Study population
We retrospectively included 317 subjects (CA: n = 100, HCM: n = 217) who underwent CMR scans between January 2016 and June 2020. This study was approved by the institutional review board and in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants.
CA was diagnosed on the basis of positive myocardial biopsy or extracardiac biopsy in conjunction with a mean LV wall thickness (septum and posterior wall) ≥ 12 mm on CMR[14]. The diagnosis of HCM was based on the presence of unexplained LV wall thickness ≥ 15 mm or ≥ 13 mm with a family history of HCM or apical hypertrophy in the absence of other conditions capable of producing a similar degree of hypertrophy[15]. HCM patients with previous septal ablation or myectomy were excluded.
The exclusion criteria for all subjects were valvular heart disease (greater than mild stenosis or greater than moderate regurgitation), significant coronary artery disease, and other confirmed systemic diseases. Images of poor quality were excluded. Hypertensive patients with concentric hypertrophy were also excluded.
Cardiovascular magnetic resonance
CMR imaging was performed in accordance to a standard protocol, as previously published by our group[16]. All CMR images were performed using a 3.0T scanner (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany) with a 30-channel phased-array receiver coil. Cine images were obtained using a balanced steady-state free-precession sequence in consecutive short-axis views covering the entire LV (from the level of the mitral valve annulus to the LV apex) with the following parameters: repetition time (TR), 39.34 ms; echo time (TE) 1.22 ms; flip angle, 38°; field of view (FOV), 284 × 399 mm2; matrix size, 139 × 208 mm2; and slice thickness, 8 mm. T2-weighted short inversion time inversion recovery (T2-STIR) imaging was performed with the following parameters: TR, 2RR; TE, 71 ms; flip angle, 180°; FOV, 243 x 300 mm2; matrix size, 256 x 166 mm2; and slice thickness, 8 mm. Late gadolinium enhancement (LGE) imaging was performed at an average of 10–15 min after contrast injection by using a segmented-turbo-FLASH–phase-sensitive inversion recovery (PSIR) sequence (TR/TE = 750.00/1.18 ms; flip angle = 40°; slice thickness = 8 mm; FOV = 400 × 270 mm2; and matrix size = 184 × 256 mm2).
Imaging analysis
Conventional CMR parameters including LV end-diastolic volume (EDV), LV end-systolic volume (ESV), LVEF, LV mass, maximal LV wall thickness (MWT) and LGE extent were calculated using commercially available software (CVI42; Circle Cardiovascular Imaging, Inc., Calgary, Canada). The extent of LGE was quantified by 5 standard deviations (SDs) above the signal intensity of remote normal myocardium[17].
Texture analysis was performed on non-contrast T2-weighted CMR scans using 3D Slicer based on the Pyradiomics library[18]. The regions of interest (ROIs) were manually delineated in the basal septum of the left ventricle by a radiologist with 4 years of experience in cardiovascular imaging who was blinded to the patients’ information. ROI delineation was repeated twice in a subset of 30 randomly selected patients by the same radiologist for intraobserver analysis and by another radiologist with 15 years of experience for interobserver analysis.
Feature extraction and selection
A total of 837 features were extracted from T2-weighted images during the process of image filtering and feature extraction. Stepwise feature selection and dimension reduction were performed due to the high number of texture features. First, the intraclass correlation coefficient (ICC) was calculated to assess the intra- and interobserver reproducibility of the selected features. Features with ICCs < 0.75 were excluded, ICCs ranging from 0.75 to 1 considered “excellent”[19]. The Boruta algorithm[20], corrplot by carret[21] and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation[22] were performed in a stepwise manner for dimension reduction. Furthermore, both the CA and HCM groups were randomly divided into a training dataset and a testing dataset (7:3). To select variables that allow for the discrimination of myocardial tissue alterations in HCM and CA patients, the classification tree model[23], a commonly used machine learning algorithm, was employed to calculate discriminative performance in the training cohort and validated in the testing cohort.
After the radiomics signature was established, the diagnostic efficiency and accuracy of the model was validated in patients with similar hypertrophy matched by LV mass index as well as age, sex and maximum wall thickness (MWT).
Statistics analysis
Statistical analysis during the construction of the radiomics signature was performed in R (version 4.0.1; R Foundation for Statistical Computing, Vienna, Austria)[24] with RStudio (version 1.3.959; RStudio, Boston, Mass)[25]. The R packages used for statistical analyses are described in the supplementary materials. Other statistical analyses were conducted with SPSS (Version 19; IBM, Armonk, NY). The normality of the data distribution was determined using the Kolmogorov-Smirnov test. Continuous data are expressed as the means ± SDs or medians with interquartile ranges. The t-test or the Mann-Whitney U-test was conducted, as appropriate. The diagnostic accuracy of the optimal radiomic parameters was evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The diagnostic sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were also calculated. ROC curves were compared using DeLong’s test. P < 0.05 was considered statistically significant.