Supplementary Tables 1-11 from Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma
Year: 2023
Type: supplementary-materials
Abstract: Supplemental Table 1. Patient, slide, and tile distribution during the 5-fold cross validation for training the neural network on the NYU dataset. Supplemental Table 2. Odds Ratios (OR) and 95% Confidence Intervals (CI) from univariate analyses of baseline clinical and demographic variables. Supplemental Table 3. AUC for each of 5 independent test runs and the average AUC for both classifiers when applied to Vanderbilt slides scanned with the Aperio AT2. Supplemental Table 4. AUC for each of 5 independent test runs and the average AUC for both classifiers when applied to Vanderbilt slides scanned with the Leica SCN400. Supplemental Table 5. AUC with 95% confidence intervals for the DCNN and multivariable classifiers when applied exclusively to lymph node or soft tissue from the Vanderbilt dataset. Supplemental Table 6. Results of 3 independent test runs and the average performance of the DCNN classifier on slides from Vanderbilt scanned at 10x magnification. Shown are area under the curve (AUC) and 95% confidence intervals (CI). Supplemental Table 7. Hazard Ratios (HR) with 95% Confidence Intervals (CI) for the clinical variables used in the multivariable prediction model. Supplemental Table 8. AUC and 95% confidence intervals for the neural network prediction, ECOG performance status, treatment category, and each combination of the variables. Supplemental Table 9. Value importance of treatment category, baseline ECOG, and DCNN are reported as the absolute value of the Z-score. Supplemental Table 10. Confusion matrix for predictions of PFS on the cohort of Vanderbilt patients when slides were scanned with an Aperio AT2 scanner. Supplemental Table 11. Confusion matrix for predictions of PFS on the cohort of Vanderbilt patients when slides were scanned with a Leica SCN400 scanner.
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4361938240