Recent Advances in Radiological Imaging for Head and Neck Squamous Cell Carcinoma
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 1 September 2025 | Viewed by 62
Special Issue Editors
Interests: radiomics; imaging; high dimensional data; prediction modeling; clinical trials
Interests: molecular imaging; PET/CT; radiomics; clinical trials; data analysis
Special Issue Information
Dear Colleagues,
Head and Neck Squamous Cell Carcinoma (HNSCC), the seventh most common cancer globally, is characterized by poor prognosis and a poor survival rate. Radiological imaging, like computed tomography (CT), positron emission tomography (PET), or PET combined with CT, plays a vital role in the diagnosis and localization of primary tumors, such as identifying tumor stages, regional and distant metastasis and recurrence, planning treatment modalities, assessing therapeutic responses, and the management and prognosis of patients.
This Special Issue aims to highlight the recent advances and clinical insights gained in multidisciplinary research in radiological imaging relevant to HNSCC. This Special Issue considers critical systematic reviews and original research articles encompassing technological developments, high-quality data generation, and advanced mathematical, statistical, and computational interventions to inform (a) innovations in radiomic workflows in image acquisition, reconstruction, segmentation, and pre-processing, as well as (b) applications of radiological images to aid in diagnosis, treatment modalities, and the management and prognosis of HNSCC patients.
Dr. Mintu Nath
Dr. Fergus McKiddie
Guest Editors
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Keywords
- head and neck squamous cell carcinoma
- computed tomography (CT)
- positron emission tomography (PET)
- PET-CT
- radiological imaging
- image processing
- image segmentation
- diagnosis
- treatment modalities
- prediction and prognostic models
- mathematical and statistical models
- machine learning and deep learning techniques
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