ArticleScientific reports2020
Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 1 of them a synthesis that pooled it.
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Who cites it
80 citing papers in PubMed, 1 synthesis or guideline pooled it, 191 citations in OpenAlex.
- Application of machine learning for lung cancer survival prognostication-A systematic review and meta-analysis.Frontiers in artificial intelligence · 2024Pooled it
- Predicting 5-Year Mortality in Non-Small-Cell Lung Cancer Using the Korean Central Cancer Registry: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Empirical evaluation of variability and multi-institutional generalizability of deep learning survival models: application to renal cancer CT scans.Computers in biology and medicine · 2026Article
- Development and multicenter validation of a predictive model for malignant pleural effusion recurrence.iScience · 2026Article
- A Prognostic Signature for Lung Adenocarcinoma in Patients Who Have Never Smoked.Cancer discovery · 2026Article
- Skeletal Muscle Gauge as a Prognosticator of Survival in Resected Early-Stage Non-small Cell Lung Cancer.Annals of surgical oncology · 2026Article
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Machine Learning Models for Disease-Free Survival Analysis after Liver Resection for Hepatocellular Carcinoma: A Multicentric French Collaborative Study.Liver cancer · 2026Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Transformer-based representation learning for robust gene expression modeling and cancer prognosis.Scientific reports · 2025Article
- Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.Scientific reports · 2025Article
- Development and validation of an immune-related gene set (IRGS) model for prognostic and immunotherapeutic assessment in lung adenocarcinoma (LUAD).Journal of thoracic disease · 2025Article
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- A prognostic signature for lung adenocarcinoma in people who have never smoked.bioRxiv : the preprint server for biology · 2025Article
- Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Classification of lung cancer severity using gene expression data based on deep learning.BMC medical informatics and decision making · 2025Article
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- Evaluating the three-level approach of the U-smile method for imbalanced binary classification.PloS one · 2025Article
- MADSurv: An Uncertainty-Aware Framework for Multimodal Cancer Survival Analysis.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- The Emerging Mechanisms and Therapeutic Potentials of Dendritic Cells in NSCLC.Journal of inflammation research · 2025Review
20 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (DNN) combining heterogeneous data sources of gene expression and clinical data to accurately predict the overall survival of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC biomarkers were used to group patients into biomarker- and biomarker+ subgroups. Then, by using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene biomarkers. Finally, the combined 15 biomarkers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival status of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%). Using the capability of deep learning, we believe that our prediction can be a promising index that helps oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.
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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.