Evidence map›Paper›PMID 39899218›Full record

SynthesisNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2025

The utility of diffusion tensor imaging in the assessment of trigeminal neuralgia pathophysiology and clinical outcome: a systematic review.

Salvatore Marino, Francesco Pambianco, Giuseppe Maria Della Pepa, Alessandro Izzo, Manuela D'Ercole, Quintino Giorgio D'Alessandris, Alessandro Olivi, Nicola Montano

Abstract readSystematic Review
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Synthesis in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 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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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

8 authors.

Salvatore MarinoInstitute of Neurosurgery, Catholic University, Rome, Italy. salvo.marino130592@gmail.com.ORCID http://orcid.org/0000-0003-2968-9713
Francesco PambiancoInstitute of Neurosurgery, Catholic University, Rome, Italy.
Giuseppe Maria Della PepaInstitute of Neurosurgery, Catholic University, Rome, Italy.
Alessandro IzzoInstitute of Neurosurgery, Catholic University, Rome, Italy.
Manuela D'ErcoleInstitute of Neurosurgery, Catholic University, Rome, Italy.
Quintino Giorgio D'AlessandrisInstitute of Neurosurgery, Catholic University, Rome, Italy.
Alessandro OliviInstitute of Neurosurgery, Catholic University, Rome, Italy.
Nicola MontanoInstitute of Neurosurgery, Catholic University, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Trigeminal neuralgia (TN) is a prevalent and debilitating condition. Despite significant advancements in the management of TN, its etiopathogenesis remains unclear, and predicting clinical outcomes following surgical or radiosurgical interventions continues to pose challenges. Diffusion tensor imaging (DTI) has emerged as a valuable tool in various neurosurgical domains, offering insights into the myelination, inflammation, and infiltration of neural fibers, including cranial nerves. Over the past two decades, numerous studies have sought to characterize DTI parameters in patients with TN. The present review aims to synthesize the current understanding of the utility of DTI in trigeminal neuralgia. In line with PRISMA-P guidelines, we therefore identified relevant studies reported in literature, and 28 papers were included. For each study reviewed, specific DTI parameters-namely fractional anisotropy (FA), apparent diffusion coefficient (ADC), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD)-were individually extracted, analyzed, and correlated with TN etiology and post-treatment clinical outcomes. We categorized results into two sections. In the first we examine the trends in DTI parameters across different subtypes of TN, including those with microvascular compression, without microvascular compression, and associated with demyelinating diseases. In the second we seek to highlight the key DTI features that may be predictive of more favorable clinical outcomes.

Indexed as

Diffusion Tensor ImagingTrigeminal NeuralgiaHumansCranial nervesCranial neuropathiesDiffusion Tensor ImagingNeurovascular conflictTrigeminal neuralgia

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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.