MétaCan
Menu
← Back to cohort

The impact of neoadjuvant treatment (NAT) on surgery in early breast cancer (EBC): A real-world data study.

2022· article· en· W4281619183 on OpenAlexaff
Thibaut Sanglier, Ryan Ross, Kristoph Klein-Panneton, Raf Poppe, Vincent Antao, Eleftherios P. Mamounas, Henry Cain

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsRoche (Canada)
FundersF. Hoffmann-La Roche
KeywordsMedicineLumpectomyBreast cancerAxillary Lymph Node DissectionAxillaNeoadjuvant therapySentinel nodeSurgeryMastectomyStage (stratigraphy)NatLymph nodeBreast surgeryBreast-conserving surgeryCancerInternal medicineSentinel lymph node

Abstract

fetched live from OpenAlex

e12609 Background: NAT is being used with increasing frequency in the multimodal treatment of EBC. Oncologically, NAT provides an opportunity to assess response to early systemic therapy and tailor adjuvant treatment. The surgical objective of NAT is down staging the tumor, which may lead to improved chances of breast conservation surgery; potentially decreasing surgical morbidity. Down staging of axillary disease may also lead to minimizing the need for complete axillary lymph node dissection (ALND) and its associated surgical morbidity. We aimed to describe the potential impact of NAT on surgical de-escalation in the primary breast tumor and axilla in patients (pts) with HER2-positive, node-positive EBC treated in the community setting. Methods: We conducted a retrospective cohort study using a nationwide US electronic health record-derived de-identified database (Flatiron Health). Pts were diagnosed from Jan 1, 2011 to Sep 30, 2021, had reported clinical staging cN≥1, underwent primary breast surgery (index date), and had records indicating initiation of systemic treatment before surgery. Early clinical benefit includes, post-NAT: a) nodal pathologic complete response (pCR; pN=0); b) pCR (absence of residual invasive disease in the breast and axillary lymph nodes at NAT completion, subsequently confirmed at primary surgery); c) down staging (lowered T or N stage). Surgical operations recorded at index date were ALND and sentinel node excision (SNE), mastectomy (MT), and lumpectomy (LT). Results: 174 pts were included. At diagnosis, median age was 55 years, 162 pts (93%) presented with invasive ductal carcinoma, and <5 had bilateral BC. 124 pts (71%) were treated with dual HER2 blockade. As for early clinical benefit, nodal pCR was achieved in 106 pts (61%), pCR in 90 (52%), and down staging in 137 (79%). Types of surgical procedure for the axilla were SNE only for 62 pts (36%), ALND only for 84 (48%), and SNE and ALND for 26 (15%). Surgical procedures for the breast were MT for 118 pts (68%), of which 59 were bilateral, and LT for 55 (32%). Compared with not achieving nodal pCR, achieving nodal pCR was associated with increased odds of SNE only (odds ratio [OR] 2.59 [95% confidence interval (CI) 1.28, 5.53]), decreased odds of ALND (OR 0.47 [95% CI 0.23, 0.93]), increased odds of LT (OR 2.05 [95% CI 1.00, 4.39]), and decreased odds of MT (OR 0.47 [95% CI 0.22, 0.96]). Conclusions: In a real-world population of pts with HER2-positive EBC, NAT use was associated with early clinical benefit, potentially leading to less invasive primary surgery of the breast and axilla. The high use of MT and bilateral MT despite a high pCR rate demonstrates the multiple factors at play behind surgical and pt decision making. These data illustrate how NAT use can benefit pts by de-escalating surgical treatment options. Limitations include the modest sample size and lack of association between early and long-term clinical benefit.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.167
GPT teacher head0.504
Teacher spread0.337 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicBreast Cancer Treatment Studies→French-language works237,207→