ArticleDiagnostics (Basel, Switzerland)2024
Enhancing Lung Cancer Survival Prediction: 3D CNN Analysis of CT Images Using Novel GTV1-SliceNum Feature and PEN-BCE Loss Function.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multimodal multitask deep learning for grading management system in non-small cell lung cancer.Nature communications · 2026Article
- Integrating central-slice anatomy and MRF-mapped radiomics dynamics for lung adenocarcinoma subtyping.Quantitative imaging in medicine and surgery · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Transfer learning based deep architecture for lung cancer classification using CT image with pattern and entropy based feature set.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Lung cancer is a prevalent malignancy associated with a high mortality rate, with a 5-year relative survival rate of 23%. Traditional survival analysis methods, reliant on clinician judgment, may lack accuracy due to their subjective nature. Consequently, there is growing interest in leveraging AI-based systems for survival analysis using clinical data and medical imaging. The purpose of this study is to improve survival classification for lung cancer patients by utilizing a 3D-CNN architecture (ResNet-34) applied to CT images from the
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