# Information-Theoretic Surprise-Driven Approach to Enhance Decision Making in Healthcare

> **NIH NIH R21** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2023 · $554,471

## Abstract

PROJECT SUMMARY
Traditional guidelines used in clinical decision-making alone are often insufficient in accurately stratifying patients
for diagnostic testing. For instance, the National Comprehensive Cancer Network (NCCN) guidelines for
stratifying patients for germline genetic testing of breast cancer fails to identify nearly 50% of the patients who
might have a BRCA mutation. With advancements in imaging techniques and black-box machine learning
algorithms, radiomics has emerged as a promising tool for making predictions in a wide range of health conditions
such as breast and ovarian cancer, Alzheimer’s disease, and coronary heart disease. Based on these studies,
the underlying hypothesis of this research is that imaging phenotypes obtained from radiomics together
with traditional guidelines can screen patients for underlying diseases (in this research, germline BRCA
mutation) with a higher positive predictive value than the traditional guidelines alone. Here, we refer to
the NCCN guidelines as the traditional guidelines.
However, little is known about the causal relationship between these deleterious health conditions and
quantitative imaging phenotypes. Together with the lack of standards for quantifying and reporting imaging
phenotypes across multiple institutions, it is currently not feasible to integrate them into clinical decision-making.
To this end, this research will focus on the following two specific aims to address these challenges and
subsequently validate the hypothesis. Specific Aim 1: MRI harmonization via amplitude synchronization to
mitigate the scanner-to-scanner variability. Specific Aim 2: Causal inference and information theory to
discover the causal relationships between BRCA mutation and imaging phenotypes and subsequently integrate
them into clinical decision making.
While the proposed research focuses on stratifying patients for germline BRCA testing based on magnetic
resonance imaging phenotypes, the methodology and algorithms generalize to other health conditions and
imaging modalities. The outcome of this research will lead to a new paradigm of clinical decision making where
medical practitioners would be able to link imaging phenotypes with underlying health conditions—akin to how
abnormal levels on comprehensive metabolic panels act as indicators of potential health problems—and prepare
for appropriate interventions.

## Key facts

- **NIH application ID:** 10575550
- **Project number:** 1R21EB033923-01
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** Ashif Iquebal
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $554,471
- **Award type:** 1
- **Project period:** 2023-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10575550

## Citation

> US National Institutes of Health, RePORTER application 10575550, Information-Theoretic Surprise-Driven Approach to Enhance Decision Making in Healthcare (1R21EB033923-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10575550. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*