The Role of Machine Learning to Detect Occult Neck Lymph Node Metastases in Early‐Stage (<scp>T1</scp>‐<scp>T2</scp>/<scp>N0</scp>) Oral Cavity Carcinomas
Our results seem to confirm that radiomic features, extracted by machine learning methods, are effective tools in detecting occult neck metastases in early-stage OCCs. The clinical relevance of this study is that radiomics could be used routinely as a preoperative tool to support diagnosis and to help surgeons in the surgical decision-making process, particularly regarding surgical indications for neck lymph node treatment.
Authors
- Vincenzo Abbate (ORCID: https://orcid.org/0000-0002-7905-0531)
- Andrea Elefante (ORCID: https://orcid.org/0000-0002-9093-7096)
- Giovanni Dell’Aversana Orabona (ORCID: https://orcid.org/0000-0001-6479-6130)
- Riccardo Nocini (ORCID: https://orcid.org/0000-0001-5086-4390)
- Stefania Troise (ORCID: https://orcid.org/0000-0001-8421-0328)
- Paola Bonavolontà (ORCID: https://orcid.org/0000-0002-3326-4825)
- Lorenzo Ugga (ORCID: https://orcid.org/0000-0001-7811-4612)
- Umberto Committeri (ORCID: https://orcid.org/0000-0002-9444-0052)
- Renato Cuocolo (ORCID: https://orcid.org/0000-0002-1452-1574)
- Raffaele Merola (ORCID: https://orcid.org/0000-0002-0159-1426)
- Maria Esposito (ORCID: https://orcid.org/0000-0001-7095-7957)
- Serena Capasso
- Maria Positano
Institutions
- University of Verona (IT)
- University of Salerno (IT)
- ASM Terni (Italy) (IT)
- University of Naples Federico II (IT)
Publication Details
- Journal
- Head & Neck
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1002/hed.28189
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36