目的 联合常规MRI及表观扩散系数(apparent diffusion coefficient,ADC)图的影像组学特征构建多参数MRI影像组学模型术前预测胰腺导管腺癌(pancreatic ductal adenocarcinoma,PDAC)淋巴结转移(lymph node metastasis,LNM),并与建立的常规MRI影像组学模型和临床模型比较预测效能,探索基于ADC图影像组学的附加价值.材料与方法 218例PDAC按照7∶3的比例随机分为训练集和验证集.纳入临床及常规影像特征构建临床影像学模型.提取常规MRI图像(T1WI、T2WI、动脉期图像及门静脉期图像)及ADC图的影像组学特征.在训练集中采用最小绝对收缩和选择算子筛选出与LNM最相关的特征用于模型构建.构建基于常规MRI影像组学模型(影像组学模型1)和联合常规MRI和ADC图的影像组学模型(影像组学模型2).使用受试者工作特征曲线下面积(area under the curve,AUC)评估模型预测效能.采用DeLong检验比较模型间的AUC值的差异是否有统计学意义.校准曲线评估模型的准确性.决策曲线分析评估模型的临床价值.结果 临床影像学模型、影像组学模型1、影像组学模型2术前预测LNM的AUC值在训练集和验证集中分别是0.741和0.674、0.818和0.702、0.854和0.839.影像组学模型2术前预测LNM的AUC值高于临床影像学模型(训练集P=0.009,验证集P=0.023)及影像组学模型1(训练集P=0.044,验证集P=0.041),差异均具有统计学意义.影像组学模型1的预测效能与临床影像学模型相比,差异不具有统计学意义(训练集P=0.095,验证集P=0.759).三个模型的校准曲线均显示预测值与实际值具有较好的一致性.决策曲线显示影像组学模型2比影像组学模型1和临床影像学模型具有更高的净效益.结论 联合常规MRI及ADC图构建的多参数MRI影像组学模型具有术前预测PDAC患者LNM的潜能,且其效能优于常规MRI影像组学模型及临床影像学模型.
The value of multi-parametric MRI radiomics model in predicting lymph node metastasis of pancreatic ductal adenocarcinoma
Objective:A multi-parametric MRI radiomics model was constructed by combining the radiomics features of conventional MRI and apparent diffusion coefficient(ADC)map to predict lymph node metastasis(LNM)in pancreatic ductal adenocarcinoma(PDAC),and the prediction performance was compared with the established conventional MRI radiomics model and clinical model,to explore the added value of ADC map radiomics.Methods and Materials A total of 218 patients with PDAC were randomly divided into a training cohort and a validation cohort at a ratio of 7∶3.Clinical and conventional imaging features were used to construct the clinical imaging model.Then the radiomics features were extracted based on conventional MRI images(T1WI,T2WI,arterial phase images and portal venous phase images)and ADC maps.Least absolute shrinkage and selection operator was used to select the most relevant features of LNM in the training cohort for model construction.Radiomics models based on conventional MRI images(represented as radiomics model 1)and radiomics models combined with conventional MRI images and ADC maps(represented as radiomics model 2)were constructed.The area under the curve(AUC)of receiver operator characteristic was used to evaluate the prediction performance of the models.DeLong validation was used to compare the difference of AUC values between models.The calibration curve was used to evaluate the accuracy of the model.The clinical value of the model was evaluated by decision curve analysis.Results:The AUC values of clinical and radiographic model,radiomics model 1 and radiomics model 2 for preoperative prediction of LNM in the training and validation cohorts were 0.741 and 0.674,0.818 and 0.702,0.854 and 0.839,respectively.The AUC value of radiomics model 2 for preoperative prediction of LNM was significantly higher than that of the clinical and radiographic model(P=0.009 in the training cohort;P=0.023 in the validation cohort)and radiomics model 1(P=0.044 in the training cohort;P=0.041 in the validation cohort).The prediction performance of radiomics model 1 was not significantly different from that of the clinical radiographic model(P=0.095 in the training cohort;P=0.759 in the validation cohort).The calibration curves of the three models showed good agreement between the predicted values and the actual values.Decision curve analysis curve showed that radiomics model 2 had a higher net benefit than radiomics model 1 and clinical imaging model.Conclusions:The multi-parametric MRI radiomics model by combining conventional MRI and ADC map radiomics can improve the diagnostic efficiency of predicting LNM of PDAC,which is significantly better than the conventional MRI radiomics model and clinical-radiographic model.
国家科技期刊平台
NSTL
万方数据
