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Cancers
Special Issues
Technical and Technological Innovations in Head and Neck Cancer Surgery
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Technical and Technological Innovations in Head and Neck Cancer Surgery

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 125

Special Issue Editor

Dr. Marta Filauro

E-Mail Website
Guest Editor
IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
Interests: head and neck cancer

Special Issue Information

Dear Colleagues,

Head and neck surgery, particularly in the context of cancer treatment, has seen significant advances over recent years, largely driven by technological innovation. These innovations have led to more precise and less invasive procedures, improved outcomes, and a better quality of life for patients. Several cutting-edge technologies are transforming the field, from enhanced imaging techniques to robotic surgery.

One of the most transformative advancements in head and neck surgery has been the development of minimally invasive surgical techniques, particularly robot-assisted surgery and transoral exoscopic laryngeal surgery (TOES).

Moreover, artificial intelligence (AI) and machine learning (ML) are increasingly being applied to improve decision-making and surgical outcomes in head and neck surgery.

AI algorithms can analyze patient data (such as medical history, imaging, and lab results) to predict the likelihood of cancer recurrence, post-surgical complications, or recovery outcomes. These tools help clinicians make more informed decisions regarding treatment plans and surgical approaches. On the other hand, machine learning systems can automatically analyze radiographic images (CT, MRI, PET scans, and videolaryngoscopy images) to identify tumors, even in early stages or in challenging-to-detect areas. This improves the chances of early diagnosis, allows for faster treatment, and can help detect subtle changes that might be missed by the human eye.

In light of this, the purpose of this Special Issue is to collect original studies and literature reviews on technological innovations that may lead to an improvement in the oncological outcomes and quality of life of head and neck cancer patients. In this Special Issue, original research articles and reviews are welcome

We look forward to receiving your contributions.

Dr. Marta Filauro
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • head and neck
  • squamous cell carcinoma
  • larynx
  • oral cavity
  • free flap
  • exoscope
  • TORS

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

16 pages, 2323 KiB  
Article
Real-Time Intraoperative Decision-Making in Head and Neck Tumor Surgery: A Histopathologically Grounded Hyperspectral Imaging and Deep Learning Approach
by Ayman Bali, Saskia Wolter, Daniela Pelzel, Ulrike Weyer, Tiago Azevedo, Pietro Lio, Mussab Kouka, Katharina Geißler, Thomas Bitter, Günther Ernst, Anna Xylander, Nadja Ziller, Anna Mühlig, Ferdinand von Eggeling, Orlando Guntinas-Lichius and David Pertzborn
Cancers 2025, 17(10), 1617; https://doi.org/10.3390/cancers17101617 (registering DOI) - 10 May 2025
Abstract
Background: Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility. Objective: To develop and evaluate a novel hyperspectral [...] Read more.
Background: Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility. Objective: To develop and evaluate a novel hyperspectral imaging (HSI) workflow that integrates deep learning with three-dimensional (3D) tumor modeling for real-time, label-free tumor margin delineation in head and neck squamous cell carcinoma (HNSCC). Methods: Freshly resected HNSCC samples were snap-frozen and imaged ex vivo from multiple perspectives using a standardized HSI protocol, resulting in a 3D model derived from HSI. Each sample was serially sectioned, stained, and annotated by pathologists to create high-resolution 3D histological reconstructions. The volumetric histological models were co-registered with the HSI data (n = 712 Datacubes), enabling voxel-wise projection of tumor segmentation maps from the HSI-derived 3D model onto the corresponding histological ground truth. Three deep learning models were trained and validated on these datasets to differentiate tumor from non-tumor regions with high spatial precision. Results: This work demonstrates strong potential for the proposed HSI system, with an overall classification accuracy of 0.98 and a tumor sensitivity of 0.93, underscoring the system’s ability to reliably detect tumor regions and showing high concordance with histopathological findings. Conclusion: The integration of HSI with deep learning and 3D tumor modeling offers a promising approach for precise, real-time intraoperative tumor margin assessment in HNSCC. This novel workflow has the potential to improve surgical precision and patient outcomes by providing rapid, label-free tissue differentiation. Full article
(This article belongs to the Special Issue Technical and Technological Innovations in Head and Neck Cancer Surgery)
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