MétaCan
Menu
← Retour à la cohorte
Enregistrement W4396590990 · doi:10.1158/1538-7445.sabcs23-po3-11-10

Abstract PO3-11-10: Second primary non-breast cancers in young breast cancer survivors

2024· article· en· W4396590990 sur OpenAlexaff
B. Zhang, Kristen D. Brantley, Shoshana Rosenberg, Gregory J. Kirkner, Laura C. Collins, Kathryn Ruddy, Rulla M. Tamimi, Lidia Schapira, Virginia F. Borges, Ellen Warner, Steven E. Come, Eric P. Winer, Ann H. Partridge

Notice bibliographique

RevueCancer Research · 2024
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueBreast Cancer Treatment Studies
Établissements canadiensSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésBreast cancerMedicineOncologyCancerInternal medicineGynecology

Résumé

récupéré en direct d'OpenAlex

Abstract Adolescent and young adults (AYAs) (15-39 years) have a heightened risk of developing a second primary malignancy due to extended periods of survivorship, higher incidence of germline cancer predisposing variants, and increased susceptibility to treatment-induced malignancies. Research on survivors of specific AYA cancer types, including breast cancer (BC) survivors, is limited. Among BC survivors, while much survivorship research focuses on risk of locoregional recurrence or contralateral BC, little is known about overall risk, and risk factors, of developing second non-breast primaries. This prospective cohort study examined women diagnosed with BC from 2006-2016 at age ≤40 years enrolled in the Young Women’s BC Study (YWS) (N=1297). Women were excluded from the analytic cohort if initially diagnosed with Stage IV breast cancer (N=64). To enable investigation of how BC treatments may impact second primary risk, women were also excluded if they reported another cancer before primary BC diagnosis (N=3). Patient characteristics, treatment information, and clinical events were collected via serial surveys. Tumor characteristics and detailed treatment data were obtained from medical record review. Five- and 10-year risk of second non-breast primary was estimated via the cumulative incidence function after applying the Fine-Gray competing risks model with time starting at primary BC diagnosis. Death, metastasis, or diagnosis with a second primary BC were considered as competing events. Univariate and multivariate Fine-Gray sub-distribution models were used to estimate sub-distribution hazard ratios (sHRs) and 95% confidence intervals (CI) for second non-breast primary cancer risk. Risk factors considered included age, race, body mass index, smoking, alcohol use, income, family history of cancer, germline pathogenic variant (PV) carrier status, tumor stage, grade, and ER status, primary surgery type, and receipt of radiation, chemotherapy, or endocrine therapy (yes/no). Multivariate models tested individual associations with additional adjustment for radiation therapy and PV carrier status, as both are consistently associated with second primary in the literature. Over a median follow-up of 10.1 years (inter-quartile range (IQR) =7.9-12.1 y), 47 patients (4%) developed a second non-breast primary cancer. Median age at second non-breast primary was 43 years (IQR=39-46), and median time between primary BC and second non-breast primary was 7.3 years (IQR=4.1-9.3). Second primary types included melanoma (n=10), thyroid (n=10), ovarian (n=4), sarcoma (n=4), uterine (n=3), rectal (n=3), bladder (n=2), cervical (n=2), head and neck (n=2), lung (n=2), lymphoma (n=2), pancreatic (n=2), and kidney (n=1). During the study, 22 patients (2%) developed a second primary BC, 167 (19%) developed metastasis, and 15 (1%) died from causes other than BC. Among the patients who developed a second primary BC, two later developed another non-breast cancer (ovarian and brain cancer). When incorporating competing risks, five and 10-year cumulative incidence of second non-breast primary was 1.4% and 3.2%, respectively. No patient or treatment factors were statistically significantly associated with second non-breast primary in univariate or multivariate models, including radiation and PV carrier status. In this population of young BC survivors, 10-year cumulative incidence of second non-breast primary cancer was 3.2%, with the most common second cancers being melanoma and thyroid cancer. Incidence rates of all second primary cancer types in this cohort were higher than population-based incidence rates for healthy women under 50 years of age, highlighting the importance of long-term surveillance for other cancer events in this young population. While risk of second non-breast primary was not associated with primary BC treatment in this study, cases were limited, and the follow-up interval was relatively short. Citation Format: Bessie Zhang, Kristen Brantley, Shoshana Rosenberg, Gregory Kirkner, Laura Collins, Kathryn Ruddy, Rulla Tamimi, Lidia Schapira, Virginia Borges, Ellen Warner, Steven Come, Eric Winer, Ann Partridge. Second primary non-breast cancers in young breast cancer survivors [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 PO3-11-10.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,038

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,351
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueCancer Research→Même sujetBreast Cancer Treatment Studies→Travaux en français237 207→