MétaCan
Menu
Back to cohort

Second primary breast cancer in young breast cancer survivors.

2023· article· en· W4379345936 on OpenAlexaff
Kristen D. Brantley, Shoshana M. Rosenberg, Laura C. Collins, Kathryn J. Ruddy, Rulla M. Tamimi, Lidia Schapira, Virginia F. Borges, Ellen Warner, Steven E. Come, Gregory J. Kirkner, Craig Snow, Ann H. Partridge

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersBreast Cancer Research Foundation
KeywordsMedicineBreast cancerCancerProportional hazards modelInternal medicineOncologyGenetic testingProspective cohort studyCohortQuartileCancer registryGynecologyConfidence interval

Abstract

fetched live from OpenAlex

10503 Background: Prior data suggests that the risk of second primary breast cancer (SPBC) is higher among women diagnosed with primary breast cancer (BC) before age 40. There is evidence that risk differs by primary tumor characteristics, cancer treatments, and genetic predisposition. However, studies investigating risk of SPBC by clinical risk factors are often limited by low numbers of young women and lack of complete patient data, particularly regarding germline genetic risk. Methods: Using the Young Women’s BC Study (YWS), a prospective cohort of 1,302 women ≤40 years diagnosed with BC from 2006-2016, we sought to compare clinical and treatment characteristics among women who developed an SPBC vs. those who did not. The analytic cohort included women diagnosed with Stage 0-III BC who did not undergo bilateral mastectomy (N = 685). Demographics, genetic testing and results, clinical events, and treatment data were collected through self-report on baseline and follow-up surveys (every 6 months for 3 years, then annually). Detailed tumor and treatment information, confirmation of genetic testing results, and new and recurrent cancer events were obtained through regular and triggered medical record review. Univariate and multivariate Cox proportional hazards models were used to examine associations of age, race, genetic testing results, hormone receptor status, stage, and grade of primary tumor, and cancer treatments with SPBC. Participants were censored at the time of local recurrence, metastasis, death, or loss of follow-up. Results: At a median follow-up time of 9.9 years [inter-quartile range (IQR) = 6.6-12.0 y], 17 patients developed an SPBC (2.5%), in whom 3 occurred in the ipsilateral breast after lumpectomy as primary BC surgery. Median time from primary BC diagnosis to SPBC diagnosis was 4.2 years (IQR = 3.3-5.6 y). Compared to those who did not develop an SPBC, a greater percentage of women who developed an SPBC had in situ disease as their primary BC (24% v. 6%) and were of Ashkenazi descent (12% v. 5%). In the 577 women who had had genetic testing, 544 (94%) were non-carriers of pathogenic variants. Risk of SPBC was 2.2% (N = 12) in non-carriers and 9.1% (N = 3) in the 33 carriers. In Cox univariate analyses, carriers had a 4.1 times higher risk of SPBC compared to non-carriers (HR = 4.14, 95% confidence interval (CI) = 1.67-14.66, p = 0.03), and primary diagnosis with in situ BC was associated with a 5-fold increased risk of SPBC (HR = 5.02, 95% CI = 1.63-15.41, p = 0.005). Both factors remained strongly associated with SPBC when tested together in a multivariate model. Conclusions: This prospective study demonstrated a low risk of SPBC in the first 10 years after diagnosis of early BC in young women who are not germline pathogenic variant carriers. The finding that risk of SPBC was higher after in situ primary BC at a young age warrants further investigation. These findings have important implications for the treatment and follow-up care of young women with BC.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.078
GPT teacher head0.443
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 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicMultiple and Secondary Primary CancersFrench-language works237,207