Computational Imaging of Renal Structures for Diagnosing Diabetic Nephropathy

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Project Summary At the current rate, one in three U.S. adults will be diabetic by 2050. A disease secondary to diabetes is diabetic nephropathy (DN), which causes end-stage renal disease (ESRD) for >225K U.S. patients (50% of all ESRD cases), accounting for >$19K in yearly Medicare costs for each patient. Measurement of minute urinary albumin (microalbuminuria) is the most common non-invasive clinical biomarker of DN. In order to conclusively define DN severity, pathologists conduct qualitative manual estimation of glomerular structural damage in renal biopsies. However, renal glomerular structure in DN biopsies does not often correlate with less invasive clinical biometrics (e.g., estimated glomerular filtration rate, urine protein, serum creatinine and glucose levels). This traditional diagnostic method is approximate, subjected to user bias, time-consuming, and has low diagnostic precision in early disease stages; further, manual hand identified features may not always accurately predict disease progression. Computational image analysis offers the opportunity to project clinical biometrics onto glomerular histological structures. This method provides finer precision in identifying structural changes that lead to physiological changes, which in turn reduces the required clinical resources and time for diagnosis, and provides clinicians with greater feedback to improve early intervention. We have developed computational tools to quantify renal structures in human DN biopsies. Our tools quantify glomerular features in histological renal tissue images more efficiently than manual methods. We have also derived a quantitative progression risk score (PRS) describing DN progression risk estimated off only a single biopsy point. Here, we will rigorously analyze the performance of these methods to predict disease progression using histological images of human DN renal biopsies. We will computationally quantify morphologically diverse DN-indicative intra-glomerular features. We will analytically integrate computationally derived glomerular features with clinical biometrics in order to develop patient-specific PRS to identify patients at risk of renal failure. Since human renal DN data is sparse, we will also use murine data, which can be generated in large amounts in a controlled fashion, to initially train the computational models. We will then refine the model for clinical use by fine-tuning the parameters using human data. The innovation is in the novel integration of traditional clinical detection methods with traditional diagnostic methods, under a computational schema for enhanced precision. This integration will lead to computational disease predicting biomarkers of the earliest measurable renal DN dysfunction. We will study the predictive power of these markers to foretell future clinical endpoints from earlier time points. These methods support the development of quantifiable prognostic and predictive information, which is dynamic over the disease course, easily discriminated, and is highly informative for modeling disease progression or response to therapy. This study will 1) enable earlier clinical predictions, thus extending windows for interventions of evolving DN; and 2) work as a pilot platform for future studies to computationally derive renal biomarkers predictive of other diseases.