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Record W4403134216 · doi:10.1016/j.eclinm.2024.102862

Multivariable models of outcomes with [177Lu]Lu-PSMA-617: analysis of the phase 3 VISION trial

2024· article· en· W4403134216 on OpenAlexaff
Ken Herrmann, Andrei Gafita, Johann S. de Bono, Oliver Sartor, Kim N., Bernd J. Krause, Kambiz Rahbar, Scott T. Tagawa, Johannes Czernin, Ghassan El‐Haddad, Connie C Wong, Zhaojie Zhang, Celine Wilke, Osvaldo Mirante, Michael J. Morris, Karim Fizazi

Bibliographic record

VenueEClinicalMedicine · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
FundersNational Cancer InstituteMedical Research CouncilNational Institutes of HealthMemorial Sloan-Kettering Cancer CenterNovartis
KeywordsMedicineMultivariable calculusPhase (matter)

Abstract

fetched live from OpenAlex

Background [ 177 Lu]Lu-PSMA-617 ( 177 Lu-PSMA-617) prolonged life in patients with metastatic castration-resistant prostate cancer (mCRPC) in VISION (NCT03511664). However, distinguishing between patients likely and unlikely to respond remains a clinical challenge. We present the first multivariable models of outcomes with 177 Lu-PSMA-617 built using data from VISION, a large prospective phase 3 clinical trial powered for overall survival. Methods Adults with progressive post androgen receptor pathway inhibitor and taxane prostate-specific membrane antigen (PSMA)-positive mCRPC received 177 Lu-PSMA-617 plus protocol-permitted standard of care (SoC) or SoC alone. In this post hoc analysis, multivariable Cox proportional hazards models of overall survival (OS) and radiographic progression-free survival (rPFS), and a logistic regression model of prostate-specific antigen response (≥50% decline; PSA50) were constructed and evaluated using C-index or receiver operating characteristic (ROC) analyses with bootstrapping validation. Nomograms were constructed for visualisation. Findings Patients were randomised between June 2018 and October 2019. Data from all 551 patients in the 177 Lu-PSMA-617 arm were analysed in multivariable modelling. The OS nomogram (C-index, 0.73; 95% confidence interval [CI], 0.70–0.76) included whole-body maximum standardised uptake value (SUV max ), time since diagnosis, opioid analgesic use, aspartate aminotransferase, haemoglobin, lymphocyte count, presence of PSMA-positive lesions in lymph nodes, lactate dehydrogenase (LDH), alkaline phosphatase (ALP), and neutrophil count. The rPFS nomogram (C-index, 0.68; 0.65–0.72) included SUV max , time since diagnosis, opioid analgesic use, lymphocyte count, presence of liver metastases by computed tomography, LDH, and ALP. The PSA50 nomogram (area under ROC curve, 0.72; 95% CI, 0.68–0.77) included SUV max , lymphocyte count and ALP. Performances of the OS and rPFS models were maintained when they were reconstructed excluding SUV max . Interpretation These models of outcomes with 177 Lu-PSMA-617 are the first built using prospective phase 3 data. They show that a combination of pretreatment laboratory, clinical, and imaging parameters, reflecting both patient and tumour status, influences outcomes. These models are important for aiding treatment selection, patient management, and clinical trial design. Funding Novartis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.478
Teacher spread0.388 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized 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

Citations23
Published2024
Admission routes1
Has abstractyes

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