Abstract B047: Development and validation of an improved risk stratification model for ovarian cancer
Notice bibliographique
Résumé
Abstract Based on previous models, among individuals who are not known to carry a pathogenic variant, lifetime risk of ovarian cancer ranges between ~0.1% and ~11%. Risk stratification to identify the people at the higher end of this lifetime risk range is of paramount importance for prevention efforts. Previous risk stratification models for ovarian cancer were based on a limited number of risk/protective factors. Further, we have also shown that risk estimates differ by menopausal status which most models have not considered. We aimed to develop and internally validate a risk stratification model for ovarian cancer that considers 15 unequivocal risk/protective factors and properly accounts for effect modification by menopausal status. We used data from nine studies (7,984 cases, 12,260 controls) participating in the Ovarian Cancer Association Consortium (OCAC). The data were split into a training set and a test set that comprised 80% and 20% of the OCAC dataset, respectively. Seven risk factors (body mass index, height, later age at menopause, menopausal hormonal therapy use, first-degree family history of ovarian cancer, endometriosis, and a polygenic score of 36 common genetic variants) and eight protective factors (later age at menarche, parity, breastfeeding, incomplete pregnancy, later age at last pregnancy, tubal ligation, combined oral contraceptive use and depot-medroxyprogesterone acetate use) were included. Other risk/protective factors such as talcum powder or aspirin use were not included due to high proportions of missing values. We fit multiplicative logistic regression models separately by menopausal status group in the training set to determine the associations between the factors and ovarian cancer. All models were adjusted for race/ethnicity, education level, age and OCAC study. In the test set, we calculated a summary relative risk for every combination of the 15 risk/protective factors (hereafter called a risk profile) based on the estimates from the training set. The summary relative risk was then translated into an absolute risk: the frequency-weighted average of all the profile-specific relative risks was scaled to the average absolute risk, and then this scaling factor was applied to each profile-specific relative risk and its confidence interval (CI). The range of absolute lifetime risks observed in the test set was 0.1%-7.2% using 15 factors, accounting for menopausal status. The area under the receiving operating curve (AUC) was 0.67 (95% CI 0.65-0.68). This is slightly higher than the previous risk stratification models (AUC=0.55-0.66). External validation of our risk stratification model in a longitudinal cohort is warranted, as our findings suggest that there is a subset of individuals at the higher end of the risk range who would be potential candidates for primary prevention strategies including salpingectomy. Citation Format: Minh Tung Phung, Alice W. Lee, Karen McLean, Lilah Khoja, Hoda Anton-Culver, Elisa V. Bandera, Jennifer Anne Doherty, Renee T. Fortner, Marc T. Goodman, Francesmary Modugno, Paul D. P. Pharoah, Kathryn L. Terry, Penelope M. Webb, Anna H. Wu, Andrew Berchuck, Gillian E. Hanley, Bhramar Mukherjee, Malcolm C. Pike, Celeste Leigh Pearce, Britton Trabert. Development and validation of an improved risk stratification model for ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B047.
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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 | 0,009 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».