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Imaging response to immune checkpoint inhibitors in patients with advanced melanoma: A retrospective observational cohort study.

2023· article· en· W4379338513 on OpenAlexaffabout
Mehul Gupta, Igor Stukalin, Siddhartha Goutam, Daniel E. Meyers, Daniel Yick Chin Heng, Tina Cheng, Jose Gerard Monzon, Vishal Navani

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsInstitute of Cancer ResearchUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineIpilimumabNivolumabInternal medicinePembrolizumabProgressive diseaseOncologyResponse Evaluation Criteria in Solid TumorsContext (archaeology)Retrospective cohort studyCohortCancerImmunotherapyDisease

Abstract

fetched live from OpenAlex

e21524 Background: The association of objective imaging response with first line immune checkpoint inhibitor (ICI) therapy regimes in advanced melanoma remains uncharacterized in a real-world context. We compared likelihood of objective response between patients receiving first line anti-programmed cell death protein 1 (anti-PD1) monotherapies (pembrolizumab or nivolumab), or combination nivolumab with the cytotoxic T-cell lymphocyte-antigen 4 inhibitor ipilimumab and outlined the association between objective response and baseline characteristics and survival outcomes. Methods: We conducted a multi-center retrospective cohort analysis of advanced melanoma patients receiving first line ICI therapy from 2013-2020 in Alberta, Canada. Best imaging response was assessed by Response Evaluation Criteria in Solid Tumors, Version 1.1. The primary outcome was likelihood of objective imaging response (complete or partial response) between patients receiving anti-PD1 monotherapy and those receiving combination nivolumab-ipilimumab. Secondary outcomes were the identification of baseline characteristics associated with non-response and the association of imaging response with overall survival (OS) and time to next treatment (TTNT) endpoints. Results: Of the 255 patients included, 49/255 (19.2%) had complete response 112/255 (43.9%) had partial response, 28/255 (11.0%) had stable disease, and 66/255 (25.9%) had progressive disease. Median OS was not evaluable (NE) (95% CI NE-NE) for complete responders, NE (95% CI 52.93 months-NE) for partial responders, 21.30 months (95% CI 15.20 months-NE) for stable disease, and 7.72 months (95% CI 5.77-10.6 months) for progressive disease (log rank p < 0.0001). A robust delineation of TTNT by imaging response was also seen. Likelihood of objective response was similar between patients treated with nivolumab-ipilimumab compared to those receiving anti-PD1 monotherapy (OR 1.87 95% CI 0.85-4.20, p = 0.124). Normal LDH level (OR 2.13; 95% CI 1.01-4.35, p = 0.047), non-mucosal primary site (OR 9.09; 95% CI 2.44-33.33, p < 0.001), and absence of BRAF V600E mutation (OR 3.23; 95% CI 1.39-7.69, p = 0.007) were independently associated with likelihood of objective imaging response in multivariable analysis. Conclusions: In this real-world analysis, we demonstrate no significant difference in likelihood of objective response between patients treated with anti-PD1 monotherapy and combination nivolumab-ipilimumab, and demonstrate that a normal LDH level, non-mucosal primary site, and absence of BRAF V600E mutation are associated with response. Imaging response was also strongly associated with both OS and TTNT irrespective of therapy received. These results may help inform treatment selection, and aid in counseling of advanced melanoma patients treated with first line ICI therapy in a routine clinical practice setting.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.414
Teacher spread0.364 · 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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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