Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas.

The aim of this work is to identify MRI texture features able to predict the response to radio-chemotherapy (RT-CHT) in patients with naso-oropharyngeal carcinoma (NPC-OPC) before treatment in order to help clinical decision making. Textural features were derived from ADC maps and post-gadolinium T1-images on a single MRI machine for 37 patients with NPC-OPC. Patients were divided into two groups (responders/non-responders) according to results from MRI scans and 18F-FDG-PET/CT performed at follow-up 3–4 and 12 months after therapy and biopsy. Pre-RT-CHT lesions were segmented, and radiomic features were extracted. A non-parametric Mann–Whitney test was performed. A p-value < 0.05 was considered significant. Receiver operating characteristic curves and area-under-the-curve values were generated; a 95% confidence interval (CI) was reported. A radiomic model was constructed using the LASSO algorithm. After feature selection on MRI T1 post-contrast sequences, six features were statistically significant: gldm_DependenceEntropy and DependenceNonUniformity, glrlm_RunEntropy and RunLengthNonUniformity, and glszm_SizeZoneNonUniformity and ZoneEntropy, with significant cut-off values between responder and non-responder group. With the LASSO algorithm, the radiomic model showed an AUC of 0.89 and 95% CI: 0.78–0.99. In ADC, five features were selected with an AUC of 0.84 and 95% CI: 0.68–1. Texture analysis on post-gadolinium T1-images and ADC maps could potentially predict response to therapy in patients with NPC-OPC who will undergo exclusive treatment with RT-CHT, being, therefore, a useful tool in therapeutical–clinical decision making.

Head and neck cancer represents the seventh most common neoplasm worldwide, with squamous cell carcinoma being the most represented histologic variant. The rising incidence of the neoplastic pathology of this district, coupled with the drastic changes in its epidemiology over the past decades, have posed significant challenges to physicians worldwide in terms of diagnosis, prognosis, and treatment. In order to meet these challenges, a considerable amount of effort has been spent by the authors of the recent literature to explore new technologies and their possible employment for the better diagnostic and prognostic definition of head and neck squamous cell carcinoma (HNSCC). Among these technologies, a growing interest has been gathering around the possible applications of dual-energy computed tomography (DECT) in head and neck pathology. Dual-energy computed tomography (DECT) utilizes two distinct X-ray energy spectra to obtain two datasets in a single scan, allowing for material differentiation based on unique attenuation profiles. DECT offers key benefits such as enhanced contrast resolution, reduced beam-hardening artifacts, and precise iodine quantification through monochromatic reconstructions. It also creates material decomposition images, like iodine maps, aiding in tumor characterization and therapy assessment. This paper aims to summarize recent findings on the use of DECT in HNSCC, providing a comprehensive overview to aid further research and exploration in the field.

Objective: Identifying MRI texture parameters able to distinguish inflammation, fibrosis, and residual cancer in patients with naso-oropharynx carcinoma after radiochemotherapy (RT-CHT). Material and methods: In this single-centre, observational, retrospective study, texture analysis was performed on ADC maps and post-gadolinium T1 images of patients with histological diagnosis of naso-oropharyngeal carcinoma treated with RT-CHT. An initial cohort of 99 patients was selected; 57 of them were later excluded. The final cohort of 42 patients was divided into 3 groups (inflammation, fibrosis, and residual cancer) according to MRI, 18F-FDG-PET/CT performed 3–4 months after RT-CHT, and biopsy. Pre-RT-CHT lesions and the corresponding anatomic area post-RT-CHT were segmented with 3D slicer software from which 107 textural features were derived. T-Student and Wilcoxon signed-rank tests were performed, and features with p-value < 0.01 were considered statistically significant. Cut-off values—obtained by ROC curves—to discriminate post-RT-CHT non-tumoural changes from residual cancer were calculated for the parameters statistically associated to the diseased status at follow-up. Results: Two features—Energy and Grey Level Non-Uniformity—were statistically significant on T1 images in the comparison between ‘positive’ (residual cancer) and ‘negative’ patients (inflammation and fibrosis). Energy was also found to be statistically significant in both patients with fibrosis and residual cancer. Grey Level Non-Uniformity was significant in the differentiation between residual cancer and inflammation. Five features were statistically significant on ADC maps in the differentiation between ‘positive’ and ‘negative’ patients. The reduction in values of such features between pre- and post-RT-CHT was correlated with a good response to therapy. Conclusions: Texture analysis on post-gadolinium T1 images and ADC maps can differentiate residual cancer from fibrosis and inflammation in early follow-up of naso-oropharyngeal carcinoma treated with RT-CHT.

The aim of this single-center, observational, retrospective study was to investigate magnetic resonance imaging (MRI) biomarkers for the assessment of radiotherapy (RT)-induced xerostomia. Twenty-seven patients who underwent radiation therapy for oropharyngeal cancer were divided into three groups according to the severity of their xerostomia—mild, moderate, and severe—clinically confirmed with the Common Terminology Criteria for Adverse Events (CTCAE). No severe xerostomia was found. Conventional and functional MRI (perfusion- and diffusion- weighted imaging) performed both pre- and post-RT were studied for signal intensity, mean apparent diffusion coefficient (ADC) values, k-trans, and area under the perfusion curves. Contrast-enhanced T1 images and ADC maps were imported into 3D slicer software, and salivary gland volumes were segmented. A total of 107 texture features were derived. T-Student and Wilcoxon signed-rank tests were performed on functional MRI parameters and texture analysis features to identify the differences between pre- and post-RT populations. A p-value < 0.01 was defined as acceptable. Receiver operating characteristic (ROC) curves were plotted for significant parameters to discriminate the severity of xerostomia in the pre-RT population. Conventional and functional MRI did not yield statistically significant results; on the contrary, five texture features showed significant variation between pre- and post-RT on the ADC maps, of which only informational measure of correlation 1 (IMC 1) was able to discriminate the severity of RT-induced xerostomia in the pre-RT population (area under the curve (AUC) > 0.7). Values lower than the cut-off of −1.473 × 10⁻¹¹ were associated with moderate xerostomia, enabling the differentiation of mild xerostomia from moderate xerostomia with a 73% sensitivity, 75% specificity, and 75% diagnostic accuracy. Therefore, the texture feature IMC 1 on the ADC maps allowed the distinction between different degrees of severity of RT-induced xerostomia in the pre-RT population. Accordingly, texture analysis on ADC maps should be considered a useful tool to evaluate salivary gland radiosensitivity and help identify patients at risk of developing more serious xerostomia before radiation therapy is administered.

Objectives: To evaluate the diagnostic performance of contrast-enhanced CT and CT-based radiomics in detecting thyroid cartilage invasion in patients with laryngeal squamous cell carcinoma (LSCC), using histopathology as the reference standard. Method: This retrospective single center study included 60 patients with histologically confirmed glottic LSCC who underwent pre-treatment contrast-enhanced CT between January 2015 and December 2025. Two head and neck radiologists independently assessed thyroid cartilage invasion based on established imaging criteria. Texture analysis was performed on manually segmented thyroid cartilage volumes, extracting 107 textural features. Feature selection and model construction were performed using LASSO logistic regression, whereas diagnostic performance was evaluated using receiver operating characteristic (ROC) curve. Results: CT-based radiologist assessment showed moderate diagnostic performance in detecting thyroid cartilage invasion, with 63.3% sensitivity, 90.0% specificity and 76.7% accuracy (Cohen’s κ = 0.534). Radiomics analysis identified shape-related and textural features as potential markers of cartilage invasion. The final radiomics model, incorporating “Shape_Maximum 2D Diameter Slice” and “glcm_Imc1,” achieved an AUC of 0.73, 81% sensitivity, 55% specificity and 67% accuracy. While radiologist evaluation offered higher specificity, radiomics demonstrated greater sensitivity, particularly in detecting subtle early-stage infiltration. Conclusions: Radiologist assessment and radiomics demonstrated different diagnostic strengths in the evaluation of thyroid cartilage invasion in glottic cancer. Although radiomics alone was not sufficient to guide clinical decision-making, the present findings suggested that it may provide additional quantitative information beyond visual assessment. Further studies will be needed to determine whether integration of radiomic features into the diagnostic workflow can improve the assessment of thyroid cartilage invasion.