Abstract

To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann–Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.

Details

Title
Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma
Author
Lu, Chenyang 1 ; Xia, Yangyang 2 ; Han, Jiamin 1 ; Chen, Wei 3 ; Qiao, Xu 4 ; Gao, Rui 1 ; Jiang, Xuewen 2 

 Shandong University, School of Control Science and Engineering, Jinan, People’s Republic of China (GRID:grid.27255.37) (ISNI:0000 0004 1761 1174) 
 Shandong University, Key Laboratory of Urinary Precision Diagnosis and Treatment, Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Jinan, People’s Republic of China (GRID:grid.27255.37) (ISNI:0000 0004 1761 1174) 
 Shandong First Medical University and Shandong Academy of Medical Sciences, Department of Radiology, Taian, People’s Republic of China (GRID:grid.410587.f) 
 Shandong University, School of Control Science and Engineering, Jinan, People’s Republic of China (GRID:grid.27255.37) (ISNI:0000 0004 1761 1174); Shandong First Medical University and Shandong Academy of Medical Sciences, Department of Radiology, Taian, People’s Republic of China (GRID:grid.410587.f) 
Pages
12043
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3060641072
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.