Association between Rural Residence and Processes of Care in Pancreatic Cancer Treatment.

Background: Pancreatic adenocarcinoma (PDAC) is an aggressive malignancy associated with poor outcomes. Surgical resection and receipt of multimodal therapy have been shown to improve outcomes in patients with potentially resectable PDAC; however treatment and outcome disparities persist on many fronts. The aim of this study was to analyze the relationship between rural residence and receipt of quality cancer care in patients diagnosed with non-metastatic PDAC.

Can global longitudinal strain (GLS) with magnetic resonance prognosticate early cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients, a prospective study?

Purpose: To determine if Artificial Intelligence-based computation of global longitudinal strain (GLS) from left ventricular (LV) MRI is an early prognostic factor of cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients. The main hypothesis based on the patients receiving antineoplastic chemotherapy treatment was CTRCD risk analysis with GLS that was independent of LV ejection fraction (LVEF).

Methods: Displacement Encoding with Stimulated Echoes (DENSE) MRI was acquired on 32 breast cancer patients at baseline and 3- and 6-month follow-ups after chemotherapy.

Fragmentation of Care in Pancreatic Cancer: Effects on Receipt of Care and Survival.

Background: The impact of fragmentation of care (FC), i.e., receipt of care at > 1 institution, on treatment of pancreatic cancer is unknown.

Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network.

This study's purpose was to develop a direct MRI-based, deep-learning semantic segmentation approach for computing global longitudinal strain (GLS), a known metric for detecting left-ventricular (LV) cardiotoxicity in breast cancer. Displacement Encoding with Stimulated Echoes cardiac image phases acquired from 30 breast cancer patients and 30 healthy females were unwrapped via a DeepLabV3 + fully convolutional network (FCN). Myocardial strains were directly computed from the unwrapped phases with the Radial Point Interpolation Method.

Surgical Resection of Colorectal Liver Metastases: Attitudes and Practice Patterns in the Deep South.

Background: Metastatic disease is the leading cause of mortality in colorectal cancer. Resection of colorectal liver metastases, when possible, is associated with improved long-term survival and the possibility of cure. However, nationwide studies suggest that liver resection is under-utilized in the treatment of colorectal liver metastases.

A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity.

Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of cardiotoxicity damage related to breast cancer chemotherapy. This study investigated an automated LV chamber quantification tool via segmentation with a supervised deep convolutional neural network (DCNN) before strain analysis with DENSE images. Segmentation for chamber quantification analysis was conducted with a custom DeepLabV3+ DCNN with ResNet-50 backbone on 42 female breast cancer datasets (22 training-sets, eight validation-sets and 12 independent test-sets).

Traumatic Radial Artery Pseudoaneurysm: Vein Graft Repair through a Single Incision.

Radial artery pseudoaneurysms are rare clinical entities usually occurring after penetrating trauma or iatrogenic injury. The radial artery is the least common location for peripheral artery pseudoaneurysms, and no clear standard of treatment exists. We present a case of an adolescent male who developed a radial artery pseudoaneurysm after slitting his wrists.

Fully automated and comprehensive MRI-based left-ventricular contractility analysis in post-chemotherapy breast cancer patients.

Objective: This study investigated the occurrence of cardiotoxicity-related left-ventricular (LV) contractile dysfunction in breast cancer patients following treatment with antineoplastic chemotherapy agents.

Methods: A validated and automated MRI-based LV contractility analysis tool consisting of quantization-based boundary detection, unwrapping of image phases and the meshfree Radial Point Interpolation Method was used toward measuring LV chamber quantifications (LVCQ), three-dimensional strains and torsions in patients and healthy subjects. Data were acquired with the Displacement Encoding with Stimulated Echoes (DENSE) sequence on 21 female patients and 21 age-matched healthy females.

Can post-chemotherapy cardiotoxicity be detected in long-term survivors of breast cancer via comprehensive 3D left-ventricular contractility (strain) analysis?

Purpose: This study applied a novel and automated contractility analysis tool to investigate possible cardiotoxicity-related left-ventricular (LV) dysfunction in breast cancer patients following treatment with anti-neoplastic chemotherapy agents (CTA). Subclinical dysfunction otherwise undetected via LV ejection fraction (LVEF) was determined.

Methods: Deformation data were acquired with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence on 16 female patients who had CTA-based treatment.

The Diagnostic and Therapeutic Challenge of Anal Intraepithelial Neoplasia.

Purpose Of Review: No single modality of care serves as the defined best practice for the treatment of anal intraepithelial neoplasia (AIN). This review aims to present the common treatment modalities germane to AIN while considering evolving evidence.

Recent Findings: AIN affords an opportunity to evaluate and treat patients before the development of invasive diseases.

Introduction to a mechanism for automated myocardium boundary detection with displacement encoding with stimulated echoes (DENSE).

Objective: Displacement ENcoding with Stimulated Echoes (DENSE) is an MRI technique developed to encode phase related to myocardial tissue displacements, and the displacement information directly applied towards detecting left-ventricular (LV) myocardial motion during the cardiac cycle. The purpose of this study is to present a novel, three-dimensional (3D) DENSE displacement-based and magnitude image quantization-based, semi-automated detection technique for myocardial wall motion, whose boundaries are used for rapid and automated computation of 3D myocardial strain.

Methods: The architecture of this boundary detection algorithm is primarily based on pixelwise spatiotemporal increments in LV tissue displacements during the cardiac cycle and further reinforced by radially searching for pixel-based image gradients in multithreshold quantized magnitude images.