The impact of facility type and volume on treatment and overall survival in craniopharyngioma.

BACKGROUND: Craniopharyngiomas are uncommon benign sellar and parasellar tumors with high overall survival (OS) and recurrence rates. Treatment is often surgical but may include adjuvant therapies. The impact of adjuvant therapy and surgical approach have been evaluated, however, facility volume and type have not. The purpose of this study is to analyze the influence of facility volume and type on treatment modalities, extent of surgery and survival of craniopharyngioma. METHODS: The 2004-2016 National Cancer Database (NCDB) was queried for patients diagnosed with craniopharyngioma. Facilities were classified by type (academic vs. non-academic) and low-volume center (LVC) (Treating < 8 patients over the timeline) versus high-volume center (HVC), (Treating ≥ 8 patients over the timeline). Differences in treatment course, outcomes, and OS by facility type were assessed. RESULTS: 3730 patients (51.3% female) with mean age 41.2 ± 22.0 were included with a 5-year estimated OS of 94.8% (94.0-95.5%). 2564 (68.7%) patients were treated at HVC, of which 2142 (83.5%) were treated at academic facilities. Patients treated at HVC's were more likely to undergo both surgery and radiation. Surgical approach at HVC was more likely to be endoscopic. Patients treated at HVC demonstrated significantly higher 5-year OS compared to patients treated at LVC (96% [95% CI 95.6-97.1% versus 91.2% [95% CI 89-92.7%] with lower risk of mortality (Hazard ratio [95% CI] = 0.69 [0.56-0.84]). CONCLUSION: Treatment of craniopharyngioma at HVC compared to LVC is associated with improved OS, lower 30- and 90-day postoperative mortality risk, and more common use of both radiotherapy and endoscopic surgical approach.

Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study.

INTRODUCTION: This study investigates how quantitative texture analysis can be used to non-invasively identify novel radiogenomic correlations with clear cell renal cell carcinoma (ccRCC) biomarkers. METHODS: The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma open-source database was used to identify 190 sets of patient genomic data that had corresponding multiphase contrast-enhanced CT images in The Cancer Imaging Archive. 2,824 radiomic features spanning fifteen texture families were extracted from CT images using a custom-built MATLAB software package. Robust radiomic features with strong inter-scanner reproducibility were selected. Random forest, AdaBoost, and elastic net machine learning (ML) algorithms evaluated the ability of the selected radiomic features to predict the presence of 12 clinically relevant molecular biomarkers identified from the literature. ML analysis was repeated with cases stratified by stage (I/II vs. III/IV) and grade (1/2 vs. 3/4). 10-fold cross validation was used to evaluate model performance. RESULTS: Before stratification by tumor grade and stage, radiomics predicted the presence of several biomarkers with weak discrimination (AUC 0.60-0.68). Once stratified, radiomics predicted KDM5C, SETD2, PBRM1, and mTOR mutation status with acceptable to excellent predictive discrimination (AUC ranges from 0.70 to 0.86). CONCLUSIONS: Radiomic texture analysis can potentially identify a variety of clinically relevant biomarkers in patients with ccRCC and may have a prognostic implication.

Geographic Distribution of Otolaryngology Advance Practice Providers and Physicians.

OBJECTIVES: Advanced practice providers (APPs), namely physician assistants (PAs) and nurse practitioners (NPs), play an increasing role in meeting growing demands for otolaryngologic services, particularly in rural communities. This study analyzes the geographic distribution of otolaryngology providers, which is essential to addressing future demands. STUDY DESIGN: Cross-sectional study. SETTING: Medicare Provider Utilization and Payment Data for 2017. METHODS: Current Procedural Terminology codes were used to identify APPs providing 10 common otolaryngologic services. Geographic distribution was evaluated by calculating densities of APPs and otolaryngologists per 100,000 persons in urban versus rural counties as defined by the National Center for Health Statistics Urban-Rural Classification Scheme. RESULTS: We identified cohorts of 8573 otolaryngologists, 1148 NPs, and 895 PAs. There were significantly higher population-controlled densities of otolaryngologists and APPs in urban counties as compared with rural counties. The majority of otolaryngologists (92.1%) and APPs (83.3%) were in urban counties. However, the proportion of APPs (16.7%) in rural counties was significantly higher than the proportion of otolaryngologists (7.9%) in rural counties (P < .01). A significant majority of rural counties (72.2%) had zero identified providers, and a greater proportion of rural counties (5.0%) were served exclusively by APPs as compared with urban counties (3.2%). CONCLUSIONS: Although otolaryngologists and APPs mostly practiced in urban counties, a relatively higher proportion of APPs practiced in rural counties when compared with otolaryngology physicians. The majority of rural counties did not have any otolaryngologic providers. Given the expected shortages of otolaryngology physicians, APPs may play a critical role in addressing these gaps in access.

Association Between Insurance Type and Outcomes of Reconstructive Head and Neck Cancer Surgery.

OBJECTIVES/HYPOTHESIS: Although the benefits of expanding health insurance coverage are clear, there are limited studies comparing the different types of insurance. This study aims to determine the association between insurance type and outcomes in patients with head and neck cancer undergoing reconstructive surgery in the United States. METHODS: Population-based cross-sectional study of the 2012-2014 National Inpatient Sample. We identified 1,314 patients with head and neck cancers undergoing tumor ablative surgery followed by pedicled or free flap reconstruction of oncologic defects. Insurance type was classified as private, Medicare, Medicaid, self-pay, or other. The primary outcome was extended length of stay (LOS), defined as greater than 14 days, which represented the 75th percentile of the study sample. Secondary outcomes included acute medical complications, surgical complications, morbidities, and costs. Analyses were adjusted for gender, geographic location, and various medical comorbidities. RESULTS: In univariate analysis, insurance type was associated with extended LOS (P = .001), medical complications (P = <.001), and mortalities (P = .020). After controlling for other covariates in the multivariate analysis, compared to private insurance, Medicare and Medicaid were both associated with significantly higher odds of extended LOS (adjusted odds ratio [OR] [95% confidence interval (CI)] = 1.73 [1.09-2.76] and 2.22 [1.38-3.58], respectively). Medicare was associated with significantly higher odds of medical complications, but Medicaid was not (adjusted OR [95% CI] = 1.53 [1.02-2.31] and 1.64 [0.97-2.78], respectively). CONCLUSIONS: Medicaid and Medicare were independently associated with extended LOS after reconstructive head and neck cancer surgery. Medicare was associated with higher rates of medical complications. Efforts to address LOS should target care planning and coordination. LEVEL OF EVIDENCE: NA Laryngoscope, 132:1946-1952, 2022.

Pediatric chronic sinusitis: diagnosis and management.

PURPOSE OF REVIEW: Review the diagnosis and management of pediatric chronic sinusitis given recent advances in both surgical and medical management. RECENT FINDINGS: Balloon catheter dilation (BCD) of the sinuses has been used as an adjunct to adenoidectomy or in lieu of traditional endoscopic sinus surgery. BCD has been shown to be a safe technique in children although its efficacy compared to maxillary sinus irrigation or traditional sinus surgery cannot be determined based on current studies. SUMMARY: New advances in BCD and biologics may serve as useful adjuncts in surgical and medical therapy respectively with additional research needed to better delineate the optimal indications for each in the treatment continuum.

CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.

OBJECTIVES: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV). METHODS: A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC). RESULTS: The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification. CONCLUSION: Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC. KEY POINTS: • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively.