ReviewJournal of inflammation research2026
Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.
Review in Journal of inflammation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Immunotherapy rechallenge in gastric cancer: resistance mechanisms, molecular stratification, and precision decision-making.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint inhibitors (ICIs) have significantly improved the clinical outcomes for patients with non-small cell lung cancer (NSCLC). However, patient heterogeneity and the limitations of current biomarkers contribute to variations in therapeutic responses. Identifying potential beneficiaries of immunotherapy and predicting efficacy remain critical challenges. In recent years, artificial intelligence (AI) has become increasingly applied in cancer treatment, particularly for modeling clinical data and predicting patient prognosis. By integrating multi-omics data such as radiomics, pathomics, genomics, transcriptomics, proteomics, and microbiomics, AI enables comprehensive biomarker discovery and facilitates prediction of immunotherapy responses and potential toxicities in NSCLC patients. Despite these advancements, challenges such as data standardization, limited interpretability, and technical barriers persist. This review summarizes the application of AI in predicting immunotherapy efficacy for NSCLC patients and discusses the challenges and future directions in the context of precision medicine.
Indexed as
Identifiers
What OpenQuestion holds
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.