Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

Multi-modal machine learning to guide adjuvant therapy in surgically resectable colorectal cancer

Award Number: R01CA270437
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/02/2023
PERIOD OF PERFORMANCE END DATE: 04/30/2028

Group Awards By:

View Award Description

Multi-modal machine learning to guide adjuvant therapy in surgically resectable colorectal cancer - Project Summary / Abstract Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the second leading cause of cancer death worldwide. There is an unmet need for accurate, cost-efficient, and broadly accessible risk- stratification tools to identify patients at increased risk of recurrence , who are most likely to benefit from adjuvant therapy. Current standard-of-care risk stratification approaches are inadequate. Every CRC surgical candidate undergoes pathologic and radiologic evaluation of their tumor; these two modalities represent a rich, readily accessible and, thus far, underutilized resource for developing new risk-stratification tools. Deep learning (DL) has demonstrated great potential for augmenting physicians on an increasing range of diagnostic and prognostic tasks in pathology, radiology, and clinical medicine. We hypothesize that applying integrated DL-based analysis to multimodal (pathologic, radiologic, and electronic medical record (EMR)) data will yield greatly improved stratification of CRC patients for adjuvant treatment planning. We propose to build the first comprehensive, publicly-available, expert-annotated multimodal CRC dataset for deep learning, containing annotated CRC pathology whole-slide images (WSI), preoperative CT and MRI images, and structured clinical EMR data. Using this dataset, we will develop both single and multi-modality DL models for risk stratification of surgically-resectable (Stage I-III) CRC patients.To test our hypothesis, we will compare the performance of multi-modality models with that of single-modality models and existing methods of stratification. This project benefits from the complementary expertise and resources of a unique interdisciplinary team spanning the fields of machine learning, pathology, radiology, and oncology.


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 = $644,202 )
2026	2026	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Cancer Detection and Diagnosis Research	000	4	5/7/2026	NON-COMPETING CONTINUATION	$644,202
														Subtotal = $644,202

Issue Date FY: 2025 ( Subtotal = $624,102 )
2025	2025	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Cancer Detection and Diagnosis Research	001	3	5/28/2025	NON-COMPETING CONTINUATION	$62,410
2025	2025	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Cancer Detection and Diagnosis Research	000	3	4/10/2025	NON-COMPETING CONTINUATION	$561,692
														Subtotal = $624,102

Issue Date FY: 2024 ( Subtotal = $594,649 )
2024	2024	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Cancer Detection and Diagnosis Research	000	2	4/3/2024	NON-COMPETING CONTINUATION	$594,649
														Subtotal = $594,649

Issue Date FY: 2023 ( Subtotal = $629,635 )
2023	2023	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Cancer Detection and Diagnosis Research	000	1	5/2/2023	NEW	$629,635
														Subtotal = $629,635

Grand Total All Awards = $2,492,588

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Multi-modal machine learning to guide adjuvant therapy in surgically resectable colorectal cancer

Award Number: R01CA270437

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 05/02/2023

PERIOD OF PERFORMANCE END DATE: 04/30/2028

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer