Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos.
Lu ZhangYicheng JiangZhe JinWenting JiangBin ZhangChangmiao WangLingeng WuLuyan ChenQiuying ChenShuyi LiuJingjing YouXiaokai MoJing LiuZhiyuan XiongTao HuangLiyang YangXiang WanGe WenXiao Guang HanWeijun FanShuixing ZhangPublished in: Cancer imaging : the official publication of the International Cancer Imaging Society (2022)
Our multi-task deep learning framework provided a real-time effective approach for decoding DSA videos and can offer clinical-decision support for TACE treatment in intermediate-stage HCC patients in real-world settings.
Keyphrases
- deep learning
- clinical decision support
- end stage renal disease
- artificial intelligence
- machine learning
- ejection fraction
- convolutional neural network
- newly diagnosed
- chronic kidney disease
- computed tomography
- electronic health record
- prognostic factors
- peritoneal dialysis
- magnetic resonance imaging
- combination therapy
- neural network
- contrast enhanced