BACKGROUND AND PURPOSE: Human papillomavirus–associated oropharyngeal squamous cell carcinoma is increasing in prevalence and typically occurs in younger patients than human papillomavirus–negative squamous cell carcinoma. While imaging features of human papillomavirus–positive versus human papillomavirus–negative squamous cell carcinoma nodal metastases have been described, characteristics distinguishing human papillomavirus–positive from human papillomavirus–negative primary squamous cell carcinomas have not been well established. The purpose of this project was to evaluate the use of texture features to distinguish human papillomavirus–positive and human papillomavirus–negative primary oropharyngeal squamous cell carcinoma. MATERIALS AND METHODS: Following institutional review board approval, 40 patients with primary oropharyngeal squamous cell carcinoma and known human papillomavirus status who underwent contrast-enhanced CT between December 2009 and October 2013 were included in this study. Segmentation of the primary lesion was manually performed with a semiautomated graphical-user interface. Following segmentation, an in-house-developed texture analysis program extracted 42 texture features from each segmented volume. A t test was used to evaluate differences in texture parameters between human papillomavirus–positive and human papillomavirus–negative squamous cell carcinomas. RESULTS: Of the 40 included patients, 29 had human papillomavirus–positive oropharyngeal squamous cell carcinoma and 11 had human papillomavirus–negative oropharyngeal squamous cell carcinoma. Significant differences were seen in the histogram parameters median (P = .006) and entropy (P = .016) and squamous cell carcinoma entropy (P = .043). CONCLUSIONS: There are statistically significant differences in some texture features between human papillomavirus–positive and human papillomavirus–negative oropharyngeal tumors. Texture analysis may be considered an adjunct to the evaluation of human papillomavirus status and characterization of squamous cell carcinoma.

Objectives: To evaluate the influence of MRI scanning parameters on texture analysis features.Methods: Publicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25‐mm doped, gel‐filled tubes, and 1 20‐mm tube containing 0.25 mM Gd‐DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight‐channel head coil included T1WIs with multiple flip angles (flip‐angle = 2,5,10,15,20,25,30), TR/TE = 4.09–5.47/0.90–1.35 ms, NEX = 1 and DCE with 30° flip‐angle, TR/TE=4.09–5.47/0.90–1.35, and NEX = 1,4. DICOM data were imported into an in‐house developed texture analysis program which extracted 41‐texture features including histogram, gray‐level co‐occurrence matrix (GLCM), and gray‐level run‐length (GLRL). Two‐tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip‐angle, number of excitations (NEX), scanner platform).Results: Significant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003–0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001–0.018) and scanner platform (Q < 0.0001), GLRL features (long‐run emphasis, high gray‐level run emphasis, high gray‐level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003–0.022) and scanner platform (Q < 0.0001).Conclusion: Significant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

BACKGROUND AND PURPOSE: The accurate prediction of prognosis and failure is crucial for optimizing treatment strategies for patients with cancer. The purpose of this study was to assess the performance of pretreatment CT texture analysis for the prediction of treatment failure in primary head and neck squamous cell carcinoma treated with chemoradiotherapy. MATERIALS AND METHODS: This retrospective study included 62 patients diagnosed with primary head and neck squamous cell carcinoma who underwent contrast-enhanced CT examinations for staging, followed by chemoradiotherapy. CT texture features of the whole primary tumor were measured using an in-house developed Matlab-based texture analysis program. Histogram, gray-level co-occurrence matrix, gray-level run-length, gray-level gradient matrix, and Laws features were used for texture feature extraction. Receiver operating characteristic analysis was used to identify the optimal threshold of any significant texture parameter. We used multivariate Cox proportional hazards models to examine the association between the CT texture parameter and local failure, adjusting for age, sex, smoking, primary tumor stage, primary tumor volume, and human papillomavirus status. RESULTS: Twenty-two patients (35.5%) developed local failure, and the remaining 40 (64.5%) showed local control. Multivariate analysis revealed that 3 histogram features (geometric mean [hazard ratio = 4.68, P = .026], harmonic mean [hazard ratio = 8.61, P = .004], and fourth moment [hazard ratio = 4.56, P = .048]) and 4 gray-level run-length features (short-run emphasis [hazard ratio = 3.75, P = .044], gray-level nonuniformity [hazard ratio = 5.72, P = .004], run-length nonuniformity [hazard ratio = 4.15, P = .043], and short-run low gray-level emphasis [hazard ratio = 5.94, P = .035]) were significant predictors of outcome after adjusting for clinical variables. CONCLUSIONS: Independent primary tumor CT texture analysis parameters are associated with local failure in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.

The pathogenesis and radiologic features of apical periodontitis are reviewed, with emphasis on the differential considerations of other odontogenic and non-odontogenic lesions that can cause lucency around the tooth apex. Periapical lucencies are often seen incidentally at head and neck imaging studies performed for indications not related to the teeth. These lesions are, however, occasionally manifestations of diseases that have a wide range of effects and may at times represent the source of symptoms that prompted the study. The vast majority of periapical lucencies are the result of apical periodontal or pulpal disease. If found in an advanced state or left untreated, disease related to the tooth may spread to adjacent tissues, including the sinuses, orbits, deep fascial spaces of the neck, and intracranial structures, and result in a significant increase in patient morbidity and mortality. Although the majority of periapical lucencies seen on radiographs and computed tomographic images occur secondary to apical periodontal or pulpal disease, not all lucencies near the tooth root are due to infection. Lucency near the tooth root may be seen in the setting of other diseases of odontogenic and non-odontogenic origin, incl...

Venous variants and pathologic abnormalities are the most common causes for pulsatile tinnitus. Advances in CT, MRI, and endovascular techniques allow for improved diagnostic accuracy and an increa... Venous variants and pathologic abnormalities are the most common causes of pulsatile tinnitus. These conditions include causes of turbulence within normally located veins and sinuses, and abnormall...

Granulomatous diseases may affect several organs in the head and neck, and recognition of radiologic findings that suggest granulomatous disease will facilitate appropriate clinical management. Granulomatous diseases have a varied etiology that includes autoimmune, infectious, idiopathic, and hereditary causes. The unifying factor in these diseases is the formation of granulomas, which histologically are mononuclear inflammatory cells or macrophages surrounded by lymphocytes. Granulomatous diseases often have systemic manifestations that affect organs throughout the body. Granulomatous diseases with head and neck manifestations include granulomatosis with polyangiitis, Churg-Strauss syndrome, Behçet disease, chronic granulomatous disease, and sarcoidosis. Infectious causes include tuberculosis, cat-scratch disease, syphilis, leprosy, actinomycosis, rhinoscleroma, and fungal infections. In the head and neck, granulomatous disease may affect the orbits, sinonasal cavities, salivary glands, aerodigestive tract, temporal bone, or skull base. Imaging findings include sinonasal opacification, ocular and other soft-tissue masses, osseous erosion, airway narrowing, lymphadenopathy, and salivary gland in...