# Development and Assessment of Decision Supporting System for Renal studies

> **NIH NIH R01** · EMORY UNIVERSITY · 2020 · $351,000

## Abstract

Project Summary/Abstract
 The pressures imposed by a rapidly expanding knowledge base, gaps in training, limited experience
and escalating time constraints create a dilemma for diagnostic radiologists and their patients. How can
radiologists consistently provide quality diagnostic interpretations? How can radiologists reduce intra- and
interobserver variability in interpretation such that the diagnosis is less dependent on the interpreting
radiologist and more accurately reflects the presence or absence of the underlying disease? Our long-term
objectives are (1) to develop a general statistical methodology for the development and implementation of a
decision supporting system (DSS) to help physicians to make informed decisions in radiologic diagnosis and to
reduce intra- and interobserver variability and (2) to develop a new general statistical inferential framework that
can determine if the performance of our DSS is equivalent to that of an expert or a panel of experts.
 Our immediate goal and proof of concept is motivated by the need to develop a DSS to improve the
care of nephrology patients referred for a nuclear medicine renal scans, an area where many radiologists lack
both training and experience. A renal scan is obtained by injecting a radioactive tracer, 99mTc MAG3 and
sequentially imaging that tracer over a 20-30 min period as it is removed from the blood by the kidneys and
passes down the ureters into the bladder. When obstruction is suspected, the patient often receives a potent
diuretic and sequential images over the kidney are obtained for an additional 20 min. Radiologists typically use
a few specific points on the kidney time activity curves (renogram) to assist in interpretation of the study. We
propose to integrate clinical data with automated image analysis to provide a comprehensive interpretation of
MAG3 renal scans in a structured format. Rather than using a few isolated features on the renogram, we
propose to develop a latent class modeling approach for predicting kidney obstruction that jointly models
renogram curve data (functional data [13,49]) resulting from renal images and expert ratings as well as other
relevant clinical variables (Aim 1). Extensions will be developed for handling missing data that are present in
this type of studies. In order to evaluate the newly developed DSS, we propose to develop a new general
statistical inferential framework that can determine if the performance of our DSS is equivalent to that of an
expert or a panel of experts. The methodology is developed for both categorical and continuous ratings of the
disease status (Aim 2). We plan to validate the DSS with an independent data sample (Aim 3). [We plan to
conduct two pilot studies with (a) nuclear medicine residents and (b) radiology residents to determine the
feasibility of applying DSS to clinical setting under Aim 4]. While intended to be of direct benefit to the
interpretation of renal scans, the DSS and statistical methodology to be...

## Key facts

- **NIH application ID:** 9987593
- **Project number:** 5R01DK108070-05
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** AMITA K. MANATUNGA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $351,000
- **Award type:** 5
- **Project period:** 2016-09-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987593

## Citation

> US National Institutes of Health, RePORTER application 9987593, Development and Assessment of Decision Supporting System for Renal studies (5R01DK108070-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9987593. Licensed CC0.

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