Primary hepatocellular carcinoma is a common cancer. Many patients are found with intermediate-advanced stage, rapid development, poor treatment and high mortality. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) can discover the early lesions and therefore plays an important role in diagnosis, treatment and prognosis of patients with hepatocellular carcinoma. It especially has obvious advantages in detecting metastasis and monitoring recurrence. However, 18F-FDG PET/CT imaging has poor quality and low diagnosis rate. Understanding the advantages and limitations of 18F-FDG PET/CT can provide better basis for clinical diagnosis and treatment for hepatocellular carcinoma patients. This article briefly introduces the research and application of 18F-FDG PET/CT in the diagnosis and treatment of hepatocellular carcinoma.
Autoimmune pancreatitis (AIP) is a unique subtype of chronic pancreatitis, which shares many clinical presentations with pancreatic ductal adenocarcinoma (PDA). The misdiagnosis of AIP often leads to unnecessary pancreatic resection. 18F-FDG positron emission tomography/ computed tomography (PET/CT) could provide comprehensive information on the morphology, density, and functional metabolism of the pancreas at the same time. It has been proved to be a promising modality for noninvasive differentiation between AIP and PDA. However, there is a lack of clinical analysis of PET/CT image texture features. Difficulty still remains in differentiating AIP and PDA based on commonly used diagnostic methods. Therefore, this paper studied the differentiation of AIP and PDA based on multi-modality texture features. We utilized multiple feature extraction algorithms to extract the texture features from CT and PET images at first. Then, the Fisher criterion and sequence forward floating selection algorithm (SFFS) combined with support vector machine (SVM) was employed to select the optimal multi-modality feature subset. Finally, the SVM classifier was used to differentiate AIP from PDA. The results prove that texture analysis of lesions helps to achieve accurate differentiation of AIP and PDA.