Liver biopsy-based validation, confirmation and comparison of the diagnostic performance of established and novel non-invasive steatotic liver disease indexes: Results from a large multi-center study.

BACKGROUND

Non-invasive tools (NIT) for metabolic-dysfunction associated liver disease (MASLD) screening or diagnosis need to be thoroughly validated using liver biopsies.

PURPOSE

To externally validate NITs designed to differentiate the presence or absence of liver steatosis as well as more advanced disease stages, to confirm fully validated indexes (n = 7 NITs), to fully validate partially validated indexes (n = 5 NITs), and to validate for the first time one new index (n = 1 NIT).

METHODS

This is a multi-center study from two Gastroenterology-Hepatology Departments (Greece and Australia) and one Bariatric-Metabolic Surgery Department (Italy). Overall, n = 455 serum samples of patients with biopsy-proven MASLD (n = 374, including 237 patients with metabolic-dysfunction associated steatohepatitis (MASH)) and Controls (n = 81) were recruited. A complete validation analysis was performed to differentiate the presence of MASLD vs. Controls, MASH vs. metabolic-dysfunction associated steatotic liver (MASL), histological features of MASH, and fibrosis stages.

RESULTS

The index of NASH (ION) demonstrated the highest differentiation ability for the presence of MASLD vs. Controls, with the area under the curve (AUC) being 0.894. For specific histological characterization of MASH, no NIT demonstrated adequate performance, while in the case of specific features of MASH, such as hepatocellular ballooning and lobular inflammation, ION demonstrated the best performance with AUC being close to or above 0.850. For fibrosis (F) classification, the highest AUC was reached by the aspartate aminotransferase to platelet ratio index (APRI) being ~0.850 yet only with the potential to differentiate the severe fibrosis stages (F3, F4) vs. mild or moderate fibrosis (F0-2) with an AUC > 0.900 in patients without T2DM. When we excluded patients with morbid obesity, the differentiation ability of APRI was improved, reaching AUC = 0.802 for differentiating the presence of fibrosis F2-4 vs. F0-1. The recommended by current guidelines index FIB-4 seemed to differentiate adequately between severe (i.e., F3-4) and mild or moderate fibrosis (F0-2) with an AUC = 0.820, yet this was not the case when FIB-4 was used to classify patients with fibrosis F2-4 vs. F0-1. Trying to improve the predictive value of all NITs, using Youden's methodology, to optimize the suggested cut-off points did not materially improve the results.

CONCLUSIONS

The validation of currently available NITs using biopsy-proven samples provides new evidence for their ability to differentiate between specific disease stages, histological features, and, most importantly, fibrosis grading. The overall performance of the examined NITs needs to be further improved for applications in the clinic.