Abstract：Objective To verify the effectiveness in preoperative differentiation between fat-poor angiomyolipoma (fp_AML) and
clear cell renal cell carcinoma (ccRCC) by analyzing radiomic features based on CT-enhanced assessment in different ranges of
peritumoral tissue. Methods The preoperative CT-enhanced scanning images of 150 patients with renal tumors with diameter ≤4 cm were
analyzed retrospectively, including 103 cases of ccRCC and 47 cases of fp_AML. The tumor area, different ranges of peritumor
areas and the volume of interests of the tumor and peritumor model were manually delineated on the CT-enhanced images in
corticomedullary phase. The training and test sets were divided according to the ratio of 7∶3. After extracting and screening the
radiomics features, a Logistic regression model was established. The effectiveness of each model to differentiate ccRCC from
fp_AML was evaluated by using the receiver operating characteristic curve. Results Among the six models, the best performing
model was the combined tumor mass volume (TMV) and 2 mm beyond the edge of the tumor (peritumoral volume, PTV0~2) model
with the area under curve (AUC) of 0.990, specificity of 0.96, sensitivity of 0.88 and accuracy of 0.93 for the training data, and
AUC of 0.931, specificity of 0.87, sensitivity of 0.71 and accuracy of 0.82 for the validation model. Among the four non-combined
models, the discrimination efficiency of PTV2~2 (2 mm inside and outside the tumor boundary) model significantly outperformed the
remaining three individual models, with AUC of 0.911, specificity of 0.92, sensitivity of 0.70, and accuracy of 0.85 for the training
data and an AUC of 0.917, specificity of 0.97, sensitivity of 0.71, and accuracy of 0.89 for the validation set. Conclusion Among the
CT-based individual models, the PTV2~2 peritumor model performs is best, that is, the peritumoral range of PTV2~2 is more effective in
identifying fp_AML and ccRCC; among the combined tumor and peritumor models, the combined TMV and PTV0~2 model performs
best, which can more accurately and comprehensively reflect the characteristics and heterogeneity of tumors, and can be used as the
most effective method to identify fp_AML and ccRCC.
贺琬淋，方维东. 术前基于CT的瘤周和瘤体放射学特征鉴别ccRCC与fp_AML[J]. 中国医疗设备, 2022, 37(8): 123-127.
HE Wanlin, FANG Weidong. Preoperative Differential Diagnosis Between ccRCC and fp_AML Based on
CT Peritumoral and Tumoral Radiomic Features. China Medical Devices, 2022, 37(8): 123-127.
