adolescent, and Young Adult Cancer From the International Guideline Harmonization Group. 4. Cancer Care Ontario’s (CCO) “Magnetic Resonance Imaging Screening of Women at High Risk for Breast Cancer“, practice guideline; https://www.easternhealth.ca/wp-content/uploads/sites/2/2018/06/Breast-Magnetic-Resonance-MRI-and-High-Risk-Guideline_2017.pdf. and www.cancercare.on.ca 5. William J, Benjamin OA et al. Breast cancer screening and Diagnosis Version 1 2019.2. NCCN clinical practice guidelines in oncology. J Natl. ComprCaNetwork 2020;18(4):452-78. www.nccn.org. version 1 2022 6. National Institute for Health and Care Excellence (NICE). Familial breast cancer: Classification and care of people at risk of familial breast cancer and management of breast cancer and related risks in people with a family history of breast cancer. www.guidance.nice.org.uk/CG80/Guidance. 7.The Dutch guideline breast screening outside the National screening scheme: https://richtlijnendatabase.nl/richtlijn/borstkanker/screening/screening_buiten_het_bob/screening_buiten_het_bevolkingsonderzoek.html. Assessed 19 September 2022. 8. Mann EM, Athanasiou A, Baltzer PAT et al. Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI). European Radiology 2022;32: 4036-45. 9. Tilanus-Linthorst MMA, Obdeijn AIM, Hop WCJ et al. Age and a BRCA1 or-2 mutation predict breast cancer growth rates in the UK, Dutch and Canadian MRI-screening studies. Clin Ca Res 2007. 10. Saadatmand S, Geuzinge HA, Rutgers EJT, et al. MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial. Lancet Oncol. 2019;20(8):1136-1147. 11. Bakker MF, de Lange SV, Pijnappel RM et al. Supplemental MRI screening for women with extremely dense breast tissue. N Engl.J Med 2019; 381;22:2091-2102. 12. Geuzinge HA, Obdeijn IM, Rutgers EJT et al. Cost-effectiveness of breast cancer screening with magnetic resonance imaging for women at familial risk. JAMA Oncology 2020;6(9):13-81-1389. 13. Geuzinge HA, BakkerMF, Heijnsdijk EAM et al. Cost-Effectiveness of Magnetic Resonance Imaging Screening for Women With Extremely Dense Breast Tissue. J Natl Cancer Inst. 2021 Nov 2;113(11):1476-1483. 14. Chiarelli AM, Blackmore et al. Performance measures of MagneticResonanceImaging plus Mammography in the high risk Ontaria breast screening program. JNCI 2020;112(2):136-144. 15. Obdeijn IM, Heijnsdijk EA, Hunink MG et al. Mammographic screening in BRCA1 mutation carriers postponed until age 40: Evaluation of benefits, costs and radiation risks using models. Eur J Ca 2016; 63:135-42. 16. Obdeijn et al. The supplemental value of mammographic screening over MRI alone in BRCA2 screening. Br Ca Res Treat 2020 ; 181 :581-88. 17. Vreemann S, v Zelst JC, Mann RM et al. Breast Cancer Res 2018;20(1):84 The added value of mammography in different age-groups of women with and without BRCA mutation screened with MRI. Citation Format: Madeleine M.A. Tilanus-Linthorst. Screening for high risk patients: Does everyone need annual MRI with mammogram? [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr ED2-2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".