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Enregistrement W2408119890 · doi:10.4137/bcbcr.s38432

A Retrospective Study Evaluating the Impact of Preoperative Breast MRI on Surgical Decision-Making in Young Patients (≤50 Years) with Invasive Breast Cancer

2016· article· en· W2408119890 sur OpenAlexaff
Som D. Mukherjee, Nicole Hodgson, Peter Lovrics, Kavita Dhamanaskar, Terry Minuk, Jonathan Sussman

Notice bibliographique

RevueBreast Cancer Basic and Clinical Research · 2016
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueBreast Cancer Treatment Studies
Établissements canadiensSt. Joseph's HospitalMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMedicineBreast cancerLumpectomyBreast MRIMammographyBreast ultrasoundMagnetic resonance imagingRadiologyMastectomyBreast surgeryPhysical examinationUltrasoundRetrospective cohort studyBreast imagingBreast diseaseCancerSurgeryInternal medicine

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Breast magnetic resonance imaging (MRI) is considered a more sensitive diagnostic test for detecting invasive breast cancer than mammography or breast ultrasound. Breast MRI may be particularly useful in younger premenopausal women with higher density breast tissue for differentiating between dense fibroglandular breast tissue and breast malignancies. The main objective of this study was to determine the impact of preoperative breast MRI on surgical decision-making in young women with breast cancer. METHODS: A retrospective review of patients with newly diagnosed invasive breast cancer and age of ≤50 years was performed. All patients underwent physical examination, preoperative mammogram, breast ultrasound, and bilateral breast MRI. Two breast cancer surgeons reviewed the preoperative mammogram report, breast ultrasound report, and physical examination summary and were asked if they would recommend a lumpectomy, a quandrantectomy, or a mastectomy. A few weeks later, the two surgeons were shown the same information with the breast MRI report and were asked what type of surgery they would now recommend. In each case, MRI was classified by two adjudicators as having affected the surgical outcome in a positive, negative, or neutral fashion. A positive impact was defined as the situation where breast MRI detected additional disease that was not found on physical examination, mammogram, or breast ultrasound and led to an appropriate change in surgical management. A negative impact was defined as the situation where breast MRI led the surgeon to recommend more extensive surgery, with less extensive disease actually found at pathology. No impact was defined as the situation where MRI findings did not alter surgical recommendations or outcomes. RESULTS: Of 37 patients whose charts were reviewed, five patients were deemed to be ineligible due to having received neoadjuvant chemotherapy, having previous breast implants, or having had their tumor fully excised during biopsy. In total, 32 patients met the inclusion criteria of this study and were appropriate for analysis. The median age of our study patient population was 42 years. The pathologic diagnosis was invasive ductal carcinoma in 91% (29/32) of patients and invasive lobular carcinoma in 9% (3/32) of patients. For surgeon A, clinical management was altered in 21/32 (66%) patients, and for surgeon B, management was altered in 13/32 (41%) patients. The most common change in surgical decision-making after breast MRI was from breast-conserving surgery to a mastectomy. Mastectomy rates were similar between both surgeons after breast MRI. After reviewing the pathology results and comparing them with the breast MRI results, it was determined that breast MRI led to a positive outcome in 13/32 (41%) patients. Breast MRI led to no change in surgical management in 15/32 (47%) patients and resulted in a negative change in surgical management in 4/32 (13%) patients. Bilateral breast MRI detected a contralateral breast cancer in 2/32 (6%) patients. CONCLUSIONS: Preoperative breast MRI alters surgical management in a significant proportion of younger women diagnosed with breast cancer. Prospective studies are needed to confirm these findings and to help determine if this change in surgical decision-making will result in improved local control.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,087
Score d'incertitude au seuil0,786

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,051
Tête enseignante GPT0,466
Écart entre enseignants0,415 · 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 tête enseignante, 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

Citations13
Publié2016
Routes d'admission1
Résumé présentoui

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