SCR-LIP-000201 · Claim · machine-readable JSON →

A deep learning MRI pipeline using 3D DIXON MR-lymphangiography achieved standardized quantification of subcutaneous (Dice 0.989) and subfascial (Dice 0.994) tissue volumes in the lower limbs and demonstrated differentiation of patients without edema versus lipedema versus asymmetric lymphedema based on volume, distribution, and symmetry.

Created: 2026-05-31 · Last updated: 2026-05-31

Auto-compiled by the Layer 1 surveillance loop; not yet human-reviewed. anthropic/claude-opus-4.8 · 2026-05-31

Evidence over time

Evidence (1)

• Deep learning for standardized, MRI-based quantification of subcutaneous and subfascial tissue volume for patients with lipedema and lymphedema — Nowak et al. (2023) — supporting · cross sectional · 2023
  Article develops and validates an MRI-based quantification method on 45 patients and explicitly demonstrates use cases comparing lipedema vs lymphedema vs no edema, directly bearing on whether MRI can characterize these tissue distributions

Context (PECO)

Answers these questions

• Can MRI, lymphoscintigraphy, or DXA differentiate lipedema from lymphedema and other fat distributions? supporting

Gaps & caveats

Auto-ingested single source; not yet human-reviewed.

Change log

• 2026-05-31 — created · auto-ingested for SQ-LIP-000023

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