A5090408246- Radiomics and Machine Learning in Medical Imaging
- Pancreatic and Hepatic Oncology Research
- Advanced MRI Techniques and Applications
- MRI in cancer diagnosis
- Hepatocellular Carcinoma Treatment and Prognosis
- COVID-19 diagnosis using AI
- Medical Imaging Techniques and Applications
- Liver Disease Diagnosis and Treatment
- COVID-19 Clinical Research Studies
- Advanced NMR Techniques and Applications
- Renal cell carcinoma treatment
- Cancer Genomics and Diagnostics
- Advanced X-ray and CT Imaging
- Photoacoustic and Ultrasonic Imaging
- Atomic and Subatomic Physics Research
- Dental Radiography and Imaging
- AI in cancer detection
- Radiation Dose and Imaging
- Gallbladder and Bile Duct Disorders
- Optical Imaging and Spectroscopy Techniques
- Neuroendocrine Tumor Research Advances
- Mass Spectrometry Techniques and Applications
- Protease and Inhibitor Mechanisms
- Lanthanide and Transition Metal Complexes
- SARS-CoV-2 and COVID-19 Research
Klinikum rechts der Isar
2015-2025
Technical University of Munich
2016-2025
München Klinik
2022
Helmholtz Zentrum München
2016
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe course. Here we evaluate clinical imaging parameters estimating need intensive care unit (ICU) treatment. We collected clinical, laboratory data from 65 with confirmed COVID-19 based on polymerase chain reaction (PCR) testing. Two radiologists evaluated severity findings in computed tomography (CT) images scale 1 (no...
The purpose of this retrospective study was to evaluate the value contrast-enhanced computed tomography (CE-CT) image features at baseline and after neoadjuvant chemotherapy in predicting histopathological response patients with adenocarcinoma gastroesophageal junction (GEJ). A total 105 a diagnosis GEJ were examined by CE-CT preoperatively chemotherapy. All underwent surgical resection. Histopathological parameters tumor regression grading according Becker et al. collected 93 patients. Line...
Development of a supervised machine-learning model capable predicting clinically relevant molecular subtypes pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.The retrospective observational study assessed 55 surgical PDAC patients. Molecular were defined by immunohistochemical staining KRT81. Tumors manually segmented and 1606 features extracted with PyRadiomics. A gradient-boosted-tree algorithm was trained on 70% the patients (N = 28) tested...
Abstract Background To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC). Methods One hundred two histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued enrolled served independent validation cohort (IVC). Tumors segmented on preoperative apparent diffusion...
To bridge the translational gap between recent discoveries of distinct molecular phenotypes pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies tools informing improving clinical decision process. Radiomics machine learning approaches can offer non-invasive whole tumor analytics for imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven ductal adenocarcinoma...
To evaluate factors associated with survival following transarterial 90Y (yttrium) radioembolization (TARE) in patients advanced intrahepatic cholangiocarcinoma (ICC).This retrospective multicenter study analyzed the outcome of three tertiary care cancer centers ICC resin microsphere TARE. Patients were included either after failed previous anticancer therapy, including relapse surgical resection, or for having a minimum 25% total liver volume affected by ICC. stratified and response was...
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with a poor prognosis. Accurate preoperative assessment using computed tomography (CT) to determine resectability crucial in ensuring patients are offered the most appropriate therapeutic strategy. Despite use of classification guidelines, any interobserver variability between reviewing surgeons and radiologists may confound decisions influencing patient treatment pathways.
Bedside chest X-rays (CXR) for catheter position control may add up to a considerable radiation dose patients in the intensive care unit (ICU). In this study, image quality and reduction potentials of novel X-ray scatter correction software (SkyFlow, Philips Healthcare, Hamburg, Germany) were evaluated. CXRs 'LUNGMAN' (Kyoto Kagaku Co., LTD, Kyoto, Japan) thoracic phantom with portacath system, central venous line dialysis performed an experimental set-up multiple tube voltage current...
Extraction of murine cardiac functional parameters on a beat-by-beat basis is limited with the existing imaging modalities due to insufficient three-dimensional temporal resolution.Faster volumetric methods enabling in vivo characterization are poised advance cardiovascular research and provide better understanding mechanisms underlying diseases.We present new approach based analyzing contrast-enhanced optoacoustic (OA) images acquired at high frame rate without using gating or other...
Abstract The in vivo assessment of tissue metabolism represents a novel strategy for the evaluation oncologic disease. Hepatocellular carcinoma (HCC) is high-prevalence, high-mortality tumor entity often discovered at late stage. Recent evidence indicates that survival differences depend on metabolic alterations tissue, with particular focus glucose and lactate production. Here, we present an imaging technique phenotyping rat models HCC. Endogenous HCC was induced Wistar rats by oral...
Importance Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems improve the diagnostic accuracy of radiologists, as shown for variety imaging tasks. Improvements performance, particular reduction false-negative findings, may useful patient care. Objective To develop evaluate deep learning algorithm able to differentiate colon carcinoma (CC) acute...
Abstract Gadolinium(III)‐based contrast agents improve the sensitivity and specificity of magnetic resonance imaging (MRI), especially when targeted are applied. Because nonlinear correlation between agent concentration in tissue MRI signal obtained vivo, quantification certain biological or pathophysiological processes by remains a challenge. Up to now, no technology has been able provide spatially resolved directly within tissue, which would allow more precise verification vivo results....
Objective— Neutrophils accumulate in early atherosclerotic lesions and promote lesion growth. In this study, we evaluated an elastase-specific near-infrared imaging agent for molecular using hybrid fluorescence tomography/x-ray computed tomography. Approach Results— Murine neutrophils were isolated from bone marrow incubated with the neutrophil-targeted Neutrophil Elastase 680 FAST proof of principle experiments, verifying that elastase-targeted fluorescent is specifically cleaved activated...
To examine the effect of high-b-value computed diffusion-weighted imaging (cDWI) on solid lesion detection and classification in pancreatic intraductal papillary mucinous neoplasm (IPMN), using endoscopic ultrasound (EUS) histopathology as a standard reference.Eighty-two patients with known or suspected IPMN were retrospectively enrolled. Computed images at b = 1000 s/mm2 calculated from (b 0, 50, 300, 600 s/mm2) DWI for conventional full field-of-view (fFOV, 3 × 4 mm3 voxel size) DWI. A...
Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction patient prognosis. Many studies focus on promoting newly developed imaging without rigorous comparison to other established parameters. To assess true value leverage potential all efforts in this field, multi-parametric evaluation available PDAC survival is warranted. Here we present multiparametric analysis predictive...
Abstract Purpose In this prospective exploratory study, we evaluated the feasibility of [ 18 F]fluorodeoxyglucose ([ F]FDG) PET/MRI-based chemotherapy response prediction in pancreatic ductal adenocarcinoma at two weeks upon therapy onset. Material and methods a mixed cohort, seventeen patients treated with neoadjuvant or palliative intent were enrolled. All imaged by F]FDG PET/MRI before after onset chemotherapy. Response per RECIST1.1 was then assessed 3 months PET/MRI-derived parameters...
Background: PDAC remains a tumor entity with poor prognosis and 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response patient survival. Non-invasive prediction of individual outcome however an unresolved task. Methods: Discrete cellularity regions resection specimen (n = 43) were analyzed by routine histopathological work up. Regional CT-derived Hounsfield Units (HU, n 66) well iodine...
Objectives To evaluate proton density fat fraction (PDFF) and T2* measurements of the liver with combined parallel imaging (sensitivity encoding, SENSE) compressed sensing (CS) accelerated chemical shift encoding-based water-fat separation. Methods Six-echo Dixon was performed in 89 subjects. The first acquisition variant used acceleration based on SENSE a total factor equal to 2.64 (acquisition labeled as SENSE). second combination CS 4 CS+SENSE). Acquisition times were compared between...
Abstract Purpose Development of a supervised machine-learning model capable predicting clinically relevant molecular subtypes pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features. Methods The retrospective observational study assessed 55 surgical PDAC patients. Molecular were defined by immunohistochemical staining KRT81. Tumors manually segmented and 1606 features extracted with PyRadiomics . A gradient-boosted-tree algorithm (XGBoost) was...