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

Abstract

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.

Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models

“…The most notable use and development have been made in the field of neurooncology 1 – 4 . Application of radiomics has recently gained interest in cardiac MRI (CMRI) and few studies have evaluated its utility in patients with ventricular hypertrophy 5 – 7 , myocardial infarction 8 , tachyarrhythmia 9 , and myocarditis 10 , 11 .…”

Progress in radiomics of common heart disease based on cardiac magnetic resonance imaging

“…GlcmV1SumOfSqs, GlcmV5SumAverg, GrlmVGLevNonUn, GlcmV5AngScMom, and Gab8H4Mag 2018 Baebler et al [17] T1-weighted 62 GLevNonU, WavEnLL, and Fraction AUC = 0.95 2020 Neisius et al [47] T1 mapping/LGE 217 LRLGE LGE, late gadolinium enhancement; AUC, Area Under Curve; HR, hazard ratio; Local binary patterns; LBP (The gray value of the center pixel is taken as the threshold, and the corresponding binary code is obtained to represent the local texture feature); X0 features (They are extracted from the original image).…”

“…Results showed that the four texture features were gray level non -uniformity (P < 0.001), energy of wavelet coefficients in low frequency sub-bands (P < 0.001), fraction (P < 0.001) and sum average (P = 0.007), which provided a new parameter for the evaluation of HCM by contrast free T1 weighted images. Neisius et al [47] used texture analysis on T1 mapping images of HCM patients to identify a delayed enhancement on LGE sequences. The results showed that the five texture features, LRLGE-45 • , LBP-22, LBP-16, homogeneity-4, and LBP-1, had the greatest contribution value to the DTE classifier.…”

Title: Cardiac MRI derived radiomics based machine learning (ML) models in identifying pulmonary hypertension (PH)- Impact of segmentation, radiomics side experiments and DAFIT approach on model performance

“…For each mask, 348 features were extracted using the MaZda software. These included histogram (9), cooccurrence matrix (220), run-length matrix (20), gradient (5), autoregressive (5), geometrical (73) and wavelet (16) features. Details about these features are provided elsewhere and beyond the scope of this work.…”

“…The most notable use and development have been made in the eld of neurooncology. [1][2][3][4] Application of radiomics has recently gained interest in cardiac MRI (CMRI) and few studies have evaluated its utility in patients with ventricular hypertrophy, [5][6][7] myocardial infarction, [8] tachyarrhythmia [9], and myocarditis. [10; 11] One of the limitations of radiomics based models is lack of reproducibility and generalizability due to which they haven't yet translated to clinical practice.…”