Supervised Classification of Lymph Nodes based on ADC Maps Construction from Whole Body Diffusion Weighted MRI

Author(s): Radhia Ferjaoui, Mohamed Ali Cherni, Nour El Houda Kraiem and Tarek Kraiem

Background and Aim: The aim of this study was to evaluate and analyze the different Apparent Diffusion Coefficient (ADC) values of components of heterogeneous lymph nodes by using the K-means technique, compared with a whole-lesion mean ADC value alone in discriminating benign and malignant pathologies. Methods: In this paper, we propose a new method based on functional information to recognize the malignancy of lymph nodes in DW MRI images. Twenty patients with a total of 102 lesions were included in this work, and the regions of interest (ROIs) were automatically extracted using the segmentation process based on the Chan-Vese algorithm. The functional information is obtained through the reconstruction of ADC maps with two diffusion factors: b-values at 0 and at 600 s.mm2 for each ROI. Then the classification by K-means into solid and non-solid parts was done and the feature means ADC values were calculated for each cluster separately. And the distinguishing between cancerous lesions and benignant was done by using the K-nearest neighbors classifier (K-NN). Results: The results showed that the mean ADC values (in 10-3.mm².s-1) of necrotic part 1.03±0.03 were significantly higher than those measured in the solid parts 0.84±0.02. The optimal ADC threshold value for differentiating benign from malignant lymph nodes was determined using the analysis of the receiver operating characteristic(ROC) at 1.12 * 10-3 .mm².s-1 with the sensitivity (SE), specificity (SP) and area under the curve (AUC) being 94.12%, 89.19% and 0.972%, respectively. And the mean ADC values of benign and malignant lymph nodes were 2.1 * 10-3 .mm².s-1 and 0.80±0.27 * 10-3 mm².s-1 respectively. The ADC values obtained for benign lymph nodes were higher ADC values than those in malignant lesions. Conclusion: Thus, the proposed computeraided diagnosis is a helpful tool for automatic lymph nodes classification into clusters and it can successfully distinguish solid from non-solid parts in lymph nodes from the Whole body. It can also help users in predicting lesions pathologies (malignant or benign) based on the computer-aided diagnosis (CAD) system based on the K-NN classifier with accuracy higher than 93.43% and F1_measure and Geometric-mean values reach respectively 96%, 86.84%, when used ROIs placed in the solid partitions.

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