基于3D-ResNet深度影像特征的胆囊癌生存预测模型

Predictive model for survival in gallbladder cancer patients based on 3D-ResNet deep image features

建立一个精准的个体化胆囊癌患者生存预测模型,分析、寻找新的胆囊癌预后因素,对于患者预后评估、治疗模式选择、手术患者筛选、术后辅助治疗方案确定及医疗资源合理使用均具有重要意义。本文提出一种基于3D-ResNet提取深度影像特征建立胆囊癌患者生存预后模型的方法,通过迁移学习以及训练3D-ResNet自动提取患者CT的深度特征,并利用提取的深度影像特征,通过Cox比例风险回归模型建立胆囊癌患者的生存预测模型。实验结果表明,基于深度影像特征建立的胆囊癌患者预后因子在预测患者生存时的C指数达到0.734,利用深度影像特征预后因子预测患者的1、3、5年存活率AUC分别达到0.833、0.791、0.813。本方法对胆囊癌预后预测有着良好的指示作用。

Establishing an accurate individualized predictive model for survival in gallbladder cancer patients and finding new prognostic factors for gallbladder cancer are of great significance for prognosis evaluation,treatment model selection,surgical patient screening,postoperative adjuvant treatment plan determination,and rational utilization of medical resources.A method to build a predictive model for survival in gallbladder cancer patients based on deep image features extracted by 3D-ResNet is proposed in the study.The deep features of the patient's CT image are automatically extracted through transfer learning and 3D-ResNet training,and the extracted deep image features are used to establish a predictive model for survival in gallbladder cancer patients through the Cox proportional hazard regression model.The experimental results show that the C index of prognostic factors obtained based on deep image features is 0.734 for predicting survival in gallbladder cancer patients,and that the AUC of 1-year,3-year and 5-year survival rates predicted by deep image features reaches 0.833,0.791 and 0.813,respectively.The proposed method has a good indication for predicting the prognosis of gallbladder cancer.