Department of Radiology, The First Affiliated Hospital of China Medical University, No.155 Nanjing Road Heping Area, Shenyang, 110000, Liaoning Province, China.
GE Health, Shanghai, 200000, China.
Abdom Radiol (NY). 2020 Aug;45(8):2449-2458. doi: 10.1007/s00261-020-02461-2.
To develop and validate a novel method based on radiomics for the preoperative differentiation of benign and malignant gallbladder polypoid lesions (PLG).
A total of 145 patients with pathological proven gallbladder polypoid lesions ≥ 1 cm were included in this retrospective study. All the patients underwent abdominal contrast-enhanced computed tomography (CT) examinations 3 weeks before cholecystectomy from January 2013 to January 2019. Seventy percent of the cases were randomly selected for the training dataset, and 30% of the cases were independently used for testing. Radiomics features extracted from portal venous-phase CT of the PLG and clinical features were analyzed, and the LASSO regression algorithm was used for data dimension reduction. Multivariable logistic regression was used to generate radiomics signatures, clinical signatures, and combination signatures. The receiver operating characteristic (ROC) curve and decision curve were plotted to assess the differentiating performance of the three signatures.
The area under the ROC curve (AUC) of the radiomics signature and clinical signature was 0.924 and 0.861 in the testing dataset, respectively. For the radiomics signature, the accuracy was 88.6%, with 88.0% specificity and 89.5% sensitivity. When combined, the AUC was 0.931, the specificity was 84.0%, and the sensitivity was 89.5%. The differences between the AUC values of the two sole models and the combination model were statistically nonsignificant.
Radiomics based on CT images can be helpful to differentiate benign and malignant gallbladder polyps ≥ 1 cm in size.
开发并验证一种基于影像组学的新方法,用于术前鉴别良恶性胆囊息肉样病变(PLG)。
本回顾性研究纳入了 145 例经病理证实的≥1cm 胆囊息肉样病变患者。所有患者均于胆囊切除术前行腹部增强 CT 检查(CT),时间为 2013 年 1 月至 2019 年 1 月。70%的病例被随机选取作为训练数据集,30%的病例则被独立用于测试。对 PLG 门静脉期 CT 提取的影像组学特征和临床特征进行分析,使用 LASSO 回归算法进行数据降维。使用多变量逻辑回归生成影像组学特征、临床特征和联合特征。绘制受试者工作特征(ROC)曲线和决策曲线以评估这三种特征的鉴别性能。
在测试数据集中,影像组学特征和临床特征的 ROC 曲线下面积(AUC)分别为 0.924 和 0.861。对于影像组学特征,其准确率为 88.6%,特异度为 88.0%,敏感度为 89.5%。两者联合时,AUC 为 0.931,特异度为 84.0%,敏感度为 89.5%。两个单独模型和联合模型的 AUC 值之间差异无统计学意义。
基于 CT 图像的影像组学有助于鉴别直径≥1cm 的良恶性胆囊息肉。