Document Detail

Computer aided staging of lymphoma patients with FDG PET/CT imaging based on textural information.
MedLine Citation:
PMID:  24081876     Owner:  NLM     Status:  Publisher    
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 PET and 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 non lymphomatous 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 ROC curve (AUC) of 0.91.
Carole Lartizien; Matthieu Rogez; Emilie Niaf; Fabien Ricard
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-9-27
Journal Detail:
Title:  IEEE journal of biomedical and health informatics     Volume:  -     ISSN:  2168-2208     ISO Abbreviation:  IEEE J Biomed Health Inform     Publication Date:  2013 Sep 
Date Detail:
Created Date:  2013-10-1     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101604520     Medline TA:  IEEE J Biomed Health Inform     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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