Abstract PO1-08-10: Impact of Oncologist-Led Genetic Counseling and Testing on Prophylactic Mastectomy Rates Among Multi-Ethnic Women with Operable Breast Cancer
Notice bibliographique
Résumé
Abstract Introduction: Approximately 5-10% of breast cancers are attributed to an inherited pathogenic variant (PV) in breast cancer predisposition genes, such as BRCA1 and BRCA2. Identifying women with hereditary breast cancer syndromes is critical to inform risk-appropriate screening and prevention strategies. For example, contralateral prophylactic mastectomy (CPM) has been shown to reduce the risk of new breast primaries without a demonstrated survival benefit. In a prior study, we found that women with pathogenic/likely pathogenic (P/LP) variants were over four times more likely to undergo CPM compared to those with benign/likely benign (B/LB) variants. In recent years due to expanded indications for genetic testing and social distancing during the COVID-19 pandemic, oncologist-led genetic testing and telehealth genetic counseling has gradually replaced in-person genetic counseling visits. We aimed to understand how changes in the delivery of genetic counseling and testing services impacted CPM rates among multi-ethnic women with operable breast cancer in the post COVID-19 period. Methods: We conducted a retrospective cohort study among 1,080 women diagnosed with unilateral breast cancer who underwent germline genetic testing between 2013 and 2022 at Columbia University Irving Medical Center (CUIMC) in New York, NY. The pre COVID-19 period was defined as 2013-2019 and the post COVID-19 period as 2020-2022. Demographics, including age, race/ethnicity and marital status, and clinical characteristics, such as year of diagnosis, breast cancer stage, tumor hormone receptor status, family history of breast cancer, and genetic test results, were extracted from the electronic health record (EHR). We used univariable and multivariable logistic regression analyses to estimate the odds ratio (OR) and 95% confidence interval (95% CI) associated with each variable and receipt of CPM. Results: Among 1080 evaluable women, mean age at diagnosis was 51.2 years old (SD, 12.3) with 39.6% non-Hispanic White, 27.8% Hispanic, 11.4% non-Hispanic Black, 9.0% Asian, and 12.2% multi-racial/other. Within the overall study population, 12.1% of women had P/LP variants and 21.4% had variants of unknown significance (VUS) results. Non-Hispanic Whites and Blacks had the highest frequency of P/LP variants, whereas VUS results were more common among Hispanics and Asians (see Table). Twenty-three percent of women in the study population underwent CPM. In multivariable analysis, younger age at diagnosis, more advanced stage breast cancer, family history of breast cancer, Hispanic race and P/LP results on germline genetic testing were associated with increased CPM rates. Hispanic women were over 60% more likely to undergo CPM compared to non-Hispanic White women (adjusted OR=1.63, 95% CI=1.07-2.47). No significant change in CPM rates was observed in the post-COVID era compared to the pre-COVID era. Conclusion: We aimed to understand how the transition to telehealth and oncologist-led genetic testing affected CPM rates. Although we did not observe a change in CPM rates in the post-COVID era, Hispanic women had significantly more VUS results and were over 60% more likely to undergo CPM compared to non-Hispanic White women. In future studies, we hope to improve risk communication of genetic test results and the risks and benefits of CPM, particularly in vulnerable and underserved populations. Table 1 Genetic testing results and rates of contralateral prophylactic mastectomy stratified by race/ethnicity in women diagnosed with operable breast cancer and undergoing germline genetic testing from 2013-2022 at Columbia University Irving Medical Center, New York, NY Citation Format: Alissa Michel, Kelly Luo, Vicky Ro, Matthew Fine, Meghna Trivedi, Wendy Chung, Roshni Rao, Tarsha Jones, Elana Levinson, Carrie Koval-Burt, Donna Russo, Ilana Chilton, Rita Kukafka, Katherine Crew. Impact of Oncologist-Led Genetic Counseling and Testing on Prophylactic Mastectomy Rates Among Multi-Ethnic Women with Operable Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-08-10.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».