Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning.

This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning..

Survey of Laboratory Stewardship Governance at US Academic Medical Centers.

Background: Efforts to appropriately utilize laboratory tests have been underway for several decades. However, limited information is available regarding the status of laboratory stewardship at academic medical centers. Prior to initiating a laboratory stewardship committee, a study was initiated to gain insights from peer institutions.

Evaluating Outcomes for Women with Metastatic Breast Cancer: Palliative Care Consultations, Hospital Charges, and Length of Stay.

: Women with late-stage metastatic breast cancer are at an increased risk of pain and distress from symptoms and often struggle with associated emotional and financial burden of their disease. Palliative care is known to alleviate symptom burden in patients with end-stage, terminal diseases but is often underutilized in both inpatient and outpatient settings. The current study aims to investigate the prevalence of palliative care consultation on inpatients with metastatic breast cancer and examine the association between palliative care consultation and length of hospital stay and total hospital charges.

AI tool for predicting MGMT methylation in glioblastoma for clinical decision support in resource limited settings.

Glioblastoma is an aggressive brain cancer with a poor prognosis. The O6-methylguanine-DNA methyltransferase (MGMT) gene methylation status is crucial for treatment stratification, yet economic constraints often limit access. This study aims to develop an artificial intelligence (AI) framework for predicting MGMT methylation.

Substance use disorder and severe maternal morbidity: is there a differential impact?

Background: Substance use disorder (SUD) is a disease characterized by behavior patterns of substance use leading to dysfunction in cognition, mood, and quality of life. The prevalence of perinatal SUD in the United States continues to rise and has adverse effects on the maternal-infant dyad. Mirroring the rise in SUD is an increasing prevalence of severe maternal morbidity (SMM).