RT Journal Article
SR Electronic
T1 Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 736
OP 740
DO 10.3174/ajnr.A1901
VO 31
IS 4
A1 Srinivasan, A.
A1 Galbán, C.J.
A1 Johnson, T.D.
A1 Chenevert, T.L.
A1 Ross, B.D.
A1 Mukherji, S.K.
YR 2010
UL http://www.ajnr.org/content/31/4/736.abstract
AB 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-ADCL; high, ADC-ADCH) and 3 partitions (ADCL; intermediate, ADC-ADCI; ADCH) 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 ADCL 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 (ADCH, ADCI; whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADCH and ADCL 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. ADCapparent diffusion coefficientDWIdiffusion-weighted imagingFAflip angleROCreceiver operator characteristicSCCsquamous cell carcinomaSNUCsinonasal undifferentiated carcinomaVOIvolume of interest