ArticleInternational journal of computer assisted radiology and surgery2012
Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine.
Article in International journal of computer assisted radiology and surgery, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 34 citations in OpenAlex.
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- The Use of Artificial Intelligence in Dentistry Practices.The Eurasian journal of medicine · 2022Article
- Review
- Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.Journal of clinical medicine · 2022Review
- Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images.Sensors (Basel, Switzerland) · 2022Article
- Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy.Journal of personalized medicine · 2021Review
- Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview.British journal of cancer · 2021Review
- Artificial intelligence in molecular imaging.Annals of translational medicine · 2021Review
- Radiomic biomarkers for head and neck squamous cell carcinoma.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Review
- Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images.Journal of digital imaging · 2019Article
- Big Data in Head and Neck Cancer.Current treatment options in oncology · 2018Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePositron 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.
methodsCandidate 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.
resultsResults 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.
conclusionThe 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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