ArticlePLOS digital health2026
Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.
Article in PLOS digital health, 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current image-based deep learning models that predict the benefits of immunotherapy in non-small cell lung cancer (NSCLC) require high-performance hardware. We aimed to develop and externally validate a clinician-operable prognostic model that integrates clinical and imaging data in a format usable by clinicians on standard central-processing-unit (CPU) hardware. This multicenter study included 1,379 patients with NSCLC treated with immunotherapy from five Chinese hospitals and the Memorial Sloan-Kettering (MSK) Cancer Center. A pairwise association encoder (PAE) converted routinely collected baseline clinical variables into edge weights of a patient-similarity graph, while radiomics features extracted from pretreatment chest computed tomography (CT) were embedded as node attributes in a Deep Hypergraph for NSCLC (DHGN). DHGN was trained on progression-free survival (PFS) and validated on overall survival (OS). Model performance was compared with three established control models and a published deep learning benchmark. Ten thoracic oncology experts independently implemented and tested each pipeline. Trained on PFS, DHGN yielded C-indices of 0.72, 0.71, and 0.71, and 0.70, 0.71, and 0.69 when validated on OS. DHGN outperformed the clinical-only, radiomics-only, composite, and EfficientNetV2 models for both endpoints (all P < 0.0001). It correctly identified patients likely to achieve PFS > 24 months and those unlikely to reach 12 months, which was superior to tumor mutation burden (TMB) and PD-L1 expression. Higher DHGN scores strongly predicted longer survival outcomes (hazard ratio: 0.10, 95% CI: 0.07-0.16, P < 0.0001) and improved the prognostic accuracy of patient stratification based on PD-L1 expression. Based on clinicians' practical deployment, the DHGN significantly reduced the operational complexity and computational requirements (P < 0.01) for model development and application in clinical settings. In conclusion, we proposed a clinician-friendly population graph model that fuses baseline clinical data with CT radiomics on widely available CPU hardware accurately stratifies NSCLC patients for immunotherapy benefits, potentially redefining benchmarks for non-invasive prognostic biomarkers and enabling broader clinical translation.
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.