Digital pathology, deep learning, and cancer: a narrative review.

Background And Objective: Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care.

Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier.

Background: Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse.

A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics.

Background: Micropapillary/solid (MP/S) growth patterns of lung adenocarcinoma are vital for making clinical decisions regarding surgical intervention. This study aimed to predict the presence of a MP/S component in lung adenocarcinoma using radiomics analysis.

Methods: Between January 2011 and December 2013, patients undergoing curative invasive lung adenocarcinoma resection were included.

Candida auris in the Age of Resistance.

( is an opportunistic ascomycetous budding yeast that has been emerging as an invasive, multidrug-resistant pathogen over the past 10 years since its discovery. This fungi is the first to be labeled as a public health threat according to the Centers for Disease Control (CDC) and has since become a major problem in the United States. This serves as a detailed overview of the various factors contributing to the pathogenicity of .

Automated Lung Cancer Detection Using Artificial Intelligence (AI) Deep Convolutional Neural Networks: A Narrative Literature Review.

Lung cancer is the number one cause of cancer-related deaths in the United States as well as worldwide. Radiologists and physicians experience heavy daily workloads, thus are at high risk for burn-out. To alleviate this burden, this narrative literature review compares the performance of four different artificial intelligence (AI) models in lung nodule cancer detection, as well as their performance to physicians/radiologists reading accuracy.

Impact of epoetin alfa on left ventricular structure, function, and pressure volume relations as assessed by cardiac magnetic resonance: the heart failure preserved ejection fraction (HFPEF) anemia trial.

Anemia, a common comorbidity in older adults with heart failure and a preserved ejection fraction (HFPEF), is associated with worse outcomes. The authors quantified the effect of anemia treatment on left ventricular (LV) structure and function as measured by cardiac magnetic resonance (CMR) imaging. A prospective, randomized single-blind clinical trial (NCT NCT00286182) comparing the safety and efficacy of epoetin alfa vs placebo for 24 weeks in which a subgroup (n=22) had cardiac magnetic resonance imaging (MRI) at baseline and after 3 and 6 months to evaluate changes in cardiac structure and function.