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

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 -mapping enables identification of focal fibrosis, the substrate of LGE. However HCM-specific heterogeneous fibrosis distribution leads to subtle T -maps changes that are difficult to identify.

PURPOSE

To apply radiomic texture analysis on native T -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 -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 -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 -maps and tested using a decision tree ensemble (DTE) classifier.

RESULTS

The selected texture features discriminated between LGE(+) and LGE(-) T -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 -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.