基于临床因素及CT特征构建鉴别肾细胞癌亚型的积分系统  

A Scoring System Based on Clinical Factor and CT Features for Identifying Renal Clear Cell Carcinoma Subtypes

作　　者：赵才勇 陈超[2] 严志强 陈文 康书朝 崔凤[1] ZHAO Caiyong;CHEN Chao;YAN Zhiqiang(Depatment of Radiology,Hangzhou Hospital of Traditional Chinese Medicine,Hangzhou,Zhejiang Province 310007,P.R.China)

机构地区：[1]杭州市中医院放射科,310007 [2]浙江大学医学院附属邵逸夫医院放射科,310016

出　　处：《临床放射学杂志》2022年第11期2077-2082,共6页Journal of Clinical Radiology

基　　金：杭州市生物医药和健康产业发展扶持科技基金资助项目(编号:2021WJCY355)。

摘　　要：目的探讨基于一般临床因素和CT影像特征构建的积分预测系统在鉴别肾透明细胞癌(ccRCC)和非透明细胞癌(non-ccRCC)中的价值。方法搜集经病理证实的150例肾细胞癌患者作为回顾性分析研究对象,包括97例ccRCC和53例non-ccRCC,随机分成训练集(100例)和验证集(50例)。通过Mann-Whitney U检验、χ^(2)检验及二元Logistic回归分析一般临床资料(年龄、性别)和CT影像特征筛选组间有统计学意义的特征因子,并进行加权赋分得到积分模型。用ROC曲线(AUC)评价模型预测效能。最后将积分模型分为3个积分区间。结果二元Logistic回归分析显示性别、囊变坏死、皮髓质期强化程度和强化模式是鉴别ccRCC和non-ccRCC的独立因素,该模型的ROC曲线AUC值为0.924(95%CI 0.860~0.987)。积分模型包括男性(2分)、囊变坏死(2分)、皮髓质期明显强化(3分)和流出型强化模式(3分),其ROC曲线AUC值为0.908(95%CI 0.840~0.975),应用Youden指数确定最佳阈值(4.5),相对应的敏感度、特异度分别为91.0%、78.8%。将积分模型分为3个积分区间:0~1分、2~4分、5~10分。随着积分增加,训练集、验证集各积分区间ccRCC的发生率均逐步增高。结论基于一般临床因素及CT影像特征构建的积分预测系统对临床鉴别ccRCC和non-ccRCC具有重要价值。Objective To investigate the value of a scoring system based on clinical factor and CT imaging features in distinguishing clear cell renal cell carcinoma(ccRCC)and non-clear cell renal cell carcinoma(non-ccRCC).Methods A total of 150 patients diagnosed as ccRCC(n=97)or non-ccRCC(n=53)by pathology were retrospectively analyzed by dividing them randomly into a training cohort(n=100)and a validation cohort(n=50).Mann-Whitney U test,Chi-squared test and binary Logistic regression analysis were used to select the independent predictor of clinical and CT imaging features and establish the score model.The receiver operating characteristic(ROC)curve(AUC)was used to assess the discriminatory power of the models.The score model was divided into three score ranges.Results Binary Logistic regression analysis showed that gender,necrosis or cystic,enhancement degree in corticomedullary phase and enhancement pattern were independent predictors in distinguishing ccRCC and non-ccRCC.The AUC of the primary predictive model was 0.924(95%CI 0.860~0.987).Four independent predictors were included in the score model:gender(male),2 points;presence of necrosis or cystic,2 points;significant enhancement in corticomedullary phase,3 points;enhancement pattern(washout),3 points.The AUC of the score model was 0.908(95%CI 0.840~0.975).This scoring system presented with a sensitivity of 91.0%and a specificity of 78.8%when using 4.5 points as cutoff value.Three score ranges were also proposed as follows:0-1 points;2-4 points;5-10 points.The number of patients with ccRCC in the three ranges significantly increased with the increasing scores in the training cohort and validation cohort.Conclusions The scoring system has significant values for distinguishing ccRCC and non-ccRCC based on four clinical and CT imaging features.

关 键 词：肾透明细胞癌 肾非透明细胞癌 计算机断层扫描 积分系统 

分 类 号：R737.11[医药卫生—肿瘤] R730.44[医药卫生—临床医学]