细胞外体积分数联合临床指标构建的列线图预测结直肠癌p53表达  

Construction of a Nomogram Based on Extracellular Volume Fraction and Clinical factors for Predicting P53 Expressionin Colorectal Carcinoma

作　　者：周兰妮 欧阳富盛 郭保亮 王丽雯 潘佳玲 陈铭 ZHOU Lanni;OUYANG Fusheng;GUO Baoiang(Department of Radiology,Shunde Hospital,Southern Medical University(The First People's Hospital of Shunde),Foshan,Guangdong Province 528308,P.R.China)

机构地区：[1]南方医科大学顺德医院(佛山市顺德区第一人民医院),528308

出　　处：《临床放射学杂志》2024年第5期776-782,共7页Journal of Clinical Radiology

基　　金：广东省医学科学技术研究基金项目(编号:A2023204、 A2021483、 A2020089);佛山市自筹经费类科技创新项目(编号:2220001004816、2220001004195)。

摘　　要：目的 构建基于CT的细胞外体积分数(ECV)结合临床指标预测结直肠癌(CRC)的p53表达状态的列线图模型并进行验证。方法 回顾性搜集2019年1月至2022年3月在本院行手术治疗的147例CRC患者的资料,根据病理结果分为p53高危表达组和p53低危表达组。按照7∶3的比例将所有患者随机分为训练组(n=103)和验证组(n=44)。分别采用Logistic回归和最小绝对收缩和选择算子(LASSO)筛选出预测因子,构建风险预测模型并进行比较,取受试者工作特征曲线(ROC)曲线下面积(AUC)面积大者建立列线图,从区分度、校准度和临床净收益等方面评估列线图的性能和临床效益。结果 多因素Logistic回归分析显示性别(OR=2.073,P<0.05)、肿瘤最大径(OR=1.023,P<0.05)、ECV(OR=0.958,P<0.05)是p53高危表达的独立危险因素;LASSO算法筛选出6个预测因子,分别为性别、肿瘤最大径、ECV、平衡期-平扫CT差值(ΔHU_(tumor))、糖类抗原199(CA199)、中性粒细胞与淋巴细胞比值(NLR)。ROC分析显示,基于LASSO法构建的列线图模型具有较好的预测效能(AUC=0.752,95%CI:0.605~0.899,P=0.005),校准曲线和决策分析曲线分别验证了模型较好的校准能力和临床净收益。结论 联合ECV和临床指标构建的列线图模型具有较好的区分度、校准能力、临床净收益,可应用于无创识别p53高危表达的患者,为临床诊断和治疗方案的制定提供参考。Objective To construct and validate a nomogram model based on extracellular volume fraction(ECV) and clinical factors for predicting the expression of p53 in colorectal cancer(CRC). Methods 147 patients with CRC who underwent surgery in our hospital from January 2019 to March 2022 were selected retrospectively. According to pathology, they were divided into two groups: high risk p53 expression group and low risk p53 expression group. Patients were randomly divided into training group(103 cases) and validation group(44 cases).Logistic regression(LR) and lasso regression(LASSO) was used respectively to screen the variables with predictive value, then established several predictive models. The diagnostic efficacy was compared using ROC curves. A nomogram was built basing on the model whose area under the curve was the biggest. The efficiency and clinical benefit of the nomogram model were evaluated by degree of differentiation, calibration and decision curve analysis(DCA). Results The results of multiple Logistic regression showed that sex(OR=2.073,P<0.05),Maximum diameter of tumor(OR=1.023,P<0.05) and ECV(OR=0.958,P<0.05) were independent predictors of p53 high risk expression. LASSO regression screened out 6 potential predictive factors, which was sex, maximum diameter of tumor, ECV,ΔHU tumor, Carbohydrate antigen 199(CA199),neutrophil to lymphocyte ratio(NLR).ROC curves showed that the nomogram model based on LASSO regression has better prediction efficiency(AUC=0.752,95%CI:0.605-0.899,P=0.005).And the calibration curve and DCA curve indicated good calibration and clinical benefit of the model. Conclusion The nomogram model constructed by combining ECV and clinical indicators has good discrimination, calibration and clinical benefit, which can be used to identify CRC patients with p53 high-risk expression non-invasively, and provide reference for clinical diagnosis and treatment.

关 键 词：结直肠癌 细胞外体积分数 p53 列线图 计算机断层成像 

分 类 号：R735.34[医药卫生—肿瘤] R730.44[医药卫生—临床医学]