摘要：Abstract 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 UP , 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.
其他摘要：Abstract 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 UP , 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.
