# Computational Feature Profiling and Modeling for Prostate Cancer Detection and Risk Stratification

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $641,385

## Abstract

PROJECT SUMMARY/ABSTRACT
Prostate cancer is the most common and second deadliest non-skin cancer in American men, accounting for
26% of new cancer diagnoses and 9% of cancer deaths in men. Active surveillance, radical prostatectomy and
radiotherapy are commonly used treatments for clinically localized prostate cancer. However, current risk
stratification methods cannot be used effectively to avoid subjecting patients with clinically indolent cancers to
unnecessary interventions, causing significant morbidity and cost. The primary components currently involved
in screening are the digital rectal exam (DRE) and serum biomarkers, such as PSA, PCA3, PHI, and 4Kscore.
Unfortunately, despite advances in these tests, overdiagnosis remains a major problem due to limited
specificity. As a result, 90% of patients diagnosed with prostate cancer receive treatment, even though up to
60% of those patients could be candidates for active surveillance. Such treatment often results in long-term
reductions in functional outcomes.
The research objective of this R01 is to develop novel markers and models to both more accurately detect
aggressive cancer and to forecast its arrival. Using a large cohort of patients, we first plan to identify novel
pathomic and germline features that indicate the presence of aggressive cancer or its precursors. We then
plan to implement an integrative graph convolutional network (GCN) combined with a convolutional neural
network (CNN) to generate new multi-modal representations of underlying cancer state within the entire
prostate. The framework will combine multiparametric magnetic resonance imaging (mpMRI), digital histology
images, germline features, biomarkers, and other predictors. We will also implement a baseline nomogram risk
model for comparison, as well as several new nomogram models that incorporate our newly identified features.

## Key facts

- **NIH application ID:** 10803606
- **Project number:** 1R01CA279666-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Corey Wells Arnold
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $641,385
- **Award type:** 1
- **Project period:** 2023-12-05 → 2028-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10803606

## Citation

> US National Institutes of Health, RePORTER application 10803606, Computational Feature Profiling and Modeling for Prostate Cancer Detection and Risk Stratification (1R01CA279666-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10803606. Licensed CC0.

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