Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.
Xiangmeng ChenBao FengKuncai XuYehang ChenXiaobei DuanZhifa JinKunwei LiRonggang LiWansheng LongXueguo LiuPublished in: European radiology (2023)
• A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.
Keyphrases
- deep learning
- contrast enhanced
- end stage renal disease
- lymph node metastasis
- newly diagnosed
- magnetic resonance
- squamous cell carcinoma
- artificial intelligence
- machine learning
- chronic kidney disease
- ejection fraction
- convolutional neural network
- computed tomography
- peritoneal dialysis
- decision making
- patient reported