Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer with 18F-FDG PET/CT images.
Yuan ZhuShan CongQiyang ZhangZhenxing HuangXiaohui YaoYou ChengDong LiangZhanli HuShao DanPublished in: Biomedical physics & engineering express (2024)
Our approach enables the quantitative assessment of medical images and a deeper understanding of both superficial and deep tumor characteristics. This noninvasive method has the potential to identify BM-related features with statistical significance, thereby aiding in the development of targeted treatment plans for NSCLC patients.
Keyphrases
- deep learning
- convolutional neural network
- end stage renal disease
- ejection fraction
- chronic kidney disease
- small cell lung cancer
- newly diagnosed
- artificial intelligence
- optical coherence tomography
- prognostic factors
- machine learning
- peritoneal dialysis
- magnetic resonance imaging
- white matter
- computed tomography
- resting state
- patient reported outcomes
- chronic pain
- patient reported
- replacement therapy
- tyrosine kinase