Bibliographic Details
| Title: |
Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis. |
| Authors: |
Hwang, In-Chang1,2 (AUTHOR) inchang.hwang@gmail.com, Chun, Eun Ju3,4 (AUTHOR) humandr@snubh.org, Kim, Pan Ki5 (AUTHOR), Kim, Myeongju6 (AUTHOR), Park, Jiesuck1,2 (AUTHOR), Choi, Hong-Mi1,2 (AUTHOR), Yoon, Yeonyee E.1,2 (AUTHOR), Cho, Goo-Yeong1,2 (AUTHOR), Choi, Byoung Wook5,7 (AUTHOR) |
| Source: |
PLoS ONE. 1/22/2025, Vol. 20 Issue 1, p1-16. 16p. |
| Subject Terms: |
*MACHINE learning, *LEFT ventricular hypertrophy, *CARDIAC magnetic resonance imaging, *CARDIAC amyloidosis, *ANGIOKERATOMA corporis diffusum |
| Abstract: |
Aims: T1 mapping on cardiac magnetic resonance (CMR) imaging is useful for diagnosis and prognostication in patients with light-chain cardiac amyloidosis (AL-CA). We conducted this study to evaluate the performance of T1 mapping parameters, derived from artificial intelligence (AI)-automated segmentation, for detection of cardiac amyloidosis (CA) in patients with left ventricular hypertrophy (LVH) and their prognostic values in patients with AL-CA. Methods and results: A total of 300 consecutive patients who underwent CMR for differential diagnosis of LVH were analyzed. CA was confirmed in 50 patients (39 with AL-CA and 11 with transthyretin amyloidosis), hypertrophic cardiomyopathy in 198, hypertensive heart disease in 47, and Fabry disease in 5. A semi-automated deep learning algorithm (Myomics-Q) was used for the analysis of the CMR images. The optimal cutoff extracellular volume fraction (ECV) for the differentiation of CA from other etiologies was 33.6% (diagnostic accuracy 85.6%). The automated ECV measurement showed a significant prognostic value for a composite of cardiovascular death and heart failure hospitalization in patients with AL-CA (revised Mayo stage III or IV) (adjusted hazard ratio 4.247 for ECV ≥40%, 95% confidence interval 1.215–14.851, p-value = 0.024). Incorporation of automated ECV measurement into the revised Mayo staging system resulted in better risk stratification (integrated discrimination index 27.9%, p = 0.013; categorical net reclassification index 13.8%, p = 0.007). Conclusions: T1 mapping on CMR imaging, derived from AI-automated segmentation, not only allows for improved diagnosis of CA from other etiologies of LVH, but also provides significant prognostic value in patients with AL-CA. [ABSTRACT FROM AUTHOR] |
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