基于CT和MRI影像组学的机器学习方法在胰腺癌诊断中的应用价值

Application value of CT and MRI radiomics based on machine-learning method in diagnosing pancreatic cancer

目的探讨基于CT和MRI影像组学的机器学习方法在胰腺癌诊断中的应用价值。方法收集2014年1月至2021年12月间上海交通大学附属第一人民医院经手术病理确诊且均行CT增强扫描、MRI平扫或MRI增强扫描检查的62例胰腺癌患者的临床资料,按照患者手术时间先后顺序,将2014年1月至2020年12月间患者纳入训练集(49例),2021年1月至2021年12月间患者纳入验证集(13例)。采用3D Slicer4.8.1软件对胰腺癌和癌旁组织CT、MRI图像感兴趣区域勾画并进行分割,采用Python进行特征提取,采用Lasso回归模型从训练集数据中筛选最优特征集,构建机器学习决策树模型。绘制受试者工作特征曲线(ROC),计算曲线下面积(AUC),评价3种影像组学特征模型在胰腺癌诊断中的价值。结果从CT增强、MRI平扫+增强图像中分别获取1767个CT特征和1674个MRI特征。对于胰腺癌与癌旁组织鉴别诊断模型,CT增强数据模型获得含6个特征的最优特征集,MRI平扫获得含16个特征的最优特征集,MRI增强获得含15个特征的最优特征集。基于CT增强数据的诊断模型在训练集的AUC值为0.98,在验证集的AUC值为1;MRI平扫与MRI增强数据模型在训练集与验证集中的AUC值均为1。3种影像组学特征建立的机器学习决策树模型诊断胰腺癌与癌旁组织的特异度和灵敏度均为100%。对于脾动脉包绕鉴别诊断模型,CT增强数据模型未获得最优特征集,无诊断效能,MRI平扫和增强分别获得含5、4个特征的最优特征集;采用MRI平扫数据建立的模型在训练集与验证集的AUC值分别为0.862、0.750,诊断灵敏度为93.8%、50.0%,特异度为78.6%、100%。MRI增强数据建立的模型在训练集与验证集的AUC值分别为0.950、0.861,诊断灵敏度为90.0%、93.6%,特异度为100%、78.6%。结论基于CT增强、MRI平扫及增强影像组学的诊断模型对于鉴别胰腺癌与癌旁组织均有>90%的准确性。基于MRI增强影像组学的诊断模型对于鉴别胰腺癌是否存在脾动脉包绕效能最佳。

Objective To investigate the application value of CT and MRI imageomics based on machine learning method in the diagnosis of pancreatic cancer.Methods The clinical data of 62 patients with surgically resected and pathologically confirmed pancreatic cancer,who underwent enhanced CT scan,MRI plain or enhanced scan in Shanghai General Hospital between January 2014 and December 2021 were collected.According to the chronological order of surgery,49 patients from January 2014 to December 2020 were enrolled in the training set and 13 patients from January 2021 to December 2021 were enrolled in the validation set.3D-slicer 4.8.1 software was used to draw the region of interest in each layer of CT and MRI images for cancerous and paracancerous tissue segment.Image features were extracted by Python and the optimal feature set from the training set data was obtained by using Lasso regression model.The machine learning decision tree model was constructed.The receiver operating characteristic curve(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the value of these three kinds of imageomics models in the diagnosis of pancreatic cancer.Results The 1767 CT features and 1674 MRI features were obtained from enhanced CT scan,MRI plain scan and enhanced MRI scan,respectively.For the differential diagnosis model of cancerous tissue and paracancerous tissue,the enhanced CT scan data model obtained the optimal feature set involving 6 features,the MRI plain scan model obtained the optimal feature set involving 16 features,and the enhanced MRI scan model obtained the optimal feature set involving 15 features.The diagnostic model based on enhanced CT scan had an AUC of 0.98 in the training set and 1 in the verification group.The AUC of the MRI plain scan and enhanced MRI scan models in both the training set and the validation set was 1.The specificity and sensitivity of machine learning decision tree model based on the three kinds of imageomics models in the diagnosis of cancerous tissue and paracancerous tissue were 100%.For the differential diagnosis model of splenic artery wrapping,the enhanced CT scan model didn′t obtain the optimal features and had no diagnostic efficacy.The MRI plain scan model and enhanced MRI scan model obtained the optimal feature set involving 5 and 4 features,respectively.The AUC of the MRI plain scan model in the training set and the validation set were 0.862 and 0.750,respectively,with diagnostic sensitivity of 93.8%and 50.0%,and specificity of 78.6%and 100%,respectively.The AUC of the enhanced MRI scan model in the training set and the validation set were 0.950 and 0.861,respectively,with diagnostic sensitivity of 90.0%and 93.6%,and specificity of 100%and 78.6%,respectively.Conclusions Based on the radiomics of CT enhanced,MRI plain scan and enhanced MRI scan,the machine learning diagnostic model has an accuracy of more than 90%in differentiating pancreatic cancer from paracancerous tissue.For the differentiation of splenic artery wrapping in pancreatic cancer,the diagnostic model based on enhanced MRI scan haS the best diagnostic efficiency.