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Diagnostics
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Integrative Approaches in Head and Neck Cancer Imaging
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Integrative Approaches in Head and Neck Cancer Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2732

Special Issue Editor

Dr. Calin G. Buzea

E-Mail Website
Guest Editor
1. Clinical Emergency Hospital “Prof. Dr. Nicolae Oblu”, 700309 Iasi, Romania
2. National Institute of Research and Development for Technical Physics, IFT, 700050 Iasi, Romania
Interests: theoretical physics; medical physics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "Integrative Approaches in Head and Neck Cancer Imaging", focuses on the latest advancements in imaging for diagnosing, treating, and monitoring head and neck cancers. This edition seeks to highlight interdisciplinary efforts that integrate radiology, oncology, surgery, and pathology to enhance the precision of cancer care.

Head and neck cancers, originating in regions like the oral cavity, pharynx, and larynx, require accurate diagnosis for their effective management, and this is heavily reliant on advanced imaging technologies like MRI, CT, PET, and ultrasound. The complex anatomy of these areas poses significant challenges that require innovative imaging solutions.

This issue aims to present cutting-edge integrative imaging strategies that combine various modalities and computational techniques to improve diagnostic accuracy and treatment outcomes for head and neck cancer. We seek contributions related to the following areas:

  1. Advanced imaging technologies tailored to head and neck cancers.
  2. Multimodal imaging frameworks that enhance diagnostic and therapeutic precision.
  3. Image-guided therapies that improve surgical and radiation outcomes.
  4. The use of artificial intelligence and machine learning in image analysis.
  5. Clinical trials evaluating new imaging protocols.
  6. Reviews on the evolution of imaging technology and future trends in head and neck oncology.

This issue is intended for radiologists, oncologists, surgeons, and researchers, and we encourage submissions that highlight collaborative research and innovative solutions in imaging.

Contributions are welcomed in the form of original research, review articles, and clinical studies that enhance our understanding and push forward the boundaries of imaging technology in oncological care. This collection aims to foster further research and innovation, significantly impacting clinical practices and patient outcomes in the field of head and neck cancer imaging.

Dr. Calin G. Buzea
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 short 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. Diagnostics 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 2600 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 cancer
  • imaging technologies
  • multimodal imaging
  • image-guided therapy
  • artificial intelligence in imaging

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Published Papers (2 papers)

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Research

14 pages, 6527 KiB  
Article
The Prediction of Radiation-Induced Trismus by the Apparent Diffusion Coefficient Values of Masseter Muscles before Chemoradiotherapy in Locally Advanced Nasopharyngeal Carcinomas
by Umur Anil Pehlivan, Efsun Somay, Cigdem Yalcin and Erkan Topkan
Diagnostics 2024, 14(20), 2268; https://doi.org/10.3390/diagnostics14202268 - 12 Oct 2024
Viewed by 1012
Abstract
Purpose: Although the apparent diffusion coefficient (ADC) value from diffusion-weighted imaging can provide insights into various pathological processes, no studies have examined the relationship between the pre-concurrent chemoradiotherapy (CCRT) mean ADC (ADCmean) values of the masseter muscles and radiation-induced trismus (RIT) [...] Read more.
Purpose: Although the apparent diffusion coefficient (ADC) value from diffusion-weighted imaging can provide insights into various pathological processes, no studies have examined the relationship between the pre-concurrent chemoradiotherapy (CCRT) mean ADC (ADCmean) values of the masseter muscles and radiation-induced trismus (RIT) in locally advanced nasopharyngeal carcinoma (LA-NPC) patients. Therefore, the current research aimed to investigate the significance of pre-CCRT masseter muscle ADCmean values for predicting the RIT rates in LA-NPC patients treated with definitive CCRT. Materials and Methods: The pre-CCRT ADCmean values of the masseter muscles and the post-CCRT RIT rates were evaluated. A receiver operating characteristic curve analysis was employed to determine the optimal ADCmean cutoff. The primary objective was to examine the relationship between the pre-CCRT masseter muscle ADCmean values and the post-CCRT RIT rates. Results: Seventy-seven patients were included. The optimal ADCmean cutoff value was 1381.30 × 10−6 mm2/s, which divided the patients into two groups: an ADCmean < 1381.30 × 10−6 mm2/s (n = 49) versus an ADCmean > 1381.30 × 10−6 mm2/s (n = 28). A masseter muscle ADCmean > 1381.30 × 10−6 mm2/s was found to be associated with significantly higher RIT rates than an ADCmean < 1381.30 × 10−6 mm2/s (71.42% vs. 6.12%; p < 0.001). The multivariate analysis results confirmed a pre-CCRT masseter muscle ADCmean > 1381.30 × 10−6 mm2/s as an independent predictor of RIT. Conclusions: Our study presents the first evidence establishing a connection between elevated masseter muscle ADCmean values and higher RIT rates in LA-NPC patients following CCRT. If confirmed with further research, these findings may help to categorize the risk of RIT in these patients. Full article
(This article belongs to the Special Issue Integrative Approaches in Head and Neck Cancer Imaging)
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27 pages, 5119 KiB  
Article
Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics
by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea and Cristian Constantin Volovăț
Diagnostics 2024, 14(18), 2091; https://doi.org/10.3390/diagnostics14182091 - 21 Sep 2024
Cited by 1 | Viewed by 1348
Abstract
Introduction: Accurate prediction of tumor dynamics following Gamma Knife radiosurgery (GKRS) is critical for optimizing treatment strategies for patients with brain metastases (BMs). Traditional machine learning (ML) algorithms have been widely used for this purpose; however, recent advancements in deep learning, such as [...] Read more.
Introduction: Accurate prediction of tumor dynamics following Gamma Knife radiosurgery (GKRS) is critical for optimizing treatment strategies for patients with brain metastases (BMs). Traditional machine learning (ML) algorithms have been widely used for this purpose; however, recent advancements in deep learning, such as autoencoders, offer the potential to enhance predictive accuracy. This study aims to evaluate the efficacy of autoencoders compared to traditional ML models in predicting tumor progression or regression after GKRS. Objectives: The primary objective of this study is to assess whether integrating autoencoder-derived features into traditional ML models can improve their performance in predicting tumor dynamics three months post-GKRS in patients with brain metastases. Methods: This retrospective analysis utilized clinical data from 77 patients treated at the “Prof. Dr. Nicolae Oblu” Emergency Clinic Hospital-Iasi. Twelve variables, including socio-demographic, clinical, treatment, and radiosurgery-related factors, were considered. Tumor progression or regression within three months post-GKRS was the primary outcome, with 71 cases of regression and 6 cases of progression. Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. The study further explored the impact of incorporating features derived from autoencoders, particularly focusing on the effect of compression in the bottleneck layer on model performance. Results: Traditional ML models achieved accuracy rates ranging from 0.91 (KNN) to 1.00 (Extra Trees). Integrating autoencoder-derived features generally enhanced model performance. Logistic Regression saw an accuracy increase from 0.91 to 0.94, and SVM improved from 0.85 to 0.96. XGBoost maintained consistent performance with an accuracy of 0.94 and an AUC of 0.98, regardless of the feature set used. These results demonstrate that hybrid models combining deep learning and traditional ML techniques can improve predictive accuracy. Conclusion: The study highlights the potential of hybrid models incorporating autoencoder-derived features to enhance the predictive accuracy and robustness of traditional ML models in forecasting tumor dynamics post-GKRS. These advancements could significantly contribute to personalized medicine, enabling more precise and individualized treatment planning based on refined predictive insights, ultimately improving patient outcomes. Full article
(This article belongs to the Special Issue Integrative Approaches in Head and Neck Cancer Imaging)
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