Evidence map›Paper›PMID 40740563›Full record

ReviewMedical journal of the Islamic Republic of Iran2025

An Assessment of the Diagnostic Accuracy of Magnetic Resonance Diffusion Kurtosis Imaging in Diagnosing Degenerative Lumbar Spine Disease: A Scoping Review.

Neil Abraham Barnes, Winniecia Dkhar, Rajagopal Kadavigere, Abhimanyu Pradhan

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In one paragraph

Review in Medical journal of the Islamic Republic of Iran, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Neil Abraham BarnesDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Winniecia DkharDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.ORCID https://orcid.org/0000-0001-5963-3230
Rajagopal KadavigereDepartment of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
Abhimanyu PradhanDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Degenerative disorders of the lumbar spine are a common cause of chronic pain and disability, particularly in older adults. They involve the progressive degeneration of intervertebral discs, which leads to symptoms such as lower back pain, nerve compression, and neurological deficits. While traditional MRIs are a widely used and effective tool for diagnosing many spinal conditions, including degenerative disorders of the lumbar spine, they do have limitations when it comes to detecting early microstructural changes in the intervertebral discs and other spinal components. This limitation can delay diagnosis and intervention in the early stages of degeneration, which is critical for preventing progression. The scoping review aims to sensitize the current evidence on the diagnostic performance of Diffusion Kurtosis Imaging (DKI) in detecting and characterizing degenerative changes in the lumbar spine. Methods: Using the Arksey and O'Malley framework, we performed an extensive literature search across Scopus, Embase, and PubMed to identify relevant studies published between January 2010 and 2024. The selection criteria included 3T MRI studies utilizing DKI techniques aimed at evaluating degenerative changes in the lumbar spine. Results: The total number of studies included in this study was 3 out of 355, where our preliminary findings indicate that the DKI parameters demonstrated an early detection of microstructural changes within the degenerative lumbar spine as compared to the healthy controls. Accordingly, these changes are highly correlated with clinical symptoms and the extent of degeneration observed on conventional MRI, suggesting that DKI is more sensitive to detecting early microstructural changes. Conclusion: MR Diffusion Kurtosis Imaging offers a substantial potential for detecting subtle microstructural changes in the lumbar spine degeneration at an early stage, that is crucial for earlier diagnosis and intervention. The integration of DKI sequence into clinical practice could result in an increase in diagnostic accuracy and patient outcomes.

Indexed as

Degenerative SpineDiffusion Kurtosis ImagingLumbar SpineMagnetic Resonance Imaging

Identifiers

PMID40740563
PMCPMC12309333

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.