Concomitant presentation of eosinophilic or oncocytic mucoepidermoid carcinoma, immunoglobulin G4-related disease, and adult-onset asthma and periocular xanthogranuloma: Case report of 3 uncommon clinical entities.

RATIONALE: Immunoglobulin (Ig) G4-related disease (IgG4-RD) reportedly has a strong relationship with adult-onset asthma and periocular xanthogranuloma (AAPOX) and may be linked to sclerosing mucoepidermoid carcinoma (MEC). We present a rare case of IgG4-RD and AAPOX occurring in a patient with resected eosinophilic or oncocytic MEC. PATIENT CONCERNS: A 52-year-old woman was referred to our rheumatology clinic in 2020 to be evaluated for suspected IgG4-RD. DIAGNOSES: The patient had diagnoses of periorbital xanthelasmas, worsening glucocorticoid-dependent chronic rhinosinusitis and adult-onset asthma, and cervical lymphadenopathy persisting 2 years after resection of a low-grade MEC of a minor salivary gland. INTERVENTIONS: Because the patient's symptomatic relief was glucocorticoid dependent, IgG4-RD was suspected, and she was referred to our medical center. Her amylase and lipase levels were elevated. Serum IgG4 levels were initially within normal limits, but IgG4-RD was diagnosed because of the presence of lymphadenopathy and evidence of pancreatitis, which was shown on positron emission tomography/computed tomography. Furthermore, the IgG4 levels later increased without explanation. After the patient began combination therapy with a glucocorticoid (prednisone) and methotrexate, her symptoms improved but recurred when the daily oral glucocorticoid dosage decreased below 10 mg. An excisional biopsy of her right submandibular gland in 2021 yielded results consistent with IgG4-RD. In addition, AAPOX was diagnosed, given the presence of periocular edema and plaques, adult-onset asthma, and rhinosinusitis. OUTCOME: The patient was carcinoma free at last follow-up and was receiving medication to treat the other conditions. LESSONS: The diagnosis of these 3 concomitant, uncommon entities required approximately 7 years of medical investigations. Clinicians should know that IgG4-RD, AAPOX, and MEC may occur together.

Association of meningitis and clival canal defect: case illustration, management, and systematic review of the literature.

OBJECTIVE: Improper embryological development of the clivus, a bony structure that comprises part of the skull base, can lead to a clival canal defect. Previously thought to be a benign condition, clival canals have been reported to be associated with meningitis and meningoceles. In this review, the authors sought to present an unpublished case of a patient with a clival canal defect associated with meningitis and to evaluate all other reported cases. METHODS: In October 2020, a search of PubMed, Web of Science, and Scopus was conducted to identify all cases of clival canals reported from January 1, 1980, through October 31, 2020. RESULTS: Including the case presented herein, 13 cases of clival canals, 11 in children (84.6%) and 2 in adults (15.4%), have been identified. Of the pediatric patients, 5 (45.5%) had an associated meningocele, and 8 (72.7%) had meningitis. Nine of the 13 patients (69.2%) had defects that were treated surgically, 5 (38.5%) by a transnasal approach and 4 (30.8%) by a transoral approach. Two patients (15.4%) were treated with drainage and antibiotics, 1 patient (7.7%) was treated solely with antibiotics, and 1 patient (7.7%) was not treated. In the literature review, 8 reports of clival canals were found to be associated with meningitis, further contributing to the notion that the clival canal may be an overlooked source of recurrent infection. In several of these cases, surgical repair of the lesion was curative, thus preventing continued episodes of meningitis. CONCLUSIONS: When a patient has recurrent meningitis with no clear cause, taking a closer look at clival anatomy is recommended. In addition, if a clival canal defect has been identified, surgical repair should be considered a safe and effective primary treatment option.

Incidence of Concomitant Semicircular Canal Dehiscence With Otosclerosis.

Objective: The concurrence of otosclerosis and superior semicircular canal dehiscence (SSCD) presents a diagnostic challenge and failure to differentiate between these 2 diagnoses results in mischaracterization and unsuccessful surgery. The objective of this study is to identify the incidence of SSCD in patients who have computed tomography (CT) evidence of otosclerosis. Study Design: Retrospective chart review. Setting: Tertiary referral hospital. Patients: Adults with CT scan of the temporal bone diagnosed with radiological unilateral or bilateral fenestral otosclerosis from January 1995 to April 2018. Methods: Retrospective review of patient imaging from a multi-center tertiary-referral health system from January 1995 to April 2018. Imaging was reviewed to quantify the incidence of SSCD among patients with CT-diagnosed bilateral fenestral otosclerosis. Poor quality imaging was excluded from review. Results: One-thousand two-hundred eight patients (1214 CT scans) were identified with otosclerosis, of which 373 were diagnosed with fenestral otosclerosis (663 ears) with imaging of sufficient quality for review. This population was predominantly female (57.2%) with bilateral fenestral otosclerosis (78%). Of these, 23 ears (3.5%) had definitive evidence of SSCD, with an additional 15 ears (2.3%) with possible radiographic evidence of SSCD. There was no significant difference in laterality between the SSCD and otosclerosis. Conclusions: Among 373 patients with fenestral otosclerosis per CT temporal bone imaging at a tertiary referral hospital, as many as 8.3% of patients had radiographic evidence of SSCD. Given this incidence, it continues to be important to consider SSCD when diagnosing and treating otosclerosis.

Endoscopic Endonasal and Transmaxillary Resection of a Nasopharyngeal Angiofibroma.

Juvenile nasopharyngeal angiofibromas (JNAs) are highly vascularized benign lesions that mainly arise in adolescent male patients. Current treatment options include observation, radiotherapy, or surgical resection. Surgical resection is the treatment of choice as it achieves immediate and complete results, thereby minimizing the risk of recurrence. Here we have reported on a transnasal-transmaxillary endoscopic approach for resection of a JNA in a 16-year-old male patient. The patient presented with 1 year of progressive nasal obstruction and intermittent episodes of right-sided epistaxis. Because of the highly vascular nature of the tumor, embolization of the internal maxillary artery and branches was performed 1 day prior to surgery. Under endoscopic visualization, the right nasal cavity was entered and the tumor was debulked. An anterior maxillary antrotomy via a sublabial approach was completed for further access. A maxillary antrostomy, ethmoidectomy, and sphenoidotomy was performed (Video 1). The pterygopalatine fossa was entered, dissected, and the tumor was circumferentially freed from the masticator space laterally, the orbit and skull base superiorly, and the basisphenoid posteriorly. The pterygoid plates and basisphenoid were drilled down to ablate residual tumor. The patient tolerated the procedure well and postoperative magnetic resonance imaging confirmed gross total resection. The sinonasal cavity was widely patent and no tumor recurrence was noted 5 months postoperatively. JNAs can be challenging to treat as they can invade extensively into local structures. A careful consideration of treatment options and approaches are necessary when faced with these lesions.

Machine learning of biomarkers and clinical observation to predict eosinophilic chronic rhinosinusitis: a pilot study.

BACKGROUND: Subtyping chronic rhinosinusitis (CRS) by tissue eosinophilia has prognostic and therapeutic implications, and is difficult to predict using peripheral eosinophil counts or polyp status alone. The objective of this study was to test machine learning for prediction of eosinophilic CRS (eCRS). METHODS: Input variables were defined as peripheral eosinophil count, urinary leukotriene E4 (uLTE4) level, and polyp status. The output was diagnosis of eCRS, defined as tissue eosinophil count >10 per high-power field. Patients undergoing surgery for CRS were retrospectively reviewed for complete datasets. Univariate analysis was performed for each input as a predictor of eCRS. Logistic regression and artificial neural network (ANN) machine learning models were developed using random and surgeon-specific training/test datasets. RESULTS: A total of 80 patients met inclusion criteria. In univariate analysis, area under the receiver operator characteristic curve (AUC) for peripheral eosinophil count and uLTE4 were 0.738 (95% confidence interval [CI], 0.616 to 0.840) and 0.728 (95% CI, 0.605 to 0.822), respectively. Presence of polyps was 94.1% sensitive, but 51.7% specific. Logistic regression models using random and surgeon specific datasets resulted in AUC of 0.882 (95% CI, 0.665 to 0.970) and 0.945 (95% CI, 0.755 to 0.995), respectively. ANN models resulted in AUC of 0.918 (95% CI, 0.756 to 0.975) and 0.956 (95% CI, 0.828 to 0.999) using random and surgeon-specific datasets, respectively. Model comparison of logistic regression and ANN was not statistically different. All machine learning models had AUC greater than univariate analyses (all p < 0.003). CONCLUSION: Machine learning of 3 clinical inputs has the potential to predict eCRS with high sensitivity and specificity in this patient population. Prospective investigation using larger and more diverse populations is warranted.

Design and Implementation of a Robotic Surgery Training Experience Logging System.

PURPOSE: Residents currently log robotic cases in the ACGME system as a "surgeon" if they performed any critical step of the procedure on the surgeon console. There is no standardization as to which steps or how much of the procedure should be performed by the resident. It was our objective to establish a tool for logging the true operative experience in robotic surgery to aid in assessing surgical competency as well as curriculum development. MATERIALS AND METHODS: We propose a tool to log surgical skill progression, experience, and feedback for robotic cases. A web-based robotic experience logging system (RoboLog) was developed with procedures deconstructed to their major steps. Trainees may request the supervising attending review their performance. RoboLog provides automated summary reports to both residents and attendings. RESULTS: RoboLog was successfully developed and piloted with a total of 310 cases logged over 1 year. A reporting structure was developed where residents could view statistics on several data points such as step-specific involvement and feedback from attending staff. Detailed data on resident experience were obtained. For instance, 82% of the 151 robotic prostatectomies were logged as "surgeon", yet urethral transection had <35% resident involvement. CONCLUSIONS: Our current system for logging robotic experience is lacking given the fact that resident involvement on the surgical console is variable. Widespread usage of a logging system with more insight into step-specific involvement is needed. RoboLog fills this need and can be used to track robotic training progress and aid in development of a standardized curriculum.

Automating linear accelerator quality assurance.

PURPOSE: The purpose of this study was 2-fold. One purpose was to develop an automated, streamlined quality assurance (QA) program for use by multiple centers. The second purpose was to evaluate machine performance over time for multiple centers using linear accelerator (Linac) log files and electronic portal images. The authors sought to evaluate variations in Linac performance to establish as a reference for other centers. METHODS: The authors developed analytical software tools for a QA program using both log files and electronic portal imaging device (EPID) measurements. The first tool is a general analysis tool which can read and visually represent data in the log file. This tool, which can be used to automatically analyze patient treatment or QA log files, examines the files for Linac deviations which exceed thresholds. The second set of tools consists of a test suite of QA fields, a standard phantom, and software to collect information from the log files on deviations from the expected values. The test suite was designed to focus on the mechanical tests of the Linac to include jaw, MLC, and collimator positions during static, IMRT, and volumetric modulated arc therapy delivery. A consortium of eight institutions delivered the test suite at monthly or weekly intervals on each Linac using a standard phantom. The behavior of various components was analyzed for eight TrueBeam Linacs. RESULTS: For the EPID and trajectory log file analysis, all observed deviations which exceeded established thresholds for Linac behavior resulted in a beam hold off. In the absence of an interlock-triggering event, the maximum observed log file deviations between the expected and actual component positions (such as MLC leaves) varied from less than 1% to 26% of published tolerance thresholds. The maximum and standard deviations of the variations due to gantry sag, collimator angle, jaw position, and MLC positions are presented. Gantry sag among Linacs was 0.336 ± 0.072 mm. The standard deviation in MLC position, as determined by EPID measurements, across the consortium was 0.33 mm for IMRT fields. With respect to the log files, the deviations between expected and actual positions for parameters were small (<0.12 mm) for all Linacs. Considering both log files and EPID measurements, all parameters were well within published tolerance values. Variations in collimator angle, MLC position, and gantry sag were also evaluated for all Linacs. CONCLUSIONS: The performance of the TrueBeam Linac model was shown to be consistent based on automated analysis of trajectory log files and EPID images acquired during delivery of a standardized test suite. The results can be compared directly to tolerance thresholds. In addition, sharing of results from standard tests across institutions can facilitate the identification of QA process and Linac changes. These reference values are presented along with the standard deviation for common tests so that the test suite can be used by other centers to evaluate their Linac performance against those in this consortium.