Table 1.
Descriptive statistics ± standard deviation for the populations under study.
Fig 1.
The Speckle Tracking methodology.
(A) Healthy control subject. (B) Individual affected by Hypertrophic Cardiomyopathy.
Fig 2.
Interpolation of shape variables (here the sole PC1 is shown) at electrophysiological homologous times.
Note that homologous times are not equally spaced during cardiac revolution.
Fig 3.
The shape of trajectories and their morphological meaning in the size and shape space.
(A) Epicardial trajectory. (B) Endocardial trajectory. (C) Shape changes explained by the first three PC scores of epicardium. (D) Shape changes explained by the first three PC scores of endocardium. Animated GIF of these figures are in the Supporting Information as well as the corresponding figures of data transported on the shape space.
Fig 4.
PCA analyses performed on the shape of the trajectories themselves.
PC1-PC2 and PC1-PC3 scatterplots are shown. (A) Size and shape space. (B) Shape space.
Fig 5.
The shape of trajectories, constituted by the nine values of first three PC scores of LV shape interpolated at the nine homologous times, explained by the first three PC scores of the LV trajectory shape analyses.
(A) LV trajectories identified in the size and shape space. (B) LV trajectories identified in shape space. We stress here that these PC scores are referred to the shape of the trajectories themselves not directly to the shape of LV.
Table 2.
Trajectories attributes ANOVA and MANOVA results for differences between the control sample and HCM individuals.
Fig 6.
Box plots of the univariate attributes of trajectories.
(A) Size and shape space. (B) Shape space.
Table 3.
Logistic regression AIC and p-values for the 9 homologous times for 3DSTE parameters.
Table 4.
Logistic regression AIC and p-values for the 9 homologous times for LV shape static parameters.
Fig 7.
Results of logistic regressions using Control/HCM as a binary response and the entire bulk of morphological parameters used in this study.
The y-axis always represents the Akaike Information Criterion value for any analysis, while the x-axis represents the nine homologous times. Smaller AICs indicate the best models. (A) STE global parameters; corresponding p-values can be found in Table 3. (B) Trajectory attributes in the size and shape space; as these attributes are proper of the entire shape trajectory they are not referred to any particular homologous time; corresponding p-values can be found in Table 5. (C) Trajectory attributes in the shape space: as these attributes are proper of the entire shape trajectory they are not referred to any particular homologous time; corresponding p-values can be found in Table 5. (D) LV static shape analyses using pure shapes, shape transported in the size and shape space and shapes transported in the shape space; corresponding p-values can be found in Table 4. (E) Shape differences from the R peak for the same types of parameters in D; corresponding p-values can be found in Table 4. Among static parameters we found that global longitudinal displacement is the best traditional descriptor at end systolic homologous time. Differences of pure epicardial shapes from R peak perform nearly in the same way. Static shapes (pure shapes in size and shape space, shapes transported in the size and space and shapes transported in the shape space), instead, are performing also in diastole. Endocardial shapes transported in both size and shape space and shape space show the lowest AICs. The courses of transported shapes and pure shapes follow inverse patterns. All trajectory attributes returned significant logistic regressions (Table 5) except for endocardial trajectory size and RV coefficient, both computed in the shape space.
Table 5.
Logistic regression AIC and p-values for trajectory attributes.
Fig 8.
ROC curves for the best STE descriptors and our best static morphometric descriptors in systole and diastole.
(A) Endocardial shape transported in the size and shape space vs. global longitudinal displacement. (B) Epicardial shape transported in the size and shape space vs. regional global torsion. Shaded areas represent the 95% of confidence interval for ROC curves. The p-values refer to the Delong test for differences in ROC prediction.
Fig 9.
ROC curves for epicardium and endocardium transported in the size and shape space and in the shape space in three meaningful homologous times covering the entire cardiac revolution.
(A-C) Size and shape space. (D-F) Shape space. Delong method was adopted to test differences in ROC curve prediction.
Fig 10.
Two-way MANOVA (for shape) and ANOVA (for size) models.
(A-D) Univariate interaction plots for the first 4 PCs of shape data transported in the shape space; these plot are aimed at showing that single PCs behave in different manner thus making necessary the use of a the multivariate test. (E) Just for sake of visualization we show the interaction plot where y-axis is represented by the Canonical Correlation scores coming from a multivariate model that includes the first 10 PCs as a response and Control/HCM as binary predictor; multivariate euclidean distances between the 4 categories (Control/HCM/Endocardium/Epicardium) can be found in Table 6. (F) Interaction plot for size.
Table 6.
Multivariate Euclidean distances between first 10 PC scores of data transported in the shape space for the 2 way MANOVA design.
Fig 11.
UPGMA analyses performed at the nine homologous times on pure shapes.
(A) Endocardial shapes. (B) Epicardial shapes. Circles represent the analysis where g+p- individuals cluster together with HCM patients.
Fig 12.
UPGMA analyses performed at the nine homologous times on shapes transported on the size and shape space.
(A) Epicardial shapes. (B) Endocardial shapes. Circles represent the analysis where g+p- individuals cluster together with HCM patients.
Fig 13.
UPGMA analyses performed at the nine homologous times on shapes transported on the shape space.
(A) Epicardial shapes. (B) Endocardial shapes. Circles represent the analysis where g+p- individuals cluster together with HCM patients.
Fig 14.
Step by step flowchart of the entire procedure presented in the paper.