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Neural Network Machine Learning for Determining Surgical Appropriateness in Head and Neck Subspecialty Referrals
Angeline A Truong1, John Paul Arios1, Kevin Xin2,3
1Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, CA, USA.
Objective:
Timely, accurate referrals to head and neck cancer surgery are essential for survival but are often delayed or misrouted, contributing to late-stage presentation and disparities. We aim to develop and validate a supervised neural network to predict surgical appropriateness at the time of referral.
Methods:
Training data included >200 000 de-identified patient records from the National Cancer Database and Surveillance, Epidemiology, and End Results registries. External validation was conducted on 39 consecutive referrals at a tertiary care center (2020-2023) using demographics, tumor site, histology, TNM stage, and grade. Model outputs were compared to treatment recommendations from head and neck surgeons.
Results:
The model achieved 79% accuracy, 85% sensitivity, 50% specificity, and 90% positive predictive value in identifying surgical candidates. Performance was consistent across sex, age, and socioeconomic subgroups, with a trend toward improved accuracy in lower-stage disease.
Conclusions:
This externally validated tool demonstrates potential to streamline referral triage, expedite surgical consultation, and enhance equitable access to head and neck cancer care.
