Pinaki Sarder

Personal Information
Title Assistant Professor
Expertise None Selected
Institution SUNY at Buffalo
Data Summary
Grants/SubContracts 1
Progress Reports 1
Publications 5
Protocols 0
Committees 1


Computational Imaging of Renal Structures for Diagnosing Diabetic Nephropathy
Summary – Proteinuria is the manifestation of a heterogeneous spectrum of kidney diseases that involve excessive loss of blood serum proteins to the urine. Damage to renal micro-compartments, including renal glomeruli, presents with proteinuria and may eventually lead to kidney failure. Medicare spends ~$24 billion annually on the care of >500K U.S. patients with end-stage renal disease. Kidney biopsies are often required to diagnose proteinuric renal disease. The traditional approach to diagnosing proteinuria includes qualitative or semi-quantitative manual estimation of glomerular structural damage in the renal biopsy. This process is approximate, subjected to user bias, time-consuming, and has low diagnostic precision in early disease stages. By projecting functional physiological measurements of glomeruli (e.g., eGFR, urine protein and serum creatinine levels) onto their histological structure, computational histological image analysis can more precisely identify structural changes that lead to physiological changes; this in turn reduces the required clinical resources and time for diagnosis, and provides clinicians with greater feedback on therapeutic efficacy. We have developed computational methods to analyze histological images of the heterogeneous renal microscopic architecture in proteinuric renal disease. In this proposal, we will analyze the performance of these methods to predict disease progression in human renal biopsies of proteinuric diabetic nephropathy (DN). 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 disease quantification models. The innovation lies in the novel integration of traditional clinical detection methods with traditional diagnostic methods, under a computational schema that enables enhanced precision. This integration will lead to computational disease-predictive biomarkers of renal dysfunction in DN. We will investigate the predictive power of these markers to foretell future clinical endpoints from an earlier time point (i.e., DN progression from stage I to stage III or stage II to stage III). These methods support the development of quantifiable prognostic and predictive information, which is dynamic over the disease course, easily discriminated, and holds high informative power for modeling disease progression or response to therapy. This study will 1) enable earlier clinical predictions, thereby 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. Relevance – Automated detection of renal structural change with integration of physiological parameters will improve objectivity among clinicians, standardize renal diagnoses, and reduce time to a precise prognosis. Saving diagnostic time allows more patients to be treated in shorter amounts of time, reducing healthcare costs. Our tools will provide clinicians with invaluable quantitative information about their patient’s disease trajectory, enable identification of DN-specific signs earlier in disease progression, and extend windows of opportunity for early therapeutic interventions.

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