[Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma].

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.目的： 探讨CT影像组学预测弥漫大B细胞淋巴瘤(DLBCL)一线化疗疗效的潜在价值。 方法： 收集2013年1月至2018年5月在山西省肿瘤医院诊治的DLBCL患者的治疗前CT图像和临床资料，根据Lugano 2014疗效评价标准，分为难治性患者(73例)和非难治性患者(57例)。基于患者化疗前的CT图像，每例患者提取850个CT影像组学特征。采用最小绝对收缩与选择算法、单因素和多因素logistic回归分析筛选出与DLBCL一线化疗疗效相关的临床因素和CT影像组学特征，分别建立影像组学模型和列线图模型。采用受试者工作特征曲线(ROC)、校准曲线和临床决策曲线评价模型预测DLBCL一线化疗疗效的临床价值。 结果： 筛选出6个与DLBCL一线化疗疗效高度相关的CT影像组学特征，其中一阶特征1个、灰度共生矩阵特征1个、灰度依赖矩阵特征3个、邻域灰度差矩阵特征1个，建立相应的影像组学模型，其在训练集和验证集中的ROC曲线下面积(AUC)分别为0.82和0.73。联合有效临床因素和影像组学评分建立列线图模型，其在训练集和验证集中的AUC分别为0.95和0.91。校准曲线和临床决策曲线显示，列线图模型具有良好的一致性且在DLBCL的疗效评估方面具有较高的临床价值。 结论： 基于临床因素和影像组学特征建立的列线图模型在预测DLBCL一线化疗疗效方面具有潜在的临床价值。.