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Mentorship facilitates successful matching for surgical specialties. A formal mentorship plan may counteract restricted mentorship opportunities due to the COVID-19 pandemic.We surveyed medical students applying to surgery specialties who participated in our formalized mentorship program (MUniversity of Wisconsin School of Medicine and Public Health.Fourth-year medical students who matched into ACGME-accredited surgical specialties.MA formalized mentorship program fostered successful mentoring relationships despite limitations from the COVID-19 pandemic.

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. 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. 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. 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.

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.

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.

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.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.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.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.

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.

The diagnosis of pancreatic cystic lesions is increasingly common. The majority of pancreatic cysts are now diagnosed incidentally on cross-sectional imaging. Lack of clear evidence-based guidelines and overall poor understanding of the natural history of pancreatic cysts contribute to complexity of managing patients with pancreatic cysts. Pancreatic cystic neoplasm types differ in their presentation, histologic features, imaging characteristics, and predisposition to develop invasive malignancy. The diagnostic strategies to determine cyst type and presence of malignancy—cross-sectional imaging, endoscopic ultrasonography, and analyses of pancreatic cyst fluid aspirates—have improved over time. However, accurate characterization of cysts remains challenging. Several large groups, including the American College of Radiology, the American Gastroenterological Association, the European Study Group on Cystic Tumours of the Pancreas, and the International Association of Pancreatology, have released cyst management guidelines or recommendations that have important differences. In this review, we provide an overview of the most common pancreatic cystic neoplasm, evaluate recent advancements in diagnostic techniques, and compare current management guidelines. This review contains 7 figures, 5 tables, and 77 references. Key Words: intraductal papillary mucinous neoplasm, management guidelines, multidisciplinary teams, mucinous cystic neoplasm, pancreatic cyst, pancreatic cystic neoplasm, serous cystadenoma, solid pseudopapillary neoplasm, surgical oncology

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., Transport of cargo into dendritic spines is required for synaptic plasticity. McVicker et al. describe a method of activity-dependent transport of a kinesin KIF1A and its cargo synaptotagmin-IV along microtubules that are transiently polymerized into dendritic spines.