---
title: >-
  Predicting advanced fibrosis using non-invasive clinical tests and modern
  machine learning methods in TARGET-NASH.
description: >-
  Explore this publication on real-world evidence: Predicting advanced fibrosis
  using non-invasive clinical tests and modern machine learning methods in…
date: '2020-01-01'
category: Publications
tags:
  - EASL
  - Poster Presentation
  - R&D
  - Care
  - Hepatology
  - MASH/MASLD
  - Pharma Partner
  - Health System Partner
canonical_url: >-
  https://www.pedestalhealth.com/resources/publications/easl/predicting-advanced-fibrosis-using-non-invasive-clinical/
source: Pedestal Health
license: © 2026 Pedestal Health. All rights reserved.
slug: predicting-advanced-fibrosis-using-non-invasive-clinical
id: 2k4h4uzPOm1sTTnCx2WVS5
contentType: article
---

## Challenge

Standard FIB-4 and NFS cutoffs for advanced fibrosis were developed using conventional statistical methods, but it was unclear whether modern machine learning approaches applied to their constituent variables could achieve meaningfully better sensitivity-specificity tradeoffs in a real-world NAFLD cohort.

## Solution

TARGET-NASH biopsy and NIT data were used to train and validate logistic regression, lasso, boosted tree, and neural network models using the individual components of FIB-4 and NFS, comparing model performance across standard and profit-matrix-weighted classification approaches.

## Impact

Demonstrating that machine learning models applied to standard NIT variables achieve improved performance tradeoffs—especially when weighted toward identifying advanced fibrosis—provides the methodological foundation for developing next-generation non-invasive staging algorithms for NAFLD trials.