Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis

Tenório, Ariane Priscilla Magalhães; Ferreira, José Raniery; Dalto, Vitor Faeda; Faleiros, Matheus Calil; Assad, Rodrigo Luppino; Louzada‐Júnior, Paulo; Nogueira-Barbosa, Marcello Henrique; Rangayyan, Rangaraj M.; Azevedo-Marques, Paulo Mazzoncini de

doi:10.1007/s10278-021-00559-7

Cited by 11 publications

(12 citation statements)

References 29 publications

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“…Radiomics analysis has been applied to assess inflammatory sacroiliitis. A radiomics model with a machine learning classifier extracting features from fat-suppressed fluid-sensitive MR images showed sensitivities of 70% to 100% and specificities of 85% to 92% for differentiating normal sacroiliac joints from sacroiliitis 27 . The performance was overall similar to the assessments of 2 radiologists.…”

Section: Radiomics Of Nononcologic Musculoskeletal Radiologymentioning

confidence: 63%

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology

Kijowski

Fritz

2022

Invest Radiol

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Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging-and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics-and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.

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Section: Radiomics Of Nononcologic Musculoskeletal Radiologymentioning

confidence: 63%

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology

Kijowski

Fritz

2022

Invest Radiol

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“…Das Modell zeigte eine Sensitivität von 70 % bis 100 % und eine Spezifität von 85 % bis 92 % bei der Unterscheidung zwischen normalen Sakroiliakalgelenken und der Sakroiliitis. Die Leistung des Modells war vergleichbar mit der Beurteilung durch zwei Radiologen (mit jeweils vier Jahren Erfahrung in muskuloskelettaler Diagnostik) [27].…”

Section: Ansätze Aus Dem Bereich Radiomicsunclassified

Beurteilung der Sakroiliitis mittels künstlicher Intelligenz – Fortschritte und Limitationen

Adams

Vahldiek

Poddubnyy

et al. 2023

Aktuelle Rheumatologie

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ZusammenfassungDie Sakroiliitis ist eine entzündliche Erkrankung des Sakroiliakalgelenks, die durch Faktoren wie Infektionen, Traumata und Autoimmunerkrankungen ausgelöst werden kann. Sie verursacht Schmerzen und Steifheit im unteren Rücken, weshalb eine frühzeitige Diagnose für eine optimale Behandlung entscheidend ist. Die Diagnose ist anspruchsvoll und erfordert klinische Beurteilung, Labortests und bildgebende Verfahren wie Röntgen, MRT oder CT. In den letzten Jahren hat sich die künstliche Intelligenz (KI) als vielversprechendes Instrument für die Beurteilung von Veränderungen im Rahmen der Sakroiliitis herausgestellt. KI-Algorithmen analysieren verschiedene bildgebende Verfahren, um strukturelle Veränderungen und Entzündungen im Sakroiliakalgelenk zu erkennen, zu quantifizieren und einzuordnen. Die Anwendung von KI kann die Diagnosegenauigkeit und Effizienz des Radiologen bzw. des Rheumatologen bei der Beurteilung von Sakroiliitis durch bildgebende Verfahren verbessern. KI-Algorithmen können strukturelle Veränderungen und Entzündungen im Sakroiliakalgelenk quantifizieren und Vorhersagemodelle für den Krankheitsverlauf erstellen. Herausforderungen wie der Bedarf an qualitativ hochwertigen Daten und die Minimierung von Verzerrungen und Fehlern in den Daten und Algorithmen müssen jedoch bewältigt werden. Weitere Studien sind erforderlich, um das volle Potenzial der KI bei der Beurteilung von Sakroiliitis auszuschöpfen. Der Einsatz von KI kann jedoch die Ergebnisse für Patienten verbessern, indem er eine frühzeitige Diagnose und Behandlung ermöglicht.

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“…[ 121 ] Another study found certain features differed between axial and peripheral SpA and could distinguish subtypes with excellent accurac. [ 120 ]…”

Section: Introductionmentioning

confidence: 99%

Beyond the horizon: Innovations and future directions in axial-spondyloarthritis

Venerito,

Del Vescovo,

Lopalco

et al. 2023

Arch Rheumatol

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Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the spine and sacroiliac joints. This review discusses recent advances across multiple scientific fields that promise to transform axSpA management. Traditionally, axSpA was considered an immune-mediated disease driven by human leukocyte antigen B27 (HLA-B27), interleukin (IL)-23/IL-17 signaling, biomechanics, and dysbiosis. Diagnosis relies on clinical features, laboratory tests, and imaging, particularly magnetic resonance imaging (MRI) nowadays. Management includes exercise, lifestyle changes, non-steroidal anti-inflammatory drugs and if this is not sufficient to achieve disease control also biological and targeted-synthetic disease modifying anti-rheumatic drugs. Beyond long-recognized genetic risks like HLA-B27, high-throughput sequencing has revealed intricate gene-environment interactions influencing dysbiosis, immune dysfunction, and aberrant bone remodeling. Elucidating these mechanisms promises screening approaches to enable early intervention. Advanced imaging is revolutionizing the assessment of axSpA's hallmark: sacroiliac bone-marrow edema indicating inflammation. Novel magnetic resonance imaging (MRI) techniques sensitively quantify disease activity, while machine learning automates complex analysis to improve diagnostic accuracy and monitoring. Hybrid imaging like synthetic MRI/computed tomography (CT) visualizes structural damage with new clarity. Meanwhile, microbiome analysis has uncovered gut ecosystem alterations that may initiate joint inflammation through HLA-B27 misfolding or immune subversion. Correcting dysbiosis represents an enticing treatment target. Moving forward, emerging techniques must augment patient care. Incorporating patient perspectives will be key to ensure innovations like genetics, microbiome, and imaging biomarkers translate into improved mobility, reduced pain, and increased quality of life. By integrating cutting-edge, multidisciplinary science with patients' lived experience, researchers can unlock the full potential of new technologies to deliver transformative outcomes. The future is bright for precision diagnosis, tightly controlled treatment, and even prevention of axSpA.

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Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis

Cited by 11 publications

References 29 publications

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology

Beurteilung der Sakroiliitis mittels künstlicher Intelligenz – Fortschritte und Limitationen

Beyond the horizon: Innovations and future directions in axial-spondyloarthritis

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