Xiang Jun-Yi, Huang Xiao-Shan, Feng Na, Zheng Xiao-Zhong, Rao Qin-Pan, Xue Li-Ming, Ma Lin-Ying, Chen Ying, Xu Jian-Xia
Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Front Oncol. 2023 Feb 15;13:1065440. doi: 10.3389/fonc.2023.1065440. eCollection 2023.
To establish a logistic regression model based on CT and MRI imaging features and Epstein-Barr (EB) virus nucleic acid to develop a diagnostic score model to differentiate extranodal NK/T nasal type (ENKTCL) from diffuse large B cell lymphoma (DLBCL).
This study population was obtained from two independent hospitals. A total of 89 patients with ENKTCL (n = 36) or DLBCL (n = 53) from January 2013 to May 2021 were analyzed retrospectively as the training cohort, and 61 patients (ENKTCL=27; DLBCL=34) from Jun 2021 to Dec 2022 were enrolled as the validation cohort. All patients underwent CT/MR enhanced examination and EB virus nucleic acid test within 2 weeks before surgery. Clinical features, imaging features and EB virus nucleic acid results were analyzed. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors of ENKTCL and establish a predictive model. Independent predictors were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the predictive model and score model.
We searched for significant clinical characteristics, imaging characteristics and EB virus nucleic acid and constructed the scoring system multivariate logistic regression and converted regression coefficients to weighted scores. The independent predictors for ENKTCL diagnosis in multivariate logistic regression analysis, including site of disease (nose), edge of lesion (blurred), T2WI (high signal), gyrus like changes, EB virus nucleic acid (positive), and the weighted score of regression coefficient was 2, 3, 4, 3, 4 points. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. The AUC of the scoring model in the training cohort were 0.925 (95% CI, 0.906-0.990) and the cutoff point was 5 points. In the validation cohort, the AUC was 0.959 (95% CI, 0.915-1.000) and the cutoff value was 6 points. Four score ranges were as follows: 0-6 points for very low probability of ENKTCL, 7-9 points for low probability; 10-11 points for middle probability; 12-16 points for very high probability.
The diagnostic score model of ENKTCL based on Logistic regression model which combined with imaging features and EB virus nucleic acid. The scoring system was convenient, practical and could significantly improve the diagnostic accuracy of ENKTCL and the differential diagnosis of ENKTCL from DLBCL.
基于CT和MRI成像特征以及EB病毒核酸建立逻辑回归模型，以开发一种诊断评分模型，用于区分结外NK/T鼻型淋巴瘤（ENKTCL）与弥漫性大B细胞淋巴瘤（DLBCL）。
本研究人群来自两家独立医院。回顾性分析了2013年1月至2021年5月共89例ENKTCL患者（n = 36）或DLBCL患者（n = 53）作为训练队列，并纳入了2021年6月至2022年12月的61例患者（ENKTCL = 27；DLBCL = 34）作为验证队列。所有患者在手术前2周内接受了CT/MR增强检查和EB病毒核酸检测。分析临床特征、成像特征和EB病毒核酸结果。进行单因素分析和多因素逻辑回归分析以确定ENKTCL的独立预测因素并建立预测模型。根据回归系数对独立预测因素进行评分加权。绘制受试者工作特征（ROC）曲线以确定预测模型和评分模型的诊断能力。
我们寻找了显著的临床特征、成像特征和EB病毒核酸，并构建了评分系统，进行多因素逻辑回归并将回归系数转换为加权分数。多因素逻辑回归分析中ENKTCL诊断的独立预测因素包括病变部位（鼻）、病变边缘（模糊）、T2WI（高信号）、脑回样改变、EB病毒核酸（阳性），回归系数的加权分数分别为2、3、4、3、4分。在训练队列和验证队列中进行ROC曲线、AUC和校准测试以评估评分模型。训练队列中评分模型的AUC为0.925（95%CI，0.906 - 0.990），截断点为5分。在验证队列中，AUC为0.959（95%CI，0.915 - 1.000），截断值为6分。四个评分范围如下：0 - 6分表示ENKTCL可能性极低；7 - 9分表示可能性低；10 - 11分表示可能性中等；12 - 16分表示可能性极高。
基于逻辑回归模型结合成像特征和EB病毒核酸的ENKTCL诊断评分模型。该评分系统方便、实用，可显著提高ENKTCL的诊断准确性以及ENKTCL与DLBCL的鉴别诊断能力。
