基于支持向量机的MRI影像组学方法鉴别不同病理分型原发性肝癌的价值

Support vector machine based MRI radiomics to identify primary hepatic carcinomas with different pathological types

目的 探讨基于支持向量机（SVM）的MRI影像组学方法鉴别不同病理分型原发性肝癌的价值.方法 回顾性分析2013年7月至2017年2月浙江大学附属第一医院经手术或穿刺病理证实为原发性肝癌,且术前行MRI平扫和增强扫描的294例（305个病灶）患者,其中肿块型胆管细胞癌96例（97个病灶）、肝细胞肝癌107例（107个病灶）、混合型肝癌91例（101个病灶）.患者均行肝脏MRI平扫和动态增强动脉期、门静脉期和平衡期扫描.按照训练数据与验证数据2：1的比例,选取203个病灶作为训练集（肿块型胆管细胞癌65个、肝细胞肝癌71个、混合型肝癌67个）,102个作为验证集（肿块型胆管细胞癌32个、肝细胞肝癌36个、混合型肝癌34个）.应用美国GE Analysis Kit（AK）软件,手动勾画MRI增强平衡期病灶,应用LASSO算法使用10折交叉验证的方法选择特征参数及降维,采用Spearman法计算特征间参数间的冗余性,采用SVM法构建预测模型,使用数据集在诊断模型上的准确性来评估模型效能.结果 训练集共提取了280个定量影像特征参数,LASSO降维算法选择31个影像特征参数,去冗余处理后剩余影像特征21个.由于存在休斯效应,支持向量机选取前11个特征参数具有最佳泛化能力,其中直方图类参数4个,纹理类特征2个,灰度共生矩阵类4个,灰度步长矩阵类1个.应用SVM观测该11个影像特征数据,经回归分析,构建了原发性肝癌的预测模型.该模型在训练集的准确率为80.3%（163/203）.将验证集的102个数据带入该模型中,其准确率为75.5%（77/102）.验证集混合型肝癌准确率85.3%（29/34）,3个病灶误诊为肿块型胆管细胞癌,2个误诊为肝细胞肝癌;肝细胞肝癌准确率77.8%（28/36）,3个病灶误诊为混合型肝癌,5个误诊为肿块型胆管细胞癌;肿块型胆管细胞癌准确率62.5%（20/32）,9个病灶误诊为混合型肝癌,3个误诊为肝细胞肝癌.混合型肝癌预测准确率最高.结论 应用基于SVM的影像组学方法预测不同病理分型的原发性肝癌具有较高的准确性,其中对混合型肝癌的预测准确性最高.

Objective To investigate the value of support vector machine based MRI-radiomics in the differential diagnosis of primary hepatic carcinomas （PHCs）. Methods PHCs patients were retrospectively collected from July 2013 to February 2017 in the First Affiliated Hospital of Zhejiang University.All patients underwent unenhanced and enhanced MRI liver scan before surgery,and confirmed by pathology. A total of 294 PHCs patients （305 lesions）, including 96 cases （97 lesions） of massive type cholangiocarcinoma （mCC）, 107（107 lesions）of hepatocellular carcinoma （HCC）, and 91 （101 lesions） of mixed hepatocellular and cholangiocellular carcinomas（HCC-CC）.All patients underwent unenhanced and dynamic enhanced MRI liver scan including arterial, portal venous and equilibrium phases. Two hundred and three lesions （65 mCC, 71 HCC, 67 HCC-CC） were assigned into the training set, the remaining 102 lesions（32 mCC,36 HCC,34 HCC-CC）into the validation set,according to a ratio of 2：1.The entire lesions were delineated manually using a region of interest on equilibrium phase of enhanced MRI by using a home-made dedicated software（Analysis Kit,AK,General Electrics,US）.Then the least absolute shrinkage and selection operator （LASSO） regression was used to select image features with a method of 10 fold cross-validation, and to reduce the dimensionality. The spearman method was used afterwards to condense the image features by removing redundant.A predictive model of diagnosing the different types of PHCs was established based on support vector machines（SVM）,and the accuracy of applying the model in the data sets was used to evaluate the diagnostic efficacy of the model. Results A total of 280 quantitative imaging features were extracted in the training set.Thirty one imaging features were selected after LASSO regression and dimensionality reduction,and 21 features were remained after redundancy removing.The SVM showed the best generalization ability when the first 11 imaging features were used due to the Hughes effect.The 11 imaging features include 4 parameters of histogram,2 of textures,4 of gray-level co-occurrence matrix and 1 of gray-level run length matrix. A predictive model for PHCs was established after the study of the 11 imaging features and a regression analysis using SVM.The accuracy of the predictive model was 80.3% （163/203） in differentiating PHCs in the training set. The accuracy of the model was 75.5% （77/102） after applying it in the validation set. The diagnostic accuracy for HCC-CC, HCC and mCC was 85.3% （29/34）, 77.8% （28/36） and 62.5% （20/32）, respectively, in the validation set. For HCC-CC, 3 cases were misdiagnosed as mCC and 2 cases as HCC.For HCC,3 cases were misdiagnosed as HCC-CC and 5 cases as mCC.For mCC,9 cases were misdiagnosed as HCC-CC and 3 cases as HCC.The model showed the highest accuracy in predicting HCC-CC.Conclusion Radiomics method based on SVM may have a high accuracy in predicting different pathologic types of PHC,with the highest accuracy for HCC-CC.