Assessment of autosomal dominant polycystic kidney disease severity using deep learning–based kidney volumetry in CT

Objectives Accurate quantification of total kidney volume (TKV) and Mayo imaging classification (MIC) is essential for assessing disease severity in autosomal dominant polycystic kidney disease (ADPKD). This study aimed to develop and validate a deep learning–based method using noncontrast computed tomography (CT) scans to automate TKV measurement and MIC assignment. Materials and methods A retrospective dataset of 60 ADPKD patients was used to train a three-dimensional (3D) UNet model for kidney segmentation and TKV estimation. Kidney axes were calculated using principal component analysis (PCA). The model was tested on consecutive scans from 30 patients (median interval: 3.6 years) and 28 external patient scans. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and measurement reliability was assessed using the intraclass correlation coefficient (ICC). MIC was assigned and compared across timepoints. Results The model achieved high segmentation accuracy (DSC: 0.93 ± 0.04 [mean ± standard deviation] and 0.94 ± 0.03 for consecutive scans; 0.96 ± 0.08 for external validation). Manual and automated TKV measurements were strongly correlated (ICC: 0.94–0.99, p < 0.001), with a mean volume error of 7.61 ± 7.26%. MIC assignment matched in 90–93.3% of cases. Kidney dimensions (length > 16.5 cm, width > 9.5 cm, depth > 8 cm) predicted high-risk MIC classes with positive predictive values > 88%. Longitudinal analysis showed significant increases in TKV and declines in estimated glomerular filtration rate–eGFR ( p < 0.001). Conclusion The proposed method enables accurate TKV quantification and MIC classification from noncontrast CT, supporting severity assessment and longitudinal monitoring in ADPKD. Key Points Question Can a deep learning method using noncontrast CT accurately automate total kidney volume measurement and Mayo Imaging Classification assignment in autosomal dominant polycystic kidney disease? Findings In internal and external testing, automated kidney segmentation showed high agreement with reference measurements and correctly assigned Mayo Imaging Classification in most cases with overall accuracy. Relevance statement Automated analysis of noncontrast CT provided reliable kidney volume quantification and disease severity classification, reducing manual workload while enabling longitudinal assessment of disease progression in autosomal dominant polycystic kidney disease.