ArticleiScience2023
The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 6 citations in OpenAlex.
- Complex biological systems analysis and deep learning for prognostic prediction of esophageal squamous cell carcinoma.iScience · 2026Article
- Multi-omics analysis of the mechanism by ATP13A2 regulates the tumor microenvironment and prognosis in hepatocellular carcinoma.Cancer cell international · 2026Article
- Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non-Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study.JMIR bioinformatics and biotechnology · 2026Article
- A deep-learning approach to predict mortality in very-low-birth-weight infants treated in the intensive care units using DeepSurv.Frontiers in pediatrics · 2026Article
- Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication.Scientific reports · 2025Article
- Deep learning model outperforms traditional models in clinical data-based prognostic prediction for adult-type diffuse glioma.Discover oncology · 2025Article
- Elucidating the role of SIRT2 in hepatocellular carcinoma through multi-omics and deep learning.Discover oncology · 2025Article
- Development and validation of machine learning models for distant metastasis of primary hepatic carcinoma: a population-based study.Discover oncology · 2025Article
- Development of PDAC diagnosis and prognosis evaluation models based on machine learning.BMC cancer · 2025Article
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The traditional prognostic model may induce the possibility of incorrect assessment of mortality risk under the assumption of linearity. It is urgent to develop a non-linearity precise prognostic model for achieving personalized medicine in lung cancer. In our study, we develop and validate a prognostic model "Modified-DeepSurv" for patients with lung carcinoma based on deep learning and evaluate its value for prognosis, while Cox proportional hazard regression was used to develop another model "CPH." The C-index of the Modified-DeepSurv and CPH was 0.956 (95% confidence interval [CI]: 0.946-0.974) and 0.836 (95% CI: 0.774-0.896), respectively, in the training cohort, while the C-index of the Modified-DeepSurv and CPH was 0.932 (95%CI: 0.908-0.964) and 0.777 (95%CI: 0.633-0.919), respectively, in the test dataset. The Modified-DeepSurv model visualization was realized by a user-friendly graphic interface. Modified-DeepSurv can effectively predict the survival of lung cancer patients and is superior to the conventional CPH model.
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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.