New Trends and Advances in Head and Neck Oncology

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Surgery".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 418

Special Issue Editors


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Guest Editor
Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy
Interests: craniofacial surgery; pediatric surgery; augmented reality; orthognathic surgery; bimaxillary surgery; head and neck surgery
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum, University of Bologna, Via S Vitale 59, 40125 Bologna, Italy
Interests: oral and maxillofacial surgery; maxillofacial surgery; orthognathic surgery; maxillofacial abnormalities; computer assisted aurgery; augmented reality; 3D printing; craniofacial abnormalities; craniofacial surgery; head and neck surgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Head and neck oncology represents a complex and dynamic field at the intersection of surgery, oncology, and advanced technology. Over the past decade, significant strides have been made in the diagnosis, treatment, and reconstruction of head and neck cancers, fueled by innovations that continue to reshape patient outcomes. This Special Issue aims to highlight the latest advancements and explores the transformative impact of emerging technologies in the management of head and neck malignancies.

Key topics in this Special Issue include the integration of robotic-assisted surgery, advancements in precision medicine, and the use of artificial intelligence for early detection and treatment planning. We also aim to examine breakthroughs in imaging modalities, minimally invasive techniques, and personalized therapeutic strategies, including immunotherapy and targeted therapies. In parallel, advancements in reconstructive surgery and 3D printing are revolutionizing functional and aesthetic rehabilitation for patients undergoing oncologic treatment.

By bringing together leading experts and researchers, this Special Issue aims to provide a comprehensive overview of cutting-edge approaches that are setting new standards of care in head and neck oncology. We invite readers to explore these innovations, which hold the potential to improve survival rates, reduce morbidity, and enhance quality of life for patients facing these challenging diagnoses.

Dr. Federica Ruggiero
Dr. Giovanni Badiali
Guest Editors

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Keywords

  • head and neck surgery
  • robotic-assisted surgery
  • reconstruction

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Published Papers (1 paper)

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Research

11 pages, 881 KB  
Article
Is the Ensemble Machine Learning Model a Reliable Method for Detecting Neoplastic Infiltration of Thyroid Cartilage in Laryngeal Cancers?
by Sermin Can, Ömer Türk, Muhammed Ayral, Günay Kozan, Mehmet Önür, Eyyüp Yagız and Mehmet Akdag
Medicina 2025, 61(11), 1945; https://doi.org/10.3390/medicina61111945 - 30 Oct 2025
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Abstract
Background and Objectives: We aimed to apply the ensemble machine learning model to diagnose thyroid cartilage invasion detected in computer tomography (CT) images in laryngeal cancers and evaluate the diagnostic performance of the model. Materials and Methods: A total of 313 [...] Read more.
Background and Objectives: We aimed to apply the ensemble machine learning model to diagnose thyroid cartilage invasion detected in computer tomography (CT) images in laryngeal cancers and evaluate the diagnostic performance of the model. Materials and Methods: A total of 313 patients were divided into two groups: the cartilage invasion group and the no cartilage invasion group. At least four CT slices were randomly selected for each patient, resulting in a total of 1251 images used in the study. A total of 619 axial CT images from the no cartilage invasion group and 632 axial CT images from the cartilage invasion group were used in the study. We reviewed the CT images and histopathological diagnoses in all cases to determine the invasion positive- or negative-status as a ground truth. The ensemble model, comprising ResNet50 and MobileNet deep learning architectures, was applied to CT images. Results: The following were obtained by the ensemble model with the test dataset: area under the curve (AUC) 0.99, and accuracy 96.54%. This model demonstrates a very high level of performance in detecting thyroid cartilage invasion. Conclusions: The ensemble machine learning model is an effective method for detecting neoplastic infiltration of the thyroid cartilage. Moreover, it may be a valuable diagnostic tool for clinicians in assessing disease prognosis and determining appropriate treatment strategies in laryngeal cancers. In conclusion, this model could be integrated into future clinical practice in laryngology and head and neck surgery for the detection of cartilage neoplastic infiltration. Full article
(This article belongs to the Special Issue New Trends and Advances in Head and Neck Oncology)
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