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Disease-free survival as a surrogate endpoint for overall survival in adults with early-stage pancreatic cancer: A trial-level surrogacy analysis.

2025· article· en· W4406869677 on OpenAlexaff
Luís Felipe Leite da Silva, Luiz F. Costa de Almeida, Mariana Macambira Noronha, Lucas Diniz da Conceição, Marcos Belotto, Thais Baccili Cury Megid, Renata D’Alpino Peixoto

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsBC Cancer AgencyDr. Georges-L.-Dumont University Hospital Centre
Fundersnot available
KeywordsMedicinePancreatic cancerSurrogate endpointStage (stratigraphy)DiseaseClinical endpointOncologyOverall survivalInternal medicineCancerSurvival analysisRandomized controlled trial

Abstract

fetched live from OpenAlex

708 Background: In early-stage cancer, overall survival (OS) is the gold-standard endpoint for assessing therapeutic efficacy. However, long follow-up periods make OS challenging and costly to measure in clinical trials. Disease-free survival (DFS), which captures the time to recurrence or death, has been proposed as a surrogate endpoint for OS in early-stage pancreatic cancer (PC). This surrogacy relationship could allow for a more rapid assessment of treatment benefits, but its validity requires robust analysis across clinical trials. Understanding the trial-level correlation between DFS and OS is crucial to optimizing trial design and grant accelerated drug approval. Methods: We conducted a systematic search of Embase, PubMed, and Cochrane databases through August 2024, identifying randomized controlled trials (RCTs) that evaluated adjuvant or neoadjuvant systemic therapies in adult patients with PC. Included studies reported both OS and DFS outcomes. Hazard ratios for DFS (HR_DFS) were analyzed as surrogate endpoints for OS (HR_OS). Statistical analyses utilized linear regression models, weighted by trial sample sizes, to determine the strength of the correlation. Surrogacy strength was categorized by the coefficient of determination (R²), with R² ≥ 0.7 indicating a strong correlation, 0.69–0.5 a moderate correlation, and values < 0.5 indicating weak correlation. Results: Out of 3,048 studies screened, 30 RCTs met the inclusion criteria, comprising 5,275 patients in adjuvant therapy trials and 557 patients in neoadjuvant therapy trials. The median follow-up duration ranged from 16 to 53.5 months, with 12 phase II trials included. Overall, DFS showed strong surrogacy for OS in adjuvant trials (R² = 0.70; 95% CI, 0.50–0.91). Subgroup analysis indicated strong correlation in phase III trials (R² = 0.71; 95% CI, 0.46–0.96) and moderate correlation in phase II trials (R² = 0.67; 95% CI, 0.21–1.00). The strongest surrogacy was observed in studies incorporating adjuvant radiation therapy (R² = 0.81; 95% CI, 0.52–1.00). In the analysis of neoadjuvant trials, HR-DFS also presented strong correlation with OS (R² = 0.90; 95% CI, 0.56–1.00). Conclusions: There is a strong trial-level correlation between DFS and OS in both adjuvant and neoadjuvant therapy trials for early-stage pancreatic cancer, particularly in phase III studies and trials incorporating adjuvant radiation therapy. However, the strength of this surrogacy varies depending on the treatment setting and trial design.

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)0.072
metaresearch head score (Gemma)0.197
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0720.197
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.014
Bibliometrics0.0060.009
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.149
GPT teacher head0.495
Teacher spread0.345 · 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 designMeta-analysis
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
Published2025
Admission routes1
Has abstractyes

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