ArticleTomography (Ann Arbor, Mich.)2024
A Hybrid CNN-Transformer Model for Predicting N Staging and Survival in Non-Small Cell Lung Cancer Patients Based on CT-Scan.
Article in Tomography (Ann Arbor, Mich.), 2024. 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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Who cites it
9 citing papers in PubMed.
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images.Journal of imaging informatics in medicine · 2026Article
- Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China.Tropical medicine and health · 2026Article
- Optimized federated learning framework with RegNetZ and Swin-Transformer for multimodal pancreatic cancer detection1.Scientific reports · 2025Article
- Images Versus Videos in Contrast-Enhanced Ultrasound for Computer-Aided Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Transfer learning based deep architecture for lung cancer classification using CT image with pattern and entropy based feature set.Scientific reports · 2025Article
- Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Uncertainty-aware automatic TNM staging classification for [BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study proposes a hybrid model based on 3D convolutional neural networks (CNNs) and transformers for predicting the N-staging and survival rates of NSCLC patients within the NSCLC radiogenomics and Nsclc-radiomics datasets. The model achieved accuracies of 0.805, 0.828, and 0.819 for the training, validation, and testing sets, respectively. By leveraging the strengths of CNNs in local feature extraction and the superior performance of transformers in global information modeling, the model significantly enhances predictive accuracy and efficacy. A comparative analysis with traditional CNN and transformer architectures demonstrates that the CNN-transformer hybrid model outperforms N-staging predictions. Furthermore, this study extracts the one-year survival rate as a feature and employs the Lasso-Cox model for survival predictions at various time intervals (1, 3, 5, and 7 years), with all survival prediction
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