Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma
Received 8 November 2020
Accepted for publication 8 January 2021
Published 4 February 2021 Volume 2021:13 Pages 999—1008
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Eileen O'Reilly
Shengsheng Lai,1,* Lei Sun,2,* Jialiang Wu,3 Ruili Wei,4 Shiwei Luo,4 Wenshuang Ding,5 Xilong Liu,6 Ruimeng Yang,4 Xin Zhen2
1School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, People’s Republic of China; 2School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 3Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen, Guangdong, 518000, People’s Republic of China; 4Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China; 5Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China; 6Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ruimeng Yang
Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China
Objective: To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features.
Materials and Methods: A total of 137 consecutive patients with pathologically proven ccRCC (including 96 low-grade [grade 1 or 2] and 41 high-grade [grade 3 or 4] ccRCC) from January 2011 to January 2019 were enrolled in this retrospective study. Target region of interest (ROI) delineation followed by texture extraction was performed on a representative slice with the largest section of the tumor on the four-phase (unenhanced phase [UP], corticomedullary phase [CMP], nephrographic phase [NP] and excretory phase [EP]) CT images. Fifteen concatenations of the four-phase features were fed into 176 classification models (built with 8 classifiers and 22 feature selection methods), the classification performances of the 2640 resultant discriminative models were compared, and the top-ranked features were analyzed.
Results: Image features extracted from the unenhanced phase (UP) CT images demonstrated a dominant classification performance over features from the other three phases. The discriminative model “Bagging + CMIM” achieved the highest classification AUC of 0.75. The top-ranked features from the UP included one shape-based feature and five first-order statistical features.
Conclusion: Image features extracted from the UP are more effective than other CT phases in differentiating low and high nuclear grade ccRCC based on machine learning–based classification modeling.
Keywords: clear cell renal cell carcinoma, Fuhrman nuclear grade, computed tomography, machine learning, classification
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