Early-Onset Pancreatic Neuroendocrine Tumors: Clinical Presentation, Pathology Features, and Oncological Outcomes.

BACKGROUND: Early-Onset (EO) pancreatic neuroendocrine tumor (PanNET) is a rare disease, but whether it is clinically different from late-onset (LO) PanNET is unknown. Our study aimed to evaluate clinical differences and disease outcomes between EO-PanNET and LO-PanNET and to compare sporadic EO-PanNET with those with a hereditary syndrome. METHODS: Patients with localized PanNET who underwent pancreatectomy at Memorial Sloan Kettering between 2000 and 2017 were identified. Those with metastatic disease and poorly differentiated tumors were excluded. EO-PanNET was defined as <50 and LO-PanNET >50 years of age at the time of diagnosis. Family history and clinical and pathology characteristics were recorded. RESULTS: Overall 383 patients were included, 107 (27.9%) with EO-PanNET. Compared with LO-PanNET, EO-PanNET were more likely to have a hereditary syndrome (2.2% vs. 16%, P <0.001) but had similar pathology features such as tumor grade ( P =0.6), size (2.2 Vs. 2.3 cm, P =0.5) and stageof disease ( P =0.8). Among patients with EO-PanNET, those with hereditary syndrome had more frequently a multifocal disease (65% vs. 3.3%, P <0.001). With a median follow-up of 70 months (range 0-238), the 5-year cumulative incidence of recurrence after curative surgery was 19% (95% CI 12%-28%) and 17% (95% CI 13%-23%), in EO-PanNET and LO-PanNET ( P =0.3). Five-year disease-specific survival was 99% (95% CI 98%-100%) with no difference with respect to PanNET onset time ( P =0.26). CONCLUSIONS: In this surgical cohort, we found that EO-PanNET is associated with hereditary syndromes but has pathologic characteristics and oncological outcomes similar to LO-PanNET. These findings suggest that patients with EO-PanNET can be managed similarly to those with LO-PanNET.

Development and Validation of a Google Street View Pedestrian Safety Audit Tool.

BACKGROUND: Assessing aspects of intersections that may affect the risk of pedestrian injury is critical to developing child pedestrian injury prevention strategies, but visiting intersections to inspect them is costly and time-consuming. Several research teams have validated the use of Google Street View to conduct virtual neighborhood audits that remove the need for field teams to conduct in-person audits. METHODS: We developed a 38-item virtual audit instrument to assess intersections for pedestrian injury risk and tested it on intersections within 700 m of 26 schools in New York City using the Computer-assisted Neighborhood Visual Assessment System (CANVAS) with Google Street View imagery. RESULTS: Six trained auditors tested this instrument for inter-rater reliability on 111 randomly selected intersections and for test-retest reliability on 264 other intersections. Inter-rater kappa scores ranged from -0.01 to 0.92, with nearly half falling above 0.41, the conventional threshold for moderate agreement. Test-retest kappa scores were slightly higher than but highly correlated with inter-rater scores (Spearman rho = 0.83). Items that were highly reliable included the presence of a pedestrian signal (K = 0.92), presence of an overhead structure such as an elevated train or a highway (K = 0.81), and intersection complexity (K = 0.76). CONCLUSIONS: Built environment features of intersections relevant to pedestrian safety can be reliably measured using a virtual audit protocol implemented via CANVAS and Google Street View.