All that glitters is not gold: sacroiliitis

Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging

OBJECTIVE

To create an automated machine learning model using sacroiliac joint MRI imaging for early sacroiliac arthritis detection, aiming to enhance diagnostic accuracy.

METHODS

We conducted a retrospective analysis involving 71 patients with early sacroiliac arthritis and 85 patients with normal sacroiliac joint MRI scans. Transverse T1WI and T2WI sequences were collected and subjected to radiomics analysis by two physicians. Patients were randomly divided into training and test groups at a 7:3 ratio. Initially, we extracted the region of interest on the sacroiliac joint surface using ITK-SNAP 3.6.0 software and extracted radiomic features. We retained features with an Intraclass Correlation Coefficient > 0.80, followed by filtering using max-relevance and min-redundancy (mRMR) and LASSO algorithms to establish an automatic identification model for sacroiliac joint surface injury. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated. Model performance was assessed by accuracy, sensitivity, and specificity.

RESULTS

We evaluated model performance, achieving an AUC of 0.943 for the SVM-T1WI training group, with accuracy, sensitivity, and specificity values of 0.878, 0.836, and 0.943, respectively. The SVM-T1WI test group exhibited an AUC of 0.875, with corresponding accuracy, sensitivity, and specificity values of 0.909, 0.929, and 0.875, respectively. For the SVM-T2WI training group, the AUC was 0.975, with accuracy, sensitivity, and specificity values of 0.933, 0.889, and 0.750. The SVM-T2WI test group produced an AUC of 0.902, with accuracy, sensitivity, and specificity values of 0.864, 0.889, and 0.800. In the SVM-bimodal training group, we achieved an AUC of 0.974, with accuracy, sensitivity, and specificity values of 0.921, 0.889, and 0.971, respectively. The SVM-bimodal test group exhibited an AUC of 0.964, with accuracy, sensitivity, and specificity values of 0.955, 1.000, and 0.875, respectively.

CONCLUSION

The radiomics-based detection model demonstrates excellent automatic identification performance for early sacroiliitis.

Sacroiliac joint beyond sacroiliitis-further insights and old concepts on magnetic resonance imaging

The sacroiliac joint (SIJ) is an amphiarthrosis composed of a posterior syndesmosis and an anterior cartilaginous portion, with limited yet present mobility. Its main function is to transmit the load from the axial skeleton to the lower limbs and vice-versa; it is susceptible to early mechanical and degenerative changes which are much more common than inflammatory sacroiliitis. Magnetic resonance imaging (MRI) has increasingly been used to evaluate these changes, and while subchondral bone marrow edema (BME) is a common finding related to both, care must be taken when applying the ASAS research MRI definition for sacroiliitis without considering lesion BME topography, size and depth, concomitant structural damage and, of course, the clinical picture. In this review, we will discuss the anatomy and biomechanics of the SIJ, the noninflammatory causes of SIJ subchondral BME, and how these concepts combined can be used to increase our diagnostic confidence.

Etiopathogenesis of sacroiliitis: implications for assessment and management

The sacroiliac joints connect the base of the sacrum to the ilium. When inflamed, they are suspected to cause low back pain. Inflammation of the sacroiliac joints is called sacroiliitis. The severity of the pain varies and depends on the degree of inflammation. Sacroiliitis is a hallmark of seronegative spondyloarthropathies. The presence or absence of chronic sacroiliitis is an important clue in the diagnosis of low back pain. This article aims to provide a concise overview of the anatomy, physiology, and molecular biology of sacroiliitis to aid clinicians in the assessment and management of sacroiliitis. For this narrative review, we evaluated articles in English published before August 2019 in PubMed. Then, we selected articles related to the painful manifestations of the sacroiliac joint. From the retrieved articles, we found that chronic sacroiliitis may be caused by various forms of spondyloarthritis, such as ankylosing spondyloarthritis. Sacroiliitis can also be associated with inflammatory bowel disease, Crohn’s disease, gout, tuberculosis, brucellosis, and osteoarthritis, indicating common underlying etiological factors. The pathophysiology of sacroiliitis is complex and may involve internal, environmental, immunological, and genetic factors. Finally, genetic factors may also play a central role in progression of the disease. Knowing the genetic pre-disposition for sacroiliitis can be useful for diagnosis and for formulating treatment regimens, and may lead to a substantial reduction in disease severity and duration and to improved patient performance.