| 颜蕾,杨光杰,聂佩,苗文杰,龚爱迪,赵钰鋆,崔景景,贾妍,华闻达,王振光.基于CT影像组学模型鉴别肾乏脂肪血管平滑肌脂肪瘤与均质肾透明细胞癌[J].中国医学影像技术,2020,36(5):732~737 |
| 基于CT影像组学模型鉴别肾乏脂肪血管平滑肌脂肪瘤与均质肾透明细胞癌 |
| CT-based radiomics model for differential diagnosis of fat-poor renal angiomyolipoma and homogeneous-density clear cell renal cell carcinoma |
| 投稿时间：2019-08-06 修订日期：2019-11-08 |
| DOI：10.13929/j.issn.1003-3289.2020.05.022 |
| 中文关键词: 肾肿瘤 血管肌脂瘤 体层摄影术,X线计算机 影像组学 |
| 英文关键词:kidney neoplasms angiomyolipoma tomography,X-ray computed radiomics |
| 基金项目:青岛市市南区科技计划项目（2020-2-004-YY）。 |
| 作者 | 单位 | E-mail | | 颜蕾 | 青岛大学医学部, 山东 青岛 266100
青岛大学附属医院PET/CT中心, 山东 青岛 266100 | | | 杨光杰 | 青岛大学附属医院PET/CT中心, 山东 青岛 266100 | | | 聂佩 | 青岛大学附属医院放射科, 山东 青岛 266100 | | | 苗文杰 | 青岛大学医学部, 山东 青岛 266100
青岛大学附属医院PET/CT中心, 山东 青岛 266100 | | | 龚爱迪 | 青岛大学医学部, 山东 青岛 266100
青岛大学附属医院PET/CT中心, 山东 青岛 266100 | | | 赵钰鋆 | 青岛大学附属医院PET/CT中心, 山东 青岛 266100 | | | 崔景景 | 慧影医疗科技有限公司, 北京 100089 | | | 贾妍 | 慧影医疗科技有限公司, 北京 100089 | | | 华闻达 | 慧影医疗科技有限公司, 北京 100089 | | | 王振光 | 青岛大学附属医院PET/CT中心, 山东 青岛 266100 | doctorwzg2002@hotmail.com |
|
| 摘要点击次数: 2539 |
| 全文下载次数: 627 |
| 中文摘要: |
| 目的 基于增强CT影像组学特征联合临床特征建立综合模型，验证其术前鉴别肾乏脂肪血管平滑肌脂肪瘤（fp-AML）与均质肾透明细胞癌（hd-ccRCC）的效能。方法 回顾性分析经病理证实的32例fp-AML与39例hd-ccRCC。在增强CT皮质期、实质期及排泄期图像手工勾画肿瘤ROI，提取影像特征，计算观察者间及观察者内组内相关系数（ICC），采用LASSO回归进行特征选择，通过Logistic多元回归分析构建回归方程，并计算皮质期、实质期、排泄期及三期联合的影像组学得分。通过Logistic多元回归分析建立综合模型，并绘制列线图。采用Hosmer-Lemeshow拟合优度检验评价列线图的拟合度，以ROC曲线分析检测列线图的鉴别效能，以决策曲线评价列线图鉴别fp-AML和hd-ccRCC的净获益。结果 自各期图像中提取出包括强度、形状、纹理、图像滤波在内共1 029个特征，对ICC均>0.75的特征进行LASSO选择，分别于皮质期、实质期、排泄期和三期联合获得6、6、5和7个有鉴别意义的特征，AUC分别为0.83[95%CI（0.73，0.92）]、0.80[95%CI（0.70，0.91）]、0.78[95%CI（0.68，0.89）]和0.86[95%CI（0.77，0.95）]。基于三期联合影像组学得分和临床特征的列线图的AUC为0.90[95%CI（0.81，0.99）]，以之术前鉴别fp-AML和hd-ccRCC可获得较满意的诊断净获益。结论 通过列线图表示基于增强CT影像组学特征联合临床特征建立的综合模型术前鉴别fp-AML与hd-ccRCC具有较高诊断效能，有助于术前定性诊断肾肿瘤。 |
| 英文摘要: |
| Objective To observe the value of enhanced CT-based radiomics nomogram incorporated with radiomics signatures and clinical factors in differential diagnosis of fat-poor angiomyolipoma (fp-AML) and homogeneous density clear cell renal cell carcinoma (hd-ccRCC) before surgical operation. Methods Data of 71 patients with fp-AML (fp-AML group, n=32) and hd-ccRCC (hd-ccRCC group,n=39) proved by pathology were retrospectively collected. Three-dimensional ROI were contoured manually at cortical, nephrographic and excretory phase images (CP, NP and EP), and the radiomics features were extracted. Then inter- and intra- class correlation coefficients (ICC) were used to exclude the inter-observer and intra-observer difference of ROI feature extraction. The LASSO regression method was used to select radiomics features. A regression formula was constructed by multivariate Logistic regression analysis. Radiomics scores of CP, NP, EP and all 3 phases were calculated. A combined radiomics nomogram was developed by incorporating clinical factors and radiomics score using Logistic multivariate regression model. The Hosmer-Lemeshow test was performed to assess the goodness-of-fit of the nomogram. The calibration of the nomogram was assessed with calibration curves. The differential effectiveness of the radiomics nomogram was evaluated on the basis of ROC curves. The decision curve was performed to evaluate the net benefits of the nomogram for differentiating fp-AML from hd-ccRCC. Results Totally 1 029 features including intensity, shape, texture and wavelet features were extracted from all phases. Radiomics features with ICC greater than 0.75 were enrolled into the LASSO regression model. Totally 6, 6, 5 and 7 optimal features extracted from cortical, nephrographic and excretory phases and all 3 phases were selected, and AUC was 0.83 (95%CI[0.73,0.92]), 0.80 (95%CI[0.70,0.91]), 0.78 (95%CI[0.68,0.89]) and 0.86 (95%CI[0.77,0.95]), respectively. AUC of the nomogram based on radiomics score of all 3 phases and clinical factors was 0.90 (95%CI[0.81,0.99]), and decision curve indicated that this radiomics nomogram had a satisfactory overall net benefit for differentiating fp-AML from hd-ccRCC before surgical operation. Conclusion CT-based radiomics nomogram, which incorporates the radiomics signatures and clinical factors, shows favorable predictive value for differentiating fp-AML from hd-ccRCC, which might be helpful to accurate diagnosis before surgical operation. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
