Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.

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第一作者： Ruo-Han,Yin

第一单位： Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.

作者： Ruo-Han,Yin ^[1] ; You-Chang,Yang ^[1] ; Xiao-Qiang,Tang ^[1] ; Hai-Feng,Shi ^[1] ; Shao-Feng,Duan ^[2] ; Chang-Jie,Pan ^[1]

作者单位： Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China. ^[1] Precision Health Institution, GE Healthcare (China), Shanghai, China. ^[2]

关键词 Radiomics X-ray computed tomography machine learning renal clear cell carcinoma

主题词贝叶斯定理(Bayes Theorem);癌, 肾细胞(Carcinoma, Renal Cell);人类(Humans);肾肿瘤(Kidney Neoplasms);前瞻性研究(Prospective Studies);回顾性研究(Retrospective Studies);体层摄影术, X线计算机(Tomography, X-Ray Computed)

DOI 10.3233/XST-210997

PMID 34657848

发布时间 2022-04-01

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Journal of X-ray science and technology

2021年29卷6期

1149-1160页

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Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.

Journal of X-ray science and technology

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Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.

Journal of X-ray science and technology

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Journal of X-ray science and technology

相关期刊

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