| Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine. | |
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MedLine Citation:
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PMID: 22215412 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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PURPOSE: Positron emission tomography/computed tomography (PET/CT) has established values for imaging of head and neck cancers, including the nasopharyngeal carcinoma (NPC), utilizing both morphologic and functional information. In this paper, we introduce a computerized system for automatic detection of NPC, targeting both the primary tumor and regional nodal metastasis, on PET/CT. METHODS: Candidate lesions were extracted based on the features from both PET and CT images and a priori knowledge of anatomical features and subsequently classified by a support vector machine algorithm. The system was validated with 25 PET/CT examinations from 10 patients suffering from NPC. Lesions manually contoured by experienced radiologists were used as the gold standard. RESULTS: Results showed that the system successfully identified all 53 hypermetabolic lesions larger than 1 cm in size and excluded normal physiological uptake in brown fat, muscles, bone marrow, brain, and salivary glands. CONCLUSION: The system combined both imaging features and a priori clinical knowledge for classification between pathological and physiological uptake. Preliminary results showed that the system was highly accurate and promising for adoption in clinical use. |
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Authors:
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Bangxian Wu; Pek-Lan Khong; Tao Chan |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2012-1-4 |
Journal Detail:
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Title: International journal of computer assisted radiology and surgery Volume: - ISSN: 1861-6429 ISO Abbreviation: - Publication Date: 2012 Jan |
Date Detail:
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Created Date: 2012-1-4 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101499225 Medline TA: Int J Comput Assist Radiol Surg Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Department of Diagnostic Radiology, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pok Fu Lam, Hong Kong. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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