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Open Access

Peer-reviewed

Research Article

Predictors of multiple sclerosis progression: A systematic review of conventional magnetic resonance imaging studies

  • Nima Broomand Lomer ,

    Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

    nima.broomand@gmail.com

    Affiliation Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran

    ⨯
  • Kamal AmirAshjei Asalemi,

    Roles Writing – original draft, Writing – review & editing

    Affiliation Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran

    ⨯
  • Alia Saberi,

    Roles Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Neurology, Poursina Hospital, Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran

    ⨯
  • Kasra Sarlak

    Roles Writing – original draft, Writing – review & editing

    Affiliation Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran

    ⨯

Abstract

Introduction

Multiple Sclerosis (MS) is a chronic neurodegenerative disorder that affects the central nervous system (CNS) and results in progressive clinical disability and cognitive decline. Currently, there are no specific imaging parameters available for the prediction of longitudinal disability in MS patients. Magnetic resonance imaging (MRI) has linked imaging anomalies to clinical and cognitive deficits in MS. In this study, we aimed to evaluate the effectiveness of MRI in predicting disability, clinical progression, and cognitive decline in MS.

Methods

In this study, according to PRISMA guidelines, we comprehensively searched the Web of Science, PubMed, and Embase databases to identify pertinent articles that employed conventional MRI in the context of Relapsing-Remitting and progressive forms of MS. Following a rigorous screening process, studies that met the predefined inclusion criteria were selected for data extraction and evaluated for potential sources of bias.

Results

A total of 3028 records were retrieved from database searching. After a rigorous screening, 53 records met the criteria and were included in this study. Lesions and alterations in CNS structures like white matter, gray matter, corpus callosum, thalamus, and spinal cord, may be used to anticipate disability progression. Several prognostic factors associated with the progression of MS, including presence of cortical lesions, changes in gray matter volume, whole brain atrophy, the corpus callosum index, alterations in thalamic volume, and lesions or alterations in cross-sectional area of the spinal cord. For cognitive impairment in MS patients, reliable predictors include cortical gray matter volume, brain atrophy, lesion characteristics (T2-lesion load, temporal, frontal, and cerebellar lesions), white matter lesion volume, thalamic volume, and corpus callosum density.

Conclusion

This study indicates that MRI can be used to predict the cognitive decline, disability progression, and disease progression in MS patients over time.

Citation: Lomer NB, Asalemi KA, Saberi A, Sarlak K (2024) Predictors of multiple sclerosis progression: A systematic review of conventional magnetic resonance imaging studies. PLoS ONE 19(4): e0300415. https://doi.org/10.1371/journal.pone.0300415

Editor: Asokan Govindaraj Vaithinathan, University of Bahrain, BAHRAIN

Received: October 17, 2023; Accepted: February 26, 2024; Published: April 16, 2024

Copyright: © 2024 Lomer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Multiple Sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system (CNS) and leads to demyelination, axonal loss, and neurodegeneration. The disease is caused by a complex interaction of environmental and genetic factors that are not yet fully understood [1, 2]. MS presents with a wide range of symptoms including sensory disturbances, walking difficulties, vision problems, intestinal and urinary dysfunction, cognitive and emotional impairment, dizziness, vertigo, sexual problems, speech difficulties, seizures, and headaches [3, 4]. MS is classified into four subgroups based on phenotype: clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), secondary-progressive MS (SPMS), and primary-progressive MS (PPMS). RRMS is the most common form of the disease, affecting approximately 85% of patients at presentation. It is characterized by acute exacerbations followed by clinically stable periods [5]. PPMS, on the other hand, presents with a slowly progressive reduction in neurological function from the start without clinical relapses [6, 7]. Naturally, RRMS tends to convert to SPMS which is an irreversible gradual disability progression [8]. In the past, nearly 10% of RRMS patients progressed to SPMS in a 5-year period, 25% in 10 years, and 75% in 30 years. However, with the advent of more treatment options and early diagnosis, the risk of SPMS conversion has decreased to about 2%, 9%, and 27% in a 10-year, 15-year, and 20-year period, respectively [9–11]. In addition to physical disability, impairment of cognitive function is also a common manifestation of MS. Neuropsychological abnormalities are observed in 40–70% of MS patients, and cognitive impairment is a predictor of disease progression [12]. MS in cognitively impaired patients is more likely to progress in upcoming years [13]. The most common cognitive impairments in MS include reduced speed of information processing and working memory, which can disrupt data retention ability and short-term memory [14–17]. Unfortunately, the underlying mechanisms of cognitive impairment in MS are not yet fully understood [18, 19].

Magnetic resonance imaging (MRI) plays a pivotal role in the detection, prognosis, and evaluation of disease activity in MS [20–23]. Focal lesions, atrophies, and normal appearing tissue damages are among the MS pathologies that can be detected using MRI [20]. White matter lesions and deep gray matter atrophy typically arise in the early stages of the disease, while cortical atrophy and demyelination emerge in later stages [24–27].

Features of MS lesions in the brain or spinal cord, including the presence of lesions or changes in the size of certain CNS structures such as the thalamus, corpus callosum, cerebellum, limbic system, and spinal cord are not addressed in the latest version of McDonald Criteria (2017) [23] or in recent guidelines for determining disease progression or deciding for escalation or change of treatment in MS disease. Considering this, here we aimed to evaluate the potential of conventional MRI markers in predicting clinical disability, disease progression, and cognitive decline in MS patients.

Methods and materials

Eligibility criteria

We included studies with the following criteria: [1] Definite diagnosis of MS based on the revised McDonald’s criteria of 2017 [2, 23], Applied conventional MRI, and [3] Focused on evaluating the progression of disability or cognitive decline in MS patients. To ensure the quality of the data, we excluded various types of publications, including review articles, animal studies, letters and commentaries, case reports, case series, book chapters, conference abstracts, and non-English studies. Furthermore, the study only included research conducted among patients with RRMS or progressive forms of the disease, while studies conducted among patients with CIS were excluded. We excluded studies with the usage of AI (Deep learning and Machine learning methods) in the prediction of course of disease.

Search strategy

We conducted this systematic review according to the guideline of preferred reporting items for Systematic reviews and Meta-Analysis (PRISMA) [28]. Search was performed in PubMed, Embase and Web of Science databases from 2010 until July 2023 to identify the relevant studies using the keywords below:

("progressive multiple sclerosis" OR "Multiple Sclerosis, Chronic Progressive"[Mesh] OR "progressive MS" OR "primary progressive multiple sclerosis" OR "secondary progressive multiple sclerosis" OR "primary progressive MS" OR "secondary progressive MS" OR PPMS OR SPMS) AND ("relapsing remitting multiple sclerosis" OR "relapsing-remitting multiple sclerosis" OR "relapsing-remitting MS" OR "relapsing/remitting multiple sclerosis" OR "relapsing/remitting MS" OR "Multiple Sclerosis, Relapsing-Remitting"[Mesh] OR "relapsing-remitting MS" OR"relapse-onset MS" OR "relapse-onset multiple sclerosis" OR RRMS) AND (MRI OR "magnetic resonance imaging" OR "magnetic resonance imaging"[MeSH] OR imaging) AND (2010:2023[pdat])

We made a slight adjustment to our search strategy to integrate with two other databases. Initially, there were no restrictions on the type of studies, their location, or language. We screened and extracted data from all studies conducted from 2010 to July 2023 using EndNote software [29]. Flow diagram of the database searching and study selection according to the PRISMA guideline is presented below in Fig 1.

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Fig 1. PRISMA flow diagram of database searching and study selection.

https://doi.org/10.1371/journal.pone.0300415.g001

Screening and data extraction

This stage was conducted in three distinct phases by two independent authors, namely K.A.A and K.S. In the first phase, the titles and abstracts of the records were carefully screened to determine their initial eligibility for inclusion in the study. In the event of any discrepancies, the third and fourth authors, N.B.L and A.S, were consulted to resolve the issue by consensus. In the second phase, the full text of the selected records was retrieved. Only those articles that specifically studied MRI markers in relation to disability progression or cognitive decline in RRMS or progressive forms of MS were included. In the third and final phase, relevant data was extracted and recorded in a data collection table, which included important information such as the demographic features of the study participants (year of study, number and studied groups of participants, mean age, and disease duration), the imaging methodology used (field strengths in Tesla and studied parameter), and the correlations of MRI markers with disability progression and cognitive decline.

Data items

In this review, we aimed to assess any correlations of MRI markers with disability progression and cognitive decline in RRMS and progressive forms of MS. These terms are defined as follows:

Disability: Disability progression in MS is a broad term referring to the worsening of physical, cognitive and emotional symptoms during the disease course. We mainly aimed at the physical disability mostly measured by Expanded Disability Status Scale (EDSS) among included studies. Depending on the affected area of CNS, physical disability progression can manifest with different symptoms including muscle weakness, balance and coordination problems, fatigue, tremors and difficulty walking. In addition, Timed 25-Foot Walk (T25FWT) and 9-Hole Peg Test (9HPT) can be utilized to assess disability outcomes. The T25FWT assesses an individual’s time to walk 25 feet as swiftly as possible while ensuring safety. Prolonged completion times indicate increased disability levels. Meanwhile, the 9HPT evaluates arm and hand functionality, employing a small container with nine holes and pegs. Participants are instructed to place and remove the pegs from the holes individually as rapidly as possible. Longer completion times signify higher disability.

Progression: The progression of MS refers to how the disease evolves and advances over time. CIS and RRMS phenotypes tend to progress and convert to the progressive phenotypes of MS.

Cognition: Cognitive decline, a representative of disability progression in MS, refers to the progressive deterioration of cognitive functions, including memory, attention, information processing speed, executive functions, and problem-solving abilities. It can significantly impact daily functioning, work performance, and overall quality of life. Two commonly used cognitive function assessment tools in studies are the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT). The SDMT evaluates processing speed and attention by matching symbols with numbers within a time limit, while the PASAT measures processing speed, flexibility, and working memory by requiring participants to add orally presented numbers in sequence.

Risk of bias assessment

In accordance with the PRISMA guidelines [28], the quality assessment of the studies included in this systematic review was conducted using the Joanna Briggs Institute Critical Appraisal tools (JBI) specifically designed for cross-sectional studies [30]. Two independent authors (N.B.L and K.S) conducted the assessment by answering 11 questions that evaluate different domains of the studies to ascertain their potential risk of bias. The questions could be answered with ‘yes’, ‘no’ or ‘unclear’. Any discrepancies between the two reviewers were discussed and resolved by achieving a consensus. The risk of bias for each individual study was determined based on the following criteria: low risk of bias if 70% of answers scored yes, moderate risk if 50 to 69% questions scored yes and high risk of bias if yes scores were below 49% [31].

Results

A total of 3028 articles were identified after conducting a thorough search of the database, including 762 articles from PubMed, 498 from Web of Science, and 1768 from Embase. Following the elimination of duplicates, 1922 articles remained for title and abstract screening. Subsequently, 639 articles were retrieved for a full text analysis, out of which 12 records were not found. During the comprehensive full text screening, 579 articles were excluded for not meeting the inclusion criteria. An additional five studies were found through other sources and met the inclusion criteria, leading to a total of 53 studies being included in this review.

Correlating MRI markers in cortical lesions and gray matter alterations of MS patients were assessed in fifteen studies, spinal cord alterations in twelve studies, corpus callosum alterations in three studies, cerebellum alterations in six studies, thalamus alterations in eleven studies, limbic system alterations in two studies, lesion atrophy in two studies, whole brain and white matter lesion volume in nineteen studies.

MRI markers predicting the disability progression

Abnormalities in the gray matter, whether deep or cortical, including atrophy or lesion in the cortex, can predict the progression of disability among patients with MS. Several studies have shown a strong correlation between cortical lesion and EDSS score [32–38] with cortical lesion volume being a predictor of neurologic disability progression during follow-up [35]. Gray matter atrophy has also been identified as a predictor of higher EDSS scores [34, 39, 40]. In addition, the ratio of gray matter (GM) to normal appearing white matter (NAWM) in recently diagnosed RRMS patients can predict disability progression [41]. The deep gray matter has been found to be a predictor of time-to-EDSS progression [42].

In white matter (WM), EDSS score was significantly correlated with WM lesion volume, central atrophy, lesion probability in the periventricular WM at the left frontal horn and around the posterior horns and with whole-brain volume particularly with widths of third and lateral ventricle [32, 37, 43–45]. The presence of confluent lesions (in RRMS), higher number of T2 lesions, lower baseline T2-lesion volume (T2LV), lower normalized brain volume (NBV), higher percentage brain volume change (PBVC) between year 2 and baseline and presence of ≥ 4 slowly expanding lesions (SELs) were defined as prognostic factors for EDSS worsening and disability progression [46–48, 83].

In the corpus callosum, some indices, including corpus callosum index (CCI) and corpus callosum lesion volume (CCLV), which indicate corpus callosum (CC) damage, were associated with disability progression and EDSS change [49, 50].

Thalamic volume change especially in the anterior, ventral anterior, ventral lateral and pulvinar nuclei inversely correlated with EDSS [50–53]. Furthermore, the EDSS was negatively associated with thalamic iron [54].

Spinal cord changes such as atrophy (GM and WM) or lesions were indicators of disability and worsening EDSS. Some studies suggested that smaller cervical cross-sectional area (CS-SCA), especially CSA-C2, loss of spinal cord volume (SCV), baseline annualized percentage upper cervical cord cross-sectional area change (aUCCA), and the number of spinal cord segments affected by T2-lesions are all predictive factors for disability [34, 40, 55–60].

In all CNS structures, atrophied lesion volume was significantly associated with disability progression [61].

MRI markers predicting the progression of disease

Cortical lesions and gray matter volume are two most significant determinants of a progressive disease [34, 35]. Cortical lesions are more prevalent in SPMS subjects compared to RRMS subjects [35] and higher baseline cortical lesions predicted conversion to SPMS [62]. Temporal gray matter atrophy is faster in SPMS than RRMS [42]. The GM/NAWM ratio is a predictor of SPMS conversion in recently diagnosed RRMS patients, implicating that GM and NAWM are influenced differently regarding disease development since early stages of MS [41].

Some MS lesion characteristics and also atrophy of brain are among other key markers and predictors of MS progression. Notably, iron rims serve as a representation of the chronic active nature of MS lesions, indicating a more severe and damaging form of the disease [63]. In a study, the only longitudinal MRI marker that was capable of distinguishing patients who deteriorated gradually from those who remained stable was brain atrophied T2-LV [64]. In another 9.1-year longitudinal study, the number and volume of T2 hyperintense lesions and lower NBV were significantly associated with conversion to SPMS [47]. Higher annualized percentage ventricular volume change (aPVVC) during the first 2 years was observed in patients with progressive disease compared to patients with no progression [65]. Central atrophy was associated with disease progression over 5.5 years in early RRMS [45]. One study showed that significant discriminative MRI atrophy measurements in RRMS vs SPMS are as followed: Index of frontal atrophy, Index of EVANS, Huckman Index, Bicaudatus Index and Width of third ventricle. For differentiating RRMS from SPMS; Remission-Progression Index formula can be used [66]: Remission-Progression Index = (RAVLT 1–5 SUM + DSST)/Huckman Index.

The corpus callosum index is an important prognostic factor for the progression of MS. It has been observed that individuals diagnosed with SPMS exhibit lower levels of CCI at the time of diagnosis, while also experiencing a greater decline in annual CCI compared to those with RRMS [67].

Deeper nuclei impairment, higher thalamic lesion volume and higher thalamic volume reduction was seen in SPMS compared with the RRMS group [38, 51, 68]. Baseline volume and the rate of annual volume loss of the ventral lateral nucleus were significant predicting factors of disease progression [53].

Spinal cord abnormalities including atrophy and lesion and gadolinium-enhancement at disease onset and during disease are also predictors of MS progression and conversion to progressive forms [69]. SCV loss particularly cervical GM atrophy is a predicting factor for progression. Although, cervical CS-SCA, especially cross-sectional area of C2 (CSA-C2) is significantly smaller in PMS compared to RRMS, but thoracic SCAs are not significantly different between types of MS [55–57, 60]. Reduction of UCCA over 24 months is seen on all MS types and is higher in SPMS [59]. Patients who develop SPMS exhibit accelerated cord atrophy rates before conversion and decelerated rates after conversion [70]. Clusters of cord atrophy are mainly found in the lateral and posterior cord segments [71].

Atrophy of the cerebellum, especially cerebellar posterior superior lobe atrophy was higher in SPMS compared to RRMS [72–74]. Significantly higher volumes and numbers of cerebellar cortical lesions were found in SPMS and PPMS compared to RRMS and CIS [75, 76]. Although these changes manifest during the progression of the disease and may not be immediately apparent at the onset of the condition, hence they may not be regarded as reliable predictors of disease progression.

MRI markers predicting the cognitive decline

Cortical gray matter volume was an MRI predictor of cognitive decline [77]. But cortical lesion (CL) volume and CL load were not significant predictors of neuropsychological outcomes, and were only associated with impairing the more challenging cognitive tests such as Trail Making Test (TMT-B) [32, 33].

Brain atrophy was correlated with verbal memory impairment and other neurocognitive symptoms. Third ventricle width and bicaudatus ratio correlated mostly with the performed cognitive tests particularly Symbol Digit Modalities Test (SDMT) [66]. In RRMS, atrophy of WM was correlated with verbal memory performances [78].

Some MS lesion characteristics were predictors of cognitive impairment. Atrophied T2-LV among PMS patients, was related to follow-up SDMT of cognitive tests [64]. T2-lesion load (T2-LL) was recognized as an important predictor of memory function, cognitive efficiency and overall cognition [79]. Temporal, frontal and cerebellar hemispherical lesions had correlations with SDMT test performance, and a small cluster in left parietal with SDT. Inability of keeping recently learned information in memory was found to be correlated with lesions in superior parietal and left frontopolar and with adjacent regions of amygdalae and hippocampus [43]. White matter lesion volume (WMLV) was more strongly correlated with the cognitive tests (Paced Auditory Serial Addition Test (PASAT) and SDMT) compared to CL volume [32].

Decrease of thalamic volume was seen with a decrease in cognitive performance [52]. Normalized thalamic volume and anterior thalamic radiation integrity were among the predictors of cognitive decline [77, 79]. Both verbal and written parts of the SDMT test indicated moderate to strong correlations with the volume of thalamus nuclear groups [51].

In a study, CC density was another independent predictor of brief visuospatial memory test (BMVT) [49].

In RRMS, verbal memory performances correlated with atrophy of WM and left hippocampus [78]. Worse SDMT scores correlated with smaller normalized volume of the hippocampus and amygdala of each hemisphere and reduced R2t of the right hippocampus and amygdala, while worse performance on the 2s PASAT correlated with reduced R2t of the left amygdala [80]. The aforementioned alterations are indicative of cognitive impairment and therefore warrant the attention of medical professionals to evaluate potential cognitive decline in patients. However, it is important to note that these changes cannot be deemed as absolute predictors of cognitive impairment.

Lower cerebellar volumes, prominently posterior superior lobe (VI + Crus I) correlated with scores of SDMT and PASAT [72]. But these changes occurred in parallel with cognitive impairment and cannot predict it. An overview of included studies is shown in Table 1. We tried to analyze Table 1 with an aim to classify the results according to the region assessed, which ranged from cortical and gray matter, spinal cord, corpus callosum, cerebellum, thalamus, limbic system, lesion atrophy, brain volumetry, to lesions and white matter.

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Table 1. An overview of the literature regarding the studies with correlations of MRI markers with disability progression, progression of the disease and cognitive decline in studies participants.

https://doi.org/10.1371/journal.pone.0300415.t001

The evaluation of risk of bias was carried out for all the studies that were included in the analysis. Out of the total 26 cohort and 27 cross-sectional studies, only one study was found to have a high risk of bias [82], while the remaining studies were categorized as moderate or low. The detailed results of the quality assessment of the included studies are presented in Tables 2 and 3.

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Table 2. JBI risk of bias assessment for cohort studies.

https://doi.org/10.1371/journal.pone.0300415.t002

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Table 3. JBI risk of bias assessment for cross-sectional studies.

https://doi.org/10.1371/journal.pone.0300415.t003

Discussion

In recent years, MRI has emerged as a valuable tool for both the diagnosis and monitoring of MS. Extensive research has been conducted to identify predictive imaging biomarkers for MS, evaluating white and gray matter metrics to forecast disease progression. Despite being in use for almost four decades, MRI techniques are still evolving, and novel and classic metrics are being explored to improve the diagnostic process, treatment guidance, and prognosis. The significant volume of high-quality research conducted in this field of MS has enabled us to enhance our capability to correlate MRI scan outcomes with clinical evolution and pathological studies, and derive much-needed prognosis biomarkers from these data.

In this comprehensive review, we provided an insight into the potential of MRI markers to predict disability progression, disease progression, and cognitive decline in MS. The presence of lesions and alterations in certain structures of the CNS, including white matter and gray matter, corpus callosum, thalamus, and spinal cord, have been found to have a significant impact on disability progression in individuals with MS.

The progression and conversion of CIS or RRMS to progressive phenotypes of MS is a major concern among physicians and patients alike. Prognostic factors for MS progression have been identified, including various biomarkers and MRI parameters such as cortical lesion, gray matter volume change, whole brain atrophy, corpus callosum index, thalamic volume change, and certain spinal cord markers, including the presence of lesions in spinal cord or alterations in its cross-sectional area.

On the other hand, cognitive impairment is a significant and prevalent change that can occur during the course of MS. Unfortunately, it has not been deemed a sign or symptom of MS attacks or MS progression and has not been included in McDonald’s criteria until recently [23]. Despite its inevitability, cognitive impairment is often overlooked and not addressed with proper treatment and prophylaxis. Therefore, it is crucial to recognize the importance of cognitive impairment as a potential sign of disease progression and should be given the same level of attention as physical disability.

Multiple MRI parameters have been investigated as probable biomarkers for MS. Among these parameters, white matter lesions (WMLs) are a characteristic feature of MS, which are usually detected by contrast-enhanced MRI. Recent research has revealed that GM abnormalities manifest early in the course of the disease and predict both conversion to MS and the progressive accrual of disability [86]. Moreover, GM atrophy is more severe than WM atrophy in the early stages of the disease [87]. Total brain volume measurements hold significant clinical importance in MS diagnosis and monitoring. However, accurately measuring brain atrophy is crucial for detecting changes over short periods of time, and this is challenging in MS patients compared to healthy individuals due to smaller brain volumes [88–90]. While volumetrics and their derived measurements have shown promise as prognosis biomarkers for MS, the estimation of total brain atrophy in MS patients is challenging and can only be achieved after several years of longitudinal follow-up [91]. Therefore, utilizing brain atrophy as a prognosis biomarker at an individual level, particularly in the early stages of the disease, is difficult [92–95]. Furthermore, spinal cord volumetrics, especially in the cervical segment, have been found to exhibit higher atrophy in MS patients than healthy controls. Additionally, the atrophy rate of the spinal cord is higher than that of the total brain, and patients with PPMS experience more atrophy than those with RRMS [96]. Cortical lesions have been shown to have better prognostic value for clinical outcomes and disability progression than WMLs. Therefore, further research on the diagnosis, monitoring, and treatment of MS should consider cortical lesions as a valuable target [97–99].

Conclusion

This review provides evidence for the predictive potential of various MRI landmarks in MS. Lesions and changes within CNS structures such as white matter, gray matter, corpus callosum, thalamus, and spinal cord serve as potential indicators for predicting the progression of disability. Various prognostic factors are linked to the progression of MS, encompassing the presence of cortical lesions, alterations in gray matter volume, whole brain atrophy, the corpus callosum index, changes in thalamic volume, and lesions or modifications in the cross-sectional area of the spinal cord. Regarding cognitive impairment in individuals with MS, dependable predictors include cortical gray matter volume, brain atrophy, characteristics of lesions (such as T2-lesion load, temporal, frontal, and cerebellar lesions, volume of white matter lesions), thalamic volume, and density of the corpus callosum. Overall, MRI appears to be a useful tool for predicting MS disability progression, progression of disease, and cognitive decline.

Limitations and suggestions

MRI has been widely used to detect and monitor MS related abnormalities. However, its limitations in predicting disease progression have been noted. Firstly, conventional MRI measures may lack specificity in predicting disease progression. The lesions seen on MRI may not always correlate with clinical symptoms or disease progression. Secondly, conventional MRI may not detect early pathological changes in MS, especially in the absence of visible lesions. This can limit its ability to predict disease progression accurately. MS is a complex disease with various underlying mechanisms such as inflammation, neurodegeneration, and remyelination that conventional MRI may not capture entirely, limiting its predictive value. Lastly, conventional MRI primarily focuses on structural changes and may not fully reflect functional impairment or disability progression in MS patients. Therefore, while MRI is useful in detecting MS-related abnormalities, its limitations in predicting disease progression should be taken into consideration. There are several ways to address these limitations. Advanced MRI techniques, including Diffusion Tensor Imaging (DTI), Magnetization Transfer Imaging (MTI), and Functional Magnetic Resonance Imaging (fMRI), offer precise measures of MS progression by capturing microstructural changes, myelin content, and functional connectivity alterations. Moreover, quantitative MRI measures, such as brain atrophy rates, lesion volumes, and magnetization transfer ratios, provide objective biomarkers when combined with clinical data. Additionally, long-term follow-up studies with repeated MRI scans and clinical assessments can identify imaging biomarkers that better correlate with disease progression. Furthermore, combining conventional MRI with other modalities like Positron Emission Tomography (PET) or Optical Coherence Tomography (OCT) can offer a more comprehensive assessment of MS pathology and improve predictive accuracy.

Acknowledgments

The authors would like to thank the Clinical Research Development Unit of Poursina Hospital, Guilan University of Medical Sciences, Guilan, Iran.

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