A susceptibility-weighted imaging qualitative score of the motor cortex may be a useful tool for distinguishing clinical phenotypes in amyotrophic lateral sclerosis

Potential of neuroimaging as a biomarker in amyotrophic lateral sclerosis: from structure to metabolism

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by motor neuron degeneration. The development of ALS involves metabolite alterations leading to tissue lesions in the nervous system. Recent advances in neuroimaging have significantly improved our understanding of the underlying pathophysiology of ALS, with findings supporting the corticoefferent axonal disease progression theory. Current studies on neuroimaging in ALS have demonstrated inconsistencies, which may be due to small sample sizes, insufficient statistical power, overinterpretation of findings, and the inherent heterogeneity of ALS. Deriving meaningful conclusions solely from individual imaging metrics in ALS studies remains challenging, and integrating multimodal imaging techniques shows promise for detecting valuable ALS biomarkers. In addition to giving an overview of the principles and techniques of different neuroimaging modalities, this review describes the potential of neuroimaging biomarkers in the diagnosis and prognostication of ALS. We provide an insight into the underlying pathology, highlighting the need for standardized protocols and multicenter collaborations to advance ALS research.

Amyotrophic lateral sclerosis with upper motor neuron predominance: diagnostic accuracy of qualitative and quantitative susceptibility metrics in the precentral gyrus

OBJECTIVE

The study aims at comparing the diagnostic accuracy of qualitative and quantitative assessment of the susceptibility in the precentral gyrus in detecting amyotrophic lateral sclerosis (ALS) with predominance of upper motor neuron (UMN) impairment.

METHODS

We retrospectively collected clinical and 3T MRI data of 47 ALS patients, of whom 12 with UMN predominance (UMN-ALS). We further enrolled 23 healthy controls (HC) and 15 ALS Mimics (ALS-Mim). The Motor Cortex Susceptibility (MCS) score was qualitatively assessed on the susceptibility-weighted images (SWI) and automatic metrics were extracted from the quantitative susceptibility mapping (QSM) in the precentral gyrus. MCS scores and QSM-based metrics were tested for correlation, and ROC analyses.

RESULTS

The correlation of MCS score and susceptibility skewness was significant (Rho = 0.55, p < 0.001). The susceptibility SD showed an AUC of 0.809 with a specificity and positive predictive value of 100% in differentiating ALS and ALS Mim versus HC, significantly higher than MCS (Z = -3.384, p-value = 0.00071). The susceptibility skewness value of -0.017 showed specificity of 92.3% and predictive positive value of 91.7% in differentiating UMN-ALS versus ALS mimics, even if the performance was not significantly better than MCS (Z = 0.81, p = 0.21).

CONCLUSION

The MCS and susceptibility skewness of the precentral gyrus show high diagnostic accuracy in differentiating UMN-ALS from ALS-mimics subjects. The quantitative assessment might be preferred being an automatic measure unbiased by the reader.

CLINICAL RELEVANCE STATEMENT

The clinical diagnostic evaluation of ALS patients might benefit from the qualitative and/or quantitative assessment of the susceptibility in the precentral gyrus as imaging marker of upper motor neuron predominance.

KEY POINTS

• Amyotrophic lateral sclerosis diagnostic work-up lacks biomarkers able to identify upper motor neuron involvement. • Susceptibility-weighted imaging/quantitative susceptibility mapping-based measures showed good diagnostic accuracy in discriminating amyotrophic lateral sclerosis with predominant upper motor neuron impairment from patients with suspected motor neuron disorder. • Susceptibility-weighted imaging/quantitative susceptibility mapping-based assessment of the magnetic susceptibility provides a diagnostic marker for amyotrophic lateral sclerosis with upper motor neuron predominance.

Iron quantitative analysis of motor combined with bulbar region in M1 cortex may improve diagnosis performance in ALS

OBJECTIVES

To explore whether the combined analysis of motor and bulbar region of M1 on susceptibility-weighted imaging (SWI) can be a valid biomarker for amyotrophic lateral sclerosis (ALS).

METHODS

Thirty-two non-demented ALS patients and 35 age- and gender-matched healthy controls (HC) were retrospectively recruited. SWI and 3D-T1-MPRAGE images were obtained from all individuals using a 3.0-T MRI scan. The bilateral posterior band of M1 was manually delineated by three neuroradiologists on phase images and subdivided into the motor and bulbar regions. We compared the phase values in two groups and performed a stratification analysis (ALSFRS-R score, duration, disease progression rate, and onset). Receiver operating characteristic (ROC) curves were also constructed.

RESULTS

ALS group showed significantly increased phase values in M1 and the two subregions than the HC group, on the all and elderly level (p < 0.001, respectively). On all-age level comparison, negative correlations were found between phase values of M1 and clinical score and duration (p < 0.05, respectively). Similar associations were found in the motor region (p < 0.05, respectively). On both the total (p < 0.01) and elderly (p < 0.05) levels, there were positive relationships between disease progression rate and M1 phase values. In comparing ROC curves, the entire M1 showed the best diagnostic performance.

CONCLUSIONS

Combining motor and bulbar analyses as an integral M1 region on SWI can improve ALS diagnosis performance, especially in the elderly. The phase value could be a valuable biomarker for ALS evaluation.

KEY POINTS

• Integrated analysis of the motor and bulbar as an entire M1 region on SWI can improve the diagnosis performance in ALS. • Quantitative analysis of iron deposition by SWI measurement helps the clinical evaluation, especially for the elderly patients. • Phase value, when combined with the disease progression rate, could be a valuable biomarker for ALS.

Editorial Comment: Iron-sensitive MR imaging of the primary motor cortex to differentiate hereditary spastic paraplegia from other motor neuron diseases

KEY POINTS

• Conventional and advanced MR techniques may aid in the diagnosis of motor neuron disease.• Iron-sensitive MR imaging of the primary motor cortex may reveal changes to help differentiate hereditary spastic paraplegia (HSP) from UMM predominant amyotrophic lateral sclerosis (UMN-ALS) and primary lateral sclerosis (PLS).• Additional research in this area is necessary.

Diagnostic Value of Magnetic Resonance Susceptibility-Weighted Imaging Scanning in Different Types of Early Prostate Cancer

To investigate the cost of MRI-sensitive imaging (SWI) for early-stage prostate cancer. In 2019, the research group included a total of 60 leukemia patients, all of whom were diagnosed with prostate-specific antigen (PSA). According to the range of PSA values, they were group A (18 cases), group A 0-44 mg/ml (18 cases), and group B 4-1010 mg/ml (26 cases). 10 mg/ml was divided into C group (16 cases). Another 60 patients with benign prostatic hyperplasia treated at the same time served as a control group. All patients underwent sensitive MRI scanning, followed by diagnostic and clinical evaluation of weighted MRI scanning to diagnose various types of prostate cancer. The results showed that there was no difference in Ve levels among the three groups (P > 0.05); the SUSE score and Ktrans and Kep levels of the patients in group C were higher in groups B, A, and A (P < 0.05). In patients with early leukemia, SUSE score was significantly correlated with Ktrans and Kep levels (P < 0.05), but not with Ve and P > 0.05 levels. Magnetic resonance imaging can be used to diagnose prostate cancer. It can differentiate and diagnose different types of prostate cancer early. This is important for evaluating the benefits of prostate cancer screening and treatment.

Feature selection from magnetic resonance imaging data in ALS: a systematic review

Background:

With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection.

Methods:

We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported.

Results:

Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS.

Conclusions:

These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.