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Sentinel node biopsy following neoadjuvant chemotherapy in biopsy proven node positive breast cancer: The SN FNAC study.

2013· article· en· W4240649963 on OpenAlexaff
Jean-François Boileau, Brigitte Poirier, Mark Basik, Claire Holloway, Louis Gaboury, Lucas Sidéris, Sarkis Meterissian, Angel Arnaout, Muriel Brackstone, David R. McCready, Stephen E. Karp, Frances C. Wright, Rami Younan, Louise Provencher, E. Patocskai, Atilla Ömeroğlu, André Robidoux

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsCentre Hospitalier de l’Université de MontréalPrincess Margaret Cancer CentreHôpital du Saint-SacrementHealth Sciences CentreOttawa HospitalUniversity Health NetworkLondon Health Sciences CentreHôpital Maisonneuve-RosemontUniversité de MontréalInstitute for Research in Immunology and CancerMcGill University Health CentreSunnybrook Health Science CentreJewish General Hospital
Fundersnot available
KeywordsMedicineSentinel nodeAxillaBreast cancerBiopsyAxillary DissectionProspective cohort studyClinical endpointSurgeryCancerRadiologySentinel lymph nodeClinical trialInternal medicine

Abstract

fetched live from OpenAlex

1018 Background: A significant and increasing proportion of patients (>30%) with biopsy proven node positive breast cancer will obtain a pathological complete response (pCR) in the axilla after neoadjuvant chemotherapy (NAC). If sentinel node biopsy (SNB) can accurately identify these patients, they could potentially avoid the morbidity of an axillary node dissection. The primary aim of this study is to evaluate the identification rate (IR), false negative rate (FNR) and accuracy of SNB in this setting. The accuracy of post NAC axillary ultrasound and clinical examination are evaluated as secondary endpoints. Methods: Patients with biopsy proven node positive breast cancer (T0-3, N1-2, M0) treated with NAC were eligible to participate in this multi-centre prospective trial. Following NAC, axillary ultrasound and clinical examination results were obtained. At time of surgery, all participants underwent both a SNB and a completion node dissection. A SNB IR greater than 90% and a FNR of less than 10% were pre-determined as being optimal. Results: From September 2009 to December 2012, 153 patients were accrued to the study. 7 patients were not eligible and 5 patients had not yet undergone surgery at the time of analysis. Axillary pCR rate = 34.0% (48/141). SNB IR = 87.2% (123/141), 95% CI [81.7%-92.7%] and FNR = 9.9% (8/81), 95% CI [3.4%-16.4%]. If only one sentinel node was removed, FNR = 19.0%(4/21); if there were 2 or more sentinel nodes, FNR = 6.6% (4/61) (p < 0.0001). Accuracy of SNB, axillary ultrasound and clinical examination were 93.5%, 63.2%, and 45.5% respectively. Conclusions: SNB following NAC in biopsy proven node positive breast cancer is associated with a suboptimal IR. FNR (less than 10%) and accuracy of SNB in this study are comparable to that of patients that present with clinically negative nodes. The FNR decreases when more than one sentinel node is identified. However, in an era where regional nodal radiation is increasingly used, the relevance of leaving residual disease in the undissected axilla after NAC is unknown and remains to be investigated. Clinical trial information: NCT00909441.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.394
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2013
Admission routes1
Has abstractyes

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