A novel CT-based radiomics approach for kidney function evaluation in ADPKD: a pilot study.

Abstract

Background: Management of autosomal dominant polycystic kidney disease (ADPKD) might take advantage of the use of new tools to predict risk of progression towards end-stage kidney disease (ESKD). The aim of this study is to explore the potential of radiomic features obtained from computed tomography (CT) scans for the prediction of kidney function decline over time of ADPKD patients.

Methods: We retrospectively selected a cohort of 58 ADPKD patients who routinely underwent CT scan for total kidney volume (TKV) assessment from February 2020 to March 2021. An expert radiologist generated a region-of-interest segmentation for cystic kidneys from which we extracted 217 radiomic features. In a subgroup of 51 patients with at least three serum creatinine measurements, on the basis of estimated glomerular filtration rate we identified 26 rapid progressors to ESKD (>3 mL/min/1.73 m²/year), and we developed a radiomic model to discriminate rapid from non-rapid progressors. Area under the curve (AUC) of the receiver operating characteristic (ROC) and sensitivity were employed to evaluate models' performance.

Results: The most statistically significant radiomic feature (F_cm.corr) (P-value = .04) associated with rapid progression showed an AUC (95% confidence interval) of 0.78 (0.65-0.90) and a sensitivity of 0.92 (0.78-0.98). On the contrary, the logistic regression model based on the height-adjusted TKV (ht-TKV) presented a lower AUC (95% confidence interval) of 0.65 (0.49-0.80), with a sensitivity 0.62 (0.42-0.78).

Conclusions: We developed a model based on the radiomic feature F_cm.corr that was able to discriminate rapid progressors. Further validation studies on larger and external cohort are warranted to corroborate our findings and to confirm the role of radiomics in ADPKD management.