Prediction of Kidney Transplant Function with Machine Learning from Computational Ultrasound Features

Abstract

Prognosis of kidney function in post-transplant patients is important when considering invasive investigations, intervention, or re-transplantation. Ultrasound imaging is a non-invasive tool that may contain subtle textures associated with kidney function. To address this, we developed a prediction model utilizing machine learning and computational image features to predict decline in estimated glomerular filtration rate (eGFR), a key measure of kidney function. Post-transplant ultrasound scans and eGFR values from N = 819 transplant patients were obtained. A multi-stage pipeline was built to first automatically segment the cortex, medulla, and central echo complex from ultrasound. Imaging features (104 total) related to shape, intensity statistics, texture and ultrasound speckle were computed. A random forest (RF) classifier was trained topredict 5-year eGFR decline from the feature set. For comparison, validation was repeated with using only clinical variables and with the Kidney Failure Risk Equation (KFRE). Predictive features were identified by feature-wise decrease in impurity and a mean validation AUC ($\pm$ standard deviation) of 0.81 $\pm$ 0.03 was achieved. Comparison AUCs were 0.62 $\pm$ 0.04 for the clinical variable model and 0.67 $\pm$ 0.03 for the KFRE model. 2-dimensional elongation, cluster shade, and Nakagami speckle shape were the most predictive features. This study provides support that computational image features combined with machine learning may potentially serve as a non-invasive eGFR decline prediction tool to aid post-transplant care.