Feature selection from magnetic resonance imaging data in ALS: a systematic review
TD Kocar, HP Mueller, AC Ludolph… - … advances in chronic …, 2021 - journals.sagepub.com
TD Kocar, HP Mueller, AC Ludolph, J Kassubek
Therapeutic advances in chronic disease, 2021•journals.sagepub.comBackground: 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 …
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 …
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.
Sage Journals以上显示的是最相近的搜索结果。 查看全部搜索结果