Fully automated 3D body composition analysis and its association with overall survival in head and neck squamous cell carcinoma patients.

Objectives: We developed a method for a fully automated deep-learning segmentation of tissues to investigate if 3D body composition measurements are significant for survival of Head and Neck Squamous Cell Carcinoma (HNSCC) patients. Methods: 3D segmentation of tissues including spine, spine muscles, abdominal muscles, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and internal organs within volumetric region limited by L1 and L5 levels was accomplished using deep convolutional segmentation architecture - U-net implemented in a nnUnet framework. It was trained on separate dataset of 560 single-channel CT slices and used for 3D segmentation of pre-radiotherapy (Pre-RT) and post-radiotherapy (Post-RT) whole body PET/CT or abdominal CT scans of 215 HNSCC patients. Percentages of tissues were used for overall survival analysis using Cox proportional hazard (PH) model. Results: Our deep learning model successfully segmented all mentioned tissues with Dice's coefficient exceeding 0.95. The 3D measurements including difference between Pre-RT and post-RT abdomen and spine muscles percentage, difference between Pre-RT and post-RT VAT percentage and sum of Pre-RT abdomen and spine muscles percentage together with BMI and Cancer Site were selected and significant at the level of 5% for the overall survival. Aside from Cancer Site, the lowest hazard ratio (HR) value (HR, 0.7527; 95% CI, 0.6487-0.8735; p = 0.000183) was observed for the difference between Pre-RT and post-RT abdomen and spine muscles percentage. Conclusion: Fully automated 3D quantitative measurements of body composition are significant for overall survival in Head and Neck Squamous Cell Carcinoma patients.

The Role of Cardiac Magnetic Resonance in Evaluation of Idiopathic Ventricular Arrhythmia in Children.

The aim of the study was to assess the role of cardiovascular magnetic resonance (CMR) in the diagnosis of idiopathic VA in children. This retrospective single-centre study included a total of 80 patients with idiopathic ventricular arrhythmia that underwent routine CMR imaging between 2016 and 2020 at our institution. All patients underwent a 3.0 T scan involving balanced steady-state free precession cine images as well as dark-blood T2W images and assessment of late gadolinium enhancement (LGE). In 26% of patients (n = 21) CMR revealed cardiac abnormalities, in 20% (n = 16) not suspected on prior echocardiography. The main findings included: non-ischemic ventricular scars (n = 8), arrhythmogenic right ventricular cardiomyopathy (n = 6), left ventricular clefts (n = 4) and active myocarditis (n = 3). LGE was present in 57% of patients with abnormal findings. Univariate predictors of abnormal CMR result included abnormalities in echocardiography and severe VA (combination of >10% of 24 h VA burden and/or presence of ventricular tachycardia and/or polymorphic VA). CMR provides valuable clinical information in many cases of idiopathic ventricular arrhythmia in children, mainly due to its advanced tissue characterization capabilities and potential to assess the right ventricle.

Censored functional data for incomplete follow-up studies.

Functional data analysis plays an increasingly important role in medical research because patients are followed over time. Thus, the measurements of a particular biomarker for each patient are often registered as curves. Hence, it is of interest to estimate the mean function under certain conditions as an average of the observed functional data over a given period. However, this is often difficult as this type of follow-up studies are confronted with the challenge of some individuals dropping-out before study completion. Therefore, for these individuals, only a partial functional observation is available. In this study, we propose an estimator for the functional mean when the functions may be censored from the right, and thus, only partly observed. Unlike sparse functional data, the censored curves are observed until some (random) time and this censoring time may depend on the trajectory of the functional observations. Our approach is model-free and fully nonparametric, although the proposed methods can also be incorporated into regression models. The use of the functional structure of the data distinguishes our approach from the longitudinal data approaches. In addition, in this study, we propose a bootstrap-based confidence band for the mean function, examine the estimation of the covariance function, and apply our new approach to functional principal component analysis. Employing an extensive simulation study, we demonstrate that our method outperforms the only two existing approaches. Furthermore, we apply our new estimator to a real data example on lung growth, measured by changes in pulmonary function for girls in the United States.