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Abstract A105: Phase 2 trials of linifanib (ABT-869) in advanced hepatocellular, renal cell and non-small cell lung cancer: Associations of response by CT or DCE-MRI with patient outcome

2009· article· en· W2316810850 on OpenAlexaff
Han Chong Toh, Wei Peng Yong, Yu-Ning Wong, Eng‐Huat Tan, Christian Kollmannsberger, Viswanath Devanarayan, Ke Zhang, Yanping Luo, Daniel Chen, Edward Ashton, Justin L. Ricker, Dawn M. Carlson, Pei‐Jer Chen

Bibliographic record

VenueMolecular Cancer Therapeutics · 2009
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineResponse Evaluation Criteria in Solid TumorsHazard ratioOncologyInternal medicineHepatocellular carcinomaProgression-free survivalCancerClinical trialLung cancerPhases of clinical researchSorafenibInterim analysisNuclear medicineUrologyOverall survivalConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background: Linifanib is a novel orally active and selective inhibitor of VEGF and PDGF families of receptor tyrosine kinases. Linifanib has demonstrated antitumor activity in a variety of advanced solid tumors in phase 1 and 2 trials. The objective of this analysis was to assess associations between pt outcome (PFS or OS) and response by CT (per RECIST or tumor volume change), or DCE-MRI (per Ktrans), in patients (pts) enrolled in ongoing phase 2 linifanib monotherapy trials. Methods: Three open-label, phase 2 monotherapy studies were conducted internationally in advanced/metastatic solid tumor pts with hepatocellular carcinoma (HCC), non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC). Interim efficacy and safety results have been previously reported. A retrospective exploratory analysis of associations between pt outcome and CT or DCE-MRI response was conducted. An independent centralized review of tumor response assessment by CT (baseline, and Q8 wks) and DCE-MRI (baseline, and 2 wks) was performed using a uniform charter. CT assessed tumor volume and best percent change from baseline and pts were classified as PR, SD, or PD by RECIST. Baseline Ktrans and percent change in Ktrans were derived from DCE-MRI. Statistical analysis was performed using the Cox proportional hazard model and logrank test, and significance considered at the level of P≥0.05. Threshold estimates were determined by the Bootstrapping & Aggregating Thresholds from Trees (BATTing). Results: Across the 3 phase 2 trials, 236 pts were enrolled from 08/07 to 10/08 at 34 centers: 44 pts with HCC, 53 pts with RCC, and 139 pts with NSCLC. Based on the BATTing methodology, 79/191 evaluable pts had a maximum CT tumor volume decrease of >32%. This was associated with improved OS (log-rank p<0.001) and PFS (log-rank p<0.001). Pts with confirmed or unconfirmed PR (n=27) per RECIST had better OS (p=0.021) and PFS (p=0.017) than those who did not achieve a PR (n=181). Among the 236 pts, 117 had DCE-MRI scans at baseline and Day 15. Of these, 68/117 pts (58%) had baseline Ktrans above the BATTing cutoff of 0.055 and this was associated with improved OS (log-rank, p=0.011), but not PFS (log-rank, p=0.835). DCE-MRI response (Ktrans decrease) at 2 wks was not associated with significant improvement in PFS or OS. Conclusions: CT tumor volume reduction and CT response were associated with improved OS and PFS. Greater baseline Ktrans was associated with improved OS but not PFS. Ktrans changes at 2 wks were not associated with improvement in pt outcome. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A105.

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)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.035
GPT teacher head0.374
Teacher spread0.339 · 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 designNon-randomized trial
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

Citations1
Published2009
Admission routes1
Has abstractyes

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