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Vision-Language Models with Explainability for Breast Cancer Prognosis using Image and Clinical Data.

Award Number: R01CA299626
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/01/2026
PERIOD OF PERFORMANCE END DATE: 04/30/2031

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Vision-Language Models with Explainability for Breast Cancer Prognosis using Image and Clinical Data. - PROJECT SUMMARY Breast cancer is the most prevalent cancer among U.S. women, accounting for nearly one-third of new cancer diagnoses in women annually.2. Accurately predicting recurrence and mortality is critical for prognosis, treatment decisions, and improving patient outcomes. However, current clinical risk models, such as the Nottingham Index and PREDICT Breast,4,5,23,107 have limitations that hinder precision and clinical utility. More comprehensive prognostic tools are needed. Digital pathology imaging offers a promising solution by capturing tumor characteristics beyond human visual discrimination, including nuclear heterogeneity and chromatin complexity. Recent analytic advancements suggest that pathology images may capture nuanced tumor structural data beyond the discriminating capabilities of traditional microscopic evaluation by humans, offering novel parameters for refining risk prediction. This proposal aims to develop a robust Survivor Model (Aim 2) that merges whole slide image (WSI) data with traditional clinical prognostic factors to improve survival estimation. This will be complemented by developing an Explainer Model (Aim 3) to enhance transparency by generating pathologist-interpretable text descriptions of predictive image features. Our specific aims are: AIM 1. Establish benchmarks for predicting recurrence and survival at 5, 10, and 15 years using a clinical epidemiological model and a deep learning image-based model. 1a) Assess the PREDICT prognostic model107 performance using clinical data. 1b) Retrain and evaluate the SlideGraph neural network model24 using image data. AIM 2. Develop a new Survivor Model by expanding QuiltNet,25 our team’s vision-language model, to integrate clinical features and WSI data for recurrence and survival prediction, then compare it against benchmarks from Aim 1. AIM 3. Develop an Explainer Model for WSI-based survival models incorporating pathologist feedback. 3a) Create an Explainer Model that interprets survival predictions from both the Survivor Model and another survival model. 3b) Integrate pathologist feedback to refine predictions. 3c) Validate the Explainer on CALGB and the large, diverse PATHWAYS Study dataset. This work will generate a clinically actionable risk prediction tool that combines deep learning with pathologist- informed reasoning, advancing precision oncology and enhancing breast cancer prognosis. By improving risk stratification and interpretability, this model has the potential to guide treatment decisions, personalize patient care, and ultimately improve outcomes for individuals with breast cancer.


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 = $825,627 )
2026	2026	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cancer Cause and Prevention Research	000	1	4/22/2026	NEW	$825,627
														Subtotal = $825,627

Grand Total All Awards = $825,627

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Vision-Language Models with Explainability for Breast Cancer Prognosis using Image and Clinical Data.

Award Number: R01CA299626

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 05/01/2026

PERIOD OF PERFORMANCE END DATE: 04/30/2031

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