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Record W4291743997 · doi:10.3390/curroncol29080452

Survival Comparisons between Breast Conservation Surgery and Mastectomy Followed by Postoperative Radiotherapy in Stage I–III Breast Cancer Patients: Analysis of the Surveillance, Epidemiology, and End Results (Seer) Program Database

2022· article· en· W4291743997 on OpenAlexvenueno aff
Wenbin Xiang, Chaoyan Wu, Huachao Wu, Sha Fang, Nuomin Liu, Haijun Yu

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsnot available
FundersNatural Science Foundation of Hubei ProvinceNational Natural Science Foundation of China
KeywordsMedicineNomogramBreast cancerProportional hazards modelMastectomySurveillance, Epidemiology, and End ResultsBreast-conserving surgeryHazard ratioPropensity score matchingStage (stratigraphy)OncologyInternal medicineEpidemiologyUnivariate analysisRadiation therapyMultivariate analysisCancer registryGynecologyCancerConfidence interval

Abstract

fetched live from OpenAlex

Background: This study aims to evaluate the overall and breast cancer-specific survival (BCSS) after breast-conserving surgery (BCS) plus radiotherapy (RT) compared with mastectomy plus RT in resectable breast cancer. Moreover, the aim is to also identify the subgroups who benefit from BCS plus RT and establish a predictive nomogram for stage II patients. Methods: Stage I−III breast cancer patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 1990 and 2016. Patients with available clinical information were split into two groups: BCS plus RT and mastectomy plus RT. Kaplan−Meier survival analysis, univariate and multivariate regression analysis, and propensity score matching were used in the study. Hazard ratio (HR) was calculated based on stratified Cox univariate regression analyses. A prognostic nomogram by multivariable Cox regression model was developed for stage II patients, and consistency index (C-index) and calibration curve were used to evaluate the accuracy of the nomogram in the training and validation set. Results: A total of 24,590 eligible patients were enrolled. The difference in overall survival (OS) and BCSS remained significant in stage II patients both before and after PSM (after PSM: OS: HR = 0.8536, p = 0.0115; BCSS: HR = 0.7803, p = 0.0013). In stage II patients, the survival advantage effect of BCS plus RT on OS and BCSS was observed in the following subgroups: any age, smaller tumor size (<1 cm), stage IIA (T2N0, T0−1N1), ER (+), and any PR status. Secondly, the C-indexes for BCSS prediction was 0.714 (95% CI 0.694−0.734). The calibration curves showed perfect agreement in both the training and validation sets. Conclusions: BCS plus RT significantly improved the survival rates for patients of stage IIA (T2N0, T0−1N1), ER (+). For stage II patients, the nomogram was a good predictor of 5-, 10-, and 15-year BCSS. Our study may help guide treatment decisions and prolong the survival of stage II breast cancer patients.

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)0.004
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.058
GPT teacher head0.375
Teacher spread0.317 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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