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.
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。