基于术前增强CT影像特征的胆囊癌神经浸润随机森林预测模型的构建  

Development of a random forest prediction model for perineural invasion in gallbladder carcinoma based on preoperative enhanced CT image features

作　　者：杨敏[1] 李起 霍文礼 李文智[2] 李娜[2] 耿智敏[2] 杨健[1] Yang Min;Li Qi;Huo Wenli;Li Wenzhi;Li Na;Geng Zhimin;Yang Jian(Department of Radiology,the First Affiliated Hospital of Xi′an Jiaotong University,Xi′an 710061,China;Department of Hepatobiliary Surgery,the First Affiliated Hospital of Xi′an Jiaotong University,Xi′an 710061,China)

机构地区：[1]西安交通大学第一附属医院医学影像科,西安710061 [2]西安交通大学第一附属医院肝胆外科,西安710061

出　　处：《中华肝胆外科杂志》2024年第8期581-585,共5页Chinese Journal of Hepatobiliary Surgery

基　　金：国家自然科学基金(62076194);陕西省重点研发计划(2021-SF-231、2022-SF-410)。

摘　　要：目的基于术前增强CT影像特征构建胆囊癌患者神经浸润无创识别的随机森林预测模型。方法回顾性分析2022年1月至2023年12月于西安交通大学第一附属医院行意向性根治性切除术的180例胆囊癌患者的临床资料,其中男性61例,女性119例,年龄(65.3±10.2)岁。将180例患者分为训练集(n=126)和测试集(n=54),依据神经浸润情况,126例训练集患者分为神经浸润组(n=33)和非神经浸润组(n=93),54例测试集患者中神经浸润者15例,非神经浸润者39例。收集患者的性别、年龄、神经浸润、肿瘤糖类抗原19-9(CA19-9)水平、肿瘤分期等临床资料。多因素logistic回归模型分析胆囊癌神经浸润的危险因素。使用Python软件中的"feature_importance"包对临床变量与神经浸润的相关性进行重要度排序。建立胆囊癌神经浸润的随机森林预测模型,并采用受试者工作特征(ROC)曲线下面积(AUC)及混淆矩阵评估模型的预测能力。结果多因素logistic回归模型分析显示,CA19-9>39.0 U/ml(OR=5.165,95%CI:1.650~16.174)、T3分期(OR=6.037,95%CI:1.571~23.197)、T4分期(OR=9.996,95%CI:2.177~45.898)及淋巴结转移(OR=7.829,95%CI:2.705~22.627)的胆囊癌患者,神经浸润发生的风险高(均P<0.05)。重要度排序中排名前3位的因素依次为:CA19-9、淋巴结转移、T分期。结合多因素分析及重要度排序结果,选择CA19-9、淋巴结转移及T分期建立胆囊癌神经浸润的随机森林预测模型。ROC曲线分析结果显示,随机森林模型在训练集和测试集中的AUC分别为0.8250和0.7667。混淆矩阵结果显示,随机森林模型在训练集和测试集中的灵敏度分别为75.76%和73.33%,特异度分别为80.65%和76.92%,准确度分别为79.36%和75.93%。结论基于术前增强CT影像特征建立的随机森林预测模型可作为胆囊癌患者神经浸润的无创识别手段。ObjectiveTo construct a random forest prediction model for non-invasive identification of perineural invasion in gallbladder carcinoma(GBC)based on preoperative enhanced CT imaging features.MethodsThe clinical data of 180 patients who underwent curative-intent resection for gallbladder carcinoma at the First Affiliated Hospital of Xi′an Jiaotong University from January 2022 to December 2023 were retrospectively analyzed,including 61 males and 119 females with the age of(65.3±10.2)years old.The 180 patients were divided into a training set(n=126)and a testing set(n=54),and based on perineural invasion,the 126 patients in the training set were divided into the perineural invasion group(n=33)and the non-perineural invasion group(n=93),and the other 54 patients in the testing set,there were 15 patients with perineural invasion and 39 patients without perineural invasion.Clinical data such as gender,age,perineural invasion,carbohydrate antigen 19-9(CA19-9)level and tumor stage were collected from patients.Multivariate logistic regression model was used to analyze the risk factors of perineural invasion in gallbladder carcinoma patients.The correlation between clinical variables and perineural invasion was ranked in order of importance using the"feature_importance"package in Python software.Then,we developed a random forest prediction model for perineural invasion in gallbladder carcinoma patients,and the area under the receiver operating characteristic(ROC)curve and confusion matrix were used to assess the predictive ability of the model.ResultsMultivariate logistic regression model analysis showed that patients with CA19-9>39.0 U/ml(OR=5.165,95%CI:1.650-16.174),T3 stage(OR=6.037,95%CI:1.571-23.197),T4 stage(OR=9.996,95%CI:2.177-45.898),and lymph node metastasis(OR=7.829,95%CI:2.705-22.627)were with a high risk of perineural invasion occurrence(all P<0.05).The top three variables in the order of the importance ranking were CA19-9,lymph node metastasis,and T stage.Combining the results of multivariate analysis and i

关 键 词：胆囊肿瘤 增强CT 神经浸润 危险因素 随机森林 

分 类 号：R735.8[医药卫生—肿瘤] R730.44[医药卫生—临床医学]