ReviewThoracic cancer2026
Guiding the Application of Immunotherapy in Nonsmall Cell Lung Cancer: The Role of Biomarkers.
Review in Thoracic cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Although immunotherapy has completely transformed the treatment landscape of nonsmall cell lung cancer (NSCLC), its wide application is still limited by the heterogeneity of patient responses, primary and acquired resistance, as well as the management challenges of immune-related adverse events (irAEs). To achieve precise individualized immunotherapy, this review systematically summarizes the multidimensional biomarker profiles that can predict the efficacy, resistance, and safety of immunotherapy. Beyond the single PD-L1 expression, the efficacy prediction system has expanded to integrate multimodal information such as dynamic tumor microenvironment, tumor genomic characteristics, systemic inflammation/immune status, and radiomics. At the same time, studies have revealed that immune-suppressive microenvironments and specific genetic variations are key mechanisms mediating treatment resistance. Moreover, clinical characteristics, blood markers, and imaging findings provide a basis for predicting the risk of irAEs. Looking to the future, the core to overcoming current bottlenecks lies in constructing dynamic and integrated prediction models. By integrating multiomics data, longitudinal liquid biopsies, and artificial intelligence algorithms, an intelligent decision-making system is expected to be developed to achieve real-time monitoring of treatment response, early identification of resistance mechanisms, and proactive management of toxicity risks, ultimately optimizing the clinical practice of NSCLC immunotherapy and advancing it towards higher-order precision medicine.
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Registered trials
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.