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Enregistrement W3151903848 · doi:10.1093/jnci/djab055

RE: Advanced Breast Cancer Definitions by Staging System Examined in the Breast Cancer Surveillance Consortium

2021· letter· en· W3151903848 sur OpenAlexaff
Etta D. Pisano, Constantine Gatsonis, Joseph A. Sparano, Melissa A. Troester, Martin J. Yaffe, Elodia B. Cole, Mitchell D. Schnall

Notice bibliographique

RevueJNCI Journal of the National Cancer Institute · 2021
Typeletter
Langueen
DomaineComputer Science
ThématiqueAI in cancer detection
Établissements canadiensUniversity of TorontoSunnybrook Health Science Centre
Organismes subventionnairesNational Institute of Environmental Health SciencesNational Cancer InstituteECOG-ACRIN Cancer Research Group
Mots-clésBreast cancerMedicineCancerOncologyMedical physicsRadiologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

As investigators for ECOG-ACRIN’s Tomosynthesis Mammographic Imaging Screening Trial (TMIST) trial, we are writing to draw attention to conceptual issues in the outcome definitions and study population in Kerlikowske et al. (1), which limit inferences with respect to the TMIST trial. Kerlikowske et al. (1) converted the TMIST primary outcome definition into a staging system for breast cancer and compared it with other staging systems in association with 5-year breast cancer mortality. However, the primary outcome of TMIST is not a cancer staging system but simply a binary classification of cancers as “advanced” or not. TMIST’s endpoint of advanced cancers was defined to identify cancers that generally require chemotherapy, because although chemotherapy prevents many cancer-related deaths, it is also associated with clinically significant morbidity. Reducing chemotherapy-related morbidity is a valuable goal of breast cancer screening. The authors constructed an ordinal categorical response using elements of the TMIST binary endpoint and performed Receiver Operating Characteristic (ROC) analysis on this ordinal categorical response (1). Although ROC analysis cannot be performed as a binary outcome, the relevance of the ordinal comparison for TMIST is not clear. A more relevant comparison would be conducted with the binary assessment that would result from using an American Joint Committee on Cancer stage as threshold for advanced cancer. For example, if stage IIA or IIB is used as the threshold, as was done by Kerlikowske et al. (1), one can estimate measures of performance that are appropriate for binary tests. The relevant measures for predicting cancer death in 5 years, given at the bottom of Table 2 in the JNCI article for the American Joint Committee on Cancer staging systems and at the bottom of Table 3 for the TMIST definition (1), are combined in Table 1 here. Measures for predicting cancer death in 5 years by AJCC staging systems and by TMIST definitiona AJCC = American Joint Committee on Cancer; AJCC Anat = American Joint Committee on Cancer Anatomic stage; AJCC Progn = American Joint Committee on Cancer Prognostic Pathologic stage; TMIST = Tomosynthesis Mammographic Imaging Screening Trial. Measures for predicting cancer death in 5 years by AJCC staging systems and by TMIST definitiona AJCC = American Joint Committee on Cancer; AJCC Anat = American Joint Committee on Cancer Anatomic stage; AJCC Progn = American Joint Committee on Cancer Prognostic Pathologic stage; TMIST = Tomosynthesis Mammographic Imaging Screening Trial. Another important difference in the outcomes relates to follow-up. The article considers 5-year risk of death, which overrepresents deaths from Estrogen Receptor (ER)-negative cancer and neglects longer term risk of ER+ deaths. The majority of screen-detected breast cancers are ER+, and it is important to address mortality from these cancers. The 2-county trial in Sweden showed that more than 15 years of follow-up was needed to demonstrate the full mortality reduction of breast cancer screening and showed that even at 10 years, fewer than one-half of the averted deaths had been observed (2-4). Finally, Kerlikowske et al. (1) report a large (approximately 60%) proportion of advanced cancer in the Breast Cancer Surveillance Consortium (BCSC) population (Table 3), underscoring that the study population was probably not a pure screening population and likely includes symptomatic women, as commonly seen in practice-based (nontrial) data (5). These important conceptual differences limit the implications of Kerlikowske et al. (1) for TMIST. TMIST is conducted by the ECOG-ACRIN Cancer Research Group (Peter J. O’Dwyer, MD, and Mitchell D. Schnall, MD, PhD, Group Co-Chairs) and supported by the National Cancer Institute of the National Institutes of Health (NIH) (award number: UG1CA189828). Role of the funder: The funder had no role in the writing of the correspondence or decision to submit it for publication. Disclosures: The authors all receive funding from ECOG-ACRIN for their work, but have no other disclosures. Author contributions: Conceptualization: EDP, CG, JS, MAT, MY, MDS. Data Curation: CG. Formal Analysis: CG, MAT, MY. Funding Acquisition: EDP, CG, EC, MDS. Investigation: MAT, MY. Methodology: EDP, CG, MAT, MY, EC, MDS. Project Administration: EDP, CG, MAT, MY, EC, MDS. Resources: EDP, CG, MAT, MY, EC. Software: CG, MY. Supervision: EDP, CG, MAT, MY, EC. Validation: CG. Visualization: CG. Writing, original draft: EDP, CG. Writing, review and edit: EDP, CG, JS, MAT, MY, EC, MDS. Disclaimer: The content is solely the opinion of the authors and does not necessarily represent the views of the NIH. The data underlying this correspondence are available in the correspondence itself.

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)0,005
score de la tête « metaresearch » (Gemma)0,031
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,032
Score d'incertitude au seuil0,063

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,031
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0040,001
Communication savante0,0030,002
Science ouverte0,0020,002
Intégrité de la recherche0,0260,019
Charge utile insuffisante (le modèle a refusé de juger)0,0170,018

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 Opus0,050
Tête enseignante GPT0,291
Écart entre enseignants0,242 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations3
Publié2021
Routes d'admission1
Résumé présentnon

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