# RSI-AI: Predicting clinically significant prostate cancer to guide biopsy decisions by combining advanced tissue microstructure imaging with deep learning

> **NIH NIH R44** · CORTECHS LABS, INC. · 2023 · $735,773

## Abstract

Prostate biopsies are critical for the diagnosis of prostate cancer, but it is often unclear who
should be biopsied and where in the gland the biopsy should be targeted. This results in missed
diagnoses, unnecessary biopsies, and overdiagnosis and overtreatment of cancer that is not life
threatening. The goal of this proposal is to develop a set of quantitative and non-invasive tools,
RSI-AI and RSI-AI+, to help clinicians determine who should be biopsied for prostate cancer
and the locations of clinically significant lesions. RSI-AI uses deep learning to predict the
location and pathological grade of prostate cancer lesions from restriction spectrum imaging
(RSI) data. RSI is an advanced diffusion magnetic resonance imaging (MRI) technique that
models the restricted diffusion of water molecules to improve microtissue classification and
tumor detection. By utilizing RSI data in the deep learning model, RSI-AI will produce
pathological grade predictions that are more accurate than models trained with conventional MRI
data. RSI-AI+ integrates the pathological grade predictions from RSI-AI with clinical data
including age, family history, genetics, and prostate volume to accurately and comprehensively
quantify current and future risk for prostate cancer. Phase I of this proposal will develop and
validate the RSI-AI and RSI-AI+ models and compare their performance to models trained with
conventional MRI data. Phase II of this proposal will deploy RSI-AI and RSI-AI+ to the
Cortechs cloud platform, demonstrate their clinical usability and utility, and generate the
materials required for a 510K FDA submission. The clinical software generated through this
proposal will ultimately improve diagnostic yields, reduce unnecessary biopsies and
overtreatment of indolent prostate cancer, while facilitating early detection and appropriate
treatment of clinically significant prostate cancer

## Key facts

- **NIH application ID:** 10896571
- **Project number:** 4R44CA254738-02
- **Recipient organization:** CORTECHS LABS, INC.
- **Principal Investigator:** Nathan Scott White
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $735,773
- **Award type:** 4N
- **Project period:** 2021-08-15 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896571

## Citation

> US National Institutes of Health, RePORTER application 10896571, RSI-AI: Predicting clinically significant prostate cancer to guide biopsy decisions by combining advanced tissue microstructure imaging with deep learning (4R44CA254738-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10896571. Licensed CC0.

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