Idiopathic Spontaneous Pneumoperitoneum and Call for a Multi-Institutional Database.

Idiopathic spontaneous pneumoperitoneum (ISP) refers to intraperitoneal air of unknown origin when other more common etiologies such as traumatic, intrathoracic, and gynecologic etiologies have been excluded. We present a case of a 42-year-old woman with insignificant history presenting with ISP who underwent exploratory laparoscopy and thorough diagnostic workup that was ultimately unrevealing. This case report adds to the existing literature of ISP, and the authors recommend initiating a multi-institutional database to improve our understanding of ISP and contribute to developing consensus guidelines for presumed ISP.

Surgical Implications of LGBTQ+ Health Disparities: A Review.

Lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+) patients face challenging health care disparities. However, due to restrictions in reporting and collection of sexual orientation and gender identity (SOGI) demographic data, comprehensive studies of surgical disparities in the LGBTQ+ population are limited. This review aims to summarize the existing literature describing surgical disparities in LGBTQ+ patients and to identify areas of surgical care in which further studies are warranted. This review addresses the literature in infectious diseases, substance use disorders, bariatrics, cardiovascular medicine, oncology, and laryngology as relevant to surgical practice. Understanding the current landscape of knowledge in LGBTQ+ surgical disparities and the areas where gaps in research exist will help the surgeon to create a framework of practice to provide more equitable care to LGBTQ+ patients.

Surgery Acting Internship Individual Learning Plans: Fostering Mentorship in the COVID-19 Era.

OBJECTIVE: Mentorship facilitates successful matching for surgical specialties. A formal mentorship plan may counteract restricted mentorship opportunities due to the COVID-19 pandemic. DESIGN: We surveyed medical students applying to surgery specialties who participated in our formalized mentorship program (MF) and those of a prior cohort who were informally mentored (MI). Epistemic Network Analysis was used to model qualitative responses. SETTING: University of Wisconsin School of Medicine and Public Health. PARTICIPANTS: Fourth-year medical students who matched into ACGME-accredited surgical specialties. RESULTS: MF students (n = 12) met with their mentors more frequently than MI students (n = 13; p = 0.03). Both groups received career guidance, letters of recommendation and application preparation. However, the MI cohort reported greater psychological and emotional support whereas the MF cohort reported more assistance with skills development. CONCLUSIONS: A formalized mentorship program fostered successful mentoring relationships despite limitations from the COVID-19 pandemic.

Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts.

PURPOSE: Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The purpose of this study is to develop and evaluate machine learning algorithms to delineate mucinous from non-mucinous PCs using non-invasive CT-based radiomics. METHODS: A retrospective, single-institution analysis of patients with non-pseudocystic PCs, contrast-enhanced computed tomography scans within 1 year of resection, and available surgical pathology were included. A quantitative imaging software platform was used to extract radiomics. An extreme gradient boosting (XGBoost) machine learning algorithm was used to create mucinous classifiers using texture features only, or radiomic/radiologic and clinical combined models. Classifiers were compared using performance scoring metrics. Shapely additive explanation (SHAP) analyses were conducted to identify variables most important in model construction. RESULTS: Overall, 99 patients and 103 PCs were included in the analyses. Eighty (78%) patients had mucinous PCs on surgical pathology. Using multiple fivefold cross validations, the texture features only and combined XGBoost mucinous classifiers demonstrated an area under the curve of 0.72 ± 0.14 and 0.73 ± 0.14, respectively. By SHAP analysis, root mean square, mean attenuation, and kurtosis were the most predictive features in the texture features only model. Root mean square, cyst location, and mean attenuation were the most predictive features in the combined model. CONCLUSION: Machine learning principles can be applied to PC texture features to create a mucinous phenotype classifier. Model performance did not improve with the combined model. However, specific radiomic, radiologic, and clinical features most predictive in our models can be identified using SHAP analysis.

Pancreatic cyst characterization: maximum axial diameter does not measure up.

BACKGROUND: Unidimensional size is commonly used to risk stratify pancreatic cysts (PCs) despite inconsistent performance. The current study aimed to determine if unidimensional size, demonstrated by maximum axial diameter (MAD), is an appropriate surrogate measurement for volume and surface area. METHODS: Patients with cross-sectional imaging of PCs from 2012 to 2013 were identified. Cyst MAD, volume, and surface area were measured using quantitative imaging software. Non-pseudocystic PCs >1 cm were selected for inclusion to assess MAD correlation with volume and surface area. Cysts imaged twice >1 year apart were selected to evaluate volumetric growth rate. RESULTS: In total, 195 cysts were included. Overall, MAD was strongly correlated with volume (r = 0.83) and surface area (r = 0.93). However, cysts 1-2 cm and 2-3 cm were weakly correlated with volume and surface area: r = 0.78, 0.57 and 0.82, 0.61, respectively. Cyst volumes and surface areas varied widely within unidimensional size groups with 51% and 40% of volumes and surface areas overlapping unidimensional size groups, respectively. Estimated changes in volume poorly predicted measured changes in volume with 42% of cysts having >100% absolute percent difference. CONCLUSIONS: Pancreatic cyst volume and surface area may be useful adjunct measurements to risk stratify patients and surveil cyst changes and deserves further study.

Texture Analysis: An Emerging Clinical Tool for Pancreatic Lesions.

Radiologic characterization of pancreatic lesions is currently limited. Computed tomography is insensitive in detecting and characterizing small pancreatic lesions. Moreover, heterogeneity of many pancreatic lesions makes determination of malignancy challenging. As a result, invasive diagnostic testing is frequently used to characterize pancreatic lesions but often yields indeterminate results. Computed tomography texture analysis (CTTA) is an emerging noninvasive computational tool that quantifies gray-scale pixels/voxels and their spatial relationships within a region of interest. In nonpancreatic lesions, CTTA has shown promise in diagnosis, lesion characterization, and risk stratification, and more recently, pancreatic applications of CTTA have been explored. This review outlines the emerging role of CTTA in identifying, characterizing, and risk stratifying pancreatic lesions. Although recent studies show the clinical potential of CTTA of the pancreas, a clear understanding of which specific texture features correlate with high-grade dysplasia and predict survival has not yet been achieved. Further multidisciplinary investigations using strong radiologic-pathologic correlation are needed to establish a role for this noninvasive diagnostic tool.

Transport of a kinesin-cargo pair along microtubules into dendritic spines undergoing synaptic plasticity.

Synaptic plasticity often involves changes in the structure and composition of dendritic spines. Vesicular cargos and organelles enter spines either by exocytosing in the dendrite shaft and diffusing into spines or through a kinesin to myosin hand-off at the base of spines. Here we present evidence for microtubule (MT)-based targeting of a specific motor/cargo pair directly into hippocampal dendritic spines. During transient MT polymerization into spines, the kinesin KIF1A and an associated cargo, synaptotagmin-IV (syt-IV), are trafficked in unison along MTs into spines. This trafficking into selected spines is activity-dependent and results in exocytosis of syt-IV-containing vesicles in the spine head. Surprisingly, knockdown of KIF1A causes frequent fusion of syt-IV-containing vesicles throughout the dendritic shaft and diffusion into spines. Taken together, these findings suggest a mechanism for targeting dendritic cargo directly into spines during synaptic plasticity and indicate that MT-bound kinesins prevent unregulated fusion by sequestering vesicular cargo to MTs.