摘要
目的:探讨基于临床指标和CT征象构建的列线图模型术前预测结直肠癌(CRC)患者微卫星不稳定状态(MSI)的价值.方法:回顾性连续搜集2016年1月-2022年12月在本院经病理诊断为结直肠腺癌的347例患者的术前临床和CT检查资料.CT资料包括平扫及对比增强动脉期、静脉期和延迟期图像.其中,276例为微卫星稳定状态(MSS),71例为MSI.按照7∶3的比例将所有患者随机分为训练集(243例)和验证集(104例).采用单因素分析(t检验、U检验或卡方检验)对两组之间年龄、性别、病史和实验室检查等临床指标及CT征象[临床T分期、临床N分期、病变肠管的长度及最厚径、病变的部位、强化方式及各期相对CT值(CR=病变CT值/同层面腹主动脉或其分支的CT值)]的差异进行比较.随后,将有统计学意义的变量纳入多因素二元logistic回归分析,筛选出预测CRC患者MSI状态的独立危险因素并构建预测模型,随后绘制模型的列线图.分别采用ROC曲线、校准曲线和决策曲线(DC A)评估列线图模型的预测效能、预测准确性和临床实用性.结果:单因素分析结果显示血小板(PLT)水平、系统免疫炎症指数(SII)、病变部位、强化方式、四期图像上病灶的CR值在MSS组与MSI组之间的差异有统计学意义(P＜0.05).多因素逻辑回归分析结果显示PLT、SII、病变部位、强化方式和动脉期CR是CRC患者MSI状态的独立预测因子.根据多因素分析结果所构建的列线图模型具有较好的MSI预测效能:在训练集和验证集中的ROC曲线下面积(AUC)分别为0.765和0.783;校准曲线表明列线图模型的拟合优度良好;DCA结果显示列线图模型在预测CRC患者MSI状态时具有较高的临床净获益率.结论:基于临床-CT征象构建的列线图模型可作为术前检测CRC患者MSI状态的辅助工具,能够协助制定C R C患者的治疗策略和评估患者预后.
Abstract
Objective:To investigate the value of a nomogram model incorporating clinical-CT features for preoperative predicting microsatellite instability(MSI)in patients with colorectal cancer(CRC).Methods:The clinical and CT data of 347 consecutive patients with pathologically confirmed colorectal adenocarcinoma in our hospital from January 2016 to December 2022 were retrospectively collected,including 276 patients with microsatellite stability(MSS)and 71 patients with MSI.The CT examination data included pre-contrast and arterial,venous and delayed phase post-contrast images.All patients were randomly divided into two groups according to the ratio of 7∶3.There were 243 cases in the training set and 104 cases in the validation set.Univariate analysis methods(t-test,U-test or chi-square test)were used to compare the difference of clinical indicators such as age,gender,medical his-tory and laboratory examination and CT features[clinical©T-stage,cN-stage,tumour location,tumor length,maximum tumor diameter,enhancement pattern and relative-CT value(ratio of the CT value of the lesion to the CT value of the abdominal aorta or its branches at the same level,including relative CT values for plain scan(PCR),arterial phase(ACR),venous phase(VCR)and delayed phase(DCR)]between the two groups.The variables with statistical significance were included in multivariate binary logistic regression analysis to select out the independent risk factors for predicting MSI status in CRC patients and construct predictive model,then a nomogram model was developed.The predictive efficacy,predictive accuracy,and clinical utility of the nomogram model were assessed using receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA),respectively.Results:Univariate analysis showed that platelet(PLT),systemic immune-in-flammation index(SII),tumor location,enhancement pattern,CR values in four phases were statisti-cally significant between the two groups(all P＜0.05).The results of multivariate logistic regression analysis showed that PLT,SII,tumor location,enhancement pattern and ACR were independent pre-dictors for MSI status in CRC patients.The nomogram model had good MSI prediction efficacy with the area under the curve(AUC)of 0.765 and 0.783 in the training set and validation set,respectively.The calibration curve indicated that the nomogram model had a good fit,and the DCA showed that the nomogram model had high net clinical benefit in predicting MSI status in CRC patients.Conclusion:The nomogram model based on clinical-CT features can be used as an auxiliary tool for preoperative detection of MSI status in CRC patients,and can assist in the formulation of treatment strategies and assessment of patient prognosis in CRC patients.
基金项目
江苏省重点研发计划专项项目(BE2021652)
中核医疗产业有限公司"技术创新"专项(ZHYLTD2021001)
NETL
NSTL
万方数据