Radiomic Analysis of Myocardial Native T1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy

Neisius, Ulf; El‐Rewaidy, Hossam; Nakamori, Shiro; Rodriguez, Jennifer; Manning, Warren J.; Nezafat, Reza

doi:10.1016/j.jcmg.2018.11.024

Cited by 119 publications

(134 citation statements)

References 43 publications

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“…Similarly, Neisius, et. al. demonstrates that texture analysis can differentiate between HCM and hypertensive heart disease patients where a set of six texture features extracted from cardiac T 1 maps can provide an accuracy of 80% in an independent testing dataset using support vector machines classifier [16]. While these studies demonstrate the potential of texture analysis to diagnose different cardiomyopathies, they do not indicate whether texture analysis can be used as an alternative analysis approach to elucidate differences in tissue compositions.…”

Section: Introductionmentioning

confidence: 93%

“…Subjects were excluded from analyses secondary to an established diagnosis of amyloidosis, iron deposition or Anderson-Fabry disease, evidence of inflammatory processes in the myocardium or pericardium, and history of ST-segment elevation myocardial infarction. Part of this dataset (~55%) was previously reported [4,5,16,26,27].…”

Section: Study Populationmentioning

confidence: 99%

“…For example, signal variation in cardiac MR images may contain additional information reflecting underlying pathophysiology [12][13][14] that is not being quantified. Radiomics [15] and texture image analysis have been recently applied to cardiac MR images [16][17][18][19][20] to extract new quantitative features that may provide diagnostic information. That is, radiomics quantitatively extract high-dimensional feature to differentiate images beyond mean signal value such as signal heterogeneity [17].…”

Section: Introductionmentioning

confidence: 99%

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Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

et al. 2020

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Background The pattern of myocardial fibrosis differs significantly between different cardiomyopathies. Fibrosis in hypertrophic cardiomyopathy (HCM) is characteristically as patchy and regional but in dilated cardiomyopathy (DCM) as diffuse and global. We sought to investigate if texture analyses on myocardial native T 1 mapping can differentiate between fibrosis patterns in patients with HCM and DCM. Methods We prospectively acquired native myocardial T 1 mapping images for 321 subjects (55±15 years, 70% male): 65 control, 116 HCM, and 140 DCM patients. To quantify different fibrosis patterns, four sets of texture descriptors were used to extract 152 texture features from native T 1 maps. Seven features were sequentially selected to identify HCM-and DCM-specific patterns in 70% of data (training dataset). Pattern reproducibility and generalizability were tested on the rest of data (testing dataset) using support vector machines (SVM) and regression models. Results Pattern-derived texture features were capable to identify subjects in HCM, DCM, and controls cohorts with 202/237(85.2%) accuracy of all subjects in the training dataset using 10fold cross-validation on SVM (AUC = 0.93, 0.93, and 0.93 for controls, HCM and DCM, respectively), while pattern-independent global native T 1 mapping was poorly capable to identify those subjects with 121/237(51.1%) accuracy (AUC = 0.78, 0.51, and 0.74) (P<0.001 for all). The pattern-derived features were reproducible with excellent intra-and inter-observer reliability and generalizable on the testing dataset with 75/84(89.3%) accuracy.

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Section: Introductionmentioning

confidence: 93%

Section: Study Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

et al. 2020

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“…For feature selection, we used the sequential forward feature selection (SFFS) method as it has demonstrated good performance in previous CMR radiomics studies (15,26). The termination criterion was set to 2% in all experiments following literature standards, i.e., the process was stopped if an added feature did not increase model performance beyond the termination criterion.…”

Section: Identification Of Optimal Radiomic Signaturesmentioning

confidence: 99%

“…Within oncology, where radiomics is most well-developed, the incremental value of radiomics models for diagnosis and prognosis have been widely reported (8)(9)(10)(11)(12)(13)(14). In cardiology, early studies have shown promising results from CMR radiomics models for discrimination of important conditions such as myocarditis, hypertrophic cardiomyopathy, and ischemic heart disease (15)(16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Cetin

Raisi‐Estabragh

Petersen

et al. 2020

Front. Cardiovasc. Med.

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Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level. Cetin et al. CMR-Radiomics Due to CV-RF This study confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease. We demonstrate such analysis may have utility beyond conventional CMR metrics for improved detection and understanding of the early effects of cardiovascular risk factors on cardiac structure and tissue.

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Texture signatures of native myocardial T₁ as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar

Neisius

El‐Rewaidy

Küçükseymen

et al. 2020

Magnetic Resonance Imaging

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BackgroundIn patients with suspected or known hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) provides diagnostic and prognostic value. However, contraindications and long‐term retention of gadolinium have raised concern about repeated gadolinium administration in this population. Alternatively, native T1‐mapping enables identification of focal fibrosis, the substrate of LGE. However HCM‐specific heterogeneous fibrosis distribution leads to subtle T1‐maps changes that are difficult to identify.PurposeTo apply radiomic texture analysis on native T1‐maps to identify patients with a low likelihood of LGE(+), thereby reducing the number of patients exposed to gadolinium administration.Study TypeRetrospective interpretation of prospectively acquired data.SubjectsIn all, 188 (54.7 ± 14.4 years, 71% men) with suspected or known HCM.Field Strength/SequenceA 1.5T scanner; slice‐interleaved native T1‐mapping (STONE) sequence and 3D LGE after administration of 0.1 mmol/kg of gadobenate dimeglumine.AssessmentLeft ventricular LGE images were location‐matched with native T1‐maps using anatomical landmarks. Using a split‐sample validation approach, patients were randomly divided 3:1 (training/internal validation vs. test cohorts). To balance the data during training, 50% of LGE(−) slices were discarded.Statistical TestsFour sets of texture descriptors were applied to the training dataset for capture of spatially dependent and independent pixel statistics. Five texture features were sequentially selected with the best discriminatory capacity between LGE(+) and LGE(−) T1‐maps and tested using a decision tree ensemble (DTE) classifier.ResultsThe selected texture features discriminated between LGE(+) and LGE(−) T1‐maps with a c‐statistic of 0.75 (95% confidence interval [CI]: 0.70–0.80) using 10‐fold cross‐validation during internal validation in the training dataset and 0.74 (95% CI: 0.65–0.83) in the independent test dataset. The DTE classifier provided adequate labeling of all (100%) LGE(+) patients and 37% of LGE(−) patients during testing.Data ConclusionRadiomic analysis of native T1‐images can identify ~1/3 of LGE(−) patients for whom gadolinium administration can be safely avoided.Level of Evidence: 2Technical Efficacy Stage: 2J. Magn. Reson. Imaging 2020. J. Magn. Reson. Imaging 2020;52:906–919.

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Radiomic Analysis of Myocardial Native T1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy

Cited by 119 publications

References 43 publications

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Texture signatures of native myocardial T₁ as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar

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Radiomic Analysis of Myocardial Native T1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy

Cited by 119 publications

References 43 publications

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar

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Texture signatures of native myocardial T₁ as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar