Identification of Calculous Pyonephrosis by CT-Based Radiomics and Deep Learning

• Published in: Bioengineering (Basel) Vol. 11; no. 7; p. 662
• Main Authors: Yuan, Guanjie, Cai, Lingli, Qu, Weinuo, Zhou, Ziling, Liang, Ping, Chen, Jun, Xu, Chuou, Zhang, Jiaqiao, Wang, Shaogang, Chu, Qian, Li, Zhen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.06.2024
• Subjects: Abdomen; Artificial neural networks; Computed tomography; convolutional neural network; Deep learning; Feature selection; Hydronephrosis; Infections; Learning algorithms; Leukocytes (neutrophilic); Machine learning; Medical imaging; Medical records; Neural networks; Patients; Performance evaluation; pyonephrosis; Radiomics; Regression analysis; Risk factors; Urine; Urogenital system; Urological surgery; computed tomography; pyonephrosis; radiomics; convolutional neural network; hydronephrosis
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11070662

Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with calculous pyonephrosis. We recruited 182 patients receiving either percutaneous nephrostomy tube placement or percutaneous nephrolithotomy for calculous hydronephrosis or pyonephrosis. The regions of interest were manually delineated on plain CT images and the CT attenuation value (HU) was measured. Radiomics analysis was performed using least absolute shrinkage and selection operator (LASSO). A 3D-CNN model was also developed. The better-performing machine-learning model was combined with independent clinical factors to build a comprehensive clinical machine-learning model. The performance of these models was assessed using receiver operating characteristic analysis and decision curve analysis. Fever, blood neutrophils, and urine leukocytes were independent risk factors for pyonephrosis. The radiomics model showed higher area under the curve (AUC) than the 3D-CNN model and HU (0.876 vs. 0.599, 0.578; = 0.003, 0.002) in the testing cohort. The clinical machine-learning model surpassed the clinical model in both the training (0.975 vs. 0.904, = 0.019) and testing (0.967 vs. 0.889, = 0.045) cohorts.