摘要目的:探讨基于MR T 1WI的最优影像组学机器学习模型及其预测软组织肉瘤分级的价值。 方法:回顾性分析2009年5月至2018年11月青岛大学附属医院113例软组织肉瘤患者的术前MR T 1WI资料，采用随机分层抽样的方法将患者随机分为训练组（ n=80）和验证组（ n=33）。根据法国国家癌症研究中心（FNCLCC）系统将软组织肉瘤病理分级分为Ⅰ~Ⅲ三个级别。Ⅰ级为低级别，Ⅱ、Ⅲ级为高级别。训练组中18例为低级别、62例为高级别病变，验证组中7例低级别、26例高级别病变。图像进行标准化后，采用A.K软件对肿瘤感兴趣区进行特征提取，并基于不同特征选择方法（加入和不加入递归式特征消除）、机器学习算法（随机森林和支持向量机算法）和采样技术（不进行过采样、使用少数样本合成过采样技术、使用随机过采样技术），组合成12种机器学习算法组合，应用弃一法交叉验证进行验证，建立分类模型。采用受试者操作特征（ROC）曲线评价模型预测软组织肉瘤病理级别的效能。 结果:在12种机器学习算法建立的软组织肉瘤分级预测模型中，联合使用递归式特征消除和少数样本合成过采样技术的随机森林分类算法效能最佳，其在验证组中预测软组织肉瘤分级的ROC曲线下面积为0.909 （95％可信区间为0.808~1.000），准确率、灵敏度和特异度分别为84.85%、86.21％和75.00%。结论:基于影像组学的机器学习方法在预测软组织肉瘤病理分级方面有较大的应用价值。

abstractsObjective:To explore the value of T 1WI based optimal radiomics and machine learning model in predicting histological grades of soft tissue sarcoma. Methods:The preoperative MR T 1WI data of 113 patients with soft tissue sarcoma in Affiliated Hospital of Qingdao University from May 2009 to November 2018 was analyzed retrospectively. The patients were divided into training set ( n=80) and validation set ( n=33) using randomly stratified sampling mothed. According to the French Federation Nationale des Centres de Lutte Contre le Cancer (FNCLCC) system, the soft tissue sarcomas were divided into 3 pathological levels (grade Ⅰ-Ⅲ). Grade Ⅰ was defined as low grade, grade Ⅱ and Ⅲ were defined as high grade. In the training set, there were 18 cases with low-grade lesions, 62 cases with high-grade lesions. In the validation set, there were 7 cases with low-grade lesions and 26 cases with high-grade lesions. After a normalizationapproach applied on the image, the radiomics features were extracted in the regions of interest using A.K software. Based on different feature selection methods [with or without recursive feature elimination (RFE)], machine learning algorithm [random forest (RF) or support vector machine (SVM)] and sampling technology [without subsampling, with the synthetic minority oversampling technique (SMOTE) or with random oversampling examples], a total of 12 models were built and each machine-learning combination model was trained using leave-one-out cross validation. The receiver operating characteristic (ROC) curves were used to evaluate the efficacy of the model in predicting the pathological grade of soft tissue sarcoma. Results:Among the 12 different machine learning models, the optimal classification model for the prediction of soft tissue sarcoma pathological grade was a combination of RF, RFE and SMOTE, with an area under the curve of 0.909 (95% confidence interval, 0.808-1.000) in the validation set, and the accuracy, sensitivity, and specificity were 84.85%, 86.21%, and 75.00%, respectively.Conclusion:The radiomics based machine learning model can be used as an attractive application approach for predicting histological grades of soft tissue sarcoma.