Utility of the K-means clustering algorithm in differentiating apparent diffusion coefficient values of benign and malignant neck pathologies

American Journal of Neuroradiology

Volumn 31, Issue 4, 2010, Pages 736-740

  Srinivasan, A ^a   Galban C J ^a   Johnson, T D ^b   Chenevert, T L ^a   Ross, B D ^a   Mukherji, S K ^a  

^a UNIVERSITY OF MICHIGAN HEALTH SYSTEM   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADENOSQUAMOUS CARCINOMA; ADULT; AGED; ARTICLE; CLINICAL ARTICLE; COMPUTER PROGRAM; CONTRAST ENHANCEMENT; DIFFUSION COEFFICIENT; FEMALE; HUMAN; HYPOPHARYNX CARCINOMA; MALE; NECK CANCER; NECK TUMOR; NEURILEMOMA; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; OROPHARYNX CARCINOMA; PAROTID GLAND CARCINOMA; PATHOLOGY; SARCOIDOSIS; SINUSITIS; SPINDLE CELL SARCOMA; SQUAMOUS CELL CARCINOMA; TONGUE CARCINOMA;

ADULT; ALGORITHMS; DIAGNOSIS, DIFFERENTIAL; DIFFUSION MAGNETIC RESONANCE IMAGING; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MALE; MIDDLE AGED; OTORHINOLARYNGOLOGIC DISEASES; OTORHINOLARYNGOLOGIC NEOPLASMS; ROC CURVE; SENSITIVITY AND SPECIFICITY; SOFTWARE; YOUNG ADULT;

EID: 77950803163     PISSN: 01956108     EISSN: None     Source Type: Journal    
DOI: 10.3174/ajnr.A1901     Document Type: Article

References (11)

1. Vandecaveye V, De Keyzer F, Nuyts S, et al. Detection of head and neck squamous cell carcinoma with diffusion weighted MRI after (chemo)-radiotherapy: correlation between radiologic and histopathologic findings. Int J Radiat Oncol Biol Phys 2007;67:960-971 (Pubitemid 46320065)
2. Vandecaveye V, de Keyzer F, Vander Poorten V, et al. Evaluation of the larynx for tumour recurrence by diffusion-weighted MRI after radiotherapy: initial experience in four cases. Br J Radiol 2006;79:681-687 (Pubitemid 44283158)
3. Maeda M, Maier SE. Usefulness of diffusion-weighted imaging and the apparent diffusion coefficient in the assessment of head and neck tumors. J Neuroradiol 2008;35:71-78
4. Srinivasan A, Dvorak R, Perni K, et al. Differentiation of benign and malignant pathology in the head and neck using3Tapparent diffusion coefficient values: early experience. AJNR Am J Neuroradiol 2008;29:40-44
5. Wang J, Takashima S, Takayama F, et al. Head and neck lesions: characterization with diffusion-weighted echo-planar MR imaging. Radiology 2001;220:621-630 (Pubitemid 32799967)
6. Hartigan JA, Wong MA. Algorithm AS136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society, Series C (Applied Statistics) 1979; 28:100-108
7. MacQueen JB. Some methods for classification and analysis of multivariate observations. In: Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, California: University of California Press; 1967:281-297
8. Kanungo T, Mount DM, Netanyahu NS, et al. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 2002;24:881-892 (Pubitemid 34835464)
9. Holzapfel K, Duetsch S, Fauser C, et al. Value of diffusion-weighted MR imaging in the differentiation between benign and malignant cervical lymph nodes. Eur J Radiol 2009;72:381-387
10. Vandecaveye V, De Keyzer F, Hermans R. Diffusion-weighted magnetic resonance imaging in neck lymph adenopathy. Cancer Imaging 2008;8:173-180
11. Maeda M, Kato H, Sakuma H, et al. Usefulness of the apparent diffusion coefficient in line scan diffusion-weighted imaging for distinguishing between squamous cell carcinomas and malignant lymphomas of the head and neck. AJNR Am J Neuroradiol 2005;26:1186-1192 (Pubitemid 43821971)