Evidence map›Paper›PMID 38124790›Full record

ArticleJTO clinical and research reports2023

Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study.

Sevinj Yolchuyeva, Elena Giacomazzi, Marion Tonneau, Fabien Lamaze, Michele Orain, François Coulombe, Julie Malo, Wiam Belkaid, Bertrand Routy, Philippe Joubert and 1 more

Open access · goldAbstract read
In one paragraph

Article in JTO clinical and research reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. CT-Based Radiomics forCancers · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 3 countries.

Sevinj YolchuyevaDepartment of Mathematics and Computer Science, Université du Québec à Trois Rivières, Quebec, Canada.
Elena GiacomazziDepartment of Mathematics and Computer Science, Université du Québec à Trois Rivières, Quebec, Canada.
Marion TonneauCentre de Recherche du Centre Hospitalier Universitaire de Montréal, Quebec, Canada.
Fabien LamazeQuebec Heart & Lung Institute Research Center, Quebec, Canada.
Michele OrainQuebec Heart & Lung Institute Research Center, Quebec, Canada.
François CoulombeQuebec Heart & Lung Institute Research Center, Quebec, Canada.
Julie MaloCentre de Recherche du Centre Hospitalier Universitaire de Montréal, Quebec, Canada.
Wiam BelkaidCentre de Recherche du Centre Hospitalier Universitaire de Montréal, Quebec, Canada.
Bertrand RoutyCentre de Recherche du Centre Hospitalier Universitaire de Montréal, Quebec, Canada.
Philippe JoubertQuebec Heart & Lung Institute Research Center, Quebec, Canada.
Venkata S K ManemDepartment of Mathematics and Computer Science, Université du Québec à Trois Rivières, Quebec, Canada.
Lung Institute · USCentre hospitalier universitaire de Québec · CAUniversité du Québec à Trois-Rivières · CAUniversité de Lille · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although the immune checkpoint inhibitors, nivolumab and pembrolizumab, were found to be promising in patients with advanced NSCLC, some of them either do not respond or have recurrence after an initial response. It is still unclear who will benefit from these therapies, and, hence, there is an unmet clinical need to build robust biomarkers. Methods: Patients with advanced NSCLC (N = 323) who were treated with pembrolizumab or nivolumab were retrospectively identified from two institutions. Radiomics features extracted from baseline pretreatment computed tomography scans along with the clinical variables were used to build the predictive models for overall survival (OS), progression-free survival (PFS), and programmed death-ligand 1 (PD-L1). To develop the imaging and integrative clinical-imaging predictive models, we used the XGBoost learning algorithm with ReliefF feature selection method and validated them in an independent cohort. The concordance index for OS, PFS, and area under the curve for PD-L1 was used to evaluate model performance. Results: We developed radiomics and the ensemble radiomics-clinical predictive models for OS, PFS, and PD-L1 expression. The concordance indices of the radiomics model were 0.60 and 0.61 for predicting OS and PFS and area under the curve was 0.61 for predicting PD-L1 in the validation cohort, respectively. The combined radiomics-clinical model resulted in higher performance with 0.65, 0.63, and 0.68 to predict OS, PFS, and PD-L1 in the validation cohort, respectively. Conclusions: We found that pretreatment computed tomography imaging along with clinical data can aid as predictive biomarkers for PD-L1 and survival end points. These imaging-driven approaches may prove useful to expand the therapeutic options for nonresponders and improve the selection of patients who would benefit from immune checkpoint inhibitors.

Indexed as

Biomarker discoveryImmunotherapyMachine learningRadiomics

Identifiers

PMID38124790
PMCPMC10730368
OpenAlexW4388798703

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.