Typical and atypical imaging features of cardiac amyloidosis

Antonopoulos, Alexios S; Almogheer, Batool; Azzu, Alessia; Alati, E; Papagkikas, Panagiotis; Cheong, Jun; Clague, Jonathan; Wechalekar, Kshama; Baksi, John; Alpendurada, Francisco

doi:10.1016/j.hjc.2020.08.008

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…tafamidis and patisiran) on the natural course of disease. Despite advances in non-invasive diagnostic tools for cardiac amyloidosis, 159 mainly the clinical use of 99m Tc-PYP or 99m Tc-DPD bone scintigraphy, 59,118,160 timely diagnosis of ATTR cardiomyopathy is still challenging and often delayed by several years. 161 While a diagnostic approach based on the presence of red flags (i.e.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and clinical outcomes of transthyretin amyloidosis: a systematic review and meta‐analysis

Antonopoulos

Panagiotopoulos

Kouroutzoglou

et al. 2022

European J of Heart Fail

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Section: Discussionmentioning

confidence: 99%

Prevalence and clinical outcomes of transthyretin amyloidosis: a systematic review and meta‐analysis

Antonopoulos

Panagiotopoulos

Kouroutzoglou

et al. 2022

European J of Heart Fail

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“…These include physiologic measurements for mapping myocardial perfusion and blood volume, as well as relaxometry T1, T2, and T2* measures for the assessment of fibrosis, oedema, and iron as well as for assessment of tissue composition for the fat fraction [ 71 ]. A variety of disease states, such as diffuse interstitial or replacement fibrosis, oedema, or infiltrative diseases, can benefit from myocardial T1 mapping [ 72 ].…”

Section: Diagnosis Of Amyloidosismentioning

confidence: 99%

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Allegra

Mirabile

Tonacci

et al. 2023

IJMS

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Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.

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“…Cardiac magnetic resonance (CMR) is considered the state-of-the-art imaging approach for assessing myocardial disease 1 , allowing tissue characterization and fibrosis detection by late gadolinium enhancement (LGE) 2 . Myocardial T1 mapping is also helpful across a spectrum of disease conditions, including diffuse interstitial or replacement fibrosis, water, or infiltrative disorders 2 , 3 . However, a major problem with T1 mapping is that there is a significant overlap in myocardial native T1 values between health and disease; thus, while in-patient regional or even temporal variations in native T1 may be informative of disease development or progression, between-patient comparisons are less useful.…”

Section: Introductionmentioning

confidence: 99%

Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes

Antonopoulos

Boutsikou²,

Simantiris

et al. 2021

Sci Rep

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We explored whether radiomic features from T1 maps by cardiac magnetic resonance (CMR) could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes. A total of 149 patients (n = 30 with no heart disease, n = 30 with LVH, n = 61 with hypertrophic cardiomyopathy (HCM) and n = 28 with cardiac amyloidosis) undergoing a CMR scan were included in this study. We extracted a total of 850 radiomic features and explored their value in disease classification. We applied principal component analysis and unsupervised clustering in exploratory analysis, and then machine learning for feature selection of the best radiomic features that maximized the diagnostic value for cardiac disease classification. The first three principal components of the T1 radiomics were distinctively correlated with cardiac disease type. Unsupervised hierarchical clustering of the population by myocardial T1 radiomics was significantly associated with myocardial disease type (chi2 = 55.98, p < 0.0001). After feature selection, internal validation and external testing, a model of T1 radiomics had good diagnostic performance (AUC 0.753) for multinomial classification of disease phenotype (normal vs. LVH vs. HCM vs. cardiac amyloid). A subset of six radiomic features outperformed mean native T1 values for classification between myocardial health vs. disease and HCM phenocopies (AUC of T1 vs. radiomics model, for normal: 0.549 vs. 0.888; for LVH: 0.645 vs. 0.790; for HCM 0.541 vs. 0.638; and for cardiac amyloid 0.769 vs. 0.840). We show that myocardial texture assessed by native T1 maps is linked to features of cardiac disease. Myocardial radiomic phenotyping could enhance the diagnostic yield of T1 mapping for myocardial disease detection and classification.

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Typical and atypical imaging features of cardiac amyloidosis

Cited by 3 publications

References 5 publications

Prevalence and clinical outcomes of transthyretin amyloidosis: a systematic review and meta‐analysis

Prevalence and clinical outcomes of transthyretin amyloidosis: a systematic review and meta‐analysis

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes

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