Quantitative muscle MRI combined with AI-based segmentation as a follow-up biomarker for ATTRv patients: A longitudinal pilot study.

BACKGROUND AND OBJECTIVES

Intramuscular fat fraction (FF), assessed using quantitative MRI (qMRI), has emerged as a promising biomarker for hereditary transthyretin amyloidosis (ATTRv) patients. Currently, the main drawbacks to its use in future therapeutic trials are its sensitivity to change over a short period of time and the time-consuming manual segmentation step to extract quantitative data. This pilot study aimed to demonstrate the suitability of an Artificial Intelligence-based (AI) segmentation technique to assess disease progression in a real-life cohort of ATTRv patients over 1 year.

METHODS

Fifteen ATTRv patients were included in this monocentric, observational, prospective study. FF, magnetization transfer ratio (MTR), and quantitative T2 were extracted from patients' lower limb qMRI at two time points, 1 year apart, at thigh and leg levels. qMRI parameters were correlated with clinical and electrophysiological parameters assessed at the same time.

RESULTS

Global FF at leg level significantly progressed over 1 year: +1.28 ± 2.62% (p = 0.017). At thigh level, no significant change in global FF, MTR, or T2 was measured. The leg FF was strongly correlated with the main clinical and electrophysiological scores.

CONCLUSION

AI-based CNN network segmentation combined with qMRI can be used to obtain quantitative metrics for longitudinal studies in ATTRv patients. Global FF at the leg level seems to be the most sensitive MRI biomarker to track disease progression in a 1-year period. Larger studies with treatment-specific groups will now be necessary to determine the place of qMRI markers compared to the current clinical and electrophysiological scores.