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Cancers
Special Issues
Radiomics in Head and Neck Cancer Care
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Radiomics in Head and Neck Cancer Care

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 2480

Special Issue Editor

Dr. Aurora Mirabile

E-Mail Website
Guest Editor
Department of Medical Oncology, IRCCS San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy
Interests: head and neck cancer biology and treatment; immunotherapy; novel treatments; toxicity management

Special Issue Information

Dear Colleagues,

The head and neck cancer (HNC) mortality rate is high regardless of therapeutic strategy, since treatment personalization, currently based on stage, site, and histological parameters, has suboptimal performance. One of the reasons could be the heterogeneous tumor biology, which may be captured by imaging, matching qualitative features of tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), with quantitative features.

The study of this process, named “radiomics”, could optimize the therapeutic strategy on a patient-specific basis with an important clinical impact in the better personalization of HNC management from different risk groups: therapy could be escalated or de-escalated, and the follow-up could be enhanced or delayed, leading to improved outcomes and assessing the relapse risk.

This Special Issue of Cancers therefore encompasses new research articles and timely reviews on all aspects of radiomics’ role and application in head and neck cancer.

Dr. Aurora Mirabile
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. 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

  • radiomics
  • personalized therapeutic approach
  • radiology
  • improving outcome
  • head and neck cancer
  • imaging biomarkers
  • artificial intelligence

Published Papers (2 papers)

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Research

13 pages, 1583 KiB  
Article
A Radiomics-Based Machine Learning Perspective on the Parotid Gland as a Potential Surrogate Marker for HPV in Oropharyngeal Cancer
by Gordian Prasse, Agnes Glaas, Hans-Jonas Meyer, Veit Zebralla, Andreas Dietz, Kathrin Hering, Thomas Kuhnt and Timm Denecke
Cancers 2023, 15(22), 5425; https://doi.org/10.3390/cancers15225425 - 15 Nov 2023
Viewed by 886
Abstract
Background: In treatment of oropharyngeal squamous cell carcinoma (OPSCC), human papillomavirus status (HPV) plays a crucial role. The HPV-positive subtype tends to affect younger patients and is associated with a more favorable prognosis. HPV-associated lesions have been described in the parotid gland, which [...] Read more.
Background: In treatment of oropharyngeal squamous cell carcinoma (OPSCC), human papillomavirus status (HPV) plays a crucial role. The HPV-positive subtype tends to affect younger patients and is associated with a more favorable prognosis. HPV-associated lesions have been described in the parotid gland, which is included in routine imaging for OPSCC. This work aims to explore the ability of an ML system to classify HPV status based on imaging of the parotid gland, which is routinely depicted on staging imaging. Methods: Using a radiomics approach, we investigate the ability of five contemporary machine learning (ML) models to distinguish between HPV-positive and HPV-negative OPSCC based on non-contrast computed tomography (CT) data of tumor volume (TM), locoregional lymph node metastasis (LNM), and the parotid gland (Parotid). After exclusion of cases affected by streak artefacts, 53 patients (training set: 39; evaluation set: 14) were retrospectively evaluated. Classification performances were tested for significance against random optimistic results. Results: The best results are AUC 0.71 by XGBoost (XGB) for TM, AUC 0.82 by multi-layer perceptron (MLP) for LNM, AUC 0.76 by random forest (RF) for Parotid, and AUC 0.86 by XGB for a combination of all three regions of interest (ROIs). Conclusions: The results suggest involvement of the parotid gland in HPV infections of the oropharyngeal region. While the role of HPV in parotid lesions is under active discussion, the migration of the virus from the oral cavity to the parotid gland seems plausible. The imaging of the parotid gland offers the benefit of fewer streak artifacts due to teeth and dental implants and the potential to screen for HPV in cases of an absent or unlocatable tumor. Future investigation can be directed to validation of the results in independent datasets and to the potential of improvement of current classification models by addition of information based on the parotid gland. Full article
(This article belongs to the Special Issue Radiomics in Head and Neck Cancer Care)
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21 pages, 873 KiB  
Article
Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients
by Sebastian Starke, Alex Zwanenburg, Karoline Leger, Fabian Lohaus, Annett Linge, Goda Kalinauskaite, Inge Tinhofer, Nika Guberina, Maja Guberina, Panagiotis Balermpas, Jens von der Grün, Ute Ganswindt, Claus Belka, Jan C. Peeken, Stephanie E. Combs, Simon Boeke, Daniel Zips, Christian Richter, Esther G. C. Troost, Mechthild Krause, Michael Baumann and Steffen Löckadd Show full author list remove Hide full author list
Cancers 2023, 15(19), 4897; https://doi.org/10.3390/cancers15194897 - 9 Oct 2023
Viewed by 1281
Abstract
Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization [...] Read more.
Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95% confidence interval (CI): 0.22–0.36) and successfully stratified patients into groups with low and high risk of disease progression (p=0.003). On the CT dataset, ensembles of multi-outcome CNNs and of single-outcome ViTs trained with segmentation loss performed best (C-index: 0.26 and 0.26, CI: 0.18–0.34 and 0.18–0.35, respectively), both with significant risk stratification for LRC in independent validation (p=0.002 and p=0.011). Further validation of the developed multitask-learning models is planned based on a prospective validation study, which has recently completed recruitment. Full article
(This article belongs to the Special Issue Radiomics in Head and Neck Cancer Care)
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