多序列MRI影像组学联合KRAS突变列线图模型预测直肠癌患者新辅助放化疗敏感性的价值  

Value of multi-sequence MRI radiomics combined with KRAS mutation nomogram model in predicting the sensitivity of neoadjuvant chemotherapy in patients with rectal cancer

作　　者：胡鸿博[1] 张莹[2] 赵升 姜昊[1] 蔺雪 姜慧杰[1] Hu Hongbo;Zhang Ying;Zhao Sheng;Jiang Hao;Lin Xue;Jiang Huijie(Department of Imaging,the Second Affiliated Hospital of Harbin Medical University,Harbin 150086,China;Department of Pharmacology,College of Pharmacy,Harbin Medical University,Harbin 150086,China)

机构地区：[1]哈尔滨医科大学附属第二医院影像科,哈尔滨150086 [2]哈尔滨医科大学药学院药理教研室,哈尔滨150086

出　　处：《中华放射学杂志》2024年第10期1069-1074,共6页Chinese Journal of Radiology

基　　金：国家自然科学基金(62171167、U23A20478)。

摘　　要：目的构建多序列MRI影像组学联合KRAS突变列线图模型预测直肠癌患者新辅助放化疗后病理完全缓解(pCR)的效能。方法该研究为病例对照研究,回顾性收集2020年6月至2023年12月哈尔滨医科大学附属第二医院经新辅助放化疗治疗的126例直肠癌患者,对术后标本的病理反应进行分级,pCR 64例、非pCR 62例。对新辅助放化疗前病理组织进行KRAS基因检测,患者中KRAS突变型34例,KRAS野生型92例。采用随机数法将126例患者按8∶2的比例分为训练集和验证集,例数分别为101、25例。采用χ^(2)检验比较pCR组与非pCR组间KRAS突变状态的差异。对患者基线T 2WI、扩散加权成像、表观扩散系数图像提取影像组学特征,筛选出最优影像组学特征建立影像组学模型,采用logistic回归构建影像组学-KRAS联合模型,并绘制列线图。通过受试者操作特征曲线、校准曲线评价模型的应用效能。结果在训练集中pCR组与非pCR组间KRAS突变差异有统计学意义(χ^(2)=4.69,P=0.032)。在MRI图像中筛选出10个影像组学特征建立影像组学模型。在训练集和验证集中,KRAS突变、影像组学模型和影像组学-KRAS列线图模型评估新辅助放化疗后pCR的曲线下面积(AUC)分别是0.665(95%CI 0.592~0.757)、0.746(95%CI 0.651~0.895)和0.818(95%CI 0.742~0.934),验证集的AUC分别是0.613(95%CI 0.582~0.755)、0.738(95%CI 0.627~0.839)和0.833(95%CI 0.768~0.961)。DeLong检验结果显示影像组学-KRAS列线图模型的AUC高于KRAS突变、影像组学模型的AUC,差异有统计学意义(训练集:Z=0.58、0.63,P=0.024、0.022;验证集:Z=0.54、0.61,P=0.018、0.035)。校准曲线显示影像组学-KRAS列线图模型的预测概率与实际概率一致性良好。结论多序列MRI影像组学联合KRAS突变的列线图模型预测直肠癌患者新辅助放化疗后pCR的效能最佳,且实际应用价值良好。Objective To construct a multi-sequence MRI radiomics combined with KRAS mutation nomogram model to predict the efficacy of pathological complete response(pCR)in patients with rectal cancer after neoadjuvant chemoradiotherapy.Methods This study was a case-control study.A total of 126 patients with rectal cancer who were treated with neoadjuvant chemoradiotherapy in the Second Affiliated Hospital of Harbin Medical University from June 2020 to December 2023 were retrospectively collected.The pathological response of the postoperative specimens was graded,with 64 cases of pCR and 62 cases of non-pCR.KRAS gene detection was performed on the pathological tissues before neoadjuvant chemoradiotherapy.Among the patients,34 cases had KRAS mutants and 92 cases had KRAS wild-types.The 126 patients were randomly divided into a training set and a validation set at a ratio of 8∶2 by the random number method,with 101 and 25 cases,respectively.The difference in KRAS mutation status between the pCR group and the non-pCR group was compared by theχ^(2) test.The radiomic features were extracted from the baseline T 2WI,diffusion-weighted imaging,and apparent diffusion coefficient images of the patients.The optimal radiomic features were screened out to establish the radiomics model.The radiomics-KRAS joint model was constructed by logistic regression,and a nomogram was drawn.The application efficiency of the model was evaluated by the receiver operating characteristic curve and calibration curve.Results There was a statistically significant difference in KRAS mutation between the pCR group and the non-pCR group in the training set(χ^(2)=4.69,P=0.032).Ten radiomics features were screened out in MRI images to establish the radiomics model.In the training set and validation set,the areas under the curve(AUC)of KRAS mutation,radiomics model and radiomics-KRAS nomogram model for evaluating pCR after neoadjuvant chemoradiotherapy were 0.665(95%CI 0.592-0.757),0.746(95%CI 0.651-0.895)and 0.818(95%CI 0.742-0.934),respectively,and the AUC

关 键 词：直肠肿瘤 磁共振成像 影像组学 KRAS基因 

分 类 号：R735.37[医药卫生—肿瘤] R445.2[医药卫生—临床医学]