Artificial Neural Network System in Evaluating Cervical Lymph Node Metastasis of Squamous Cell Carcinoma / 대한구강악안면방사선학회지

ABSTRACT

The purpose of this study was to evaluate cervical lymph node metastasis of oral squamous cell carcinoma patients by MRI film and neural network system. MATERIALS AND METHODS: The oral squamous cell carcinoma patients(21 patients, 59 lymph nodes) who have visited SNU hospital and been taken by MRI, were included in this study. Neck dissection operations were done and all of the cervical lymph nodes were confirmed with biopsy. In MR images, each lymph node were evaluated by using 6 MR imaging criteria(size, roundness, heterogeneity, rim enhancement, central necrosis, grouping) respectively. Positive predictive value, negative predictive value, and accuracy of each single MR imaging criteria were calculated. At neural network system, the layers of neural network system consisted of 10 input layer units, 10 hidden layer units and 1 output layer unit. 6 MR imaging criteria previously described and 4 MR imaging criteria (site I-node level 2, site II-other node level, shape I-oval, shape II-bean) were included for input layer units. The training files were made of 39 lymph nodes(24 metastatic lymph nodes, 10 non-metastatic lymph nodes) and the testing files were made of other 20 lymph nodes(10 metastatic lymph nodes, 10 non-metastatic lymph nodes). The neural network system was trained with training files and the output level (metastatic index) of testing files were acquired. Diagnosis from neural network was decided according to 4 different standard metastatic index-68, 78, 88, 98 respectively and positive predictive values, negative predictive values and accuracy of each standard metastatic index were calculated. RESULTS: In the diagnosis of using single MR imaging criteria, the rim enhancement criteria had the highest positive predictive value, 0.95 and the size criteria showed the highest at negative predictive value, 0.77. The highest accurate criteria was heterogeneity with the accuracy of 0.81 and the lowest one was central necrosis with accuracy of 0.59. In the diagnosis of using neural network systems, the highest accurate standard metastatic index was 78, and that time, the accuracy was 0.90. Neural network system was more accurate than any other single MR imaging criteria in evaluating cervical lymph node metastasis. CONCLUSION: Neural network system has been shown to be more useful than any other single MR imaging criteria. In future, Neural network system will be powerful aiding tool in evaluating cervical node metastasis.