Artificial Intelligence Model for Imaging-Based Extranodal Extension Detection and Outcome Prediction in Human Papillomavirus−Positive Oropharyngeal Cancer
This single-center cohort study found that an AI-driven pipeline can successfully automate lymph node segmentation and iENE classification from pretreatment CT scans in HPV-associated OPC. Predicted iENE was independently associated with worse oncologic outcomes. External validation is required to assess generalizability and the potential for implementation in institutions without specialized imaging expertise.
Authors
- Gabriel Dayan (ORCID: https://orcid.org/0000-0001-7594-4522)
- Kristoff Nelson
- Éric Bissada
- Brian O’Sullivan (ORCID: https://orcid.org/0000-0001-8887-843X)
- Samuel Kadoury (ORCID: https://orcid.org/0000-0002-3048-4291)
- Antoine Desîlets (ORCID: https://orcid.org/0000-0002-9382-7597)
- Christine Brodeur (ORCID: https://orcid.org/0000-0002-8579-3358)
- Houda Bahig (ORCID: https://orcid.org/0000-0001-9508-0602)
- Laurent Létourneau‐Guillon (ORCID: https://orcid.org/0000-0002-6325-7538)
- Édith Filion
- Tareck Ayad (ORCID: https://orcid.org/0000-0003-3673-7875)
- Paul Tabet (ORCID: https://orcid.org/0000-0003-4897-5395)
- Apostolos Christopoulos (ORCID: https://orcid.org/0000-0002-8491-9289)
- Louis Guertin
- G. Hénique
- P.F. Nguyen-Tan
Institutions
- Centre Hospitalier de l’Université de Montréal (CA)
- Polytechnique Montréal (CA)
- Université de Montréal (CA)
Publication Details
- Journal
- JAMA Otolaryngology–Head & Neck Surgery
- Published
- 2025-09-30
- DOI
- https://doi.org/10.1001/jamaoto.2025.3225
- Citations
- 2
- Primary Topic
- Head and Neck Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 3.70