Publications by authors named "E D Gennatas"
Background: This multicenter and retrospective study investigated the additive value of tumor morphologic features derived from the functional tumor volume (FTV) tumor mask at pre-treatment (T0) and the early treatment time point (T1) in the prediction of pathologic outcomes for breast cancer patients undergoing neoadjuvant chemotherapy.
Methods: A total of 910 patients enrolled in the multicenter I-SPY 2 trial were included. FTV and tumor morphologic features were calculated from the dynamic contrast-enhanced (DCE) MRI.
Introduction: Heart failure (HF) is a frequent cause of readmissions. Despite caring for underresourced patients and dependence on government funding, safety net hospitals frequently incur penalties for failing to meet pay-for-performance readmission metrics. Limited research exists on the causes of HF readmissions in safety net hospitals.
View Article and Find Full Text PDFArticle Synopsis
- This study utilized machine learning to predict the likelihood of grade ≥2 pneumonitis or dyspnea in lung cancer patients undergoing proton beam therapy, involving 965 patients across twelve institutions.
- The research showed that 25.9% of patients experienced significant pulmonary toxicity, with specific factors like treatment technique and radiation dose influencing the risk.
- The gradient boosting model outperformed other models in accuracy, achieving a balanced accuracy of 0.67 and an area under the curve of 0.75 when demographic and treatment details were considered.
Liver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF), but high post-LT mortality has been reported. Existing post-LT models in ACLF have been limited. We developed an Expert-Augmented Machine Learning (EAML) model to predict post-LT outcomes.
View Article and Find Full Text PDFAssociation of SARS-CoV-2 nucleocapsid viral antigen and the receptor for advanced glycation end products with development of severe disease in patients presenting to the emergency department with COVID-19.
Front Immunol
April 2023
Introduction: There remains a need to better identify patients at highest risk for developing severe Coronavirus Disease 2019 (COVID-19) as additional waves of the pandemic continue to impact hospital systems. We sought to characterize the association of receptor for advanced glycation end products (RAGE), SARS-CoV-2 nucleocapsid viral antigen, and a panel of thromboinflammatory biomarkers with development of severe disease in patients presenting to the emergency department with symptomatic COVID-19.
Methods: Blood samples were collected on arrival from 77 patients with symptomatic COVID-19, and plasma levels of thromboinflammatory biomarkers were measured.