# Data-driven Diagnostics using Multimodal- AI Assisted Approaches for Early Cancer Detection

> **NIH NIH F99** · UNIVERSITY OF TEXAS MED BR GALVESTON · 2024 · $38,383

## Abstract

Project Summary:
The vast majority (90%) of cancers are epithelial in nature, and oral squamous cell carcinoma (OSCC) accounts 
for a portion of these cases by afflicting 744,884 people yearly, worldwide. Late-stage cancer diagnosis is 
associated with low survival rates, whereas individuals diagnosed at an early stage have a significantly better 
chance of survival. Current standard screening examinations often fail to identify abnormal regions with high risk 
of malignancy, and, thus, need to be improved to increase survival rate. Dr. Gracie Vargas, my sponsor, has 
developed a detection approach that combines multiple optical imaging modalities to visualize large areas 
(widefield, WF) with complementary microscopic areas (nonlinear optical microscopy, NLOM) for label-free 
identification of neoplasia. This approach has demonstrated substantial image-based alterations in high-risk 
lesions using the system in preclinical animal models. As with many novel optical systems, our research shows 
there is room for optimization in this promising technique. The handling and evaluating of the complex data and 
identifying the most important features associated with early cancer changes is an additional challenge,
particularly in the application in human specimens. Here, I propose an approach that combines label-free 
multimodal optical imaging with artificial intelligence (AI) to develop data-driven diagnostics for detection of early 
high-risk lesions with potential for malignant, to ultimately increase survival rate. In the F99 phase, I will optimize 
and evaluate a WF system by integrating a seamless spectral capability, to capture a wide range of spectral 
features in human OSCC samples. This optimization will acquire additional image-based information that applied 
to machine learning methods to extract the most important features associated with early cancer changes, while 
potentially improving the systems performance. My dissertation work during the F99 phase will equip me with 
training and expertise in the integration of biomedical optics, under the guidance of Dr. Gracie Vargas at the 
University of Texas medical Branch at Galveston, and machine learning, with support from my co-sponsor, Dr. 
Heidi Spratt, to advance translational early cancer detection. In the postdoctoral K00 phase, I will concentrate 
on developing multimodal label-free optical approaches for data-driven early cancer diagnostics. This research
involves complex decision-making that can extend beyond the capabilities of traditional machine learning. Thus, 
as I transition to the post-doctoral phase, I will train in advanced AI methods, such as deep learning, as well as 
explore the integration of patient records to enhance early cancer diagnostics. The successful completion of this 
project will advance emerging noninvasive early cancer multimodal microscopy technologies through cutting 
edge data-driven approaches, ultimately enhancing early epithelial cancer detection and...

## Key facts

- **NIH application ID:** 10989491
- **Project number:** 1F99CA294164-01
- **Recipient organization:** UNIVERSITY OF TEXAS MED BR GALVESTON
- **Principal Investigator:** Paula Villarreal
- **Activity code:** F99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $38,383
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10989491

## Citation

> US National Institutes of Health, RePORTER application 10989491, Data-driven Diagnostics using Multimodal- AI Assisted Approaches for Early Cancer Detection (1F99CA294164-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10989491. Licensed CC0.

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