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Record W4414799819 · doi:10.1093/neuonc/noaf193.070

OS09.4.A EARLY TUMOR VOLUME CHANGES PREDICT CLINICAL OUTCOMES IN MIDH1/2 GLIOMA: POST-HOC RESULTS FROM THE PHASE 3 INDIGO TRIAL

2025· article· en· W4414799819 on OpenAlexaff
Benjamin M. Ellingson, Ingo K. Mellinghoff, Martin J. van den Bent, Deborah T. Blumenthal, Mehdi Touat, Katherine B. Peters, John Clarke, Jose Carlos Méndez, Shlomit Yust‐Katz, Liam Welsh, Warren Mason, François Ducray, Yoshie Umemura, Burt Nabors, Matthias Holdhoff, Andreas F. Hottinger, Yoshiki Arakawa, Juan Manuel Sepúlveda-Sánchez, Wolfgang Wick, Riccardo Soffietti, Jennifer C. Perry, Pierre Giglio, M. de la Fuente, Elizabeth A. Maher, Dongdong Zhao, Susan Pandya, Linda S. Steelman, Islam Hassan, Patrick Y. Wen, Timothy F. Cloughesy

Bibliographic record

VenueNeuro-Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science CentreToronto General Hospital
Fundersnot available
KeywordsConfidence intervalMagnetic resonance imagingHazard ratioProportional hazards modelPlaceboVolume (thermodynamics)Isocitrate dehydrogenaseClinical trial

Abstract

fetched live from OpenAlex

Abstract BACKGROUND In the Phase 3 INDIGO trial (NCT04164901), vorasidenib, an oral, brain-penetrant, dual inhibitor of mutated isocitrate dehydrogenase 1/2 (mIDH1/2), improved progression-free survival (PFS) versus placebo in patients with mIDH1/2 diffuse glioma. While two-dimensional (2D) measurements of tumors remain the standard for clinical trials, tumor volume better captures the true extent of disease and can provide quantitative evaluation of treatment effect through analysis of growth dynamics. We utilized data from the INDIGO trial to evaluate tumor volume as a predictive marker in mIDH1/2 glioma. MATERIAL AND METHODS PFS was assessed by blinded independent review committee (BIRC) using modified Response Assessment in Neuro-Oncology for low-grade gliomas criteria (2D). Tumor volume was measured using semiautomated segmentation on magnetic resonance imaging scans at baseline and every 12 weeks (3D). Pearson correlation coefficient and mixed-effect models were used to evaluate relationships between 2D and 3D assessments. Cox proportional hazards models were used to analyze associations between baseline tumor volume, tumor growth rate and clinical outcomes, including PFS and time to next intervention (TTNI). RESULTS Despite more fluctuations and variability in 2D measurements than in 3D measurements over time, there was a strong positive correlation both for individual measurements (R=0.858) and when accounting for repeated measurements within the same patients (R=0.861). While baseline tumor volume appeared prognostic for TTNI, it only trended toward association with PFS by BIRC. Increase in volumetric growth at any time point during treatment increased the risk of progression or death (hazard ratio [HR] 1.061; 95% confidence interval [CI] 1.038, 1.084; P<0.0001) and the risk of next intervention or death (HR 1.059; 95% CI 1.037, 1.082; P<0.0001). Volumetric growth during the first 6 months of treatment was predictive of PFS (HR 1.046; 95% CI 1.026, 1.067; P<0.0001) and TTNI (HR 1.051; 95% CI 1.031, 1.071; P<0.0001) regardless of treatment assignment. CONCLUSION Using data from the Phase 3 INDIGO trial of patients with mIDH1/2 glioma, we found a high correlation between 2D and 3D assessments. While baseline tumor volume showed variable prognostic value, the substantial association between volumetric growth rate and outcomes suggests that early tumor volume changes can serve as reliable surrogate for both PFS and TTNI. These findings support the inclusion of 3D volumetric assessment in future clinical trials and treatment evaluation of diffuse gliomas. Study sponsored by Servier.

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)0.006
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.377
Teacher spread0.334 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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