980 © 2020 Annals of Medical and Health Sciences Research Original Article Research Article How to Cite this Article: Ferjaoui R, et al. Supervised Classification of Lymph Nodes based on ADC Maps Construction from Whole Body Diffusion Weighted MRI. Ann Med Health Sci Res. 2020;10: 980-988. This is an open access article distributed under the terms of the Creative Com‑ mons Attribution‑NonCommercial‑ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non‑commercially, as long as the author is credited and the new creations are licensed under the identical terms. Supervised Classifcaton of Lymph Nodes based on ADC Maps Constructon from Whole Body Difusion Weighted MRI Radhia Ferjaoui 1 *, Mohamed Ali Cherni 2 , Nour El Houda Kraiem 3 and Tarek Kraiem 1,4 1 Department of Biophysics and Medical Technologies, Research Laboratory of Biophysics and Medical Technologies (LRBTM), University of Tunis El-Manar, ISTMT, 1006, Tunis, Tunisia; 2 Department of Signal and Image Processing, LR13 ES03 SIME Laboratory, University of Tunis, ENSIT, Montfeury 1008, Tunis, Tunisia; 3 Department of Radiology, Hospital Aziza Othmana of Tunis, 1006, Tunis, Tunisia; 4 Department of Medicine, University of Tunis El Manar, Faculty of Medicine of Tunis, 1007, Tunis, Tunisia Abstract Background and Aim: The aim of this study was to evaluate and analyze the diferent Apparent Difusion Coefcient (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 difusion factors: b-values at 0 and at 600 s.mm 2 for each ROI. Then the classifcation 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 classifer (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 signifcantly higher than those measured in the solid parts 0.84±0.02. The optimal ADC threshold value for diferentiating 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), specifcity (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 computer- aided diagnosis is a helpful tool for automatic lymph nodes classifcation 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 classifer 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. Keywords: Lymph nodes; DW MRI; Computer-aided diagnosis system; Segmentation; Feature extraction; Classifcation; K-nearest neighbors; Support vector machine Corresponding author: Radhia Ferjaoui, Department of Bophysics and Medical Technologies, Research Laboratory of biophysics and Medical Technologies (LRBTM), University of Tunis El Manar, ISTMT, 1006, Tunis, Tunisia, Tel: +21694441475; E-mail: radhiaferjeoui@gmail.com Introduction Cancer is a major public health problem worldwide and is the second leading cause of death in the world. In particular, the Lymphoma, a form of cancer that affects the lymphocyte cells (B cell and T cell), is one of the most frequent cancers on the planet these days. It represents 3.2% of all cancers and about 225,000 deaths are estimated worldwide (2.7% of all deaths). [1] Typically, determination of the extent of cancer is very important for appropriate treatment planning and prognosis determination. [2,3] Hence, imaging modality (Computed Tomography (CT), Positron Emission Tomography (PET), PET/CT, Magnetic Resonance Imaging (MRI)...) plays a vital role in the initial staging and evaluation of response to therapy. However, both of these modalities (PET/CT and FDG-PET/CT) have several disadvantages such as the signifcant amount of radiation exposure which result in the development of secondary malignancies [4,5] and the relatively high cost they have. [6] Also,