FIGURE 2 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics
Year: 2023
Type: other
Abstract: Model results. A, Overview of model characteristics. The characteristics are grouped into input data, prediction head (i.e., how were the survival predictions made) and model type (whether the model involved any nonlinearities and/or convolutions). PH: proportional hazards, MLP: multilayer perceptron, *: age, sex, stage, HPV status. B–D, Performance of all models, including benchmark models, in terms of 2-year AUROC, 2-year average precision and C-index of the lifetime risk, respectively. The results are ranked by AUROC (numbers above bars indicate the overall rank of each model). Error bars represent 95% confidence intervals computed using 10,000 stratified bootstrap replicates. Dashed gray lines indicate random guessing performance (0.5 for AUROC and C-index, 0.14 for AP). E–G, show the Kaplan–Meier survival estimates in low- and high-risk groups identified by the best performing model in each category (combined, EMR only and radiomics), respectively. Test set patients were stratified into two groups based on the predicted 2-year event probability at 0.5 threshold. In each case, there were significant differences in survival between the predicted risk groups (HR, 8.64, 5.96, and 4.50, respectively, p < 10−18 for all).
Authors: Michal Kazmierski, Mattea Welch, Sejin Kim, Chris McIntosh, Katrina Rey-McIntyre, Shao Hui Huang, Tirth Patel, Tony Tadic, Michael Milosevic, Fei-Fei Liu, Adam Ryczkowski, Joanna Kazmierska, Zezhong Ye, Deborah Plana, Hugo J.W.L. Aerts, Benjamin H. Kann, Scott V. Bratman, Andrew J. Hope, Benjamin Haibe-Kains
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4382539012