BACKGROUND
The spondyloarthritis (SpA) are a group of chronic inflammatory diseases of autoimmune
nature that share common clinical and genetic features, including an association with
HLA-B27 antigen. They are among the most common rheumatic diseases with a prevalence of
0.01-2,5%. All of these conditions make the patients to move on a chronic disabling
disease.
Patients with SpA can be classified based on their clinical presentation into either
predominantly axial SpA (axSpA) or predominantly peripheral SpA. Axial SpA is
characterized by primary involvement of the sacroiliac joints (SIJs) and/or the spine,
leaading to substantial pain and disability. Until recently, the diagnosis of axSpA
relied on detecting of structural changes evocative of sacroiliitis in the SIJs using
plain radiography.
The introduction of Magnetic resonance imaging (MRI) for evaluating the SIJs has
significantly advanced the recognition of axSpA. MRI can detect early inflammatory
processes even in patients who do not yet have structural lesions. Besides, MRI has shown
superiority over radiography in detecting structural changes in the SIJs. However, the
definition of a "positive MRI" in SpA remains controversial, as both sensitivity and
specificity have their limitations. Early diagnosis of SpA has become increasingly
important, as treatments are now available, and MRI is emerging as the preferred choice
for early diagnosis. A number of randomized controlled trials of anti-tumour necrosis
factor agents in ankylosing spondylitis have demonstrated regression of inflammatory
lesions in the spine by MRI. Moreover, the role of MRI in the early diagnosis of SpA has
become better established, and imaging features of active sacroiliitis by MRI have been
defined for axSpA diagnosis.
RATIONALE OF THE STUDY
Despite the current advances in medical imaging and ongoing efforts to improve the
classification criteria for axSpA, a high proportion of axSpA patients remain
under-diagnosed, leading to delays in diagnosis that can result in a poor prognosis. The
volume of unstructured data coming from medical imaging contributes to diagnostic delays.
The integration of AI and machine learning technologies in medicine for processing large
datasets has led to faster and more accurate analysis, identification of real-world
evidence gaps, and the agile generation of evidence to address healthcare providers' and
healthcare systems' needs.
This study aims to develop an AI diagnostic tool that combines quantitative MRI data with
clinical information to aid in the early diagnosis of axSpA.
OBJECTIVES
Primary objective: To create an AI tool that allows the early diagnosis of axSpA and
lesion detection based on MRI.
Secondary objective: Clinical validation of the AI module.
Exploratory objective:
Automated characterization of lesions (oedema, erosion, fat metaplasia and
ankylosing) based on texture quantification and radiomics and deep features
analysis.
Determination of normative values for texture imaging biomarker on the SIJs.
SAMPLE DESCRIPTION
The dataset will consist of 900 MRIs, collected retrospectively. MRI exams will be
sourced from patients with active axSpA and from those with inactive or no axSpA (control
group). The control-to-case ratio will be set at 40/60, allowing the algorithm to learn
from both subsets without favoring one group over the other. Since AI can more easily
characterize normality than pathology, the proportion of non-axSpA and normal MRIs can be
lower (approximately 40%) compared to the 60% allocated to axSpA MRIs. Among the active
axSpA group, the distribution of MRIs across classification categories (oedema,
ankylosing, erosion, and fat metaplasia) should be as balanced as possible, ideally with
25% assigned to each category. Each MRI does not necessarily come from a different
patient, as they may represent different time points for the same individual.
ANALYSIS PLAN
Image Quality Control.
All the images received from sites will be checked by imaging technicians to
guarantee the homogeneity of the data
Centralized Image Interpretation.
A centralized radiological review of the MRI images will be conducted by senior MSK
expert radiologists. Each case will be evaluated by two radiologists. If there is a
disagreement between the two, a third radiologist will review the case.
The radiologists will classify the MRIs into the study's various classes and cohorts
based on the ASAS criteria for defining active sacroiliitis on MRI for the
classification of axial spondyloarthritis. All radiologists involved in the project
will receive training to detect lesions according to the ASAS criteria, and this
training will be documented and stored in the study's repository.
Annotation process.
The imaging technicians will delineate the lesions detected by the MSK expert
radiologists to generate a 3D volume. This will be then reviewed by the MSK expert
radiologist.
Imaging Biomarkers Extraction.
To obtain further information of the lesions labeled, a texture analysis will be
performed to quantify several features related to the heterogeneity of the tissue
that can be considered as an indicator of the pathological process. The radiomic
panel will be based on the following features:
Structural or shape features: Descriptive of the geometric properties of the
image. Examples of these features are volume, maximum orthogonal diameter,
maximum surface area, compactness, fractal dimension or sphericity of a lesion.
Statistical characteristics are those that are inferred by statistical
relationships. They can be in turn:
First-order or distributional: They provide information on the frequency
of individual voxel values without taking into consideration their spatial
relationships. This distribution is presented in the form of histograms,
which report the mean, median, maximum and minimum in the intensities of
the voxels, but also on the asymmetry, kurtosis, uniformity or entropy of
the distribution.
Second order or texture: They reflect the relationships between
neighboring voxels, allowing to obtain a spatial arrangement of their
intensities, thus giving an idea about the architecture and heterogeneity
of the studied tissue. These relationships are obtained by means of
statistical analyses, such as cooccurrence matrices, which measure the
probability that two neighboring voxels have the same signal intensity.
Higher order: These are combinations of features obtained by complex
statistical analysis, such as fractal analysis, on images to which filters
or mathematical transformations have been applied to maximize or minimize
patterns, remove noise, or highlight certain details.
Deep features: These are properties obtained by analyzing images with
convolutional neural networks (CNN) or other deep learning algorithms. These
algorithms are trained to be able, in an image, to automatically determine and
select those features or sets of classifying features, without the need of
human intervention.
AI Module Development.
Using the MRIs collected together with the imaging biomarkers and other clinical
information available, the data scientists will create an AI-based model that will
provide a probability score of axSpA for each subject.
To create the AI module, the database will be divided in three non-balanced sub datasets
(training, validation and test). The bigger dataset of images will be used for training
the module. After the training phase, the validation database will be used to check if
the classification is well done or the model should be trained again.
Training: Set of cases used to fit the model during the training process. These will be
the cases the model will use to tune its weights, i.e. the cases that the model will use
to "learn".
Validation: Set of cases used to evaluate the model during the training process,
therefore, are not used to tune the model weights. This dataset is used for three main
purposes:
Hyperparameters tuning.
Overfitting detection: During the training process, after each training iteration
(epoch), the model is evaluated over both the training and validation datasets.
Selection of the "best" model during the training process, this means that the
weights from the training iteration in which the best validation metrics are
obtained are stored.
Test: A separate set, not used for training or validation, will be used for the final
model evaluation.
In this study, MRI exams will be obtained from various scanners and institutions.
Therefore, the acquisition protocols and reconstruction techniques may vary between
scanners. To address this, preprocessing techniques will be applied to standardise the
images across different scanners.