Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma

Shengsheng Lai1 School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, People’s Republic of China

Lei Sun2 School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China

Jialiang Wu3 Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen, Guangdong, 518000, People’s Republic of China

Ruili Wei4 Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China

https://orcid.org/0000-0001-7322-2657

Shiwei Luo4 Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China

https://orcid.org/0000-0002-2451-3295

Wenshuang Ding5 Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China

Xilong Liu6 Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China

Ruimeng Yang4 Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of ChinaCorrespondence[email protected]

https://orcid.org/0000-0003-2768-9429

Xin Zhen2 School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of ChinaCorrespondence[email protected]

https://orcid.org/0000-0002-5037-2801

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N. A. Karelskaya, I. S. Gruzdev, V. Yu. Raguzina & G. G. Karmazanovsky. (2024) Texture analysis of CT- and MR-Images in the differential diagnosis of renal cell carcinoma: a review. Diagnostic radiology and radiotherapy 14:4, pages 7-18.
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Zhiyong Zhou, Xusheng Qian, Jisu Hu, Chen Geng, Yongsheng Zhang, Xin Dou, Tuanjie Che, Jianbing Zhu & Yakang Dai. (2023) Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma. Frontiers in Oncology 13.
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Mohamed Shehata, Rasha T. Abouelkheir, Mallorie Gayhart, Eric Van Bogaert, Mohamed Abou El-Ghar, Amy C. Dwyer, Rosemary Ouseph, Jawad Yousaf, Mohammed Ghazal, Sohail Contractor & Ayman El-Baz. (2023) Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review. Cancers 15:10, pages 2835.
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Yanwen Lu, Wenliang Ma, Xiang Dong, Mackenzie Brown, Tong Lu & Weidong Gan. (2023) Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost. Computer Modeling in Engineering & Sciences 136:1, pages 347-362.
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Meiyi Yang, Xiaopeng He, Lifeng Xu, Minghui Liu, Jiali Deng, Xuan Cheng, Yi Wei, Qian Li, Shang Wan, Feng Zhang, Lei Wu, Xiaomin Wang, Bin Song & Ming Liu. (2022) CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma. Frontiers in Oncology 12.
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Yingjie Xv, Fajin Lv, Haoming Guo, Zhaojun Liu, Di Luo, Jing Liu, Xin Gou, Weiyang He, Mingzhao Xiao & Yineng Zheng. (2021) A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma. Frontiers in Oncology 11.
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Yingjie Xv, Fajin Lv, Haoming Guo, Xiang Zhou, Hao Tan, Mingzhao Xiao & Yineng Zheng. (2021) Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study. Insights into Imaging 12:1.
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Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma

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Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma

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