Share Email Print
cover

Proceedings Paper

Prognostic power of the human psoas muscles FDG metabolism in amyotrophic lateral sclerosis
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In Amyotrophic Lateral Sclerosis (ALS) spinal cord (SC) showed a moderate increase in FDG uptake with respect to healthy subjects. The main aim of our study is to integrate the information concerning the divergent behavior of SC with skeletal muscle metabolism improving the informative potential of 18F-fluoro-2-deoxy-glucose (FDG) PET/CT imaging regarding specific pathophysiological mechanisms underlying ALS progression. We analyzed 50 ALS patients with spinal onset consecutively submitted to FDG PET/CT imaging. Obtained data were compared to the corresponding findings in 36 age and sex-matched controls. A computational method was used to extract psoas volume and attenuation coefficient from CT images. Psoas volume was normalized for patient ideal body weight (IBW). In co-registered PET images, FDG accumulation was defined by average normalized standardized uptake value (N-SUV). Average Hounsfield values (AVH) in the psoas were similar in patients and controls (39±8 AHV vs 39±11 AHV, respectively, p=ns). By contrast, ALS was associated with a significant reduction in psoas volume normalized for IBW (8.8±2.9 mL/Kg IBW vs 10.3±2.7 mL/Kg IBW, respectively, p<0.05). More interestingly, N-SUV was significantly higher in patients than in controls (0.44±0.19 vs 0.29±0.09; p<0.001). These SUV values predicted overall survival rate at Kaplan-Meyer analysis (p<0.05) with a predictive power that was confirmed by univariate as well as by multivariate Cox analysis (p<0.02). ALS is therefore associated with a psoas reduction in volume and increase in FDG uptake. The intensity of FDG uptake within this muscular district is related to disease aggressiveness.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141Z (16 March 2020); doi: 10.1117/12.2548857
Show Author Affiliations
Rita Lai, Univ. delgi Studi di Genova (Italy)
Daniela Schenone, Univ. degli Studi di Genova (Italy)
Gianmario Sambuceti, IRCCS Ospedale Policlinico San Martino Genova (Italy)
Univ. delgi Studi di Genova (Italy)
Anna Maria Massone, Univ. degli Studi di Genova (Italy)
Istituto Superconduttori, Materiali Innovativi e Dispositivi, CNR (Italy)
Cristina Campi, Univ. di Padova (Italy)
Adriano Chiò, Univ. degli Studi di Torino (Italy)
AUO Città della Salute e della Scienza Torino (Italy)
Claudia Caponnetto, Univ. degli Studi di Torino (Italy)
Angelina Cistaro, PET Ctr., IRMET SpA Affidea Torino (Italy)
Matteo Bauckneht, Univ. degli Studi di Genova (Italy)
Vanessa Cossu, Univ. degli Studi di Genova (Italy)
Silvia Morbelli, IRCCS Ospedale Policlinico San Martino (Italy)
Cecilia Marini, Istituto di Bioimmagini e Fisiologia Molecolare, CNR (Italy)
IRCCS Ospedale Policlinico San Martino (Italy)
Michele Piana, Univ. degli Studi di Genova (Italy)
Istituto Superconduttori, Materiali Innovativi e Dispositivi, CNR (Italy)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray