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Utility of non-invasive MRI assessment of tenosynovial amyloidosis for early detection of cardiac amyloidosis: UNRAVEL, a pilot study

2025· article· en· W7127919055 on OpenAlexaff
M Saldanha, Tom Solomon, A S Zenses, A Aldajani, François Tournoux, Michael Chetrit

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsMcGill University Health CentreOttawa Heart InstituteCentre Hospitalier de l’Université de MontréalMcGill University
Fundersnot available
KeywordsCarpal tunnel syndromeCarpal tunnelRetinaculumMedian nerveMagnetic resonance imagingWristAmyloidosisTransthyretin

Abstract

fetched live from OpenAlex

Abstract Background Amyloidosis is a multisystem disorder affecting the heart, peripheral nerves, and other organs. Transthyretin (ATTR) and Light-Chain (AL) amyloidosis account for most Cardiac amyloidosis (CA) cases, with ATTR being more prevalent. Recent treatments slow progression and reduce hospitalizations, but prognosis remains poor, underscoring the need for early detection and intervention. Carpal tunnel syndrome (CTS) often precedes cardiac symptoms by 5–10 years, offering a window for early detection, especially in the setting of carpal tunnel release surgery. This study explored the ability of magnetic resonance imaging (MRI) to detect amyloid deposits within the carpal tunnel as a non-invasive alternative to biopsy. We hypothesized that MRI can detect amyloid-related changes, distinguishing amyloid-positive from amyloid-negative patients. Methods This cross-sectional pilot study involving 12 subjects (50% female, mean age 70 ± 7.2 years; 6 amyloid-positive, 6 amyloid-negative) with moderate-to-severe CTS post-carpal tunnel release, biopsy, and laser microdissection mass spectrometry. Subjects with biopsy-confirmed amyloid-positive or amyloid-negative status were recruited for wrist MRI. MRI sequences included T1-weighted, proton density (PD), and magnetization transfer ratio (MTR), reviewed by a blinded musculoskeletal radiologist. Results T1 and PD imaging revealed increased thickening and cross-sectional area (CSA) in the amyloid-positive group, particularly in the flexor tendons, tenosynovium, flexor retinaculum, and median nerve within the carpal tunnel. Retinaculum thickness was 44% higher in the amyloid-positive group than in the amyloid-negative group (0.23 ± 0.03 cm vs. 0.16 ± 0.04 cm, p = 0.007), suggesting fibrosis as a potential imaging biomarker of amyloid-related tissue remodeling. Carpal tunnel and median nerve enlargement were observed, with third space CSA increasing by 36% in the amyloid-positive group (1.05 ± 0.16 cm² vs. 0.77 ± 0.16 cm², p = 0.004). Selective tendon involvement included a 26% reduction in flexor pollicis CSA (0.17 ± 0.06 cm² vs. 0.23 ± 0.04 cm², p = 0.03), and enlargement of both the flexor digitorum profundus and superficialis were observed. MTR was 118% higher in tendons of amyloid-positive patients (0.81 ± 0.38 vs. 0.37 ± 0.06, p = 0.019), suggesting increased macromolecular composition due to amyloid fibrils, and was also elevated in other structures. Conclusion This study evaluated the use of non-invasive wrist MRI for assessing amyloid deposition in the carpal tunnel of amyloid-positive patients. Our findings suggest a trend toward larger carpal tunnel structures in amyloid-positive patients and selective tissue involvement supported by the presence of macromolecular changes, suggesting the potential for MRI-based noninvasive identification of amyloidosis. Validation studies to refine the methodology in a larger sample size are ongoing.Proton-Density MRI of Amyloid CTS

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.322
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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