Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.
Hao-Ji YanJia-Sheng ZhaoHou-Dong ZuoJun-Jie ZhangZhi-Qiang DengChen YangXi LuoJia-Xin WanXiang-Yun ZhengWei-Yang ChenSu-Ping LiDong TianPublished in: Annals of surgical oncology (2024)
The CT radiomics showed the potential for accurately predicting SLNM in NSCLC patients. The ML model with dual-region radiomic features has better performance than the logistic regression or single-region models.
Keyphrases
- lymph node metastasis
- computed tomography
- machine learning
- squamous cell carcinoma
- contrast enhanced
- end stage renal disease
- papillary thyroid
- positron emission tomography
- ejection fraction
- newly diagnosed
- small cell lung cancer
- dual energy
- magnetic resonance imaging
- chronic kidney disease
- image quality
- prognostic factors
- advanced non small cell lung cancer
- magnetic resonance
- deep learning