Document Detail


Utility of the k-means clustering algorithm in differentiating apparent diffusion coefficient values of benign and malignant neck pathologies.
MedLine Citation:
PMID:  20007723     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND AND PURPOSE: Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies. MATERIAL AND METHODS: MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADC(L); high, ADC-ADC(H)) and 3 partitions (ADC(L); intermediate, ADC-ADC(I); ADC(H)) by using the K-means clustering algorithm. An unpaired 2-tailed Student t test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies. RESULTS: A statistically significant difference between the mean ADC(L) clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers (P = .03 and .022, respectively) and the 2-cluster model of reader 2 (P = .04), with the other metrics (ADC(H), ADC(I); whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADC(H) and ADC(L) in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: P = .008, area under curve = 0.850; 3 clusters: P = .01, area under curve = 0.825). CONCLUSIONS: The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.
Authors:
A Srinivasan; C J Galbán; T D Johnson; T L Chenevert; B D Ross; S K Mukherji
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2009-12-10
Journal Detail:
Title:  AJNR. American journal of neuroradiology     Volume:  31     ISSN:  1936-959X     ISO Abbreviation:  AJNR Am J Neuroradiol     Publication Date:  2010 Apr 
Date Detail:
Created Date:  2010-04-13     Completed Date:  2010-07-14     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8003708     Medline TA:  AJNR Am J Neuroradiol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  736-40     Citation Subset:  IM    
Affiliation:
Department of Radiology, University of Michigan Health System, Ann Arbor, 48109, USA. ashoks@med.umich.edu
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MeSH Terms
Descriptor/Qualifier:
Adult
Algorithms*
Diagnosis, Differential
Diffusion Magnetic Resonance Imaging / methods*
Female
Humans
Image Processing, Computer-Assisted / methods*
Male
Middle Aged
Otorhinolaryngologic Diseases / diagnosis*
Otorhinolaryngologic Neoplasms / diagnosis*
ROC Curve
Sensitivity and Specificity
Software
Young Adult
Grant Support
ID/Acronym/Agency:
P01CA085878/CA/NCI NIH HHS; P50CA093990/CA/NCI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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