Trial reportEuropean radiology2019
Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS).
Trial report in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The trial behind it
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Who cites it
16 citing papers in PubMed.
- Muscle texture features on preoperative MRI for diagnosis and assessment of severity of congenital muscular torticollis.Journal of orthopaedic surgery and research · 2024Article
- Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography.Diagnostics (Basel, Switzerland) · 2023Article
- Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023Article
- Machine learning-based radiomics to differentiate immune-mediated necrotizing myopathy from limb-girdle muscular dystrophy R2 using MRI.Frontiers in neurology · 2023Article
- Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning.Metabolites · 2022Article
- Artificial intelligence in spine care: current applications and future utility.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- A novel MRI index for paraspinal muscle fatty infiltration: reliability and relation to pain and disability in lumbar spinal stenosis: results from a multicentre study.European radiology experimental · 2022Article
- Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data.Cancers · 2022Article
- AI MSK clinical applications: spine imaging.Skeletal radiology · 2022Review
- A Radiomics Nomogram for Distinguishing Benign From Malignant Round-Like Breast Tumors.Frontiers in oncology · 2022Article
- Texture analysis using T1-weighted images for muscles in Charcot-Marie-Tooth disease patients and volunteers.European radiology · 2021Article
- Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases.Scientific reports · 2021Article
- Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength.Diagnostics (Basel, Switzerland) · 2021Article
- Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT.Frontiers in endocrinology · 2021Article
- MRI in the assessment of adipose tissues and muscle composition: how to use it.Quantitative imaging in medicine and surgery · 2020Review
- Lumbar muscle volume in postmenopausal women with osteoporotic compression fractures: quantitative measurement using MRI.European radiology · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study was to apply texture analysis (TA) on paraspinal musculature in T2-weighted (T2w) magnetic resonance images (MRI) of symptomatic lumbar spinal stenosis (LSS) patients and correlate the findings with clinical outcome measures.
methodsNinety patients were prospectively enrolled in the multi-centric Lumbar Stenosis Outcome Study (LSOS). All patients received a T2w MRI, from which we selected axial images perpendicular to the intervertebral disc at level L3/4 for TA. Regions-of-interest (ROI) were drawn of the paraspinal musculature and 304 TA features/ ROI were calculated. As clinical outcome measurements, we analysed three commonly applied measures: Spinal Stenosis Measure (SSM), Roland-Morris Disability Questionnaire (RMDQ), as well as the Numeric Rating Scale (NRS). We used two machine learning-based classifiers: Decision table, and k-nearest neighbours (k-NN).
resultsWe observed no meaningful correlation between TA in paraspinal musculature and the two clinical outcome measures SSM symptoms and SSM function, while a moderate correlation was observed regarding the outcome measures RMDQ (k-NN: r = 0.56) and NRS (Decision Table: r = 0.72).
conclusionsIn conclusion, MR TA is a viable tool to quantify medical images and illustrate correlations of microarchitectural changes invisible to a human reader with potential clinical impact. KEY POINTS: • TA is feasible on paraspinal musculature using MRI. • TA on paraspinal musculature correlates with SSM and RMDQ. • TA may enable a statement regarding clinical impact of imaging findings.
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