Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [18F]FDG maximum-intensity projection images

ConclusionsCNN models based on differences in FDG accumulation sites archive high performance in differentiating between sarcoidosis and ML.Clinical relevance statementWe developed a CNN model using MIP images of [18F]FDG PET/CT to distinguish between sarcoidosis and malignant lymphoma. It achieved high performance and could be useful in diagnosing diseases with involvement across organs and lymph nodes.Key Points• There are differences in FDG distribution when comparing whole-body [18F]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.
Source: European Radiology - Category: Radiology Source Type: research