摘要
目的 探讨基于CT靶扫描的影像组学及临床特征对肺结节恶性的预测价值。方法 回顾性分析肺结节患者93例(共102枚)临床及CT图像资料,比较恶性组59枚和良性组43枚临床及CT图像特征,采用多因素logistic回归分析肺结节恶性的危险因素,并建立临床模型。提取病例的靶扫描CT图像特征,使用逻辑回归分类器建立影像组学模型。按照7∶3的比例分为训练组71枚和测试组31枚,绘制ROC曲线评估训练组和测试组三种模型(临床模型、影像组学模型和联合模型)对肺结节恶性的预测效能。结果 恶性组和良性组CT图像特征(长径、短径、长短径比、密度、形态、毛刺、空气支气管征、胸膜凹陷征及血管聚集征)比较有统计学差异(P<0.05或P<0.01)。密度为混合磨玻璃或实性是肺结节恶性的独立危险因素(P<0.01),为临床模型。10个权重系数最高的影像特征归为影像组学模型。基于CT靶扫描的联合模型具有较高的预测效能,训练组和测试组的AUC分别为0.933(95%CI:0.880~0.987)和0.885(95%CI:0.765~1.000)(P<0.01)。结论 基于CT靶扫描的影像组学及临床特征的联合模型对肺结节恶性具有较高预测价值。
Objective To explore the predictive value of CT target scanning based radiomics and clinical features for pulmonary nodule malignancy.Methods The clinical and CT image data of 93 patients(102 pulmonary nodules)were retrospectively analyzed.The clinical and CT image features of 59 nodules in malignant group and 43 nodules in benign group were compared.The risk factors for pulmonary nodule malignancy were analyzed by multivariate logistic regression,and the clinical model was established.The features of target scan CT images were extracted and the radiomics model was established using logistic regression classifier.Seventy-one nodules in training group and 31 nodules in testing group were at a ratio of 7:3.ROC curves were drawn to evaluate the predictive efficacy of the three models,which included clinical model,radiomics model and combined model,in training group and testing group for pulmonary nodule malignancy.Results CT image features including long diameter,short diameter,ratio of long-short diameter,density,shape,burr,air bronchial sign,pleural depression sign and vascular aggregation sign,were significantly different between malignant group and benign group(P<0.05 or P<0.01).Density of mixed ground glass or solid was an independent risk factor for pulmonary nodule malignancy(P<0.01),which was a clinical model.The 10 image features with the highest weight coefficients were classified into radiomics model.The combined model based on CT target scanning showed a higher predictive efficacy,with AUC of 0.933(95%CI:0.880-0.987)in traning group and AUC of 0.885(95%CI:0.765-1.000)in testing group(P<0.01).Conclusion CT target scanning based combined model of radiomics and clinical features has high predictive value for pulmonary nodule malignancy.
作者
贾济波
陈琦
王勋
朱全新
JIA Jibo;CHEN Qi;WANG Xun;ZHU Quanxin(Deppartment of Radiology,Third People's Hospital of Kunshan,Kunshan 215300,CHINA)
出处
《江苏医药》
CAS
2024年第9期945-949,共5页
Jiangsu Medical Journal
基金
苏州市“科教兴卫”青年科技项目(KJXW2021077)
昆山市重点研发计划-社会发展科技专项项目(KS2337)。
