A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma

European Radiology - Tập 30 - Trang 1274-1284 - 2019
Pei Nie1, Guangjie Yang2, Zhenguang Wang2, Lei Yan2, Wenjie Miao2, Dapeng Hao1, Jie Wu3, Yujun Zhao2, Aidi Gong2, Jingjing Cui4, Yan Jia4, Haitao Niu5
1Radiology Department, The Affiliated Hospital of Qingdao University, Qingdao, China
2PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, China
3Pathology Department, The Affiliated Hospital of Qingdao University, Qingdao, China
4Huiying Medical Technology Co., Ltd, Beijing, China
5Urology Department, The Affiliated Hospital of Qingdao University, Qingdao, China

Tóm tắt

To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC). Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness. Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793–0.966) and the validation set (AUC, 0.846; 95% CI, 0.643–1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810–0.983) and the validation set (AUC, 0.949; 95% CI, 0.856–1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683–0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness. The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy. • Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.

Tài liệu tham khảo

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