Back

Chronic lesion expansion persists in secondary progressive multiple sclerosis and is associated with neurodegeneration

Klistorner, S.; Barnett, M.; Klistorner, A.

2026-09-25 neurology
10.64898/2026.09.23.26363839 medRxiv
Show abstract

Background: Secondary progressive multiple sclerosis (SPMS) is traditionally viewed as predominantly neurodegenerative, with limited inflammatory activity. However, chronic active lesions characterised by persistent rim inflammation and gradual expansion may continue to contribute to tissue injury. Objectives: To characterise chronic lesion dynamics in SPMS and determine whether chronic lesion expansion contributes to tissue damage and neurodegeneration. Thirty-one patients with SPMS underwent longitudinal MRI including T1-weighted, FLAIR and diffusion imaging. White matter lesions were manually segmented and chronic lesion tissue expansion (CLTE) was quantified as annualised percentage change. Associations between lesion expansion, mean diffusivity (MD) changes and brain atrophy were assessed using correlation, regression and mixed-effects modelling. Results: CLTE remained prominent, with a mean annual expansion rate of 7.1 +- 4.1%. Expansion substantially exceeded the contribution of newly formed lesions and occurred at rates comparable to relapsing multiple sclerosis. CLTE demonstrated moderate intra-patient clustering (ICC = 0.23). MD increased significantly within lesions during follow-up and was associated with lesion expansion. Chronic lesion expansion was also strongly associated with ventricular enlargement (partial r = 0.72, p < 0.001). Conclusions: Chronic lesion expansion persists in SPMS and represents a major source of tissue injury associated with microstructural degeneration and brain atrophy.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.