PET Scans Distinguish ALS Patients from Healthy Controls

An effective ALS biomarker for assessing disease progression could potentially shorten the duration of clinical trials and significantly cut their cost. One such promising approach, called electrical impedance myography, was developed by Seward Rutkove, who was awarded Prize4Life’s $1M ALS Biomarker Prize for his work (see http://www.prize4life.org/page/prizes/biomarker_prize). A new study, published online on March 10 in JAMA Neurology, describes a different approach based on 18Fluorodeoxyglucose-positron-emission tomography (FDG-PET). FDG-PET is able to distinguish between ALS patients and healthy controls based on differences in glucose metabolism in various areas of the brain. Interestingly, C9orf72-positive ALS patients exhibit metabolic patterns that are distinctive from C9orf72-negative cases. The research team, led by Philip van Damme at the University Hospital Leuven, Belgium, analyzed scans from 81 patients and 20 controls using machine learning methods, and correctly identified 95% of the ALS patients. Future plans include continuing to validate this approach and testing its diagnostic potential in early stage ALS patients.

An effective ALS biomarker for assessing disease progression could potentially shorten the duration of clinical trials and significantly cut their cost. One such promising approach, called electrical impedance myography, was developed by Seward Rutkove, who was awarded Prize4Life’s $1M ALS Biomarker Prize for his work (see http://www.prize4life.org/page/prizes/biomarker_prize). A new study, published online on March 10 in JAMA Neurology, describes a different approach based on 18Fluorodeoxyglucose-positron-emission tomography (FDG-PET). FDG-PET is able to distinguish between ALS patients and healthy controls based on differences in glucose metabolism in various areas of the brain. Interestingly, C9orf72-positive ALS patients exhibit metabolic patterns that are distinctive from C9orf72-negative cases. The research team, led by Philip van Damme at the University Hospital Leuven, Belgium, analyzed scans from 81 patients and 20 controls using machine learning methods, and correctly identified 95% of the ALS patients. Future plans include continuing to validate this approach and testing its diagnostic potential in early stage ALS patients.

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