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Development of a Machine Learning Model to Integrate Clinical, Laboratory, Sonographic, and Elastographic Data for Noninvasive Liver Tissue Characterization in NAFLD

Award Number: R01DK119860
ORGANIZATION: NATIONAL INSTITUTE OF DIABETES & DIGESTIVE & KIDNEY DISEASES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 01/20/2019
PERIOD OF PERFORMANCE END DATE: 12/31/2025

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Development of a Machine Learning Model to Integrate Clinical, Laboratory, Sonographic, and Elastographic Data for Noninvasive Liver Tissue Characterization in NAFLD - Abstract Non-alcoholic fatty liver disease (NAFLD) is exceptionally common, with an estimated one hundred million afflicted people in the United States. Detection and risk stratification of this very common disease remains a major challenge. Despite recent advances, including development of numerous therapeutic agents presently in phase 2 and 3 trials, NAFLD remains a silent disease in which the vast majority of patients accumulate progressive liver damage without signs or symptoms and, undiagnosed, receive no medical care. The NAFLD patients at highest risk of cirrhosis are those with moderate or greater liver fibrosis at the time of diagnosis, a group of patients who are described as having high risk non-alcoholic steatohepatitis (hrNASH). The current reference standard for identifying people with hrNASH is liver biopsy, which is expensive, invasive, and limited by interobserver variability. The focus of this project is to develop and validate low cost non-invasive diagnostic technology to diagnose hrNASH. We propose to accomplish this in three Specific Aims. First, we will expand and annotate an existing database of patients with chronic liver disease from 328 subjects to 1,000 subjects, ~40% of whom will have NAFLD. The database will contain ~20,000 images (~10,000 ultrasound elastography images and ~ 10,000 conventional ultrasound images) and multiple demographic and clinical data points for each subject (a total of ~30,000 clinical, laboratory, and demographic data points). We have previously developed advanced image processing techniques to make ultrasound elastography more accurate and less variable. We will use this large database to develop, customize and refine our image processing techniques for NAFLD evaluation (Aim 1), with the goal of improving ultrasound elastography diagnosis of hrNASH. Second, we will combine conventional ultrasound elastography imaging, conventional ultrasound imaging, our advanced image analysis techniques, and the demographic, clinical, and laboratory data in a machine learning model to predict hrNASH and will compare the performance of our predictive model with the FIB4, a widely-used blood test-based prediction rule (Aim 2). Third, we will validate our predictive model in an independent prospective cohort of NAFLD subjects undergoing biopsy for NAFLD risk stratification (Aim 3). We hypothesize that the combination of image processing-enhanced elastography and conventional ultrasound imagery combined with demographic, clinical, and laboratory data will have greater predictive power for hrNASH than clinical or sonographic data alone. The proposed predictive models have the potential to (1) reduce the number of liver biopsies performed for hrNASH detection, (2) facilitate recruitment for clinical trials of NAFLD therapeutics, and (3) improve care quality for the most common liver disease in the United States.


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: 2025 ( Subtotal = $0 )
2025	2023	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	5	12/12/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2023 ( Subtotal = $448,421 )
2023	2023	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	5	1/5/2023	NON-COMPETING CONTINUATION	$448,421
														Subtotal = $448,421

Issue Date FY: 2022 ( Subtotal = $448,421 )
2022	2022	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	4	12/20/2021	NON-COMPETING CONTINUATION	$403,580
2022	2022	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	001	4	3/28/2022	NON-COMPETING CONTINUATION	$44,841
														Subtotal = $448,421

Issue Date FY: 2021 ( Subtotal = $448,421 )
2021	2021	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	002	3	2/24/2021	NON-COMPETING CONTINUATION	$44,841
2021	2021	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	001	3	12/28/2020	NON-COMPETING CONTINUATION	-$44,841
2021	2021	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	3	12/23/2020	NON-COMPETING CONTINUATION	$448,421
														Subtotal = $448,421

Issue Date FY: 2020 ( Subtotal = $442,592 )
2020	2020	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	001	2	3/30/2020	NON-COMPETING CONTINUATION	$44,258
2020	2020	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	2	12/20/2019	NON-COMPETING CONTINUATION	$398,334
														Subtotal = $442,592

Issue Date FY: 2019 ( Subtotal = $462,750 )
2019	2019	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	1	1/16/2019	NEW	$462,750
														Subtotal = $462,750

Grand Total All Awards = $2,250,605

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Development of a Machine Learning Model to Integrate Clinical, Laboratory, Sonographic, and Elastographic Data for Noninvasive Liver Tissue Characterization in NAFLD

Award Number: R01DK119860

ORGANIZATION: NATIONAL INSTITUTE OF DIABETES & DIGESTIVE & KIDNEY DISEASES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 01/20/2019

PERIOD OF PERFORMANCE END DATE: 12/31/2025

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