Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer

This prognostic study of p16+ OPSCC describes the development of a CT-based imaging risk score integrating PT and metastatic cervical LN features to predict recurrence risk and identify suitable candidates for treatment tailoring. This tool could optimize treatment modulations of p16+ OPSCC at a highly granular level.

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Publication Details

Journal
JAMA Network Open
Published
2025-05-01
DOI
https://doi.org/10.1001/jamanetworkopen.2025.8094
Citations
5
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
6.80

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article

Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer

Shlomo A. Koyfman, Kailin Yang, Jonathan Lee, Mihir R. Patel et al.
5 citations
JAMA Network Open
Radiomics and Machine Learning in Medical Imaging
6.80
article

Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer

Shlomo A. Koyfman, Kailin Yang, Jonathan Lee, Mihir R. Patel, Tanmoy Dam, Amaury Leroy, Xiao T. Li, Nabil F. Saba, Krunal Pandav, Sirvan Khalighi, Sarah J. Stock, J.O. Sonuga, Pingfu Fu, Bolin Song
article en
5 citations

Abstract

This prognostic study of p16+ OPSCC describes the development of a CT-based imaging risk score integrating PT and metastatic cervical LN features to predict recurrence risk and identify suitable candidates for treatment tailoring. This tool could optimize treatment modulations of p16+ OPSCC at a highly granular level.

JAMA Network OpenVol. 8(5)
University of Iowa (US), Georgia Institute of Technology (US), Cleveland Clinic (US), Veterans Health Administration (US), Emory University Hospital (US), The Wallace H. Coulter Department of Biomedical Engineering (US), Piedmont Cancer Institute (US), Winship Cancer Institute, Case Western Reserve University (US)
U.S. Department of Defense, U.S. Department of Veterans Affairs, Bristol-Myers Squibb, Eli Lilly and Company, AstraZeneca, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, DOD Prostate Cancer Research Program, DOD Peer Reviewed Cancer Research Program
Openalex Percentile: Top 3%
Radiomics and Machine Learning in Medical Imaging
6.80
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