ArticleCancers2022
Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-based algorithm for predicting outcomes in early-stage lung cancer: An annotation-free imaging artificial intelligence study.JTCVS open · 2026Article
- New genomic and proteomic biomarker discovery in cancer: revolutionizing diagnosis and prognostication.World journal of surgical oncology · 2026Review
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.BMC cancer · 2025Article
- 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
- Radiomic clustering using graph network techniques coupled with unbalanced optimal transport.Computational and structural biotechnology journal · 2025Article
- Imaging patterns and recommendations for diagnosis, staging, and management of lung cancer.BJR open · 2025Review
- Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and Radiomics Analysis.Cardiovascular and interventional radiology · 2025Article
- Article
- Predicting overall survival in chordoma patients using machine learning models: a web-app application.Journal of orthopaedic surgery and research · 2023Article
- Review
- SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers.Patterns (New York, N.Y.) · 2023Article
- Tumor-associated prognostic factors extractable from chest CT scans in patients with lung cancer.Translational lung cancer research · 2023Review
- Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models.Cancers · 2023Article
- Article
- Artificial Neural Networks in Lung Cancer Research: A Narrative Review.Journal of clinical medicine · 2023Review
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.PloS one · 2023Article
- A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study.PloS one · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model.
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