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Computational Imaging of Renal Structures for Diagnosing Diabetic Nephropathy
Summary Data Summary
Applicant Sarder, Pinaki
E-Mail Address Pinaki.Sarder@medicine.ufl.edu
Project Title Computational Imaging of Renal Structures for Diagnosing Diabetic Nephropathy
CBU ID 17AU3792
External SubContract ID 32307-5
Diabetic Complication Nephropathy
Funding Program Group Pilot & Feasibility [PF2017]
Abstract 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.
Application PDF Application Research Plan
Status Contract Executed
Key Personnel John Tomaszewski; Gregory Wilding; Sanjay Jain; Agnes Fogo; Michael Walsh
Salary Total Costs 21115
Supply Total Costs 5282
Equipment Total Costs 0
Travel/Other Total Costs 73603
Direct Costs 100000
Indirect Costs Proposed 0
Total Costs Proposed 100000
Total Costs Approved 100000
Start Date 11/1/2017
End Date 10/31/2018
IFO Name McCabe, Rose
IFO E-Mail Address rosemary.mccabe@buffalo.edu
IACUC/IRB No. FWA00008824
IACUC/IRB Institution SUNY at Buffalo
Entity ID No. 14-1368361
Report Request Date 11/30/2018
T1D NO
TypeCount
Invoices 18
Progress Reports 1
Data Submission


Invoices
UrlCBU IDExternal IDInstitutionDateDirectIndirectInvoiceBalancePDF
  View  17AU379232307-5SUNY at Buffalo9/4/2019$639.21$379.27$1,018.48-View PDF
  View  17AU379232307-5SUNY at Buffalo8/3/2018$17,153.69$10,206.45$27,360.14-View PDF
  View  17AU379232307-5SUNY at Buffalo7/5/2018$6,970.78$4,147.61$11,118.39-View PDF
  View  17AU379232307-5SUNY at Buffalo6/18/2018$4,107.85$2,444.17$6,552.02-View PDF
  View  17AU379232307-5SUNY at Buffalo5/5/2018$2,886.47$1,717.44$4,603.91-View PDF
  View  17AU379232307-5SUNY at Buffalo5/3/2018$1,574.18$936.64$2,510.82-View PDF
  View  17AU379232307-5SUNY at Buffalo4/5/2018$7,084.14$981.24$8,065.38-View PDF
  View  17AU379232307-5SUNY at Buffalo4/2/2019$8,808.97$84.47$8,893.44-View PDF
  View  17AU379232307-5SUNY at Buffalo3/7/2018$3,424.47$2,037.56$5,462.03-View PDF
  View  17AU379232307-5SUNY at Buffalo3/4/2019$505.00$300.48$805.48-View PDF
  View  17AU379232307-5SUNY at Buffalo2/7/2019$2,220.01$1,320.90$3,540.91-View PDF
  View  17AU379232307-5SUNY at Buffalo2/2/2018$1,053.02$626.55$1,679.57-View PDF
  View  17AU379232307-5SUNY at Buffalo12/7/2018$2,772.56$1,649.67$4,422.23-View PDF
  View  17AU379232307-5SUNY at Buffalo11/8/2018$1,757.52$1,045.73$2,803.25-View PDF
  View  17AU379232307-5SUNY at Buffalo10/25/2018$2,198.50$1,308.11$3,506.61-View PDF
  View  17AU379232307-5SUNY at Buffalo1/9/2019$2,831.80$1,684.93$4,516.73-View PDF
  View  17AU379232307-5SUNY at Buffalo1/3/2020-$0.33$0.33-View PDF
  View  17AU379232307-5SUNY at Buffalo1/16/2018$1,968.83$1,171.45$3,140.28-View PDF


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