Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.
Wei ZhangHongkun YinZixing HuangJian ZhaoHaoyu ZhengDu HeMou LiWeixiong TanSong TianXijiao LiuPublished in: Cancer medicine (2021)
Deep learning based on high-resolution T2-weighted magnetic resonance images showed a good predictive performance for MSI status in rectal cancer patients. The proposed model may help to identify patients who would benefit from chemotherapy or immunotherapy and determine individualized therapeutic strategies for these patients.
Keyphrases
- deep learning
- magnetic resonance
- rectal cancer
- contrast enhanced
- high resolution
- convolutional neural network
- end stage renal disease
- locally advanced
- artificial intelligence
- ejection fraction
- chronic kidney disease
- newly diagnosed
- machine learning
- magnetic resonance imaging
- prognostic factors
- peritoneal dialysis
- computed tomography
- radiation therapy
- diffusion weighted imaging