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Texture signatures of native myocardial T 1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar
Author(s) -
Neisius Ulf,
ElRewaidy Hossam,
Kucukseymen Selcuk,
Tsao Connie W.,
Mancio Jennifer,
Nakamori Shiro,
Manning Warren J.,
Nezafat Reza
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27048
Subject(s) - hypertrophic cardiomyopathy , medicine , cardiomyopathy , magnetic resonance imaging , radiology , nuclear medicine , gadolinium , artificial intelligence , computer science , heart failure , materials science , metallurgy
Background In 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 T 1 ‐mapping enables identification of focal fibrosis, the substrate of LGE. However HCM‐specific heterogeneous fibrosis distribution leads to subtle T 1 ‐maps changes that are difficult to identify. Purpose To apply radiomic texture analysis on native T 1 ‐maps to identify patients with a low likelihood of LGE(+), thereby reducing the number of patients exposed to gadolinium administration. Study Type Retrospective interpretation of prospectively acquired data. Subjects In all, 188 (54.7 ± 14.4 years, 71% men) with suspected or known HCM. Field Strength/Sequence A 1.5T scanner; slice‐interleaved native T 1 ‐mapping (STONE) sequence and 3D LGE after administration of 0.1 mmol/kg of gadobenate dimeglumine. Assessment Left ventricular LGE images were location‐matched with native T 1 ‐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 Tests Four 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(−) T 1 ‐maps and tested using a decision tree ensemble (DTE) classifier. Results The selected texture features discriminated between LGE(+) and LGE(−) T 1 ‐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 Conclusion Radiomic analysis of native T 1 ‐images can identify ~1/3 of LGE(−) patients for whom gadolinium administration can be safely avoided. Level of Evidence: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020. J. Magn. Reson. Imaging 2020;52:906–919.