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Abstract PO4-18-01: International Validation of a Staging Model for de novo Metastatic Breast Cancer

2024· article· en· W4396632991 on OpenAlexaff
Jennifer K. Plichta, Samantha M. Thomas, Sabine Siesling, Linda de Munck, Amélie Lusque, Thomas Grinda, Lovedeep Gondara, Stephen Chia, Paula Cabrera‐Galeana, Nancy Reynoso‐Noverón, Sara López‐Tarruella, Isabel Álvarez, Stephen B. Edge, Gabriel N. Hortobágyi

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineCancerBreast cancerMetastatic breast cancerOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Given the heterogeneity in outcomes for de novo metastatic breast cancer (dnMBC), a staging system was recently developed that refines prognostic estimates for patients presenting with distant metastases (JCO 2023;10:2546). This staging model was developed using the National Cancer Database and stratified patients into 4 subgroups, termed IVA, IVB, IVC, and IVD based on 3-year overall survival (OS): IVA >70%, IVB 50-70%, IVC 25-< 50%, and IVD < 25%; the primary factors affecting higher stage grouping were negative for ER, PR, and HER2 expression and a higher number of involved organ sites. Here, we aim to validate this model using an international data set. Methods Data for dnMBC patients were obtained from the Netherlands Cancer Registry (NCR, hosted by IKNL), Epidemiological Strategy and Medical Economics (ESME) by Unicancer, British Columbia Cancer (BCCan), Instituto Nacional de Cancerología, México (INCan), and Grupo Español de Investigación en Cáncer de Mama (GEICAM); the diagnosis years included varied by organization (range 2002-2021). Stage groups were assigned based on previously published criteria, defined by T-category, grade, ER, PR, HER2, histology, organ system site of metastases (bone-only, brain-only, visceral), and number of organ systems involved. For each cohort, followup time and OS were estimated using the reverse Kapan-Meier and Kaplan-Meier method, respectively. Median rates were estimated across all cohorts by weighting on cohort sample size or number at risk. Cox proportional hazards models were used to estimate the association of stage with OS after adjustment for age at diagnosis (treatment data not available for all cohorts). For the ESME cohort, a subgroup multivariable analysis was used to estimate the association of stage with OS after adjustment for age, local (surgery or radiotherapy) and first line systemic treatments. Results The final validation cohort was comprised of N=11,199 patients from 5 international organizations: n=5063 (45.2%, IKNL), n=4139 (37.0%, ESME), n=774 (6.9%, BCCan), n=757 (6.8%, INCan), and n=466 (4.2% GEICAM). Median followup across all cohorts was 77.5 months (95% CI 74.3-80.2). Median followup (in months) for the IKNL, ESME, BCCan, INCan, and GEICAM cohorts was 45.3 (95% CI 43.6-47.1), 77.5 (95% CI 74.3-80.2), 105.0 (95% CI 95.9-106.5), 93.0 (95% CI 86.5-102.1), and 49.5 (95% CI 46.5-53.4), respectively. Patients were stratified into stage groups: IVA, n=603 (5.4%); IVB, n=5704 (50.9%); IVC, n=3356 (30.0%); IVD, n=1536 (13.7%). For all cohorts combined, the weighted average OS rates consistently decreased with increasing stage group; similar findings were noted for individual cohorts (all p< 0.001; Table). On multivariable subgroup analysis including age at diagnosis, stage group was significantly associated with OS, and the risk of death increased with increasing stage (all p< 0.001; Table). On multivariable subgroup analysis for n=4139 patients (ESME cohort), stage group remained significantly associated with OS [IVA: reference; IVB: HR 1.51 (95% CI 1.17-1.93); IVC: HR 2.11 (95% CI 1.64-2.72); IVD: HR 3.60 (95% CI 2.75-4.70)] after adjusting for age and treatment. Conclusions These findings provide external validation of the previously published staging guidelines for dnMBC. This may guide future revisions of the AJCC staging guidelines for patients with dnMBC and provide patients and providers valuable information in planning therapy and goals of care as they approach the end-of-life. Table. Citation Format: Jennifer Plichta, Samantha Thomas, Sabine Siesling, Linda de Munck, Amélie Lusque, Thomas Grinda, Lovedeep Gondara, Stephen Chia, Paula Cabrera-Galeana, Nancy Reynoso-Noverón, Sara López-Tarruella, Isabel Álvarez López, Stephen Edge, Gabriel Hortobagyi. International Validation of a Staging Model for de novo Metastatic Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-18-01.

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)0.011
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.112
GPT teacher head0.460
Teacher spread0.348 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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