一种基于常规放射组学 CT 建立的列线图模型,用于鉴别乏脂型血管平滑肌脂肪瘤和透明细胞肾细胞癌。
A convention-radiomics CT nomogram for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma.
机构信息
Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, 310000, China.
Shaoxing City Keqiao District Hospital of Traditional Chinese Medicine, Shaoxing, 312000, China.
出版信息
Sci Rep. 2021 Feb 25;11(1):4644. doi: 10.1038/s41598-021-84244-3.
This study aimed to construct convention-radiomics CT nomogram containing conventional CT characteristics and radiomics signature for distinguishing fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC). 29 fp-AML and 110 ccRCC patients were enrolled and underwent CT examinations in this study. The radiomics-only logistic model was constructed with selected radiomics features by the analysis of variance (ANOVA)/Mann-Whitney (MW), correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO), and the radiomics score (rad-score) was computed. The convention-radiomics logistic model based on independent conventional CT risk factors and rad-score was constructed for differentiating. Then the relevant nomogram was developed. Receiver operation characteristic (ROC) curves were calculated to quantify the accuracy for distinguishing. The rad-score of ccRCC was smaller than that of fp-AML. The convention-radioimics logistic model was constructed containing variables of enhancement pattern, V, and rad-score. To the entire cohort, the area under the curve (AUC) of convention-radiomics model (0.968 [95% CI 0.923-0.990]) was higher than that of radiomics-only model (0.958 [95% CI 0.910-0.985]). Our study indicated that convention-radiomics CT nomogram including conventional CT risk factors and radiomics signature exhibited better performance in distinguishing fp-AML from ccRCC.
本研究旨在构建一种基于常规 CT 特征和放射组学特征的常规放射组学 CT 列线图,用于区分乏脂性血管平滑肌脂肪瘤 (fp-AML) 与透明细胞肾细胞癌 (ccRCC)。本研究纳入了 29 例 fp-AML 患者和 110 例 ccRCC 患者,并对其进行了 CT 检查。通过方差分析 (ANOVA)/Mann-Whitney (MW)、相关性分析和最小绝对收缩和选择算子 (LASSO) 对选定的放射组学特征进行分析,构建了放射组学模型,并计算了放射组学评分 (rad-score)。基于独立的常规 CT 风险因素和 rad-score 的常规放射组学逻辑模型用于区分。然后,开发了相关的列线图。计算接收者操作特征 (ROC) 曲线以量化区分的准确性。ccRCC 的 rad-score 小于 fp-AML 的 rad-score。构建的常规放射组学逻辑模型包含增强模式、V 和 rad-score 等变量。对于整个队列,常规放射组学模型的曲线下面积 (AUC) 为 0.968[95%CI 0.923-0.990],高于仅放射组学模型的 AUC(0.958[95%CI 0.910-0.985])。我们的研究表明,包含常规 CT 风险因素和放射组学特征的常规放射组学 CT 列线图在区分 fp-AML 与 ccRCC 方面具有更好的性能。