Ignite Creation Date:
2024-10-25 @ 7:50 PM
Last Modification Date:
2024-10-26 @ 3:40 PM
Study NCT ID:
NCT06591481
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-09-08
Brief Title:
Retrospective Case-Control Study for Developing an Artificial Intelligence AI Tool for Lesion Detection Using Magnetic Resonance Imaging MRI and Clinical Variables for Early Diagnosis of Axial Spondyloarthritis axSpA
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Retrospective Case-control Study for the Development of an Artificial Intelligence AI-Based Tool of Lesion Detection Based on Magnetic Resonance Imaging MRI and Clinical Variables for Early Diagnosis of Axial Spondyloarthritis axSpA
Status:
RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
InnovaDetect
Brief Summary:
The goal of this observational study is to develop and validate an Artificial Intelligence AI tool that allows the lesion detection and early diagnosis of axial spondyloarthritis axSpA based on Magnetic Resonance Imaging MRI
This study will gather MRI scans from axSpA patients and a control group of participants
This study will gather MRI scans from axSpA patients and a control group of participants
Detailed Description:
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 001-25 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 andor 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 4060 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
1 Image Quality Control
All the images received from sites will be checked by imaging technicians to guarantee the homogeneity of the data
2 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 studys 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 studys repository
3 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
4 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
5 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 ie 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
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 001-25 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 andor 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 4060 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
1 Image Quality Control
All the images received from sites will be checked by imaging technicians to guarantee the homogeneity of the data
2 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 studys 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 studys repository
3 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
4 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
5 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 ie 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None