Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies

TL;DR

This paper introduces a method that clusters imaging sites into pseudo-domains based on scan appearance, enabling more accurate prediction of future disease status in multi-center studies despite data heterogeneity.

Contribution

It proposes a novel pseudo-domain clustering approach to handle site variability and improve predictive modeling in multi-center imaging data.

Findings

Pseudo-domain models outperform traditional models in prediction accuracy.

Clustering by visual scan appearance effectively captures site differences.

Method enhances disease outcome prediction in liver disease studies.

Abstract

In multi-center randomized clinical trials imaging data can be diverse due to acquisition technology or scanning protocols. Models predicting future outcome of patients are impaired by this data heterogeneity. Here, we propose a prediction method that can cope with a high number of different scanning sites and a low number of samples per site. We cluster sites into pseudo-domains based on visual appearance of scans, and train pseudo-domain specific models. Results show that they improve the prediction accuracy for steatosis after 48 weeks from imaging data acquired at an initial visit and 12-weeks follow-up in liver disease