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FIGURE 2 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

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posted on 2023-06-29, 14:20 authored by 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

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).

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Canadian HIV Trials Network, Canadian Institutes of Health Research (CTN, CIHR)

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ARTICLE ABSTRACT

ML combined with simple prognostic factors outperformed multiple advanced CT radiomics and deep learning methods. ML models provided diverse solutions for prognosis of patients with HNC but their prognostic value is affected by differences in patient populations and require extensive validation.

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