Pradeep, Aishwarya

ABSTRACT Background Family history of pancreatic cancer (PC), pathogenic germline variants (PGVs), and increased intrapancreatic fat (IPF) are individually associated with increased PC risk. Whether IPF is elevated in high‐risk individuals (HRIs) with PC family history or PGVs is unknown. Although MRI is the preferred modality for IPF measurement, region‐of‐interest–based approaches are prone to sampling bias because pancreatic fat is heterogeneously distributed. We aimed to compare IPF among HRIs and matched non‐HRI controls using an Artificial intelligence (AI)‐based tool for whole pancreatic IPF measurement applied to magnetic resonance imaging (MRI). Methods We developed and validated a deep‐learning pipeline for IPF measurement that incorporates automated pancreas segmentation on T1‐weighted MRI images registered to the corresponding iterative decomposition of water and fat with echo asymmetry and least squares estimation images to compute IPF using proton density fat fraction (PDFF). This pipeline was used to quantify IPF and assess IPF distribution patterns in 85 HRIs and 170 non‐HRIs matched 1:2 for age, sex, BMI, and diabetes status. A surface erosion approach was developed to minimize the impact of peripancreatic fat on IPF measurement. Linear models adjusting for matching variables were used to assess associations. Results Model‐expert agreement for whole‐pancreas PDFF was excellent (intraclass correlation coefficient 0.99; 95% CI, 0.98–0.99 mean absolute error 1.4%). Mean PDFF did not differ between HRIs and controls (25.8% [11.5] vs. 26.9% [11.4]; p = 0.32). PDFF increased with age ( p = 0.015), male sex ( p < 0.001), presence of diabetes ( p = 0.009), BMI ( p < 0.001). IPF distribution patterns did not differ by risk group. Conclusion An AI‐based approach provides accurate, scalable, and objective assessment of IPF quantity and distribution. IPF quantity and spatial distribution do not appear to differ based on germline or familial predisposition to PC.

Background As the myriad clinical and radiographic presentations of spontaneous intracranial hypotension are increasingly recognized, disease prevalence appears to rise. True incidence data remain insufficient. We sought to calculate the incidence of spontaneous intracranial hypotension among the residents of Olmsted County, Minnesota. Methods Our institution is a quaternary care center for spontaneous intracranial hypotension evaluation and provides primary neurologic care for local residents. All patients who were evaluated at our institution from 2019 to 2021 for spontaneous intracranial hypotension were reviewed, and cases of spontaneous intracranial hypotension with advanced imaging work-up using digital subtraction myelography or dynamic computed tomography myelography were identified. Patients who were residents of Olmsted County were identified. Results Fourteen patients with spontaneous intracranial hypotension were identified in Olmsted County during the study period, all of whom were adults and underwent our institution's standard spontaneous intracranial hypotension work-up. In Olmsted County, the incidence of spontaneous intracranial hypotension was 3.8 per 100,000 adult population, including 3.2 per 100,000 adult females and 2.5 per 100,000 adult males. Seven of 14 patients (50%) had a cerebrospinal fluid-venous fistula. Conclusion This study uniquely presents the incidence rate of spontaneous intracranial hypotension in the general population of Olmsted County, Minnesota. Spontaneous intracranial hypotension is more common in women than men and has an overall incidence roughly equivalent to that of aneurysmal subarachnoid hemorrhage in Olmsted County, a widely known neurologic condition. Approximately half the cases of spontaneous intracranial hypotension are due to cerebrospinal fluid-venous fistulas, which generally require specialty expertise for diagnosis and treatment.