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Enregistrement W2011407375 · doi:10.1200/jco.2014.56.2975

How Well Does Supplemental Screening Magnetic Resonance Imaging Work in High-Risk Women?

2014· letter· en· W2011407375 sur OpenAlexaboutno aff
Wendie A. Berg

Notice bibliographique

RevueJournal of Clinical Oncology · 2014
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueBRCA gene mutations in cancer
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineMagnetic resonance imagingOncologyRadiology

Résumé

récupéré en direct d'OpenAlex

There has been much recent criticism of breast cancer screening. Long-term results of randomized controlled trials of mammography on average show a decrease in breast cancer mortality of 22% in women age 50 to 74 years and 15% in women age 39 to 49 years. This benefit to mammography is offset by high rates of false positives, with 10% of women recalled for additional testing each year to diagnose cancer in 0.2% to 0.7% of women screened. Further, at least some of the cancers found with screening mammography would never otherwise be diagnosed in the patient’s lifetime. Such overdiagnosis results in potentially harmful treatments. The magnitude of such overdiagnosis is a topic of much debate, but is likely to represent at least 10% of all breast cancers found on screening mammography. Most such overdiagnosis is likely due to low-grade ductal carcinoma in situ (DCIS), and some subcentimeter low-grade, estrogen receptor–positive invasive cancers. The ability to stratify women on the basis of breast cancer risk presents additional opportunities and challenges for screening, particularly with discovery of disease-associated variations in the susceptibility genes BRCA1 and BRCA2. In the article that accompanies this editorial, one of the first challenges to be overcome by Chiarelli et al in the Ontario Breast Screening Program (OBSP) was that of assessing the family history and other risk factors of women who might be eligible for high-risk screening and performing genetic assessment as appropriate. Bellcross et al estimated that approximately 6% of women in Detroit have a family history of breast and/or ovarian cancer that met the 2005 United States Preventive Services criteria for genetic counseling. However, after providing such history, in a 2008 survey, only 20% of women at risk were correctly identified by their practitioners and referred for such counseling. It is not clear what the success rate is in the OBSP for identifying and referring such women for counseling or what percentage of women in the OBSP are considered high risk. It is also not explicitly clear what family history criteria or risk assessment tools were used by primary care physicians in Ontario; many such tools have been validated (reviewed in Nelson et al). Based on family history, after women were referred for counseling, the IBIS or BOADICEA models were used to identify women eligible for high-risk screening ( 25% lifetime risk of breast cancer), and 1,629 of 5,201 (31.3%) of those referred were eligible. Women who were known to have pathogenic mutations in BRCA1 or BRCA2 or other predisposing pathogenic mutations or prior chest radiation therapy before age 30 years and at least 8 years earlier, as well as first-degree untested relatives of such patients, were also eligible for high-risk screening. The OBSP recommendation that high-risk women age 30 to 69 years undergo annual screening with magnetic resonance imaging (MRI) as a supplement to digital mammography parallels that of the American Cancer Society. Women with a personal history of breast cancer who met other high-risk criteria were included and comprised 226 of 2,290 (9.9%) of the population studied, though details of results in this subgroup are not provided. No trials have shown that screening mammography reduces breast cancer mortality in high-risk women. Several small studies have shown reduced mammographic sensitivity, more node-positive disease, and higher interval cancer rates (ie, rates of cancer detected clinically in the interval between screens) in known or suspected BRCA1/2 carriers compared with average-risk women. Compared with women without pathogenic mutations, aggressive, estrogen receptor–negative, and triple-negative cancers are more common in BRCA1/2 carriers and tend to occur at a younger age, especially in BRCA1 carriers. Although prophylactic mastectomy greatly reduces the risk of breast cancer and of death as a result of breast cancer in women with pathogenic mutations (reviewed in Nelson et al), women who undergo mastectomy experience pain and reduced enjoyment of sex, thus making effective methods of imaging surveillance a desirable alternative. To what extent does the addition of annual MRI to mammography in high-risk women increase cancer detection compared with mammography alone? Chiarelli et al reported detection of 15 cancers in 813 screens (18.5 per 1,000; 95% CI, 10.2 to 29.7) among BRCA1 or BRCA2 carriers as a result of the first screening MRI in an organized screening program. Among 1,158 women screened as a result of family history (lifetime risk 25%), six (5.2 per 1,000, 95% CI, 1.9 to 11.2) were found to have cancer only on MRI in the first screening round. Another two cancers were found in women with prior chest radiation therapy before age 30 years. Of the total 23 cancers found by MRI alone, 17 (74%) were invasive. This represents an early report on a program started in July 2011 with follow-up through March 2013, so many important parameters remain to be assessed. Analysis of the benefits of screening in high-risk women will require consideration of factors other than cancer detection rates alone. Chiarelli et al did not report further detail of the cancers found only with MRI. Molecular subtype and node status will be important to know for invasive cancers, and additional analyses are planned. In a prior elegant analysis of Warner et al encompassing 435 BRCA1/2 carriers who underwent MRI in Ontario and 830 controls matched for JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 32 NUMBER 21 JULY 2

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)0,015
score de la tête « metaresearch » (Gemma)0,123
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,079

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,123
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0010,001
Intégrité de la recherche0,0050,004
Charge utile insuffisante (le modèle a refusé de juger)0,0050,002

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.

Tête enseignante Opus0,028
Tête enseignante GPT0,356
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

Citations15
Publié2014
Routes d'admission1
Résumé présentoui

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