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

Award Number: R01CA279666
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 12/05/2023
PERIOD OF PERFORMANCE END DATE: 11/30/2028

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Computational Feature Profiling and Modeling for Prostate Cancer Detection and Risk Stratification - 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $558,560 )
2026	2026	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cancer Detection and Diagnosis Research	000	3	12/9/2025	NON-COMPETING CONTINUATION	$558,560
														Subtotal = $558,560

Issue Date FY: 2025 ( Subtotal = $619,960 )
2025	2025	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cancer Detection and Diagnosis Research	001	2	6/3/2025	NON-COMPETING CONTINUATION	$61,995
2025	2025	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cancer Detection and Diagnosis Research	000	2	11/26/2024	NON-COMPETING CONTINUATION	$557,965
														Subtotal = $619,960

Issue Date FY: 2024 ( Subtotal = $641,385 )
2024	2024	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cancer Detection and Diagnosis Research	000	1	12/5/2023	NEW	$641,385
														Subtotal = $641,385

Grand Total All Awards = $1,819,905

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

Award Number: R01CA279666

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 12/05/2023

PERIOD OF PERFORMANCE END DATE: 11/30/2028

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