Machine Learning–Driven Risk Stratification and Adjuvant Treatment Guidance in Oral Cavity Cancer

20260 citationsJournal Article

Authors

Andrea Costantino · IRCCS Humanitas Research Hospital

N. Tsur · AdventHealth Celebration

Daniel Uralov · Thomas Jefferson University

Jianbin Zhu · Translational Research Institute for Metabolism and Diabetes

Uthman Alamoudi · AdventHealth Celebration

Giuseppe Spriano · Humanitas University

J. Scott Magnuson

Abstract

ML-derived risk stratification identifies patients with OCSCC most likely to benefit from adjuvant therapy, supporting intensification for intermediate-/high-risk patients and potential deintensification for low-risk patients. External prospective validation is warranted to enable clinical implementation.

Topics & Keywords

Head and Neck Cancer Studies Radiomics and Machine Learning in Medical Imaging Advanced Radiotherapy Techniques

Publication Details

Published in: JCO Precision Oncology

Volume 10, Issue 3, pp. e2500914-e2500914

DOI: 10.1200/po-25-00914

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Machine Learning–Driven Risk Stratification and Adjuvant Treatment Guidance in Oral Cavity Cancer

Authors

Abstract

Topics & Keywords

Publication Details