Enhanced Computed Tomography-Based Radiomics Signature Combined With Clinical Features in Evaluating Nuclear Grading of Renal Clear Cell Carcinoma
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Published on: March 30, 2015
Li Yan1, Ning Chai2, Yuanzhao Bao1
1From the Departments of Imaging.
Objective:
The aim of the study was to explore the value of enhanced computed tomography (CT)-based radiomics signature combined with clinical features in evaluating nuclear grading of clear cell renal cell carcinoma (ccRCC).
Methods:
One hundred one patients with ccRCC were classified into low- and high-grade group, and the data were divided into training set and verification set. Radiomics signatures were constructed in the training set in enhanced 3 stages and the combination of them. The predictive nomogram was constructed. The classification efficiency and the clinical practicability of the integrated radiomics model were evaluated.
Results:
The classification efficiency of enhanced 3-stage integrated histology model was higher than that of each single-phase model. The predictive nomogram incorporated the best radiomics signature, and the independent clinical risk factors showed good performance. A decision curve analysis curve shows that the net benefit of the combined model.
Conclusions:
It is feasible to evaluate the nuclear grading of ccRCC based on enhanced CT radiomics signature combined with clinical features.
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