Whole Body MRI for Non-invasive Lesion Detection and Therapy Follow-up: Study With Patients With Radiographic axSpA and Non-radiographic axSpA

Overview

This study aims to further improve and expand the WB-MRI imaging evaluation cohort of axSpA patients to complete dynamic follow-up and condition assessment. Based on the WB-MRI features, the association of WB-MRI signs with disease activity, inflammation, and structural damage will be analyzed. At the same time, in-depth data mining was conducted and machine learning was used to construct a new axSpA disease activity assessment model based on WB-MRI data, and the accuracy and reliability of the model were clearly evaluated in the discovery cohort and validation cohort.

Axial spondyloarthritis (axSpA) is a common clinical chronic inflammatory joint disease with a high disability rate. Current assessment of axSpA disease activity mainly relies on patients' self-reported symptoms and signs, which has poor accuracy and reliability. At the same time, existing assessment methods fail to include some important disease characteristics of axSpA, such as active spinal inflammation and enthesitis. Therefore, there is an urgent need to establish a new axSpA disease activity assessment system. With the development of imaging technology, whole-body MRI (WB-MRI) scanning technology is highly sensitive, can detect multiple parts at the same time, and can fuse multiple sequence images to make disease location and diagnosis more accurate. Currently, Much attention has been paid to it, but it has been less studied in axSpA. This study established a disease activity assessment model based on WB-MRI imaging assessment, which will provide a new, effective and accurate method for axSpA disease assessment. At the same time, it will provide theoretical basis and practical basis for further understanding of the clinical characteristics of axSpA, the relationship between inflammation and structural damage.

Conditions