Axillary lymph node (ALN) status is a prognostic factor for patients with breast cancer. Metastasis of sentinel lymph node (SLN) indicates ALN involvement. In this study, our purpose is to develop a quantitative approach in characterizing the metastasis of ALN on spectral CT using the largest SLN (LSLN) as the surrogate. With IRB approval, a data set of 185 patients with breast cancer was retrospectively collect at Sun Yat-Sen Memorial Hospital in Guangzhou, China. Each patient underwent a preoperative spectral CT scan. A chest and axillary dual-phasic contrast media enhanced scan were acquired with a GE Discovery CT750HD CT scanner while the patient was in supine position. The LSLN was manually identified by radiologists for quantitative image analysis. We used a total of 6 sets of dual-phasic scans including 40 keV monochromatic images, 70 keV monochromatic images, and gemstone spectral images obtained at arterial and venous phases. 82 patients were positive to biopsy-proven cancer metastasis and the remaining 103 were negative. A deep convolutional neural network (DCNN) was used to extract quantitative image features as the image representation of SLN. To assess the efficacy of quantitative image features in characterization of SLN, three machine learning classifiers including KNN, SVM, and random forest were compared. Ten-fold cross validation was used for model selection. Results indicated that the AUCs on the 6 CT images for classification of LSLN metastasis ranged from 0.71-0.78 in which the best classification were observed on 70 keV monochromatic images at arterial phase. The overall classifications in arterial phase were better than those in venous phase for low (40 keV) and mixture energy setting while the findings were reversed for high (70 keV) energy setting. Future work is underway to assess our quantitative measures in axillary staging.