Wednesday, December 31, 2025 12/31/2025

Multimodal Learning for Contextually-Aware Longitudinal PET/CT image analysis

Award Number: R01EB033782
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 12/01/2023
PERIOD OF PERFORMANCE END DATE: 11/30/2027

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Multimodal Learning for Contextually-Aware Longitudinal PET/CT image analysis - PROJECT SUMMARY 18F-Fluorodeoxyglucose (FDG) PET/CT imaging has become an essential tool for guiding and adapting treatments for lymphoma. However, the PET evaluation criteria currently used for assessing lymphoma, which consists of subjective visual scoring on a 5-point scale, is suboptimal. The visual scores suffer from high inter- observer variability and have low prognostic power for new emerging biological therapies. Quantitative PET metrics have been shown to be more predictive of clinical outcomes than visual scores, but quantitative analysis of whole-body PET/CT images is prohibitively time-consuming and impractical in routine clinical care. Deep learning (DL) has shown promise in automating the quantitative analysis of baseline FDG PET/CT images, but comprehensive evaluation of interim-therapy and post-therapy images using DL has proven difficult. Residual lymphoma has low-level uptake, which can be hard to differentiate from physiologic or treatment-related uptake, and reading physicians must use clinical histories and baseline PET images (i.e., sites of initial disease) to make reliable diagnoses. DL algorithms, on the other hand, only operate on cross-sectional images and are unable to account for historical context. Our objective is to develop DL algorithms that operate on PET/CT images from more than one time point so that algorithms can learn longitudinal dependencies for contextually-aware predictions. We also aim to develop multimodal vision-language models that can simultaneously interpret radiology text reports while performing PET/CT image analysis. These models can leverage critical information about patient history and physician interpretation when processing retrospective images. Furthermore, we will use semi-supervised learning to leverage both unlabeled datasets and labeled datasets. Our overall goal is to develop contextually-aware algorithms for automated longitudinal analysis of whole-body PET/CT images in lymphoma. These tools will be developed using diverse datasets from multiple institutions. PET metrics measured by DL will be validated as predictive markers of outcome using data from a Phase 3 clinical trial.


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 = $429,396 )
2026	2026	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	3	12/15/2025	NON-COMPETING CONTINUATION	$429,396
														Subtotal = $429,396

Issue Date FY: 2025 ( Subtotal = $535,178 )
2025	2025	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	2	11/12/2024	NON-COMPETING CONTINUATION	$481,660
2025	2025	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	001	2	5/19/2025	NON-COMPETING CONTINUATION	$53,518
														Subtotal = $535,178

Issue Date FY: 2024 ( Subtotal = $606,378 )
2024	2024	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	001	1	4/19/2024	NEW	$0
2024	2024	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	002	1	4/22/2024	NEW	$134,751
2024	2024	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	11/16/2023	NEW	$471,627
														Subtotal = $606,378

Grand Total All Awards = $1,570,952

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Multimodal Learning for Contextually-Aware Longitudinal PET/CT image analysis

Award Number: R01EB033782

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 12/01/2023

PERIOD OF PERFORMANCE END DATE: 11/30/2027

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