MétaCan
Menu
← Retour à la cohorte
Enregistrement W4396587423 · doi:10.1158/1538-7445.sabcs23-po3-19-03

Abstract PO3-19-03: Addressing USPSTF 2023 Identified Key Gaps in Knowledge in Breast Cancer Screening through TMIST (ECOG-ACRIN EA1151) or its Ancillary Studies

2024· article· en· W4396587423 sur OpenAlexaff
Etta D. Pisano, Constantine Gatsonis, Mitchell D. Schnall, Melissa A. Troester, Elodia B. Cole, Jean Cormack, Ilana F. Gareen, Martin D. Yaffe, Laura C. Collins, Amarinthia Curtis, Ruth Carlos, Kathy Miller, Christopher Comstock

Notice bibliographique

RevueCancer Research · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueRadiomics and Machine Learning in Medical Imaging
Établissements canadiensHealth Sciences CentreSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineBreast cancerCancerInternal medicineOncologyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Abstract Background The United States Preventative Services Task Force in their 2023 recommendations identified areas where more research data is needed to inform future breast cancer screening recommendations. Research areas identified are: improve clinicians and patients understanding and evaluation of dense breast tissue on a screening mammogram, benefits and harms of supplemental screening using ultrasound or MRI for women with dense breasts, health outcomes such as rates of breast cancer diagnosis requiring treatment, rates of advanced breast cancers diagnosed across consecutive screening rounds, and breast cancer-associated morbidity and mortality, causes of increased risk of breast cancer mortality in black women across spectrum of stages and biomarker patterns, understand why black women are more likely to be diagnosed with breast cancers that have biomarker patterns that are indicative of poor health outcomes, assess benefits/harms differences between annual and biennial screening for breast cancer in women overall and if there are differences between black and white women, approaches to reduce the risk of overdiagnosis leading to overtreatment of breast lesions found at screening that may not cause morbidity and mortality, natural history of DCIS, and identify prognostic indicators of breast tumors that are unlikely to affect quality or length of life. Methods The ongoing TMIST study, currently with 88,801 asymptomatic women presenting for screening mammography ages 45-74 enrolled out of 128,905, could contribute to scientific evidence to support the above research areas through existing study aims and planned ancillary studies. Supplemental Screening with US and MRI: TMIST PreSCRIB will utilize Machine Learning applied to TMIST and All of Us data, including genetics, mammograms, social determinates of health and other data to recommend individualized screening strategies for women. DxMRI is a study where women will get AbMRI at time of Dx work-up. There are plans to use these examinations plus supplemental screening MRIs performed on TMIST subjects in an enriched reader study to evaluate the role of supplemental screening MRI in moderate risk women. Rates of breast cancer treatment, consecutive screening, morbidity, and mortality: TMIST’s primary outcome is the proportion of women experiencing an advanced breast cancer and needing treatment. TMIST is also collecting information on health care utilization following a cancer diagnosis, including types of treatment given, and costs data from the screening and diagnostic work-up visits, and mortality data for study participants. Increased risk of breast cancer mortality in black women: TMIST is performing PAM50 plus p53 status, immune profile, DNA repair phenotype, and 21-gene recurrence assay on all breast cancers and a subset of benign tissue. Blood and buccal smears might also help address this issue. Ongoing work, funded by the Susan B. Komen Foundation, focuses on improving Black participation in TMIST Biorepository (currently about 45% participation of the 21% of TMIST US black subjects). Surveys are planned on perceived racism and social determinates of health as part of DxMRI Study. Screening Frequency: We are developing a collaboration with the UK-based clinical trial PROSPECTS to compare rates of all cancers and advanced cancers for annual, biennial, and 3-year screening. Overdiagnosis, natural history of DCIS, prognostic indicators of breast tumors not impacting quality of life: PRoGram- will use radiomics, genomics and pathomics to develop a greater understanding of the variability of the non-advanced cancers diagnosed in the TMIST population, including DCIS. It is hoped that this model will provide greater understanding of the risk of poor outcomes for women diagnosed with lower risk cancers, including DCIS. Citation Format: Etta Pisano, Constantine Gatsonis, Mitchell Schnall, Melissa Troester, Elodia Cole, Jean Cormack, Ilana Gareen, Martin Yaffe, Laura Collins, Amarinthia Curtis, Ruth Carlos, Kathy Miller, Christopher Comstock. Addressing USPSTF 2023 Identified Key Gaps in Knowledge in Breast Cancer Screening through TMIST (ECOG-ACRIN EA1151) or its Ancillary Studies [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 PO3-19-03.

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)0,206
score de la tête « metaresearch » (Gemma)0,316
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,206
Score d'incertitude au seuil0,979

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,2060,316
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0040,006
Bibliométrie0,0070,007
Études des sciences et des technologies0,0020,002
Communication savante0,0080,006
Science ouverte0,0050,008
Intégrité de la recherche0,0170,008
Charge utile insuffisante (le modèle a refusé de juger)0,0190,007

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 Opus0,240
Tête enseignante GPT0,531
Écart entre enseignants0,290 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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 sujetRadiomics and Machine Learning in Medical Imaging→Travaux en français237 207→