ArticleJournal of oncology2022
Integrating Radiomics with Genomics for Non-Small Cell Lung Cancer Survival Analysis.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.BMC medical imaging · 2026Pooled it
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer.Scientific reports · 2026Article
- Development of a computed tomography radiomics and CD38 integrated model: predicting immunotherapy response and investigating biological implications in non-small cell lung cancer.Journal of thoracic disease · 2026Article
- Review
- Whole lung radiomic features are associated with overall survival in patients with locally advanced non-small cell lung cancer treated with definitive radiotherapy.Radiation oncology (London, England) · 2025Observational
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- The prognostic value of radiogenomics using CT in patients with lung cancer: a systematic review.Insights into imaging · 2024Review
- Deep learning-based approaches for multi-omics data integration and analysis.BioData mining · 2024Review
- Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy.Translational lung cancer research · 2024Article
- Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach.Heliyon · 2024Article
- Observational
- Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature.Biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The objectives of our study were to assess the association of radiological imaging and gene expression with patient outcomes in non-small cell lung cancer (NSCLC) and construct a nomogram by combining selected radiomic, genomic, and clinical risk factors to improve the performance of the risk model. Methods: A total of 116 cases of NSCLC with CT images, gene expression, and clinical factors were studied, wherein 87 patients were used as the training cohort, and 29 patients were used as an independent testing cohort. Handcrafted radiomic features and deep-learning genomic features were extracted and selected from CT images and gene expression analysis, respectively. Two risk scores were calculated through Cox regression models for each patient based on radiomic features and genomic features to predict overall survival (OS). Finally, a fusion survival model was constructed by incorporating these two risk scores and clinical factors. Results: The fusion model that combined CT images, gene expression data, and clinical factors effectively stratified patients into low- and high-risk groups. The C-indexes for OS prediction were 0.85 and 0.736 in the training and testing cohorts, respectively, which was better than that based on unimodal data. Conclusions: Combining radiomics and genomics can effectively improve OS prediction for NSCLC patients.
Identifiers
What Socratic 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 Socratic graph.