Diagnostic validity of magnetic resonance parkinsonism index in differentiating patients with progressive supranuclear palsy from patients with Parkinson's disease

Identification of optimal value of magnetic resonance planimetry and the parkinsonism index for the diagnosis of Parkinson's disease and progressive supranuclear palsy

Objectives

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative conditions that have overlapping clinical and imaging features, thus making it difficult to distinguish and diagnose PSP from PD. Therefore, in this study, we aimed to investigate the optimal value of magnetic resonance planimetry and the parkinsonism index to differentiate between PSP and PD.

Methods

In this retrospective study, we recruited a total of 84 patients (27 patients with PSP, 27 patients with PD and 27 normal controls) who underwent MRI brain examinations. For each subject, we calculated the corpus callosum area, midbrain area, pons area, middle cerebellar peduncle (MCP) width and superior cerebellar peduncle (SCP) width on MRI brain images. We also calculated the pons to midbrain area (P/M) ratio, MCP/SCP ratio and magnetic resonance parkinsonism index (MRPI).

Results

Receiver operating characteristic curve (ROC) analysis was used to identify the diagnostic value of each biomarker. MRPI had a sensitivity of 70.4%, a specificity of 88.9%, and a diagnostic accuracy of 79.6% with an optimum cut off of 24.3 for differentiating PSP from PD. P/M ratio had a sensitivity of 74.1%, a specificity of 77.8%, and a diagnostic accuracy of 75.9% with an optimal cutoff of 24.3 for differentiating PSP from PD. The MCP/SCP ratio had a sensitivity of 66.7%, a specificity of 77.8%, and an accuracy of 72.2% with an optimal cut off of 4.65 for differentiating PSP from PD.

Conclusions

The study revealed that MRPI and P/M ratio are accurate markers for differentiating PSP from PD. The optimal cut-off values derived from our study can help in the early diagnosis of PD.

Differentiating between common PSP phenotypes using structural MRI: a machine learning study

BACKGROUND

Differentiating Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data.

METHODS

Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes.

RESULTS

MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients.

CONCLUSION

The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials.

Magnetic Resonance Planimetry in the Differential Diagnosis between Parkinson’s Disease and Progressive Supranuclear Palsy

The clinical differential diagnosis between Parkinson’s disease (PD) and progressive supranuclear palsy (PSP) is often challenging. The description of milder PSP phenotypes strongly resembling PD, such as PSP-Parkinsonism, further increased the diagnostic challenge and the need for reliable neuroimaging biomarkers to enhance the diagnostic certainty. This review aims to summarize the contribution of a relatively simple and widely available imaging technique such as MR planimetry in the differential diagnosis between PD and PSP, focusing on the recent advancements in this field. The development of accurate MR planimetric biomarkers, together with the implementation of automated algorithms, led to robust and objective measures for the differential diagnosis of PSP and PD at the individual level. Evidence from longitudinal studies also suggests a role of MR planimetry in predicting the development of the PSP clinical signs, allowing to identify PSP patients before they meet diagnostic criteria when their clinical phenotype can be indistinguishable from PD. Finally, promising evidence exists on the possible association between MR planimetric measures and the underlying pathology, with important implications for trials with new disease-modifying target therapies.

Development and Validation of Automated Magnetic Resonance Parkinsonism Index 2.0 to Distinguish Progressive Supranuclear Palsy‐Parkinsonism From Parkinson's Disease

Background

Differentiating progressive supranuclear palsy‐parkinsonism (PSP‐P) from Parkinson's disease (PD) is clinically challenging.

Objective

This study aimed to develop an automated Magnetic Resonance Parkinsonism Index 2.0 (MRPI 2.0) algorithm to distinguish PSP‐P from PD and to validate its diagnostic performance in two large independent cohorts.

Methods

We enrolled 676 participants: a training cohort (n = 346; 43 PSP‐P, 194 PD, and 109 control subjects) from our center and an independent testing cohort (n = 330; 62 PSP‐P, 171 PD, and 97 control subjects) from an international research group. We developed a new in‐house algorithm for MRPI 2.0 calculation and assessed its performance in distinguishing PSP‐P from PD and control subjects in both cohorts using receiver operating characteristic curves.

Results

The automated MRPI 2.0 showed excellent performance in differentiating patients with PSP‐P from patients with PD and control subjects both in the training cohort (area under the receiver operating characteristic curve [AUC] = 0.93 [95% confidence interval, 0.89–0.98] and AUC = 0.97 [0.93–1.00], respectively) and in the international testing cohort (PSP‐P versus PD, AUC = 0.92 [0.87–0.97]; PSP‐P versus controls, AUC = 0.94 [0.90–0.98]), suggesting the generalizability of the results. The automated MRPI 2.0 also accurately distinguished between PSP‐P and PD in the early stage of the diseases (AUC = 0.91 [0.84–0.97]). A strong correlation (r = 0.91, P < 0.001) was found between automated and manual MRPI 2.0 values.

Conclusions

Our study provides an automated, validated, and generalizable magnetic resonance biomarker to distinguish PSP‐P from PD. The use of the automated MRPI 2.0 algorithm rather than manual measurements could be important to standardize measures in patients with PSP‐P across centers, with a positive impact on multicenter studies and clinical trials involving patients from different geographic regions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Diagnostic Performance of the Magnetic Resonance Parkinsonism Index in Differentiating Progressive Supranuclear Palsy from Parkinson's Disease: An Updated Systematic Review and Meta-Analysis

Progressive supranuclear palsy (PSP) and Parkinson's disease (PD) are difficult to differentiate especially in the early stages. We aimed to investigate the diagnostic performance of the magnetic resonance parkinsonism index (MRPI) in differentiating PSP from PD. A systematic literature search of PubMed-MEDLINE and EMBASE was performed to identify original articles evaluating the diagnostic performance of the MRPI in differentiating PSP from PD published up to 20 February 2021. The pooled sensitivity, specificity, and 95% CI were calculated using the bivariate random-effects model. The area under the curve (AUC) was calculated using a hierarchical summary receiver operating characteristic (HSROC) model. Meta-regression was performed to explain the effects of heterogeneity. A total of 14 original articles involving 484 PSP patients and 1243 PD patients were included. In all studies, T1-weighted images were used to calculate the MRPI. Among the 14 studies, nine studies used 3D T1-weighted images. The pooled sensitivity and specificity for the diagnostic performance of the MRPI in differentiating PSP from PD were 96% (95% CI, 87-99%) and 98% (95% CI, 91-100%), respectively. The area under the HSROC curve was 0.99 (95% CI, 0.98-1.00). Heterogeneity was present (sensitivity: I² = 97.29%; specificity: I² = 98.82%). Meta-regression showed the association of the magnet field strength with heterogeneity. Studies using 3 T MRI showed significantly higher sensitivity (100%) and specificity (100%) than those of studies using 1.5 T MRI (sensitivity of 98% and specificity of 97%) (p < 0.01). Thus, the MRPI could accurately differentiate PSP from PD and support the implementation of appropriate management strategies for patients with PSP.

Accuracy of Magnetic Resonance Parkinsonism Index in Differentiating Progressive Supranuclear Palsy from Parkinson's Disease among South Indian Population: A Retrospective Case Control Study

Context  Progressive supranuclear palsy (PSP) is a neurodegenerative disorder which comes under Parkinsonism plus syndrome. As this spectrum of disease has many overlapping clinical as well as imaging findings, some quantitative parameters like magnetic resonance Parkinsonism index and midbrain/pons ratio are useful to differentiate PSP from other PD patients.

Aims  The study aimed to detect sensitivity and specificity of magnetic resonance Parkinsonism index in differentiating PSP from PD.

Settings and Design  It was a retrospective case–control study conducted in Sri Manankula Vinayagar Medical College, Puducherry, during the period of January 2018 to June 2019.

Materials and Methods  The 87 subjects, who were diagnosed and grouped into three categories (PSP, PD, and control) after performing magnetic resonance imaging brain, were reviewed. The parameters like the area of Pons and midbrain, width of MCP and SCP, P/M, M/P, and MRPI were calculated.

Statistical Analysis  One-way ANOVA and Chi-square test was used. The sensitivity, specificity, diagnostic accuracy, and cut-off values obtained with receiver operating characteristic curve analysis were determined.

Results  The mean age of presentation was approximately 75 years with male predominance. The cut-off value of MRPI obtained in this study was 13.4 with 100% sensitivity and specificity. Even though M/P ratio was found to be statistically significant among PSP patients; cut-off value was not obtained.

Conclusion  MRPI was concluded as the better tool in diagnosing PSP compared with the M/P ratio. Hence the combined qualitative as well as quantitative measurement of MRPI will increase the diagnostic accuracy of PSP.

Differentiating PSP from MSA using MR planimetric measurements: a systematic review and meta-analysis

Differential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology. Quantitative MR planimetric measurements were reported to discriminate between progressive supranuclear palsy (PSP) and non-PSP-parkinsonism. Several studies have used midbrain to pons ratio (M/P) and the Magnetic Resonance Parkinsonism Index (MRPI) in distinguishing PSP patients from those with Parkinson's disease. The current meta-analysis aimed to compare the performance of these measures in discriminating PSP from multiple system atrophy (MSA). A systematic MEDLINE review identified 59 out of 2984 studies allowing a calculation of sensitivity and specificity using the MRPI or M/P. Meta-analyses of results were carried out using random effects modelling. To assess study quality and risk of bias, the QUADAS-2 tool was used. Eight studies were suitable for analysis. The meta-analysis showed a pooled sensitivity and specificity for the MRPI of PSP versus MSA of 79.2% (95% CI 72.7-84.4%) and 91.2% (95% CI 79.5-96.5%), and 84.1% (95% CI 77.2-89.2%) and 89.2% (95% CI 81.8-93.8%), respectively, for the M/P. The QUADAS-2 toolbox revealed a high risk of bias regarding the methodological quality of patient selection and index test, as all patients were seen in a specialized outpatient department without avoiding case control design and no predefined threshold was given regarding MRPI or M/P cut-offs. Planimetric brainstem measurements, in special the MRPI and M/P, yield high diagnostic accuracy for the discrimination of PSP from MSA. However, there is an urgent need for well-designed, prospective validation studies to ameliorate the concerns regarding the risk of bias.

Magnetic resonance parkinsonism indices and interpeduncular angle in idiopathic normal pressure hydrocephalus and progressive supranuclear palsy

Purpose

The clinical presentation of idiopathic normal pressure hydrocephalus (iNPH) may overlap with progressive supranuclear palsy (PSP). The Magnetic Resonance Parkinsonism Index (MRPI), MRPI 2.0, and the interpeduncular angle (IPA) have been investigated to differentiate PSP from healthy controls (HC) and other parkinsonisms. We aimed to assess equivalences and differences in MRPI, MRPI 2.0, and IPA in iNPH, PSP, and HC groups.

Methods

We retrospectively recruited 99 subjects (30 iNPH, 32 PSP, 37 HC) from two institutions. MRI exams, acquired on either 1.5 T or 3 T scanners, included 3D T1-weighted images to measure MRPI, MRPI 2.0, and IPA. Inter- and intra-rater reliability was investigated with the intra-class correlation coefficient (ICC), and the two one-sided t tests (TOST) procedure was used to assess these markers in iNPH, PSP, and HC.

Results

For all the three measures, intra-rater and inter-rater ICC were excellent (range = 0.91–0.93).

In the comparison of iNPH and PSP with HC, differences for MRPI and MRPI 2.0 (p < 0.01 in all cases) and no equivalence (p = 1.00 in all cases) were found at TOST. iNPH and PSP MRPI showed no difference (p = 0.06) and no equivalence (p = 0.08). MRPI 2.0 was not equivalent (p = 0.06) and not different (p = 0.09) in the same two populations. PSP and HC IPA proved equivalent (p < 0.01) while iNPH IPA was different (p < 0.01) and not equivalent (p = 0.96 and 0.82) from both PSP and HC.

Conclusion

MRPI and MRPI 2.0 significantly overlap in iNPH and PSP, with risk of misdiagnosis, and for this reason may not be helpful in the differential diagnosis.

Electronic supplementary material

The online version of this article (10.1007/s00234-020-02500-1) contains supplementary material, which is available to authorized users.

Automated MRI Classification in Progressive Supranuclear Palsy: A Large International Cohort Study

BACKGROUND

The Magnetic Resonance Parkinsonism Index is listed as one of the most reliable imaging morphometric markers for diagnosis of progressive supranuclear palsy (PSP). However, the use of this index in diagnostic workup has been limited until now by the low generalizability of published results because of small monocentric patient cohorts, the lack of data validation in independent patient series, and manual measurements used for index calculation. The objectives of this study were to investigate the generalizability of Magnetic Resonance Parkinsonism Index performance validating previously established cutoff values in a large international cohort of PSP patients subclassified into PSP-Richardson's syndrome and PSP-parkinsonism and to standardize the use of the automated Magnetic Resonance Parkinsonism Index by providing a web-based platform to obtain homogenous measures around the world.

METHODS

In a retrospective international multicenter study, a total of 173 PSP patients and 483 non-PSP participants were enrolled. A web-based platform (https://mrpi.unicz.it) was used to calculate automated Magnetic Resonance Parkinsonism Index values.

RESULTS

Magnetic Resonance Parkinsonism Index values showed optimal performance in differentiating PSP-Richardson's syndrome and PSP-parkinsonism patients from non-PSP participants (93.6% and 86.5% of accuracy, respectively). The Magnetic Resonance Parkinsonism Index was also able to differentiate PSP-Richardson's syndrome and PSP-parkinsonism patients in an early stage of the disease from non-PSP participants (90.1% and 85.9%, respectively). The web-based platform provided the automated Magnetic Resonance Parkinsonism Index calculation in 94% of cases.

CONCLUSIONS

Our study provides the first evidence on the generalizability of automated Magnetic Resonance Parkinsonism Index measures in a large international cohort of PSP-Richardson's syndrome and PSP-parkinsonism patients. The web-based platform enables widespread applicability of the automated Magnetic Resonance Parkinsonism Index to different clinical and research settings. © 2020 International Parkinson and Movement Disorder Society.

Multiple system atrophy

Multiple system atrophy (MSA) is a sporadic, adult-onset, relentlessly progressive neurodegenerative disorder, clinically characterized by various combinations of autonomic failure, parkinsonism and ataxia. The neuropathological hallmark of MSA are glial cytoplasmic inclusions consisting of misfolded α-synuclein. Selective atrophy and neuronal loss in striatonigral and olivopontocerebellar systems underlie the division into two main motor phenotypes of MSA-parkinsonian type and MSA-cerebellar type. Isolated autonomic failure and REM sleep behavior disorder are common premotor features of MSA. Beyond the core clinical symptoms, MSA manifests with a number of non-motor and motor features. Red flags highly specific for MSA may provide clues for a correct diagnosis, but in general the diagnostic accuracy of the second consensus criteria is suboptimal, particularly in early disease stages. In this chapter, the authors discuss the historical milestones, etiopathogenesis, neuropathological findings, clinical features, red flags, differential diagnosis, diagnostic criteria, imaging and other biomarkers, current treatment, unmet needs and future treatments for MSA.

Progressive supranuclear palsy

Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized pathologically by 4 repeat tau deposition in various cell types and anatomical regions. Richardson's syndrome (RS) is the initially described and one of the clinical phenotypes associated with PSP pathology, characterized by vertical supranuclear gaze paly in particular downwards, postural instability with early falls and subcortical frontal dementia. PSP can manifest as several other clinical phenotypes, including PSP-parkinsonism, -pure akinesia with gait freezing, -frontotemporal dementia, - corticobasal syndrome, - speech/language impairment. RS can also have a pathologic diagnosis other than PSP, including corticobasal degeneration, FTD-TDP-43 and others. New clinical diagnostic criteria take into account this phenotypic variability in an attempt to diagnose the disease earlier, given the current lack of a validated biomarker. At present, therapeutic options for PSP are symptomatic and insufficient. Recent large neuroprotective trials have failed to provide a positive clinical outcome, however, have led to the design of better studies that are ongoing and hold promise for a neuroprotective treatment for PSP.