Clinical language search algorithm from free-text: facilitating appropriate imaging.

BACKGROUND: The comprehensiveness and maintenance of the American College of Radiology (ACR) Appropriateness Criteria (AC) makes it a unique resource for evidence-based clinical imaging decision support, but it is underutilized by clinicians. To facilitate the use of imaging recommendations, we develop a natural language processing (NLP) search algorithm that automatically matches clinical indications that physicians write into imaging orders to appropriate AC imaging recommendations. METHODS: We apply a hybrid model of semantic similarity from a sent2vec model trained on 223 million scientific sentences, combined with term frequency inverse document frequency features. AC documents are ranked based on their embeddings' cosine distance to query. For model testing, we compiled a dataset of simulated simple and complex indications for each AC document (n = 410) and another with clinical indications from randomly sampled radiology reports (n = 100). We compare our algorithm to a custom google search engine. RESULTS: On the simulated indications, our algorithm ranked ground truth documents as top 3 for 98% of simple queries and 85% of complex queries. Similarly, on the randomly sampled radiology report dataset, the algorithm ranked 86% of indications with a single match as top 3. Vague and distracting phrases present in the free-text indications were main sources of errors. Our algorithm provides more relevant results than a custom Google search engine, especially for complex queries. CONCLUSIONS: We have developed and evaluated an NLP algorithm that matches clinical indications to appropriate AC guidelines. This approach can be integrated into imaging ordering systems for automated access to guidelines.

Cost-analysis of in-office versus operating room sialendoscopy: Comparison of cost burden and outcomes.

PURPOSE: Office-based procedures in otolaryngology are increasingly utilized to increase efficiency, reduce cost, and eliminate risks associated with surgery. Gland-preserving surgical management of sialadenitis and sialolithiasis are often performed in the operating room, although many surgeons are moving this practice to clinic. We aim to determine the difference in patient charges and perioperative outcomes for salivary gland procedures performed in the clinic versus the OR. METHODS: Retrospective series of patients presenting with sialolithiasis, acute or chronic sialadenitis, and stricture between 2010 and 2019. Demographics, perioperative variables, setting, and charge data were collected. RESULTS: 528 patients underwent operative intervention (n = 427 office, n = 101 OR). Cohort demographics were comparable. Sialolithiasis was the most common presenting diagnosis in both cohorts. Both cohorts had similar rates of complete (p = 0.09) and partial (p = 0.97) response to treatment. A higher percentage of patients in the OR group reported no improvement (21.4 vs 12.2%, p = 0.034). Overall complications were similar (p = 0.582). Mean charges were statistically greater in the OR ($5560.35 OR vs $1298.33 office, p < 0.001). Operative time was significantly reduced in the office group (21.8 min vs 60.85 min, p < 0.001). CONCLUSIONS: Appropriately selected patients can be successfully treated in outpatient clinic without compromising patient safety or quality while significantly reducing the financial burden to patients and the healthcare system.

Domain specific word embeddings for natural language processing in radiology.

BACKGROUND: There has been increasing interest in machine learning based natural language processing (NLP) methods in radiology; however, models have often used word embeddings trained on general web corpora due to lack of a radiology-specific corpus. PURPOSE: We examined the potential of Radiopaedia to serve as a general radiology corpus to produce radiology specific word embeddings that could be used to enhance performance on a NLP task on radiological text. MATERIALS AND METHODS: Embeddings of dimension 50, 100, 200, and 300 were trained on articles collected from Radiopaedia using a GloVe algorithm and evaluated on analogy completion. A shallow neural network using input from either our trained embeddings or pre-trained Wikipedia 2014 + Gigaword 5 (WG) embeddings was used to label the Radiopaedia articles. Labeling performance was evaluated based on exact match accuracy and Hamming loss. The McNemar's test with continuity and the Benjamini-Hochberg correction and a 5×2 cross validation paired two-tailed t-test were used to assess statistical significance. RESULTS: For accuracy in the analogy task, 50-dimensional (50-D) Radiopaedia embeddings outperformed WG embeddings on tumor origin analogies (p < 0.05) and organ adjectives (p < 0.01) whereas WG embeddings tended to outperform on inflammation location and bone vs. muscle analogies (p < 0.01). The two embeddings had comparable performance on other subcategories. In the labeling task, the Radiopaedia-based model outperformed the WG based model at 50, 100, 200, and 300-D for exact match accuracy (p < 0.001, p < 0.001, p < 0.01, and p < 0.05, respectively) and Hamming loss (p < 0.001, p < 0.001, p < 0.01, and p < 0.05, respectively). CONCLUSION: We have developed a set of word embeddings from Radiopaedia and shown that they can preserve relevant medical semantics and augment performance on a radiology NLP task. Our results suggest that the cultivation of a radiology-specific corpus can benefit radiology NLP models in the future.

High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels.

BACKGROUND: Reidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radiology reports to generate high precision nodule predictions. METHODS: 888 CTs from Lung Nodule Analysis were used to train a 2-dimensional (2D) object detection neural network. A pipeline of 2D object detection, 3D unsupervised clustering, false positive reduction, and axial-slice numbers were used to generate nodule candidates. 47 CTs from the National Lung Cancer Screening Trial (NLST) were used for model evaluation. RESULTS: Our nodule detector achieved a precision of 0.962 at a recall of 0.573 on the NLST test set for any nodule. When adjusting for unintended nodule predictions, we achieved a precision of 0.931 at a recall 0.561, which corresponds to 0.06 false positives per CT. Error analysis revealed better detection of nodules with soft tissue attenuation compared to ground glass and undeterminable attenuation. Nodule margins, size, location, and patient demographics did not differ between correct and incorrect predictions. CONCLUSIONS: Utilization of axial-slice numbers from radiology reports allowed for development of a lung nodule detector with a low false positive rate compared to prior feature-engineering and machine learning approaches. This high precision nodule detector can reduce time spent on reidentification of prior nodules during lung cancer screening and can rapidly develop new institutional datasets to explore novel applications of computer vision in lung cancer imaging.

Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing.

BACKGROUND: A systematic approach to MRI protocol assignment is essential for the efficient delivery of safe patient care. Advances in natural language processing (NLP) allow for the development of accurate automated protocol assignment. We aim to develop, evaluate, and deploy an NLP model that automates protocol assignment, given the clinician indication text. METHODS: We collected 7139 spine MRI protocols (routine or contrast) and 990 head MRI protocols (routine brain, contrast brain, or other) from a single institution. Protocols were split into training (n = 4997 for spine MRI; n = 839 for head MRI), validation (n = 1071 for spine MRI, fivefold cross-validation used for head MRI), and test (n = 1071 for spine MRI; n = 151 for head MRI) sets. fastText and XGBoost were used to develop 2 NLP models to classify spine and head MRI protocols, respectively. A Flask-based web app was developed to be deployed via Heroku. RESULTS: The spine MRI model had an accuracy of 83.38% and a receiver operator characteristic area under the curve (ROC-AUC) of 0.8873. The head MRI model had an accuracy of 85.43% with a routine brain protocol ROC-AUC of 0.9463 and contrast brain protocol ROC-AUC of 0.9284. Cancer, infectious, and inflammatory related keywords were associated with contrast administration. Structural anatomic abnormalities and stroke/altered mental status were indicative of routine spine and brain MRI, respectively. Error analysis revealed increasing the sample size may improve performance for head MRI protocols. A web version of the model is provided for demonstration and deployment. CONCLUSION: We developed and web-deployed two NLP models that accurately predict spine and head MRI protocol assignment, which could improve radiology workflow efficiency.

An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow.

Although machine learning (ML) has made significant improvements in radiology, few algorithms have been integrated into clinical radiology workflow. Complex radiology IT environments and Picture Archiving and Communication System (PACS) pose unique challenges in creating a practical ML schema. However, clinical integration and testing are critical to ensuring the safety and accuracy of ML algorithms. This study aims to propose, develop, and demonstrate a simple, efficient, and understandable hardware and software system for integrating ML models into the standard radiology workflow and PACS that can serve as a framework for testing ML algorithms. A Digital Imaging and Communications in Medicine/Graphics Processing Unit (DICOM/GPU) server and software pipeline was established at a metropolitan county hospital intranet to demonstrate clinical integration of ML algorithms in radiology. A clinical ML integration schema, agnostic to the hospital IT system and specific ML models/frameworks, was implemented and tested with a breast density classification algorithm and prospectively evaluated for time delays using 100 digital 2D mammograms. An open-source clinical ML integration schema was successfully implemented and demonstrated. This schema allows for simple uploading of custom ML models. With the proposed setup, the ML pipeline took an average of 26.52 s per second to process a batch of 100 studies. The most significant processing time delays were noted in model load and study stability times. The code is made available at " http://bit.ly/2Z121hX ". We demonstrated the feasibility to deploy and utilize ML models in radiology without disrupting existing radiology workflow.

Content Validity of a High-Fidelity Surgical Middle Ear Simulator: A Randomized Prospective International Multicenter Trial.

OBJECTIVE: After demonstration of face validity of a surgical middle ear simulator (SMS) previously, we assessed the content validity of the simulator with otolaryngology residents. STUDY DESIGN: Multicenter randomized prospective international study. SETTING: Four academic institutions. METHODS: Novice participants were randomized into control, low-fidelity (LF), and high-fidelity (HF) groups. Control and LF produced 2 recordings from 2 attempts, and HF produced 4 recordings from 10 attempts, with trials 1, 4, 7, and 10 used for scoring. Three blinded experts graded videos of the simulated stapedectomy operation using an objective skills assessment test format consisting of global and stapedotomy-specific scales. RESULTS: A total of 152 recordings from 61 participants were included. Baseline characteristics did not differ significantly between groups. Depending on the step of the operation, inter-rater reliability ranged from 24 to 90%. For LF and HF, years of training was significantly associated with improved scores in certain objective skills assessment test subparts. HF outperformed the control group on stapes and global scores ( p < 0.05). The HF group demonstrated improvement in global score over trials, but plateaued after four trials. Scores varied greatly for participants from different institutions in certain operative steps, such as transecting incudostapedial joints, likely due to differences in instrumentation and time elapsed since manufacture. CONCLUSION: Practice with SMS led to better performance in both global and stapes-specific scores. Further studies are needed to examine construct validity and to create otology-appropriate grading systems. Variables like instrumentation and decline in flexibility of the simulator after 12 months greatly affect performance on the simulator.

Predictors of future academic productivity in pediatric otolaryngology fellowship applicants.

OBJECTIVES: To investigate which components of pediatric otolaryngology fellowship applications are more closely predictive of future academic productivity in applicants who go on to complete their fellowship training. METHODS: Applications to our institution's ACGME accredited pediatric otolaryngology fellowship program through the SF Match program for the years 2011-2016 were reviewed. Applicant files on record were utilized to extract independent variables including sex, mean USMLE score, residency program Doximity ranking, military experience, number of national honors/awards, AOA status, total number of publications listed on application, number of first author publications listed on application, and AAOHNS Committee involvement. Academic productivity was determined by number of PubMed indexed publications per year, practice setting, and H-index (Scopus). Statistical analysis consisted of multivariate and univariate regression models, with p < 0.05 being considered statistically significant. RESULTS: Multivariate regression showed that USMLE Step 1 and 2 mean score and number of publications listed on application exhibited statistically significant correlations with a higher number of future post fellowship publications per year. Residency program Doximity rank, applicant number of awards and honors, AOA status, and number of first author publications were not predictive of future academic productivity. No statistically significant associations were found between any variables and the faculty position outcome variable. CONCLUSIONS: Quantifiable criteria in pediatric otolaryngology fellowship applications, such as number of listed publications and mean USMLE scores are strongly correlated with future academic productivity metrics.

Deep Learning for Predictive Analysis of Pediatric Otolaryngology Personal Statements: A Pilot Study.

OBJECTIVE: The personal statement is often an underutilized aspect of pediatric otolaryngology fellowship applications. In this pilot study, we use deep learning language models to cluster personal statements and elucidate their relationship to applicant rank position and postfellowship research output. STUDY DESIGN: Retrospective cohort. SETTING: Single pediatric tertiary care center. METHODS: Data and personal statements from 115 applicants to our fellowship program were retrieved from San Francisco Match. BERT (Bidirectional Encoder Representations From Transformers) was used to generate document embeddings for clustering. Regression and machine learning models were used to assess the relationship of personal statements to number of postfellowship publications per year when controlling for publications, board scores, Alpha Omega Alpha status, gender, and residency. RESULTS: Document embeddings of personal statements were found to cluster into 4 distinct groups by K-means clustering: 2 focused on "training/research" and 2 on "personal/patient anecdotes." Training clusters 1 and 2 were associated with an applicant-organization fit by a single pediatric otolaryngology fellowship program on univariate but not multivariate analysis. Models utilizing document embeddings alone were able to equally predict applicant-organization fit (receiver operating characteristic areas under the curve, 0.763 and 0.750 vs 0.419; P values >.05) as compared with models utilizing applicant characteristics and personal statement clusters alone. All predictive models were poor predictors of postfellowship publications per year. CONCLUSION: We demonstrate ability for document embeddings to capture meaningful information in personal statements from pediatric otolaryngology fellowship applicants. A larger study can further differentiate personal statement clusters and assess the predictive potential of document embeddings.

Algorithmic Prediction of Delayed Radiology Turn-Around-Time during Non-Business Hours.

RATIONALE AND OBJECTIVES: Radiology turnaround time is an important quality measure that can impact hospital workflow and patient outcomes. We aimed to develop a machine learning model to predict delayed turnaround time during non-business hours and identify factors that contribute to this delay. MATERIALS AND METHODS: This retrospective study consisted of 15,117 CT cases from May 2018 to May 2019 during non-business hours at two hospital campuses after applying exclusion criteria. Of these 15,177 cases, 7,532 were inpatient cases and 7,585 were emergency cases. Order time, scan time, first communication by radiologist, free-text indications, and other clinical metadata were extracted. A combined XGBoost classifier and Random Forest natural language processing model was trained with 85% of the data and tested with 15% of the data. The model predicted two measures of delay: when the exam was ordered to first communication (total time) and when the scan was completed to first communication (interpretation time). The model was analyzed with the area under the curve (AUC) of receiver operating characteristic (ROC) and feature importance. Source code: https://bit.ly/2UrLiVJ RESULTS: The algorithm reached an AUC of 0.85, with a 95% confidence interval [0.83, 0.87], when predicting delays greater than 245 minutes for "total time" and 0.71, with a 95% confidence interval [0.68, 0.73], when predicting delays greater than 57 minutes for "interpretation time". At our institution, CT scan description (e.g. "CTA chest pulmonary embolism protocol"), time of day, and year in training were more predictive features compared to body part, inpatient status, and hospital campus for both interpretation and total time delay. CONCLUSION: This algorithm can be applied clinically when a physician is ordering the scan to reasonably predict delayed turnaround time. Such a model can be leveraged to identify factors associated with delays and emphasize areas for improvement to patient outcomes.

Malignant Pineal Parenchymal Tumors in Adults: A National Cancer Database Analysis.

BACKGROUND: Limited retrospective data exist on malignant pineal parenchymal tumors (PPTs) in adults, and there are no large previous studies that review clinical outcomes across the 3 treatment arms of surgery, radiotherapy, and chemotherapy. As a result, optimal disease management has yet to be defined. OBJECTIVE: To evaluate treatment trends and perform survival analysis in adult PPT. METHODS: The National Cancer Database was queried for histologically confirmed PPT diagnosed from 2007 to 2016. Univariate and multivariate Cox regressions were used to evaluate the prognostic impact of covariates. Kaplan-Meier survival curves were generated for comparative subanalyses. RESULTS: Of the 251 patients who met inclusion criteria, 172 had PPTs of intermediate differentiation (PPTID) and 79 had pineoblastoma. A plurality of patients with pineoblastoma were treated with trimodal therapy (39.1%), whereas patients with PPTID were commonly treated with either surgery alone or surgery and radiation (33.7% each). Factors independently associated with improved overall survival include younger patient age, female sex, lower comorbidity score, lower tumor grade, and treatment with surgery or radiation (each P < .05). Subanalyses confirm the effect of radiation on survival in patients with grade III PPTID with subtotal resection; however, no survival benefit of adjuvant radiation is demonstrated in patients with grade II PPTID with subtotal resection. CONCLUSION: Although radiotherapy and surgery were found to increase survival in all patients with PPT, there was no demonstrable survival benefit of adjuvant radiation in surgically treated patients with grade II PPTID. This suggests that adjuvant radiotherapy may not add significant survival benefit in many adult patients with grade II PPTID.

Impact of COVID-19 on Otolaryngology Literature.

OBJECTIVES/HYPOTHESIS: To understand the effect of the COVID-19 pandemic on the volume, quality, and impact of otolaryngology publications. STUDY DESIGN: Retrospective analysis. METHODS: Fifteen of the top peer-reviewed otolaryngology journals were queried on PubMed for COVID and non-COVID-related articles from April 1, 2020 to March 31, 2021 (pandemic period) and pre-COVID articles from the year prior. Information on total number of submissions and rate of acceptance were collected from seven top-ranked journals. RESULTS: Our PubMed query returned 759 COVID articles, 4,885 non-COVID articles, and 4,200 pre-COVID articles, corresponding to a 34% increase in otolaryngology publications during the pandemic period. Meta-analysis/reviews and miscellaneous publication types made up a larger portion of COVID publications than that of non-COVID and pre-COVID publications. Compared to pre-COVID articles, citations per article 120 days after publication and Altmetric Attention Score were higher in both COVID articles (citations/article: 2.75 ± 0.45, P < .001; Altmetric Attention Score: 2.05 ± 0.60, P = .001) and non-COVID articles (citations/article: 0.03 ± 0.01, P = .002; Altmetric Attention Score: 0.67 ± 0.28, P = .016). COVID manuscripts were associated with a 1.65 times higher acceptance rate compared to non-COVID articles (P < .001). CONCLUSIONS: COVID-19 was associated with an increase in volume, citations, and attention for both COVID and non-COVID articles compared to pre-COVID articles. However, COVID articles were associated with lower evidence levels than non-COVID and pre-COVID articles. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:1364-1373, 2022.

High-Fidelity Surgical Middle Ear Simulator: A Pilot Study.

OBJECTIVES: To assess a middle ear simulator as a surgical training tool among a cohort of novice trainees. STUDY DESIGN: Prospective pilot study. SETTING: The George Washington University Hospital (tertiary care academic hospital). PARTICIPANTS: Twenty one medical students and physician assistant students completed the protocol. Each student produced four recordings over 10 attempts. The two raters were attending surgeons from the George Washington University Hospital. INTERVENTIONS: Stapedotomies performed on a high-fidelity middle ear simulator. Participants were assessed at baseline with a Purdue pegboard test and trained with video and a low fidelity middle ear simulator. MAIN OUTCOME MEASURES: Two experts rated recordings on scales based upon a validated objective skills assessment test (OSAT) format. RESULTS: Inter-rater reliability was strong across all stapedotomy skills scores and global rating scores. Participants demonstrated statistically significant improvement up to the third recording (seventh attempt), but the scores on the fourth recording (tenth attempt) were not significantly different from the third. One participant failed to improve in score over 10 attempts. Pegboard test performance was not correlated with score improvement. Low-fidelity trial time was significantly correlated to stapedotomy and global rating scores. CONCLUSIONS: This pilot study serves as the first investigation of this middle ear simulator amongst a cohort of trainees. A high-fidelity middle ear simulator may serve as a measurement tool to select future surgical trainees, customize training pathways, and assess surgical capacity before graduation.

Sensorineural Hearing Loss and Tinnitus Characteristics in Patients With Idiopathic Intracranial Hypertension.

OBJECTIVES: To characterize patterns of sensorineural hearing loss (SNHL) and tinnitus in patients with idiopathic intracranial hypertension (IIH). STUDY DESIGN: Retrospective chart review. SETTING: Tertiary referral center. PATIENTS: Adult patients diagnosed with IIH via lumbar puncture (LP) between 2010 and 2020 who had available audiograms. The study included a total of 40 patients; 33 women, and 7 men with a median age of 43. INTERVENTIONS: Diagnostic LP and audiogram. MAIN OUTCOME MEASURES: Otologic symptoms, ophthalmologic signs, hearing thresholds, cerebrospinal fluid opening pressures. RESULTS: The most commonly reported symptoms were tinnitus in 28 (70%, 23 pulsatile and 5 tonal), aural fullness in 11 (28%), and vertigo in 10 (25%). Twenty-nine patients had ophthalmologic examinations and 18 had evidence of papilledema. Twenty-five (63%) patients had hearing loss in at least one ear at one frequency range. Patients presented with both unilateral and bilateral hearing loss across low, middle, and high frequency ranges. No significant association was observed between hearing loss threshold and LP opening pressure except for 250 Hz in the left ear. After stratification by tinnitus group (pulsatile, tonal, and no tinnitus), no significant difference was found between mean hearing loss threshold at different frequencies. In addition, no significant mean age difference was identified in patients within each tinnitus group. CONCLUSIONS: There was no classic pattern or presentation for hearing loss in our IIH patients. They developed sudden, unilateral, or bilateral SNHL in low, middle, or high frequency range. The degree of hearing loss did not correlate with CSF opening pressure.

Head and neck osteosarcomas: Analysis of treatment trends and survival outcomes in the United States (2004-2016).

BACKGROUND: Head and neck osteosarcoma (HNOS) is a rare primary bone tumor with limited data to guide treatment approaches. METHODS: The NCDB was used to identify patients diagnosed with HNOS. Kaplan-Meier and Cox multivariate regression were used to examine the impact of each treatment on overall survival (OS). RESULTS: We identified 821 patients diagnosed with HNOS. Utilization of neoadjuvant chemotherapy + surgery increased from zero cases in 2004 to 24% of cases in 2016. Among surgically treated patients, 5-year OS was 55.8% (CI: 51.7-60.1%). No difference was seen in OS between each treatment cohort on multivariate analysis. However, neoadjuvant chemotherapy + surgery + adjuvant chemotherapy had improved 18-month survival compared to surgery alone (95.8% vs. 78.5%, p = 0.031). CONCLUSIONS: Our study demonstrated no survival benefit in perioperative chemotherapy or radiation therapy; however, short-term survival improvement in patients receiving both neoadjuvant and adjuvant chemotherapy displays promise and requires further investigation.

Transoral robotic surgery versus nonrobotic resection of oropharyngeal squamous cell carcinoma.

The aim of this study is to evaluate the impact of transoral robotic surgery (TORS) compared to nonrobotic surgery (NRS) on overall survival in oropharyngeal squamous cell carcinoma (OPSCC). We performed a retrospective study of patients with HPV+ and HPV- OPSCC undergoing TORS or NRS with neck dissection using the National Cancer Database from the years 2010-2016. Among patients with OPSCC in our cohort, 3167 (58.1%) patients underwent NRS and 2288 (41.9%) underwent TORS. TORS patients demonstrated better overall survival than NRS patients (HPV+ patients: aHR 0.74, p = 0.02; HPV- patients: aHR 0.58, p < 0.01). Subsite analysis showed TORS was correlated with improved survival in base of tongue (BoT) primaries for both HPV+ (aHR 0.46, p = 0.01) and HPV- (aHR 0.42, p = 0.01) OPSCC. Compared to NRS, TORS is associated with improved overall survival for HPV+ and HPV- OPSCC, as well as greater overall survival for BoT primaries.

Pediatric head and neck rhabdomyosarcoma: An analysis of treatment and survival in the United States (1975-2016).

INTRODUCTION: Rhabdomyosarcoma (RMS) is the most common soft tissue head and neck sarcoma in children. Stringent analysis of survival data is imperative to optimize treatment. METHODS: The National Cancer Database (NCDB, 2004-2016) and the Surveillance, Epidemiology, and End Results Program (SEER, 1975-2016) were queried for patients ages 0-19 with RMS of the head and neck. Survival trends were analyzed using univariable logistic regression and Chi-square pairwise comparisons. Survival by treatment was analyzed using log-rank tests, Kaplan Meier, and multivariable Cox-proportional hazards regression. RESULTS: 1147 (63.3% age <10 years, 54.3% male) and 459 (71.4% age <10 years, 53.6% male) patients were identified in NCDB and SEER, respectively. In NCDB, embryonal (n = 625, 54.5%) and alveolar (n = 300, 26.2%) were the most common histology and nonparameningeal/non-orbital (n = 634, 55.3%), followed by parameningeal (n = 303, 26.4%) and orbital (n = 210, 18.3%) the most common location. Five-year overall survival (OS) was 70.3% with lower mortality risk for embryonal histology (adjusted HR [aHR] = 0.69, p = 0.0038). Orbital tumors had greatest survival (5-year OS = 92.4%) of all sites, and their mortality risk was higher with chemotherapy and radiation compared to surgery and radiation (aHR = 6.27, p = 0.0302). SEER analysis showed average increased survival by 4% per year (p < 0.0001), but no significant 5-year OS difference when comparing 1976-1980 and 2006-2010 (p = 0.0843). CONCLUSIONS: Major prognostic factors for survival in childhood RMS of the head and neck were embryonal histology, orbital site, extent of disease, and use of SRT for orbital tumors. Larger population studies are required to demonstrate survival differences between treatment modalities for other sites.

Six1 proteins with human branchio-oto-renal mutations differentially affect cranial gene expression and otic development.

Single-nucleotide mutations in human SIX1 result in amino acid substitutions in either the protein-protein interaction domain or the homeodomain, and cause ∼4% of branchio-otic (BOS) and branchio-oto-renal (BOR) cases. The phenotypic variation between patients with the same mutation, even within affected members of the same family, make it difficult to functionally distinguish between the different SIX1 mutations. We made four of the BOS/BOR substitutions in the Xenopus Six1 protein (V17E, R110W, W122R, Y129C), which is 100% identical to human in both the protein-protein interaction domain and the homeodomain, and expressed them in embryos to determine whether they cause differential changes in early craniofacial gene expression, otic gene expression or otic morphology. We confirmed that, similar to the human mutants, all four mutant Xenopus Six1 proteins access the nucleus but are transcriptionally deficient. Analysis of craniofacial gene expression showed that each mutant causes specific, often different and highly variable disruptions in the size of the domains of neural border zone, neural crest and pre-placodal ectoderm genes. Each mutant also had differential effects on genes that pattern the otic vesicle. Assessment of the tadpole inner ear demonstrated that while the auditory and vestibular structures formed, the volume of the otic cartilaginous capsule, otoliths, lumen and a subset of the hair cell-containing sensory patches were reduced. This detailed description of the effects of BOS/BOR-associated SIX1 mutations in the embryo indicates that each causes subtle changes in gene expression in the embryonic ectoderm and otocyst, leading to inner ear morphological anomalies.