Publications by authors named "Jakob WeiSS"
Article Synopsis
- This study explores the use of advanced deep learning methods to automatically measure body composition from whole-body MRI scans, aiming to assess their ability to predict mortality in the general population.
- The investigation was based on data from two large Western European cohort studies, focusing on key body composition metrics such as subcutaneous and visceral adipose tissue, skeletal muscle, and intramuscular fat.
- Results indicate significant associations between several volumetric body composition measures and mortality risk, highlighting the potential of automated techniques to improve clinical outcomes related to cardiometabolic diseases and cancer.
Background: This study assessed whether deep learning applied to routine outpatient chest X-rays (CXRs) can identify individuals at high risk for incident chronic obstructive pulmonary disease (COPD).
Methods: Using cancer screening trial data, we previously developed a convolutional neural network (CXR-Lung-Risk) to predict lung-related mortality from a CXR image. In this study, we externally validated CXR-Lung-Risk to predict incident COPD from routine CXRs.
Antithrombotic Agents after Free Tissue Transfer in the Pediatric and Adolescent Population.
J Reconstr Microsurg
November 2024
Background: Even for the experienced microsurgeon, free tissue transfer in pediatric patients is challenging, and large patient series remain scarce in the literature. Moreover, the added value of antithrombotic agents in pediatric free tissue transfer remains unclear.
Methods: We conducted a retrospective outcome analysis of pediatric free tissue transfer with respect to postoperative antithrombotic treatment at our tertiary academic center.
Background: Artificial intelligence (AI) is increasingly finding its way into routine radiological work.
Objective: Presentation of the current advances and applications of AI along the entire radiological patient journey.
Methods: Systematic literature review of established AI techniques and current research projects, with reference to consensus recommendations.
Purpose: To assess the image quality and impact on acquisition time of a novel deep learning based T2 Dixon sequence (T2) of the spine.
Methods: This prospective, single center study included n = 44 consecutive patients with a clinical indication for lumbar MRI at our university radiology department between September 2022 and March 2023. MRI examinations were performed on 1.
Background: There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap.
Methods: In this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole-body T1-weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF).
Exercise intolerance is a debilitating symptom in heart failure (HF), adversely affecting both quality of life and long-term prognosis. Emerging evidence suggests that pulmonary artery (PA) compliance may be a contributing factor. This study aims to non-invasively assess PA compliance and its dynamic properties during isometric handgrip (HG) exercise in HF patients and healthy controls, using cardiovascular magnetic resonance (CMR).
View Article and Find Full Text PDFObjectives: To assess the accuracy of a deep learning-based algorithm for fully automated detection of thoracic aortic calcifications in chest computed tomography (CT) with a focus on the aortic clamping zone.
Methods: We retrospectively included 100 chest CT scans from 91 patients who were examined on second- or third-generation dual-source scanners. Subsamples comprised 47 scans with an electrocardiogram-gated aortic angiography and 53 unenhanced scans.
Objectives: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation.
Materials And Methods: The consensus was achieved by a multi-stage process.
Importance: The association between body composition (BC) and cancer outcomes is complex and incompletely understood. Previous research in non-small-cell lung cancer (NSCLC) has been limited to small, single-institution studies and yielded promising, albeit heterogeneous, results.
Objectives: To evaluate the association of BC with oncologic outcomes in patients receiving immunotherapy for advanced or metastatic NSCLC.
Purpose: Artifacts caused by metallic implants remain a challenge in computed tomography (CT). We investigated the impact of photon-counting detector computed tomography (PCD-CT) for artifact reduction in patients with orthopedic implants with respect to image quality and diagnostic confidence using different artifact reduction approaches.
Material And Methods: In this prospective study, consecutive patients with orthopedic implants underwent PCD-CT imaging of the implant area.
Purpose: Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans. We developed a deep learning (DL) model for fully automatic BC quantification on routine staging CTs and determined its prognostic role in a clinical cohort of patients with GEAC.
View Article and Find Full Text PDFBackground: We aimed to correlate alterations in the rat sarcoma virus (RAS)/mitogen-activated protein kinase pathway in vascular anomalies to the clinical phenotype for improved patient and treatment stratification.
Methods And Results: This retrospective multicenter cohort study included 29 patients with extracranial vascular anomalies containing mosaic pathogenic variants (PVs) in genes of the RAS/mitogen-activated protein kinase pathway. Tissue samples were collected during invasive treatment or clinically indicated biopsies.
Background: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable.
Objective: To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility.
Purpose: To investigate the value of photon-counting detector CT (PCD-CT) derived virtual non-contrast (VNC) reconstructions to identify renal cysts in comparison with conventional dual-energy integrating detector (DE EID) CT-derived VNC reconstructions.
Material And Methods: We prospectively enrolled consecutive patients with simple renal cysts (Bosniak classification-Version 2019, density ≤ 20 HU and/or enhancement ≤ 20 HU) who underwent multiphase (non-contrast, arterial, portal venous phase) PCD-CT and for whom non-contrast and portal venous phase DE EID-CT was available. Subsequently, VNC reconstructions were calculated for all contrast phases and density as well as contrast enhancement within the cysts were measured and compared.
Aim: Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting.
Methods: In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study.
Objectives: To determine the diagnostic accuracy of ultra-high-resolution photon-counting detector CT angiography (UHR PCD-CTA) for evaluating coronary stent patency compared to invasive coronary angiography (ICA).
Methods: Consecutive, clinically referred patients with prior coronary stent implantation were prospectively enrolled between August 2022 and March 2023 and underwent UHR PCD-CTA (collimation, 120 × 0.2 mm).
Objective: Accurate locoregional staging is crucial for effective breast cancer treatment. Photon-counting computed tomography (PC-CT) is an emerging technology with high spatial resolution and the ability to depict uptake of contrast agents in tissues, making it a promising tool for breast cancer imaging. The aim of this study was to establish the feasibility of locoregional staging of breast cancer through contrast-enhanced thoracic PC-CT, assess its diagnostic performance, and compare it with that of digital mammography (DM).
View Article and Find Full Text PDFObjectives: Metal artifacts remain a challenge in computed tomography. We investigated the potential of photon-counting computed tomography (PCD-CT) for metal artifact reduction using an iterative metal artifact reduction (iMAR) algorithm alone and in combination with high keV monoenergetic images (140 keV) in patients with dental hardware.
Material And Methods: Consecutive patients with dental implants were prospectively included in this study and received PCD-CT imaging of the craniofacial area.
Article Synopsis
- This study examines the effectiveness of using gracilis free muscle transfer (GFMT) as a second option for facial reanimation after a failed primary reconstruction for facial paralysis.* -
- Twelve patients were analyzed, categorized based on their initial failed procedures, and outcomes were measured using specific metrics to evaluate improvements in facial movement and emotional expression.* -
- Results indicated significant postoperative enhancements in facial function across all groups, but those who underwent GFMT secondary to a failed procedure had lower oral movement scores compared to patients initially treated with static methods.*
Background: Photon-counting detector computed tomography (PCD-CT) is a promising new technology with the potential to fundamentally change workflows in the daily routine and provide new quantitative imaging information to improve clinical decision-making and patient management.
Method: The contents of this review are based on an unrestricted literature search of PubMed and Google Scholar using the search terms "photon-counting CT", "photon-counting detector", "spectral CT", "computed tomography" as well as on the authors' own experience.
Results: The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCD-CT allows for the counting of every single photon at the detector level.
Objective: Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT).
Materials And Methods: In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT.
Article Synopsis
- - Researchers studied how different MRI features, especially 3D tumor volume measurements, relate to survival outcomes in 93 patients with central nervous system lymphomas (CNSL) undergoing treatment.
- - Key findings showed that patients with over 3 lymphoma lesions and high tumor volumes had worse progression-free survival (PFS) and overall survival (OS), and the traditional IPCG criteria for treatment response were inadequate.
- - A 3D tumor volume reduction of ≥97% was identified as a crucial early indicator for better patient outcomes, demonstrating potential to enhance risk assessment and inform treatment plans for CNSL.
Article Synopsis
- Accurate small vessel stent visualization using CT is difficult, but photon-counting CT (PCD-CT) shows promise in improving this assessment compared to traditional energy-integrating CT (EID-CT).
- A study involved scanning 12 water-contrast agent filled stents with PCD-CT and EID-CT, evaluating image quality subjectively with radiologists and objectively through intensity profile calculations.
- PCD-CT produced better overall reading scores and higher diagnostic confidence, suggesting it may enhance small vessel stent imaging effectiveness, though further research is needed for clinical application.
Article Synopsis
- This study evaluates the effectiveness of the indocyanine green clearance (ICG) test in distinguishing between normal liver tissue, liver fibrosis, and cirrhosis by comparing ICG results with histopathological findings.
- Conducted on 82 patients who underwent liver examinations, the research used the Ishak scoring system to categorize liver conditions, applying statistical analyses to determine differences in ICG test outcomes among different fibrosis stages.
- Results showed significant differences in ICG parameters between liver stages, with a sensitivity of 0.40 and specificity of 0.80 for initial fibrosis detection, and 0.75 sensitivity for advanced fibrosis or cirrhosis compared to milder stages.