Background: Optical coherence tomography (OCT) provides quantitative measures of retinal layer thickness. Cigarette smoking is a risk factor for multiple sclerosis (MS) onset and disease severity: its effects on OCT metrics have not been assessed.
Objective: The objective of this study was to assess the effect of smoking history on retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform (GCIP) of MS patients by OCT.
Methods: 112 MS patients were recruited from the Brigham and Women's Hospital. Spectralis OCT scans were acquired to measure GCIP, peripapillary RNFL, and total macular volume. Multivariable linear mixed effects regression model assessed RNFL and GCIP change with fixed effects for smoking history while adjusting for optic neuritis eye status, age, disease duration, sex, baseline EDSS, and disease modifying therapies (DMTs).
Results: Smoking histories were available for 102 patients: 46 (45.10%) had a history of smoking cigarettes and 56 (54.90%) never smoked. No statistically significant differences were found between ever-smokers and never-smokers with respect to GCIP, RNFL, and macular volume.
Conclusion: Our study shows no significant difference in retinal thickness between ever-smokers and never-smokers. If confirmed, this result suggests mechanistic differences between the retina and other central nervous system (CNS) compartments in response to smoking and should be noted when considering OCT as a surrogate measure of CNS activity.
Purpose: Currently, there are many schedules for exclusive vaginal cuff brachytherapy (VCB). In 3D treatment planning for VCB dosimetry, parameters have not been analyzed. The aim of this study was to compare the most common schedules using dose-volume histogram metrics.
Background And Purpose: Conventionally, the quality of radiotherapy treatment plans is assessed through visual inspection of dose distributions and dose-volume histograms. This study developed a framework to evaluate plan quality using dose, complexity, and robustness metrics. Additionally, a method for predicting plan robustness metrics using dose and complexity metrics was introduced for cases where plan robustness evaluation is unavailable or impractical.
Objective: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.
Methods: Two popular XAIs used for explaining clinical predictive models were evaluated based on their ability to generate domain-appropriate representations, impact clinical workflow, and consistency. Explanations were benchmarked against true clinical deterioration triggers recorded in the data system and agreement was quantified.
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. Social determinants of health (SDH) significantly impact HCC outcomes, yet comprehensive insights into these effects are lacking. This scoping review aimed to examine the influence of SDH on HCC outcomes.
The hemodynamic definitions of pulmonary hypertension consider resistive loading (pulmonary vascular resistance [PVR]), but there are increasing evidence that pulsatile loading (pulmonary artery compliance [PAC]) has functional and prognostic importance. The aims of the present study on patients with left heart disease, were to evaluate a novel echocardiographic right ventricular (RV) afterload score and to investigate its relation to risk of mortality or implantation of a left ventricular assist device. Patients ( = 220) with left ventricular ejection fraction < 50% consecutively referred for heart transplant or heart failure workup underwent echocardiography and right heart catheterization.
