Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI scans of 1143 male patients (median age, 64 years; IQR, 59-68 years) undergoing MRI for suspicion of clinically significant PCa (csPCa) (International Society of Urological Pathology grade > 1) between January 2012 and May 2022 who were negative for csPCa at baseline MRI. A DL model was developed using baseline MRI and clinical parameters (age, prostate-specific antigen [PSA] level, PSA density, and prostate volume) to predict the time to PCa progression (defined as csPCa diagnosis at follow-up).

Quantifying Patient Preferences and Expectations About Diabetic Retinopathy Monitoring.

Importance: Diabetic retinopathy (DR) is the leading cause of blindness among adults in the US. The US Centers for Disease Control and Prevention recommends annual DR monitoring for all individuals with diabetes, yet monitoring rates remain below 70%.

Objective: To evaluate how patient preferences and expectations about DR monitoring are associated with expected monitoring adherence behaviors.

Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.

Objectives: This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses.

Materials And Methods: A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans.

What makes a good scientific presentation on artificial intelligence in medical imaging?

Purpose: Adequate communication of scientific findings is crucial to enhance knowledge transfer. This study aimed to determine the key features of a good scientific oral presentation on artificial intelligence (AI) in medical imaging.

Methods: A total of 26 oral presentations dealing with original research on AI studies in medical imaging at the 2023 RSNA annual meeting were included and systematically assessed by three observers.

Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics.

Objective: Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the diagnostic quality of reconstructed images.

Materials And Methods: A retrospective multisite study of 1535 patients assessed biparametric prostate MRI between 2016 and 2020.

Using deep learning to optimize the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences.

Purpose: To explore diagnostic deep learning for optimizing the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences.

Method: This retrospective study included 840 patients with a biparametric prostate MRI scan. The MRI protocol included a T2-weighted image, three DWI sequences (b50, b400, and b800 s/mm), a calculated ADC map, and a calculated b1400 sequence.

Convincing 10-Year Follow-up Results of the Banded Roux-en-Y Gastric Bypass.

Introduction: Several studies have shown the positive effect on weight loss of the banded Roux-en-Y gastric bypass (BRYGB). Thus far, studies describing the 10-year post-operative results are scarce. Therefore, the aim of this study was to describe the weight loss results, effect on associated medical problems, and complication rates during 10 years of follow-up after BRYGB.

Migration and sustainable development.

To understand the implications of migration for sustainable development requires a comprehensive consideration of a range of population movements and their feedback across space and time. This Perspective reviews emerging science at the interface of migration studies, demography, and sustainability, focusing on consequences of migration flows for nature-society interactions including on societal outcomes such as inequality; environmental causes and consequences of involuntary displacement; and processes of cultural convergence in sustainability practices in dynamic new populations. We advance a framework that demonstrates how migration outcomes result in identifiable consequences on resources, environmental burdens and well-being, and on innovation, adaptation, and challenges for sustainability governance.

Recent Developments in Speeding up Prostate MRI.

The increasing incidence of prostate cancer cases worldwide has led to a tremendous demand for multiparametric MRI (mpMRI). In order to relieve the pressure on healthcare, reducing mpMRI scan time is necessary. This review focuses on recent techniques proposed for faster mpMRI acquisition, specifically shortening T2W and DWI sequences while adhering to the PI-RADS (Prostate Imaging Reporting and Data System) guidelines.

Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry.

Carbon is cycled through the air, plants, and belowground environment. Understanding soil carbon cycling in deep soil profiles will be important to mitigate climate change. Soil carbon cycling is impacted by water, plants, and soil microorganisms, in addition to soil mineralogy.

Migration, Embeddedness, and Vulnerability During the COVID-19 Pandemic.

The COVID-19 pandemic and concomitant policy measures have disproportionally affected the lives of migrants worldwide. Focusing on inequalities between social groups, studies have tended to neglect the role of local embeddedness as a factor influencing the extent to which individuals are affected by COVID-19. In this paper, we study the vulnerabilities of people with different migration experiences in an urban setting in the early stages of the pandemic, focusing on three key livelihood assets: economic, social, and human capital (health).

Refugee settlements are highly exposed to extreme weather conditions.

Involuntary displacement from conflict and other causes leads to clustering of refugees and internally displaced people, often in long-term settlements. Within refugee-hosting countries, refugee settlements are frequently located in isolated and remote areas, characterized by poor-quality land and harsh climatic conditions. Yet, the exposure of refugee settlements to climatic events is underresearched.

Differential impact of test performance characteristics on burden-to-benefit tradeoffs for blood-based colorectal cancer screening: A microsimulation analysis.

Objectives: To inform the development and evaluation of new blood-based colorectal cancer (CRC) screening tests satisfying minimum United States (US) coverage criteria, we estimated the impact of the different test performance characteristics on long-term testing benefits and burdens.

Methods: A novel CRC-Microsimulation of Adenoma Progression and Screening (CRC-MAPS) model was developed, validated, then used to assess different screening tests for CRC. We compared multiple, hypothetical blood-based CRC screening tests satisfying minimum coverage criteria of 74% CRC sensitivity and 90% specificity, to measure how changes in a test's CRC sensitivity, specificity, and adenoma sensitivity (sizes 1-5 mm, 6-9 mm, ≥10 mm) affect total number of colonoscopies (COL), CRC incidence reduction (IR), CRC mortality reduction (MR), and burden-to-benefit ratios (incremental COLs per percentage-point increase in IR or MR).

COVID-19 responses restricted abilities and aspirations for mobility and migration: insights from diverse cities in four continents.

Research on the impacts of COVID-19 on mobility has focused primarily on the increased health vulnerabilities of involuntary migrant and displaced populations. But virtually all migration flows have been truncated and altered because of reduced economic and mobility opportunities of migrants. Here we use a well-established framework of migration decision-making, whereby individual decisions combine the aspiration and ability to migrate, to explain how public responses to the COVID-19 pandemic alter migration patterns among urban populations across the world.

Laparoscopic Conversion of Vertical Banded Gastroplasty to Roux-en-Y Gastric Bypass Gives Better Result Compared to an Open Approach.

Background: Vertical banded gastroplasty (VBG) has a relatively high failure rate in the long run, requiring revisional surgery in 30-65%. A common conversion option is to Roux-en-Y gastric bypass (RYGB), which can be performed laparoscopically or open. Current literature contains small cohorts and inconclusive results.

K2S Challenge: From Undersampled K-Space to Automatic Segmentation.

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space.

Comparative Performance of Deep Learning and Radiologists for the Diagnosis and Localization of Clinically Significant Prostate Cancer at MRI: A Systematic Review.

Background: Deep learning (DL)-based models have demonstrated an ability to automatically diagnose clinically significant prostate cancer (PCa) on MRI scans and are regularly reported to approach expert performance. The aim of this work was to systematically review the literature comparing deep learning (DL) systems to radiologists in order to evaluate the comparative performance of current state-of-the-art deep learning models and radiologists.

Methods: This systematic review was conducted in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.

Trends and Patterns of Global Refugee Migration.

This paper studies long-term trends and patterns in global refugee migration. We explore the intensity, spread, and distance of refugee migration at a global, regional, and country level between 1951 and 2018. The analysis did not detect a long-term increase in the global intensity of refugee migration.

Higher Preoperative Weight loss Is Associated with Greater Weight Loss up to 12 Months After Bariatric Surgery.

Background: Prior research suggested presurgical weight loss is associated with greater total weight loss, resulting in a more effective bariatric intervention. We aimed to assess whether preoperative weight loss is a predictor for total weight loss, and which patient factors are associated with successful weight loss.

Methods: All patients (N = 773) that underwent primary bariatric surgery between June 2017 and August 2019 were included in this single-center retrospective study.

The Perfect Storm: A Case of Thyrotoxic Crisis Masking as Altered Mental Status in the South Bronx.

Thyrotoxic crisis better known as thyroid storm is a rare life-threatening endocrine emergency that is the result of dysregulated hypermetabolic state caused by excessive production and release of thyroid hormones compromising multiple organ systems. Unfortunately, this condition carries significant mortality depending on whether the thyroid storm was associated with thyrotoxicosis. Early recognition and treatment are paramount to survival.