Description S3
Creators: name: Nandy, Debmalya
Publisher: name: figshare
Year: 2022
Subjects: subject: 70703 Veterinary Diagnosis and Diagnostics
subject: FOS: Veterinary sciences

description: <b>Description of results using three image variables and three clinical covariates in the RF classification model (Case 1).</b><br>Pre-harmonization: Total accuracy: Using the image variables and also the clinical covariates in the RF model, the lower bound total accuracy (pre-ComBat) is significantly higher than that using only three image variables in RF: medians 68.4% vs. 60.5%; p-value = 7.48 E-09. Similarly, the upper bound total accuracy is also significantly higher: medians 71.1% vs. 65.8%; p-value = 3.64 E-07 (Figure S6-A). <br>Sensitivity: Using the image variables and also the clinical covariates in the RF model, the lower bound sensitivity (pre-ComBat) is significantly higher than that using only three image variables in RF: medians 52.6% vs. 47.4%; p-value = 8.77 E-04. Similarly, the upper bound sensitivity is also significantly higher: medians 63.2% vs. 52.6%; p-value = 1.76 E-06 (Figure S6-B).<br>Specificity: Using the image variables and also the clinical covariates in the RF model, the lower bound specificity (pre-ComBat) is significantly higher than that using only three image variables in RF: medians 78.9% vs. 68.4%; p-value = 2.33 E-10. Similarly, the upper bound sensitivity is also significantly higher: medians 84.2% vs. 78.9%; p-value = 2.90 E-03 (Figure S6-C).<br>Post-harmonization: Total accuracy: Post-ComBat harmonization (scenarios c, d), the total accuracy lower bounds are significantly higher compared to their pre-ComBat and post-ComBat with only image variables in RF counterparts. For example, post-ComBat using three image variables and three clinical covariates (scenario c): (1) vs. pre-ComBat: medians 71.1% vs 68.4%; p-value = 8.80 E-04 and (2) vs. using only image variables in the RF model (scenario b): medians 71.1% vs. 65.8%; p-value = 1.84 E-04. Moreover, comparing between post-ComBat scenarios c and d: medians 71.1% vs 68.4%, p-value = 6.97 E-03 (Tables 4 and S4, Figures 2B and S6-A).<br>Sensitivity: Post-ComBat harmonization (scenarios c, d), the sensitivity lower bounds are not significantly higher compared to their pre-ComBat counterparts. For example, post-ComBat using three image variables and three clinical covariates (scenario c) vs. pre-ComBat: both medians 52.6%; p-value = 0.953 (Table S4, Figure S6-B). However, this post-ComBat sensitivity lower bound in scenario c is significantly higher than that using only image variables (scenario d): both medians 52.6%; p-value = 0.0177. Interestingly, post-ComBat sensitivity in scenario c deteriorates significantly compared to those when not using the clinical covariates in the RF model (scenario b): medians 52.6% vs. 63.2%; p-value = 2.07 E-05 (Table S4).<br>Specificity: Post-ComBat harmonization (scenarios c, d), the specificity lower bounds are again significantly higher compared to their pre-ComBat counterparts. For example, post-ComBat specificity lower bound using three image variables and three clinical covariates (scenario c) vs. pre-ComBat: medians 84.2% vs. 78.9%; p-value = 9.44 E-10 (Table S4, Figure S6-C). This post-ComBat specificity lower bound in scenario c is also significantly higher than that using only image variables (scenario d): both medians 84.2%; p-value = 2.69 E-03 (Figure S6-C) and compared to those when not using the clinical covariates in the RF model (scenario b): medians 84.2% vs. 73.7%; p-value = 3.05 E-12 (Table S4).<br>