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    CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

    Published 2025-01-01
    “…Lu Qian,1,* BinHai Fu,2,* Hong He,2 Shan Liu,1 RenCai Lu2 1Department of Pathology, the First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People’s Republic of China; 2Department of Nuclear Medicine, The First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People’s Republic of China*These authors contributed equally to this workCorrespondence: RenCai Lu, Department of Nuclear Medicine, The First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, No. 157 Jinbi Road, Kunming, Yunnan, 650032, People’s Republic of China, Tel +86 087163614503, Email lrc_09@126.comObjective: This study aimed to explore the value of a radiomic nomogram based on contrast-enhanced computed tomography (CECT) for differentiating benign and malignant solid-containing renal masses.Materials and Methods: A total of 122 patients with pathologically confirmed benign (n=47) or malignant (n=75) solid-containing renal masses were enrolled in this study. …”
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