Usefulness of radiomics and kidney volume based on non-enhanced computed tomography in chronic kidney disease - initial report.

Introduction: Chronic kidney disease (CKD) is classified according to the estimated glomerular filtration rate (eGFR), but kidney volume (KV) can also provide meaningful information. Very few radiomics (RDX) studies on CKD have used computed tomography (CT). This study aimed to determine whether non-enhanced computed tomography-based (NECT) RDX can be useful in evaluation of patients with CKD and to compare it with KV.

Methods: The NECT scans of 64 subjects with impaired kidney function classified based on < 60 ml/min/1,73 m2 and 60 with normal kidney function as controls were retrospectively analyzed. Kidney segmentation, volume measurements and RDX features extraction were performed. Machine learning models (RDX) were constructed to classify the kidneys as having structural markers of impaired or normal function.

Results: The median KV in the impaired kidney function group was 114.83 mL vs 159.43 mL (p < 0.001) in the control group. There was a statistically significant strong positive correlation between KV and eGFR (rs = 0.579, p < 0.001), and a strong negative correlation between KV and serum creatinine level (rs = -0.514, p < 0.001). The KV-based models achieved the best area under the curve (AUC) of 0.746, whereas the RDX-based models achieved the best AUC of 0.878.

Conclusions: RDX can be useful in identifying patients with impaired kidney function on NECT. RDX-based models perform better than KV-based models. RDX has the potential to identify patients with a higher risk of CKD based on imaging, which, as we believe, can indirectly assist in clinical decision-making.