- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06344364
Digital Pathology and AI for Liver Outcomes in MASLD (DPAILO-1)
Epidemiologic Liver Outcomes Retrospective Study to Confirm The Prognostic Value of the FibroNest Digital Pathology Fibrosis Biomarker (Ph-FCS) in Patients With MASLD (DPAILO-1)
Study Overview
Status
Intervention / Treatment
Detailed Description
MASH, or metabolic dysfunction-associated steatohepatitis, presents histological liver changes resembling those caused by alcohol abuse, but in the absence of alcohol intake. Common among adults with conditions like obesity and type-2 diabetes, MASH, especially its severe form, is anticipated to become a leading cause of end-stage liver disease.
Currently lacking approved treatments, MASH poses a significant burden on liver health and transplantation. Diagnosis and assessment rely on subjective histological review, prone to variability and limitations in detecting subtle changes. Consequently, there's an urgent need for accurate, continuous histological biomarkers.
The FibroNest Ph-FCS offers a promising solution, utilizing high resolution digital pathology and sophisticated algorithmic methods for sensitive and reproducible fibrosis severity assessment and prediction of clinical events. In a 2003 proof of concept retrospective study on 400 patients, its prognostic performance was excellent.
In this proposed multi-center retrospective study, we aim to confirm the Ph-FCS's prognostic value on a large cohort of 1,200 MASLD patients. We will also compare the prognostic performance of the Ph-FCS with the prognostic performance of the NASH-CR Fibrosis stages, and with non-invasive biomarkers like Fib-4 and elastography/Fibroscan, also collected retrospectively from the point of initial diagnosis.
This study seeks to:
(i) Confirm Ph-FCS's prognostic utility on a large scale.
(ii) Compare biopsy-based Ph-FCS with NASH-CRN F Stages
(iii) Compare biopsy-based Ph-FCS with non-invasive biomarkers.
Study Type
Enrollment (Actual)
Contacts and Locations
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Adult pts ( >=18 years old) with MASLD defined histologically.
- Liver biopsy with fibrosis stains available for digitization or already digitized.
- Clinical follow-up >1 year available recording liver-related outcomes either through hospitalization ICD-10 codes or through clinical observation
Exclusion Criteria:
- Liver diseases other than MASLD Note: no exclusion based on bariatric surgery, significant weight loss or enrollment in NASH clinical studies, but data is collected for data analysis / competing effects (see data analysis plan)
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Non-Liver Related Event
Absence of any of the liver events described in the second group in the patient clinical follow-up.
|
Biomarker name: FibroNest Phenotypic Fibrosis Composite Score Acronym: FibroNest Ph-FCS Type of Biomarker: Histologic based, Digital, Quantitative Image Analysis, Imaging modality Definition: A quantitative, normalized (no unit) and continuous composite score that aggregates quantitative histological features of fibrosis severity measured by high resolution quantitative image analysis.
|
|
Liver Related Event
Liver-related events include liver-related death, hepatic decompensation events (variceal hemorrhage, ascites, hepatic encephalopathy), and hepatocellular carcinoma.
|
Biomarker name: FibroNest Phenotypic Fibrosis Composite Score Acronym: FibroNest Ph-FCS Type of Biomarker: Histologic based, Digital, Quantitative Image Analysis, Imaging modality Definition: A quantitative, normalized (no unit) and continuous composite score that aggregates quantitative histological features of fibrosis severity measured by high resolution quantitative image analysis.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Performance of Hepatic Decompensation Event predictive value of the FibroNest Ph-FCS
Time Frame: Time-to-event analysis between 2 and 10 years
|
Area under Receiver Operating Characteristic Curve (AUROC) of the FibroNest Ph-FCS, as a prognostic/diagnostic biomarker for liver related events in patients with MASH.
|
Time-to-event analysis between 2 and 10 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Performance of Hepatic Decompensation Event predictive value of the FIB-4 biomarker, a non-invasive test
Time Frame: Time-to-event analysis between 2 and 10 years
|
Area under Receiver Operating Characteristic Curve (AUROC) of FIB-4, as a prognostic/diagnostic biomarker for Hepatic Decompensation Events in patients with MASH.
|
Time-to-event analysis between 2 and 10 years
|
|
Performance of Hepatic Decompensation Event predictive value of the elastography (Fibroscan) biomarker, a non-invasive test
Time Frame: Time-to-event analysis between 2 and 10 years
|
Area under Receiver Operating Characteristic Curve (AUROC) of elastography, as a prognostic/diagnostic biomarker for Hepatic Decompensation Events in patients with MASH.
|
Time-to-event analysis between 2 and 10 years
|
|
Performance of Hepatic Decompensation Event predictive value of the NASH-CRN Fibrosis Stage, a biopsy-based score for fibrosis severity
Time Frame: Time-to-event analysis between 2 and 10 years
|
Area under Receiver Operating Characteristic Curve (AUROC) of NASH-CRN F stage, as a biopsy-based prognostic/diagnostic biomarker for Hepatic Decompensation Events in patients with MASH.
|
Time-to-event analysis between 2 and 10 years
|
Collaborators and Investigators
Sponsor
Collaborators
Investigators
- Principal Investigator: Vlad Ratziu, MD, PhD, Sorbonne University
Publications and helpful links
General Publications
- Kendall TJ, Jimenez-Ramos M, Turner F, Ramachandran P, Minnier J, McColgan MD, Alam M, Ellis H, Dunbar DR, Kohnen G, Konanahalli P, Oien KA, Bandiera L, Menolascina F, Juncker-Jensen A, Alexander D, Mayor C, Guha IN, Fallowfield JA. An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease. Nat Med. 2023 Nov;29(11):2939-2953. doi: 10.1038/s41591-023-02602-2. Epub 2023 Oct 30.
- Ratziu V, Chen L, Petitjean L. et al. Novel Artificial Intelligence-Assisted Digital Pathology Quantitative Image Analysis Predicts the occurrence of Liver-related Clinical Events in the Multicentric, European, Hepatic Outcomes and Survival Fatty Liver Registry (HITSURFR) Study. Hepatology. 78(S1) S1-S2154 2084-A
- Sanyal AJ, Van Natta ML, Clark J, Neuschwander-Tetri BA, Diehl A, Dasarathy S, Loomba R, Chalasani N, Kowdley K, Hameed B, Wilson LA, Yates KP, Belt P, Lazo M, Kleiner DE, Behling C, Tonascia J; NASH Clinical Research Network (CRN). Prospective Study of Outcomes in Adults with Nonalcoholic Fatty Liver Disease. N Engl J Med. 2021 Oct 21;385(17):1559-1569. doi: 10.1056/NEJMoa2029349.
Helpful Links
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- PHN 1-080-23
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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