Innate Versus Acquired: A Review of Predictive Technical Aptitude Assessments in Surgical Trainee Selection.

Purpose Of Review: The evaluation and selection process of similarly qualified applicants for surgical residency positions in the United States (US) is challenging. Technical aptitude assessments may provide an opportunity to improve the selection process by offering insight into a candidate's technical skills. The use of these assessments prompts consideration of the degree to which technical aptitude in surgery is innate versus acquired.

Host tissue factors predict immune surveillance and therapeutic outcomes in gastric cancer.

The immune composition of solid tumors is typically inferred from biomarkers, such as histologic and molecular classifications, somatic mutational burden, and PD-L1 expression. However, the extent to which these biomarkers predict the immune landscape in gastric adenocarcinoma-an aggressive cancer often linked to chronic inflammation-remains poorly understood. We leveraged high-dimensional spectral cytometry to generate a comprehensive single-cell immune landscape of tumors, normal tissue, and lymph nodes from patients in the Western Hemisphere with gastric adenocarcinoma.

Declining Clinical Utility of Tools for Predicting Sentinel Lymph Node Biopsy Status: A Single Institution Experience from 2000 to 2021.

Introduction: Clinicopathologic data-based sentinel lymph node (SLN) prediction models are used to select patients with melanoma for sentinel lymph node biopsy (SLNB). However, the temporal performance of these models is unknown. Therefore, we investigated whether the performance and clinical utility of the Melanoma Institute of Australia, Memorial Sloan Kettering Cancer Center, and Friedman et al.

Feasibility and Efficacy of Sentinel Lymph Node Mapping in Gastric Cancer.

Background: Lymph node metastasis is a critical prognostic factor for patients with gastric carcinoma (GC). Sentinel lymph node (SLN) mapping has the potential to identify the initial site of draining lymph node metastasis and reduce the extent of surgical lymphadenectomy. This study aimed to evaluate the diagnostic accuracy of SLN mapping in GC.

Clinical Utility of Melanoma Sentinel Lymph Node Biopsy Nomograms.

Background: For patients with melanoma, the decision to perform sentinel lymph node biopsy (SLNB) is based on the estimated risk of lymph node metastasis. We assessed 3 melanoma SLNB risk-prediction models' statistical performance and their ability to improve clinical decision making (clinical utility) on a cohort of melanoma SLNB cases.

Study Design: Melanoma patients undergoing SLNB at a single center from 2003 to 2021 were identified.

Unique Genomic Alterations and Microbial Profiles Identified in Patients With Gastric Cancer of African, European, and Asian Ancestry: A Novel Path for Precision Oncology.

Objective: Here, we characterize differences in the genetic and microbial profiles of GC in patients of African (AFR), European, and Asian ancestry.

Background: Gastric cancer (GC) is a heterogeneous disease with clinicopathologic variations due to a complex interplay of environmental and biological factors, which may affect disparities in oncologic outcomes..

Are the MIA and MSKCC nomograms useful in selecting patients with melanoma for sentinel lymph node biopsy?

Background And Methods: The Melanoma Institute of Australia (MIA) and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms were developed to help guide sentinel lymph node biopsy (SLNB) decisions. Although statistically validated, whether these prediction models provide clinical benefit at National Comprehensive Cancer Network guideline-endorsed thresholds is unknown. We conducted a net benefit analysis to quantify the clinical utility of these nomograms at risk thresholds of 5%-10% compared to the alternative strategy of biopsying all patients.

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.

Background: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs.