Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [ 18 F]FDG maximum-intensity projection images.
Hikaru AokiYasunari MiyazakiTatsuhiko AnzaiKota YokoyamaJunichi TsuchiyaTsuyoshi ShiraiSho ShibataRie SakakibaraTakahiro MitsumuraTakayuki HondaHaruhiko FurusawaTsukasa OkamotoTomoya TateishiMeiyo TamaokaMasahide YamamotoKunihiko TakahashiUkihide TateishiTetsuo YamaguchiPublished in: European radiology (2023)
F]FDG PET/CT findings in patients with sarcoidosis and malignant lymphoma before treatment. • Convolutional neural networks, a type of deep learning technique, trained with maximum-intensity projection PET images from two angles showed high performance. • A deep learning model that utilizes differences in FDG distribution may be helpful in differentiating between diseases with lesions that are characteristically widespread among organs and lymph nodes.
Keyphrases
- convolutional neural network
- deep learning
- pet ct
- pet imaging
- positron emission tomography
- lymph node
- diffuse large b cell lymphoma
- artificial intelligence
- high intensity
- computed tomography
- machine learning
- contrast enhanced
- image quality
- resistance training
- magnetic resonance imaging
- combination therapy
- neoadjuvant chemotherapy
- early stage
- optical coherence tomography
- rectal cancer