Abstract P1-01-05: Conducting Ancillary Studies during an Active NCTN/NCORP Screening Trial – The TMIST (ECOG-ACRIN EA1151) Experience
Notice bibliographique
Résumé
Abstract Background: The primary aim of the Tomosynthesis Mammographic Imaging Screening Trial (TMIST) is to determine whether women randomly assigned to be screened through 3-5 rounds with tomosynthesis (TM) have fewer advanced cancers than the population screened with digital mammography (DM) over 3-8 years after entry. In addition, there are 15 secondary aims with data being collected in the areas of imaging assessment, medical physics, breast biology and pathology, long-term follow-up, and health care utilization. Women ages 45 to 74 are eligible to participate. The study will enroll 108,508 women. Participants may also volunteer to contribute blood and/or buccal smears to the TMIST biorepository. Approximately 70% of TMIST participants have agreed to do so. Because of the size of the TMIST study and vast amount of data to be collected, there is an opportunity for investigators to utilize TMIST data to support various research questions not covered in TMIST. Methods:The TMIST study team developed a process where investigators who would like access to the TMIST data can submit a concept while the trial is ongoing for access to data in a protected manner. The process starts with the project investigator reaching out to the TMIST study chair. If the study chair, lead statistician, and ECOG-ACRIN (EA) co-Principal Investigator think the project has promise; a timeline for when the project could take place (either (1) during the TMIST clinical trial or (2) after the end of the trial and publication of the primary paper) is developed. The next steps involve reviews by the TMIST Data Safety and Monitoring Board and the EA Executive Review Committee. All ancillary projects proposed will require an external funding plan and budget before the project moves out of concept review inside of EA. Once the project concept clears all required EA approvals it then goes to the National Cancer Institute (NCI) Division of Cancer Prevention (DCP) for their approval. NCI Central Institutional Review Board (CIRB) approval is also required for the project to start while the trial is still active but is not sought until funding has been received. Two projects have secured external funding, have completed the EA committees’ review processes, and have received NCI approval. One is a case control study assessing short-term breast cancer risk through image-based analysis of screening mammograms (Project PI: Jon Steingrimsson, PhD, Brown University). The second is a case control study to assess the impact of breast compression pressure versus force in screening mammography on the likelihood of developing interval breast cancers (Project PIs: Etta Pisano, MD and Aili Maki, PhD, University of Toronto). Both projects involve analysis of images where software is being applied to TMIST images on computer systems controlled by EA IT personnel. Both projects are expected to be completed in the next year. Two additional projects have been approved for grant submission through the process described above. The PreSCRiB study (PI: Elizabeth Burnside, MD MPH, U of Wisconsin) will utilize Machine Learning applied to TMIST and All of Us data, including genetics, mammograms, social determinates of health and other data to develop individualized screening strategies for women. The second project (PI; Marc Ryser, PhD, Duke University) will utilize TMIST data to validate an algorithm the investigators have developed to assess overdiagnosis. Another project that is in development and will likely be submitted for approval and funding in the next 6-9 months is a collaboration between TMIST and UK-based clinical trial PROSPECTS study teams to compare rates of all cancers and advanced cancers for annual, biennial, and 3-year screening, with analysis by age, race, ethnicity, breast density and other factors. The ongoing TMIST study, as of June 24, 2024, has enrolled 101,394 women. Total enrollment is expected by late 2024 or early 2025. Follow-up on enrolled participants is expected to end in early 2028. Citation Format: Etta Pisano, Constantine Gatsonis, Mitchell Schnall, Melissa Troester, Elodia Cole , Jean Cormack, Jon Steingrimsson, Ilana Gareen, Martin Yaffe, Laura Collins, Amarinthia Curtis, Ruth Carlos, Kathy Miller, Christopher Comstock. Conducting Ancillary Studies during an Active NCTN/NCORP Screening Trial – The TMIST (ECOG-ACRIN EA1151) Experience [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-01-05.
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 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 | 0,058 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,004 |
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 ».