Random survival forest models predict renal complications in antenatal hydronephrosis.

Authors

David A Ostrowski, Joseph R Logan, Austin Thompson, Reiley Broms, Thomas Syphan, Maria Antony, Mandy Rickard, Lauren Erdman, Dennis Head, Jessica H Hannick, Lynn L Woo, Frederick D Grant, Neeta D'Souza, Satish E Viswanath, Chris A Flask, Armando J Lorenzo, Yong Fan, Gregory E Tasian, John K Weaver

Published in

The Canadian journal of urology. Volume 33. Issue 5. Pages 1261-1272. Oct 30, 2026. Epub Oct 30, 2026.

Abstract

This study aims to build machine learning models to analyze Technetium-99m mercaptoacetyltriglycine (MAG3) renal scans and clinical variables from patients with antenatal hydronephrosis (ANH) with concern for ureteropelvic junction obstruction (UPJO) and equivocal initial MAG3 renal scans to predict the risk of renal complication development.
A retrospective cohort of patients under 1-year-old with ANH concerning for UPJO with an equivocal MAG3 renal scan evaluated at our institution from January 2009 to June 2021 was identified. Equivocal renal scans were defined as studies where the affected kidney demonstrated split function greater than 39%, and subsequent clinical recommendation was continued surveillance instead of surgery. Random survival forest models were c onstructed to predict the risk of renal complications (decreased function, increased parenchymal thinning, worsening hydronephrosis) using clinical variables (Clinical Model), or numerical features extracted from MAG3 renal scans (Imaging Model), or clinical and numeric renal scan features (Ensemble Model).
One-hundred fifty-two patients were included; 62 patients developed a renal complication, and 90 did not develop a renal complication with a minimum of 3-year follow-up. The Imaging Model (C-index 0.74; 95% CI: 0.68-0.79) and the Clinical Model (C-index 0.70; 95% CI: 0.66-0.75) performed similarly, while the Ensemble Model (C-index 0.76; 95% CI: 0.75-0.86) demonstrated improved performance compared to either individually. Differential function per unit volume of the affected kidney was the most important driver of the Clinical Model's predictions. All models identified high-risk patients at significantly higher risk of renal complications on Kaplan-Meier log-rank analysis (p < 0.0001).
Random survival forest models using clinical and/or renal scan features may improve the prediction of renal complication risk in patients with ANH, with concern for UPJO and an equivocal initial MAG3 renal scan.

PMID:
43016028
Bibliographic data and abstract were imported from PubMed on 12 Nov 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.