逻辑回归
置信区间
接收机工作特性
医学
肾细胞癌
精确检验
优势比
单变量
多元统计
单变量分析
多元分析
曼惠特尼U检验
曲线下面积
放射科
内科学
核医学
统计
数学
作者
Liling Long,X.Y. Chen,Yidi Chen,Yiwu Lei,Fuling Huang,Cheng Tang
标识
DOI:10.2174/1573405618666220513125457
摘要
Distinguishing exophytic renal urothelial carcinoma (ERUC) from exophytic renal clear-cell carcinoma (ERCCC) with collecting system invasion may be difficult as they involve similar locations and collecting system invasion.The study aimed to characterize the clinical data and computed tomography (CT) features that can aid in differentiating ERUC from ERCCC.Data from 17 patients with ERUC and 222 patients with ERCCC were retrospectively assessed. CT and clinical features exhibiting significant differences in t-tests/Mann-Whitney U-test and chi-square tests/Fisher's exact tests were analyzed using receiver operating characteristic (ROC) curves. Variables with an area under the curve (AUC) <0.7 were excluded. Univariate logistic regression analysis was used to analyze the associations of CT and clinical features with ERUC or ERCCC. Variables with odds ratio (OR) values being close to 1 in univariate logistic regression were excluded from multivariate logistic regression. A predictive model was then constructed based on the predictors (p<0 in multivariate logistic regression). Differential diagnostic performance was assessed with AUC values.Multivariate logistic regression analysis identified preserving reniform contour (OR: 45.27, 95% confidence interval [CI]: 4.982-411.39) and infiltrative growth pattern (OR: 21.741, 95% CI: 1.898-249.049) as independent predictors that can be used to distinguish ERUC from ERCCC. AUC values for preserving reniform contour, infiltrative growth pattern, and Model-1 were 0.907 (95% CI: 0.817-0.998), 0.837 (95% CI: 0.729-0.946), and 0.947 (95% CI: 0.874-1), respectively.The independent predictors and predictive model may play an important role in preoperative differentiation between ERUC and ERCCC.
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