Outcome Prediction using Image Features with Conformal Quantile Regression

Music City Center 

Estimated glomerular filtration rate (eGFR) is a continuous biomarker of kidney function and an important clinical outcome in glomerular and other kidney diseases. The use of demographic, clinical, and kidney biopsy image data to predict future eGFR and quantify prediction uncertainty is crucial for risk stratification and clinical decision-making. Conformal quantile regression (CQR) provides a statistical framework to estimate prediction intervals around continuous outcomes with statistical guarantees about coverage of the true outcomes. However, CQR has not been explored in the context of predicting eGFR using image data that include generated regressors. In this study, we conducted a simulation study to test the performance of CQR in constructing prediction intervals of continuous outcomes from generated regressors. We demonstrated that CQR is robust to additive measurement error in the generated regressors but large samples are required for optimal coverage with functionally misspecified regressors. Finally, we used real-world glomerular disease kidney biopsy image features to predict eGFRs and demonstrated that CQR prediction intervals provide reliable coverage in real data.

Computational pathology

Conformal quantile regression

Image features

Generated regressors

Prediction intervals 

Section on Statistics in Imaging