Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis.
<h4>Aims</h4>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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317741 |
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| author | In-Chang Hwang Eun Ju Chun Pan Ki Kim Myeongju Kim Jiesuck Park Hong-Mi Choi Yeonyee E Yoon Goo-Yeong Cho Byoung Wook Choi |
| author_facet | In-Chang Hwang Eun Ju Chun Pan Ki Kim Myeongju Kim Jiesuck Park Hong-Mi Choi Yeonyee E Yoon Goo-Yeong Cho Byoung Wook Choi |
| author_sort | In-Chang Hwang |
| collection | DOAJ |
| description | <h4>Aims</h4>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.<h4>Methods and results</h4>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).<h4>Conclusions</h4>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. |
| format | Article |
| id | doaj-art-42008416f85e44df94cbd1c32f5f7f10 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-42008416f85e44df94cbd1c32f5f7f102025-08-20T03:52:16ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031774110.1371/journal.pone.0317741Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis.In-Chang HwangEun Ju ChunPan Ki KimMyeongju KimJiesuck ParkHong-Mi ChoiYeonyee E YoonGoo-Yeong ChoByoung Wook Choi<h4>Aims</h4>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.<h4>Methods and results</h4>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).<h4>Conclusions</h4>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.https://doi.org/10.1371/journal.pone.0317741 |
| spellingShingle | In-Chang Hwang Eun Ju Chun Pan Ki Kim Myeongju Kim Jiesuck Park Hong-Mi Choi Yeonyee E Yoon Goo-Yeong Cho Byoung Wook Choi Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis. PLoS ONE |
| title | Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis. |
| title_full | Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis. |
| title_fullStr | Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis. |
| title_full_unstemmed | Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis. |
| title_short | Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis. |
| title_sort | automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light chain cardiac amyloidosis |
| url | https://doi.org/10.1371/journal.pone.0317741 |
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