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

Kazmierski, Michal; Welch, Mattea; Kim, Sejin; McIntosh, Chris; Rey-McIntyre, Katrina; Huang, Shao Hui; Patel, Tirth; Tadic, Tony; Milosevic, Michael; Liu, Fei-Fei; Ryczkowski, Adam; Kazmierska, Joanna; Ye, Zezhong; Plana, Deborah; Aerts, Hugo J.W.L.; Kann, Benjamin H.; Bratman, Scott V.; Hope, Andrew J.; Haibe-Kains, Benjamin

doi:10.1158/2767-9764.23600396.v1

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

journal contribution

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

Supplementary Data of figures/tables

Funding

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

History

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.