Post-Traumatic Surgical Emphysema and Sialocele with Fistula Following Knife Wounds to the Head and Neck of a 30-Year-Old Woman.

BACKGROUND Trauma to the left submandibular gland is an infrequent entity, with only a few cases reported in the literature. Recommended management consists of excision of the gland if trauma is suspected; if trauma is not clearly identified during the surgical exploration and the gland is not removed, post-traumatic complications such as fistula or sialocele may occur. In such cases, conservative measures including aspiration, pressure bandages, and anti-sialogogues are the first step of treatment and surgical excision is reserved for unsuccessful cases. CASE REPORT This report describes a case of post-traumatic surgical emphysema and sialocele with fistula following knife wounds to the head and neck of a 30-year-old woman. The patient had an incised wound to the left submandibular gland. Subsequently, a painful slow-growing mass developed and the diagnosis of sialocele was considered. Confirmation of this diagnosis was achieved by performing a fine-needle aspiration, which revealed a high amylase level within the collection. Afterwards, pressure bandages were applied and no recurrence of the sialocele was observed in imaging follow-up. CONCLUSIONS This report shows that although submandibular gland trauma is rare, it can lead to salivary fistula or sialocele. The diagnosis of sialocele can be confirmed by imaging combined with fine-needle aspiration and measurement of amylase levels in the aspirate. Identification of traumatic sialocele and fistula at an early stage will lead to the most appropriate management.

Non-union and use of proton pump inhibitors in the treatment of femoral and tibial shaft fractures: a nested case-control study.

BACKGROUND: Proton pump inhibitors (PPIs) are one of the most frequently used drugs worldwide. Previous research has shown that they could increase the risk of fracture and interfere with the fracture healing process. In this study, we analyzed the effect of PPIs on the risk of fracture non-union in patients with femoral and tibial shaft fractures. METHODS: A case-control study was conducted at our institution, including a total of 254 patients who underwent fixation surgery for a femoral or tibial shaft fracture between January 2012 and December 2017. We defined cases as patients who experienced a delayed union (case group A; n = 44), or non-union (cases group B; n = 12). Cases were matched by age, sex, and fractured bone, to 144 controls who did not experience delayed fracture union and did not require further procedures. A conditional logistic regression analysis was performed adjusted to potential confounders, and to the proportion of days covered (PDC) with PPIs. RESULTS: Adjusted ORs (95% CI) for undergoing a nail dynamization following a tibial or femoral shaft fracture were 1.38 (0.70-2.65) for any use PPIs. Patients with a longer PPI treatment courses (PDC ≥ 0.5) had an adjusted OR of 1.86 (0.70-4.76) for undergoing nail dynamization when compared with controls. Contrastingly, patients with a PDC < 0.5 had an adjusted OR of 1.03 (0.43-2.48). The adjusted OR (95% CI) for undergoing additional surgical procedures due to non-union was 4.5 (0.62-32.8) for any use of PPIs, and 12.3 (1.9-81.0) in patients with a PDC ≥ 0.5. CONCLUSIONS: A prolonged use of PPIs use was associated with a higher risk of fracture non-union in tibial and femoral shaft fractures.

Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models.

Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.

Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy.

PURPOSE: To determine whether machine learning assisted-texture analysis of multi-energy virtual monochromatic image (VMI) datasets from dual-energy CT (DECT) can be used to differentiate metastatic head and neck squamous cell carcinoma (HNSCC) lymph nodes from lymphoma, inflammatory, or normal lymph nodes. MATERIALS AND METHODS: A retrospective evaluation of 412 cervical nodes from 5 different patient groups (50 patients in total) having undergone DECT of the neck between 2013 and 2015 was performed: (1) HNSCC with pathology proven metastatic adenopathy, (2) HNSCC with pathology proven benign nodes (controls for (1)), (3) lymphoma, (4) inflammatory, and (5) normal nodes (controls for (3) and (4)). Texture analysis was performed with TexRAD® software using two independent sets of contours to assess the impact of inter-rater variation. Two machine learning algorithms (Random Forests (RF) and Gradient Boosting Machine (GBM)) were used with independent training and testing sets and determination of accuracy, sensitivity, specificity, PPV, NPV, and AUC. RESULTS: In the independent testing (prediction) sets, the accuracy for distinguishing different groups of pathologic nodes or normal nodes ranged between 80 and 95%. The models generated using texture data extracted from the independent contour sets had substantial to almost perfect agreement. The accuracy, sensitivity, specificity, PPV, and NPV for correctly classifying a lymph node as malignant (i.e. metastatic HNSCC or lymphoma) versus benign were 92%, 91%, 93%, 95%, 87%, respectively. CONCLUSION: Machine learning assisted-DECT texture analysis can help distinguish different nodal pathology and normal nodes with a high accuracy.

Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.

OBJECTIVES: This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction. METHODS: Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck. RESULTS: Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy. CONCLUSIONS: Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone. KEY POINTS: • Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.

Spectral Computed Tomography: Technique and Applications for Head and Neck Cancer.

Spectral computed tomography (CT) or dual-energy CT (DECT) is an advanced form of CT with increasing applications in head and neck radiology. This article provides an overview of the DECT technique and reviews current applications for the evaluation of neck pathology, focusing on oncologic applications. Included are an overview of the basic underlying principles and approaches for DECT scan acquisition and material characterization; a discussion of various DECT reconstructions and a brief overview of practical issues pertaining to DECT implementation, including those related to workflow impact of DECT; and a discussion of various applications of DECT for the evaluation of the neck, especially in oncology.

Dual-Energy Computed Tomography of the Neck: A Pictorial Review of Normal Anatomy, Variants, and Pathologic Entities Using Different Energy Reconstructions and Material Decomposition Maps.

There is increasing use of dual-energy computed tomography (DECT) for the evaluation of head and neck pathologic entities. Optimal DECT utilization requires familiarity with the appearance of normal tissues variants, and pathologic entities on different DECT reconstructions that may be used in clinical practice. The purpose of this article is to provide a practical, pictorial review of the appearance of normal anatomic structures and different neoplastic and nonneoplastic head and neck pathologic entities on commonly used DECT reconstructions.

Sensitivity and positive predictive value of magnetic resonance imaging in the diagnosis of elastofibroma dorsi: review of fourteen cases.

BACKGROUND: Elastofibroma dorsi (ED) is a benign soft tissue tumor that classically presents as an ill-defined mass at the inferior pole of the scapula. Several studies have indicated the benefits of using magnetic resonance imaging (MRI) to identify ED. In this study, we calculate the sensitivity and positive predictive value (PPV) of MRI in the diagnosis of ED using histopathology as the gold standard diagnostic method. Clinical characteristics of ED and radiologic features of MRI as well as treatment options are discussed. MATERIALS AND METHODS: A systematic retrospective review was performed of all ED patients treated in our center between 1999 and 2009. MRI and histopathology samples were performed in all cases. The MRI sensitivity and PPV in the diagnosis of ED were calculated. RESULTS: A total of 15 patients who were treated within the study period were reviewed; of these, were 14 (3 men, 11 women) true ED cases. MRI scan results matched the histopathology in 14 of 15 patients; 1 false-positive patient was observed, and no false-negative patients (negative MRI and positive histopathology) were noted. The PPV and sensitivity of MRI scan in the diagnosis of ED were 93.3% (95% confidence interval, 68.0%-100.0%) and 100% (95% confidence interval, 75.2%-100.0%) respectively. CONCLUSIONS: MRI is a useful tool for assessment of ED and can potentially help avoid the need for unnecessary biopsy and surgery, especially in the asymptomatic patient.