Related Experiment Video
Updated: May 26, 2026
A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine
Bangxian Wu1, Pek-Lan Khong, Tao Chan
1Department of Diagnostic Radiology, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pok Fu Lam, Hong Kong.
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.