Predictive Model for Nasopharyngeal Carcinoma Recurrence with MRI and Optimized 3D-ResNet18
Li Jiongyi1, Li Bin1*, Qiu Qianhui2*, Liu Yibin2, Tian Lianfang1
1(School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China) 2(Department of Otolaryngology & Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China)
Abstract：Recurrence of nasopharyngeal carcinoma (NPC) after treatment is an important factor of treatment failure, which is extremely harmful to the quality of life, cure rate and even survival rate of patients with NPC. Therefore, effective prediction of nasopharyngeal carcinoma recurrence plays crucial roles in the prognosis of NPC. Magnetic resonance imaging (MRI) has high resolution of soft tissue, which is a preferred inspection method of NPC. Each imaging of NPC in MRI is quite different, and the gray level of lesion tissue is uneven and the boundaries are blurred, which cause the fact that manual segmentation is difficult, costly, and has limited accuracy, while automatic segmentation of NPC lesions in MRI has low accuracy rate. As a result, low-level image feature extraction and computation based on NPC lesion segmentation has low accuracy rate as well. Performance of NPC recurrence prediction model with radiomics feature engineering and traditional machine learning methods is poor. To solve this problem, a nasopharyngeal carcinoma recurrence prediction model was proposed with MRI and Nesterov accelerating gradient optimized 3D-ResNet18. Through the automatic detection of NPC lesion by distance regularized level-set evolution and histogram equalization in MRI, the enhanced imaging data without redundancy was automatically obtained. The improved 3D-ResNet18 network model optimized by Nesterov accelerated gradient algorithm was used to extract the deep features of NPC and achieve recurrence prediction, providing guidance on the patients’ treatment plans. The research was conducted on MRI images of 140 patients with NPC to complete the model training and cross-validation analysis. Recurrence of NPC was predicted with sensitivity, specificity, accuracy, and AUC of 80.0%, 64.6%, 72.3% and 0.75 respectively. The p values of paired t-test comparing 3D-ResNet10 model and Momentum optimization method under the same conditions were 0.040 and 0.006 respectively. The results showed that the predictive model of nasopharyngeal carcinoma recurrence with improved 3D-ResNet18 can effectively predict the nasopharyngeal carcinoma recurrence.
李炯逸, 李彬, 邱前辉, 刘遗斌, 田联房. 基于MRI与优化3D-ResNet18的鼻咽癌复发预测模型[J]. 中国生物医学工程学报, 2023, 42(5): 583-593.
Li Jiongyi, Li Bin, Qiu Qianhui, Liu Yibin, Tian Lianfang. Predictive Model for Nasopharyngeal Carcinoma Recurrence with MRI and Optimized 3D-ResNet18. Chinese Journal of Biomedical Engineering, 2023, 42(5): 583-593.
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