IEEE J Biomed Health Inform - Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information.

Tópicos

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Resumo

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. 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. An original feature selection method based on combining different filter methods was proposed. The evaluation database consisted of 156 lymphomatous and 32 suspicious but nonlymphomatous regions of interest. Different types of training databases including either the PET and CT features or the PET features only, with or without feature selection, were evaluated to assess the added value of multimodality and texture information on classification performance. An optimization study was conducted for each classifier separately to select the best combination of parameters. 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.

Resumo Limpo

design computeraid diagnosi system discrimin hypermetabol cancer lesion hypermetabol inflammatori physiolog noncancer process fdg petct exam lymphoma patient detect perform support vector machin svm classifi assess base featur set includ positron emiss tomographi pet comput tomographi ct characterist deriv clinic practic sophist textur analysi origin featur select method base combin differ filter method propos evalu databas consist lymphomat suspici nonlymphomat region interest differ type train databas includ either pet ct featur pet featur without featur select evalu assess ad valu multimod textur inform classif perform optim studi conduct classifi separ select best combin paramet promis classif perform achiev svm classifi combin discrimin pet ct featur valu area receiv oper curv

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