ArticleScientific reports2025
Development of a prognostic multiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first line immunotherapy.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed.
- Radiomics-Based Prediction of Treatment Response in Non-Small Cell Lung Cancer Using Pre-Treatment CT Imaging Features.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning-Derived Risk Groups and Clinical Implementation of Survival Prediction in Lung Cancer: Evidence from a Kazakh National Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Advances in Multi-Modal Biomarkers for Immunotherapy Response in Non-Small Cell Lung Cancer: ctDNA, Microbiome, and Radiomics.Cancers · 2026Review
- Digital immune twins and ai-integrated multi-omic biomarkers: Redefining personalized immunotherapy in non-small cell lung cancer.Iranian journal of basic medical sciences · 2026Review
- How gut microbiota contribute to neuropsychiatric disorders: evidence from neuroimaging studies.Frontiers in microbiology · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
While tumor expression of PD-L1 is useful for predicting NSCLC tumor response to anti-PD-1/PD-L1 therapy (programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1)), it is not entirely reliable for therapy response prediction. Here we employ a multiomic phenotypic approach, combining radiomic, clinical and pathologic biomarkers, to develop a radiomic signature for therapy response prediction on conventional pre-therapy imaging. Tumor radiomics were extracted from baseline CT imaging on retrospective cohort (n = 243) of stage 4 NSCLC patients [squamous (n = 33); non-squamous (n = 210)] undergoing first-line anti-PD-1 therapy. Image parameter heterogeneity was mitigated using nested ComBat harmonization. A novel multiomic graph was developed by combining the constituent radiomics and radiological (SUV
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Registered trials
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