Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma.
Noriyuki FujimaV Carlota Andreu-ArasaSara K MeibomGustavo A MercierAndrew R SalamaMinh Tam TruongOsamu SakaiPublished in: European radiology (2020)
• Deep learning-based diagnosis of FDG-PET images showed the highest diagnostic accuracy to predict the treatment outcome in patients with oral cavity squamous cell carcinoma. • Deep learning-based diagnosis was shown to differentiate patients between good and poor disease-free survival more clearly than conventional T-stage, clinical stage, and conventional FDG-PET-based parameters.
Keyphrases
- deep learning
- pet ct
- positron emission tomography
- pet imaging
- squamous cell carcinoma
- computed tomography
- convolutional neural network
- free survival
- artificial intelligence
- end stage renal disease
- machine learning
- ejection fraction
- chronic kidney disease
- newly diagnosed
- prognostic factors
- lymph node metastasis
- locally advanced
- radiation therapy
- rectal cancer