Publications by authors named "C P HeuSSel"
Background: Computed tomography pulmonary angiography (CTPA) is frequently performed in patients with pulmonary hypertension (PH) and may aid non-invasive estimation of pulmonary hemodynamics. We, therefore, investigated automated volumetry of intrapulmonary vasculature on CTPA, separated into core and peel fractions of the lung volume and its potential to differentially reflect pulmonary hemodynamics in patients with pre- and postcapillary PH.
Methods: A retrospective case-control study of 72 consecutive patients with PH according to the 2022 joint guidelines of the European Society of Cardiology and the European Respiratory Society who underwent right heart catheterization (RHC) and CTPA within 7 days between August 2013 and February 2016 at Thoraxklinik at Heidelberg University Hospital (Heidelberg, Germany) was conducted.
Objectives: We hypothesized that semiquantitative visual scoring of lung MRI is suitable for GOLD-grade specific characterization of parenchymal and airway disease in COPD and that MRI scores correlate with quantitative CT (QCT) and pulmonary function test (PFT) parameters.
Methods: Five hundred ninety-eight subjects from the COSYCONET study (median age = 67 (60-72)) at risk for COPD or with GOLD1-4 underwent PFT, same-day paired inspiratory/expiratory CT, and structural and contrast-enhanced MRI. QCT assessed total lung volume (TLV), emphysema, and air trapping by parametric response mapping (PRM, PRM) and airway disease by wall percentage (WP).
Background: In COPD patients with severe right-sided emphysema, complete major and incomplete minor fissure, implantation of one-way valves in both the right upper (RUL) and middle lobes (ML) is a possible approach for endoscopic lung volume reduction. The aim of this retrospective analysis was to evaluate the response to therapy and the complication rate at 90 days (90d-FU) after combined RUL-ML valve implantation.
Methods: This retrospective, monocentric study included all patients from the Thoraxklinik Heidelberg who underwent RUL-ML valve treatment between 2012 and 2023 with available follow-up data.
Article Synopsis
- The study evaluates a deep learning model (LCP-CNN) for classifying the risk of incidentally detected pulmonary nodules, comparing its performance to traditional statistical methods like the Brock model and Lung-RADS®.
- LCP-CNN showed superior diagnostic accuracy and sensitivity across various patient cohorts, making it more effective for identifying malignant nodules compared to the other methods.
- The findings suggest that integrating deep learning systems can enhance clinical workflows for managing pulmonary nodules, regardless of a patient’s specific risk factors or conditions.
Objectives: Small lesions are the limiting factor for reducing gadolinium-based contrast agents in brain magnetic resonance imaging (MRI). The purpose of this study was to compare the sensitivity and precision in metastasis detection on true contrast-enhanced T1-weighted (T1w) images and artificial images synthesized by a deep learning method using low-dose images.
Materials And Methods: In this prospective, multicenter study (5 centers, 12 scanners), 917 participants underwent brain MRI between October 2021 and March 2023 including T1w low-dose (0.