Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis

Kepp, Felix H.; Huber, Florian A.; Wurnig, Moritz C.; Mannil, Manoj; Kaniewska, Malwina; Guglielmi, Riccardo; Grande, Filippo Del; Guggenberger, Roman

doi:10.1016/j.ejrad.2021.109755

Cited by 11 publications

(9 citation statements)

References 28 publications

(34 reference statements)

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“…Notably, our dataset displays a significantly higher count of negative cases compared to positive ones, a pattern also seen in the literature [16], as well as other radiomics studies [17]. This discrepancy led to concerns about potential biases during algorithm training.…”

Section: Segmentation and Feature Extractionsupporting

confidence: 57%

“…Their study included a total of 90 patients. However, their most accurate outcomes were generated by a model that employed T1W-CE images [17]. This model which was built upon contrast-enhanced images demonstrated the highest accuracy, probably by detecting ancillary inflammatory lesions like synovitis or enthesitis, which could suggest inflammation [4].…”

Section: Discussionmentioning

confidence: 99%

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Radiomics for the Detection of Active Sacroiliitis Using MR Imaging

Triantafyllou,

Klontzas,

Koltsakis

et al. 2023

Diagnostics

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Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field.

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Section: Segmentation and Feature Extractionsupporting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging

Triantafyllou,

Klontzas,

Koltsakis

et al. 2023

Diagnostics

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“…In KEPP et al's study, texture analysis based on radiomics was superior to qualitative evaluation in distinguishing sacroiliac arthritis from degenerative changes. The diagnostic AUC of multiple imaging sequences combined by radiologists is 0.72, the AUC of fsT1wCE is 0.87, the AUC of T1w sequence is 0.49, and the AUC of the combined fsT1wCE multiple sequence combination is 0.91 [ 41 ]. As shown in Table 4 , in this study, the SVM-bimodal model with conventional MRI sequence achieved better AUC results in the testing set compared to KEPP et al's radiomics-based texture analysis model special MRI sequences, thus further improving the efficiency of assisting in the diagnosis of sacroiliitis and our model uses conventional MRI sequences, which have better clinical applicability.…”

Section: Discussionmentioning

confidence: 99%

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

Liu,

Wu,

Cai

et al. 2024

J Orthop Surg Res

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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.

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“…So, there may be some false positives in practical diagnosis. Kepp et al (2021) indicated that two radiologists with a minimum of 5 years of experience only have an accuracy rate of 75.6% and 74.4% in the diagnosis of sacroiliitis, respectively. Furthermore, the networks generalization ability would decrease due to the distributional imbalance of sacroiliitis and especially the lack of grade IV samples.…”

Section: And Radiomics Methodsmentioning

confidence: 99%

“…Sacroiliitis is visually assessed on CT scans using gray scale descriptors, texture descriptors, which are effective prior features for mitigating the risk of over-fitting. As a result, radiomics has been applied extensively in the diagnosis of sacroiliitis (Faleiros 2018, Castro-Zunti et al 2020, Tenório et al 2020, Kepp et al 2021, Ye et al 2022. Ye et al (2022) improved the diagnostic accuracy from 74% to 78% using a model that combined the features of radiomics and clinical practice.…”

Section: And Radiomics Methodsmentioning

confidence: 99%

Sacroiliitis diagnosis based on interpretable features and multi-task learning

Liu,

Zhang,

Zhang

et al. 2024

Phys. Med. Biol.

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Objective. Sacroiliitis is an early pathological manifestation of ankylosing spondylitis (AS), and a positive sacroiliitis test on imaging may help clinical practitioners diagnose AS early. Deep learning based automatic diagnosis algorithms can deliver grading findings for sacroiliitis, however, it requires a large amount of data with precise labels to train the model and lacks grading features visualization. In this paper, we aimed to propose a radiomics and deep learning based deep feature visualization positive diagnosis algorithm for sacroiliitis on CT scans. Visualization of grading features can enhance clinical interpretability with visual grading features, which assist doctors in diagnosis and treatment more effectively.Approach. The region of interest (ROI) is identified by segmenting the sacroiliac joint (SIJ) 3D CT images using a combination of the U-net model and certain statistical approaches. Then, in addition to extracting spatial and frequency domain features from ROI according to the radiographic manifestations of sacroiliitis, the radiomics features have also been integrated into the proposed encoder module to obtain a powerful encoder and extract features effectively. Finally, a multi-task learning technique and five-class labels are utilized to help with performing positive tests to reduce discrepancies in the evaluation of several radiologists.Main results. On our private dataset, proposed methods have obtained an accuracy rate of 87.3$\%$, which is 9.8$\%$ higher than the baseline and consistent with assessments made by qualified medical professionals.Significance. The results of the ablation experiment and interpreting analysis demonstrated that the proposed methods are applied in automatic CT scan sacroiliitis diagnosis due to their excellently interpretable and portable advantages.

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Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis

Cited by 11 publications

References 28 publications

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging

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

Sacroiliitis diagnosis based on interpretable features and multi-task learning

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