Detection performance of the support vector machine (SVM) classifier was assessed based on feature sets including 105 positron emission tomography (PET) and Computed tomography (CT) characteristics derived from the clinical practice and from more sophisticated texture analysis.  Promising classification performance was achieved by the SVM classifier combined with the 12 most discriminant PET and CT features with a value of the area under the receiver operating curve of 0.91.
SVM
Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information.We have designed a computer-aided diagnosis system to discriminate between hypermetabolic cancer lesions and hypermetabolic inflammatory or physiological but noncancerous processes in FDG PET/CT exams of lymphoma patients. 
PET/CT