ArticleFrontiers in immunology2024
Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- Machine learning models for predicting cerebrospinal fluid bacterial culture outcomes in post-neurosurgical meningitis-development and temporal three-year validation.Archives of microbiology · 2026Article
- Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Development of a nomogram for predicting severe post-parathyroidectomy hypocalcemia in patients with secondary hyperparathyroidism: a retrospective cohort study.Gland surgery · 2026Article
- Machine learning models predict survival in unresectable stage III non-small cell lung cancer: Surveillance, Epidemiology, and End Results and Chinese cohort study.Journal of thoracic disease · 2026Article
- Machine Learning-Derived Risk Groups and Clinical Implementation of Survival Prediction in Lung Cancer: Evidence from a Kazakh National Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnosis model of early malignant pulmonary nodules based on clinical laboratory data.Scientific reports · 2026Article
- Development and validation of a machine-learning-based triage model incorporating systemic inflammatory, nutritional, coagulation, and tumor marker parameters for identifying baseline distant metastasis in lung cancer.Frontiers in oncology · 2026Article
- Oral and gut microbiota dysbiosis with strengthened oral-gut connectivity in post-stroke cognitive impairment.Frontiers in immunology · 2026Article
- Single-Cell and Multi-Omics-Based Characterization of Gastric Cancer Identifies TPP1 as a Potential Target for Gastric Cancer Progression and Treatment.Oncology research · 2026Article
- Predictive value of systemic immune-inflammatory markers for lung cancer-associated cerebral infarction: a retrospective cohort study.American journal of cancer research · 2026Article
- Machine learning-based prediction of postoperative venous thromboembolism in orthopedic surgery using the MIMIC-IV database and external validation.Frontiers in surgery · 2026Article
- Clinical phenotype identification based on inflammation-nutrition-coagulation biomarkers in advanced non-small cell lung cancer.Frontiers in nutrition · 2026Article
- Machine Learning Techniques for the Analysis of the Influence of Blood Gasometry Parameters on Acid-Base Homeostasis in Pediatric Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a machine learning model to predict early recurrence after surgery in NSCLC patients.Scientific reports · 2025Article
- Identification of Mitochondrial and Succinylation Modification-Related Gene Signature in Ischemic Stroke.Molecular neurobiology · 2025Article
- Harnessing natural killer cell-related genes for prognostic and therapeutic advances in lung adenocarcinoma: a predictive model for survival and immunotherapy outcomes.Translational cancer research · 2025Article
- Prognostic models for large cell neuroendocrine lung carcinoma: a machine learning and regression approach.Translational lung cancer research · 2025Article
- Big data-driven machine learning: transforming multi-omics lung cancer research.Discover oncology · 2025Article
- A new online dynamic nomogram based on the inflammation burden index to predict cardiac injury after antitumor therapy in lung cancer patients.Cardio-oncology (London, England) · 2025Article
- Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models.Diagnostics (Basel, Switzerland) · 2025Article
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Abstract
methodsA total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA).
resultsThe LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds.
conclusionThe LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.
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