基于CT检查影像组学胃神经内分泌肿瘤预后的预测模型构建及其应用价值  

Construction and application value of CT based radiomics model in predicting the prognosisof patients with gastric neuroendocrine neoplasm

作　　者：杨志浩[1] 韩懿静 程铭 王睿[1] 李靖 赵慧萍[1] 高剑波[1] Yang Zhihao;Han Yijing;Cheng Ming;Wang Rui;Li Jing;Zhao Huiping;Gao Jianbo(Department of Radiology,the First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China;Department of Radiology,Affiliated Cancer Hospital of Zhengzhou University,Zhengzhou 450008,China)

机构地区：[1]郑州大学第一附属医院放射科,郑州450052 [2]郑州大学附属肿瘤医院放射科,郑州450008

出　　处：《中华消化外科杂志》2023年第4期552-565,共14页Chinese Journal of Digestive Surgery

基　　金：国家自然科学基金(81971615);河南省科技厅科技攻关项目(212102310664)。

摘　　要：目的:构建基于CT检查影像组学胃神经内分泌肿瘤(GNEN)预后的预测模型,探讨其应用价值。方法:采用回顾性队列研究方法。收集2011年8月至2020年12月2家医学中心收治的182例(郑州大学第一附属医院124例,郑州大学附属肿瘤医院58例)GNEN患者的临床病理资料;男130例,女52例;年龄为64(56~70)岁。182例患者通过随机数字表法按7∶3随机分为训练集128例和验证集54例。182例患者均行CT增强检查。观察指标:(1)影像组学模型的构建与验证。(2)影响训练集GNEN患者预后因素分析。(3)GNEN患者预后预测模型构建与评估。偏态分布的计量资料以M(范围)表示,组间比较采用Mann-Whitney U检验。计数资料以绝对数表示,组间比较采用χ^(2)检验、校正χ^(2)检验或Fisher确切概率法。采用Kaplan-Meier法计算生存率和绘制生存曲线,采用Log-rank检验进行生存分析。单因素和多因素均采用COX回归模型。使用R软件(4.0.3版本)glmnet软件包进行最小绝对收缩和选择算子方法(LASSO)-COX回归分析,使用rms软件包(4.0.3版本)生成列线图和校准曲线图,使用Hmisc软件包(4.0.3版本)计算C-index,采用dca.R软件包(4.0.3版本)进行决策曲线分析。结果:(1)影像组学模型的构建与验证:提取182例GNEN患者1781个影像组学特征,经组内相关系数>0.75的特征筛选和LASSO-COX回归模型进一步降维后,最终筛选14个非零系数影像组学特征,计算影像组学评分(R-score),构建基于R-score的影像组学预测模型。采用R-score的最佳截断值为-0.494,将训练集128例患者分为高风险64例和低风险64例;将验证集54例患者分为高风险35例和低风险19例。影像组学预测模型预测训练集患者18、24、30个月总生存率的曲线下面积分别为0.83[95%可信区间(CI)为0.76~0.87,P<0.05]、0.84(95%CI为0.73~0.91,P<0.05)、0.91(95%CI为0.78~0.95,P<0.05);验证集上述指标分别为0.84(95%CI为0.75~0.92,P<0.05)、0.84(95%CI为0.73~0.91Objective To construct of a computed tomography(CT)based radiomics model for predicting the prognosis of patients with gastric neuroendocrine neoplasm(GNEN)and investigate its application value.Methods The retrospective cohort study was conducted.The clinicopathological data of 182 patients with GNEN who were admitted to 2 medical centers,including the First Affiliated Hospital of Zhengzhou University of 124 cases and the Affiliated Cancer Hospital of Zhengzhou University of 58 cases,from August 2011 to December 2020 were collected.There were 130 males and 52 females,aged 64(range,56−70)years.Based on random number table,all 182 patients were divided into the training dataset of 128 cases and the validation dataset of 54 cases with a ratio of 7:3.All patients underwent enhanced CT examination.Observation indicators:(1)construction and validation of the radiomics prediction model;(2)analysis of prognostic factors for patients with GNEN in the training dataset;(3)construction and evaluation of the prediction model for prognosis of patients with GNEN.Measurement data with skewed distribution were represented as M(range),and comparison between groups was conducted using the Mann‐Whitney U test.Count data were described as absolute numbers,and the chi‐square test,corrected chi‐square test or Fisher exact probability were used for comparison between groups.The Kaplan‐Meier method was used to calculate survival rate and draw survival curve,and the Log‐rank test was used for survival analysis.The COX regression model was used for univariate and multivariate analyses.The R software(version 4.0.3)glmnet software package was used for least absolute shrinkage and selection operator(LASSO)-COX regression analysis.The rms software(version 4.0.3)was used to generate nomogram and calibration curve.The Hmisc software(version 4.0.3)was used to calculate C‐index values.The dca.R software(version 4.0.3)was used for decision curve analysis.Results(1)Construction and validation of the radiomics prediction model.One thous

关 键 词：胃肿瘤 神经内分泌肿瘤 预后 体层摄影 X线计算机 影像组学 

分 类 号：R735.2[医药卫生—肿瘤]