Statistical analysis
Continuous data were expressed as mean ± standard deviation (SD) or median and interquartile range (IQR). Reproducibility within core lab and between centers was compared by Bland-Altman plot and percent difference was calculated for LV EDV, ESV, and SV as difference of the two measures divided by the mean of the two measures. Time required to complete fully automated and semi-automated contouring were compared between centers using analysis of variance (ANOVA) and as a group using student t-test. BSA was calculated using Haycock’s method7. To build the z-model of a parameter (i.e. ESV, DSV and SV), we selected an optimum exponent, α, of the index parameter (parameter/BSAα) such that: 1.) The index parameter satisfactorily follows a normal distribution and 2.)The index parameter does not depend upon BSA. Z-score was then calculated as
\(Z=\frac{[\ \left(\frac{\text{parameter}}{\text{BSA}^{\alpha}}\right)-\left(\text{mean\ value\ of\ indexed\ parameter}\right)]}{\text{SD\ of\ indexed\ parameter}}\).6Normality of an indexed parameter was evaluated using Shapiro-Wilk and Kolmogorov-Smirnov tests, Q-Q plot, skewness and kurtosis. Dependence of the indexed parameter on BSA was evaluated with a test of the slope of the linear regression of the indexed parameters on BSA. We conducted grid search with a 0.001 step size to find the optimum exponent, α, and chose the one that maximized the sum of p-value for Shapiro-Wilk test and the p-value of testing the slope of index parameter vs. BSA. During the model development, diagnostic analysis were conducted using leave-one-out method. Few data points with extreme values that influences the distribution of indexed parameter were excluded from the final z-model development. After the optimum has determined, association of indexed parameter with age and gender were further examined with respectively linear regression and Student t-test. Gender specific z-scores model hence developed because there is difference between genders in indexed parameter. A two-sided p-value <0.05 was considered statistically significant. Statistical analysis was performed using SAS version 9.4 (SAS institute, Cary, NC).