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Abstract ES7-1: Methods to minimize the false negative rate of sentinel lymph node surgery after neoadjuvant chemotherapy for node positive breast cancer

2019· article· en· W2911838586 on OpenAlexaboutno aff
JC Boughey

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerSentinel nodeSentinel lymph nodeLymph nodeAxillary Lymph Node DissectionAxillary DissectionAxillaChemotherapyDissection (medical)Neoadjuvant therapySurgeryCancerRadiologyInternal medicineOncology

Abstract

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Abstract Neoadjuvant chemotherapy (NAC) is known to decrease the extent of disease in the breast and increase rates of breast conservation. In addition, NAC also can reduce the likelihood of nodal positivity and hence decrease need for axillary node dissection and its associated morbidities. Three prospective clinical trials have assessed the false negative rate (FNR) of SLN after NAC for patients with clinically node-positive disease at presentation. The American College of Surgeons Oncology Group (ACOSOG) Z1071 study reported a false negative rate (FNR) of SLN surgery of 12.6% in patients with cN1 disease with 2 or more SLNs resected. The FNR was lower at 10.6% when dual tracer technique was utilized. Additional analysis showed that when a clip was placed in the positive node at diagnosis and the clipped node was resected as one of the SLNs, the FNR was 6.8%. The Canadian study (SN FNAC - sentinel node following neoadjuvant chemotherapy) reported a FNR of 13.3% when defining SLN with isolated tumor cells (ITC) as negative and 8.4% when including ITC in the definition of a positive SLN. The SENTINA study from Europe reported an overall FNR of 14.2%, however when excluding patients with only a single SLN removed the FNR was 9.8%. Further work with preoperative localization of the clipped node with a seed and resection of the localized clipped node along with the sentinel nodes (termed targeted axillary dissection) has been shown to have a FNR of 2.4%. Surgeons are incorporating SLN after NAC into their clinical practice for patients with a good response to NAC and thus use of SLN surgery after NAC for patients with node-positive breast cancer is increasing. There are multiple methods that can decrease the FNR of the procedure in this setting. These include use of dual tracer for SLN identification, resection of the initial biopsy-proven positive node, resection of at least 2 SLNs and use of immunohistochemical staining of the SLNs. There are several different techniques to assist with ensuring resection of the initially biopsy-proven positive lymph node which include varying methods to mark the node at diagnosis and ways to identify the node at time of SLN surgery. The node can be marked at the time of percutaneous lymph node biopsy (or at a subsequent visit prior to initiation of NAC) with a clip, a radioactive seed, or tattooed with ink. At the time of surgery, if ink or a radioactive seed was placed, the node can be identified using these techniques at the time of axillary surgery. If a clip was placed at diagnosis, this can undergo preoperative localization, with a radioactive seed or wire, to maximize likelihood of identifying the clipped node during surgery. For patients with biopsy-proven node-positive breast cancer, SLN surgery after NAC allows assessment of residual nodal disease and can enable patients who have their axillary disease eradicated by NAC to avoid ALND. Citation Format: Boughey J. Methods to minimize the false negative rate of sentinel lymph node surgery after neoadjuvant chemotherapy for node positive breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr ES7-1.

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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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.403
Teacher spread0.365 · 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 designBench or experimental
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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Citations0
Published2019
Admission routes1
Has abstractyes

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