Neighborhood socioeconomic status and race are associated with incidence disparities in oral cavity cancers.

OBJECTIVES: To determine the association between neighborhood socioeconomic status (nSES), race and incidence rate trends of oral cavity cancer (OCC). MATERIALS AND METHODS: We used data from the SEER (Surveillance, Epidemiology, and End Results) 18 Census Tract-level SES and Rurality Database (2006-2018) database of the National Cancer Institute to create cohorts of OCC patients between 2006 and 2018. Annual incidence rates were calculated and trends in rates were estimated using joinpoints regression. RESULTS: The incidence of OCC is the highest among low nSES White Americans (2.86 per 100 000 persons) and the lowest among high nSES Black Americans (1.17 per 100 000 persons). Incidence has significantly increased among Asian Americans (annual percent change [APC]: low nSES-2.4, high nSES-2.6) and White Americans (APC: low nSES-1.4, high nSES-1.6). Significant increases in the incidence of oral tongue cancer in these groups primarily drive this increase. Other increases were noted in alveolar ridge cancer among White Americans and hard palate cancer among Asian Americans. OCC incidence decreased significantly in Hispanic Americans of high nSES (APC: -2.5) and Black Americans of low nSES (APC: -2.7). Floor of mouth cancer incidence decreased among most groups. CONCLUSION: Despite the overall decreasing incidence of OCC, these trends are inconsistent among all OCC subsites. Differences are seen by race, nSES, and subsite, indicating intersectional barriers that extend beyond nSES and race and ethnicity alone. Further research on risk factors and developing interventions targeting vulnerable groups is needed.

Skills transfer to sinus surgery via a low-cost simulation-based curriculum.

BACKGROUND: Surgical skill development outside the operating room aims to improve technique and subsequent patient safety. The purpose of this study was to evaluate the correlation between technical and cognitive skills with cadaveric endoscopic sinus surgery (ESS) performance and change in ESS performance before and after implementation of a dedicated ESS simulation-based and knowledge-based curriculum. METHODS: A before-after study design was implemented among 10 medical students and 10 junior otolaryngology residents. Participants completed a knowledge-based, multiple-choice ESS pretest and watched an ESS prosection video. Participants performed 9 tasks on a previously validated low-cost, low-technology, nonbiologic sinus surgery task trainer followed by cadaveric maxillary antrostomy and anterior ethmoidectomy. Participants then completed a simulation-based and knowledge-based ESS curriculum followed by a repeat cadaveric maxillary antrostomy and anterior ethmoidectomy. Performance was graded with a 5-point global rating scale (GRS) and a 5-point ESS-specific checklist. RESULTS: We observed a stronger correlation between the multiple-choice, knowledge-based, ESS pretest scores and cadaveric ESS GRS score (r = 0.73) than between task trainer performance and cadaveric ESS GRS score (r = 0.43). We also noted a significant increase in precurriculum vs postcurriculum mean ± standard deviation (SD) cadaveric ESS checklist scores for both medical students (1.18 ± 0.25 vs 2.58 ± 0.57; p = 0.0002) and residents (2.09 ± 0.78 vs 2.88 ± 0.54; p = 0.023). The greatest improvements for residents were in performance of uncinectomy, enlargement of maxillary os, and identification of the bulla. CONCLUSION: These findings provide evidence supporting the use of ESS training curricula outside the operating room.

Anatomical Region Segmentation for Objective Surgical Skill Assessment with Operating Room Motion Data.

Background Most existing objective surgical motion analysis schemes are limited to structured surgical tasks or recognition of motion patterns for certain categories of surgeries. Analyzing instrument motion data with respect to anatomical structures can break the limit, and an anatomical region segmentation algorithm is required for the analysis. Methods An atlas was generated by manually segmenting the skull base into nine regions, including left/right anterior/posterior ethmoid sinuses, frontal sinus, left and right maxillary sinuses, nasal airway, and sphenoid sinus. These regions were selected based on anatomical and surgical significance in skull base and sinus surgery. Six features, including left and right eye center, nasofrontal beak, anterior tip of nasal spine, posterior edge of hard palate at midline, and clival body at foramen magnum, were used for alignment. The B-spline deformable registration was adapted to fine tune the registration, and bony boundaries were automatically extracted for final precision improvement. The resultant deformation field was applied to the atlas, and the motion data were clustered according to the deformed atlas. Results Eight maxillofacial computed tomography scans were used in experiments. One was manually segmented as the atlas. The others were segmented by the proposed method. Motion data were clustered into nine groups for every dataset and outliers were filtered. Conclusions The proposed algorithm improved the efficiency of motion data clustering and requires limited human interaction in the process. The anatomical region segmentations effectively filtered out the portion of motion data that are out of surgery sites and grouped them according to anatomical similarities.