User profiles for L.J. Savic
Lynn Jeanette SavicClinician Scientist, University Medicine Berlin, Germany Verified email at charite.de Cited by 1671 |
[HTML][HTML] Intra-arterial embolotherapy for intrahepatic cholangiocarcinoma: update and future prospects
LJ Savic, J Chapiro, JFH Geschwind - Hepatobiliary Surgery and …, 2017 - ncbi.nlm.nih.gov
… LJ Savic reports scholarships from the German National Academic Foundation and the
Medical Excellence Initiative by the Manfred Lautenschläger Foundation outside the submitted …
Medical Excellence Initiative by the Manfred Lautenschläger Foundation outside the submitted …
Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI
Objectives To develop and validate a proof-of-concept convolutional neural network (CNN)–based
deep learning system (DLS) that classifies common hepatic lesions on multi-phasic …
deep learning system (DLS) that classifies common hepatic lesions on multi-phasic …
[HTML][HTML] Irreversible electroporation in interventional oncology: where we stand and where we go
LJ Savic, J Chapiro, B Hamm… - RöFo-Fortschritte auf …, 2016 - thieme-connect.com
Irreversible electroporation (IRE) is the latest in the series of image-guided locoregional tumor
ablation therapies. IRE is performed in a nearly non-thermal fashion that circumvents the „…
ablation therapies. IRE is performed in a nearly non-thermal fashion that circumvents the „…
Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning—an artificial intelligence concept
Purpose To use magnetic resonance (MR) imaging and clinical patient data to create an
artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial …
artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial …
Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features
Objectives To develop a proof-of-concept “interpretable” deep learning prototype that
justifies aspects of its predictions from a pre-trained hepatic lesion classifier. Methods A …
justifies aspects of its predictions from a pre-trained hepatic lesion classifier. Methods A …
MR elastography in cancer
The mechanical traits of cancer include abnormally high solid stress as well as drastic and
spatially heterogeneous changes in intrinsic mechanical tissue properties. Whereas solid …
spatially heterogeneous changes in intrinsic mechanical tissue properties. Whereas solid …
Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictors of tumor response in hepatocellular carcinoma after DEB-TACE
IT Schobert, LJ Savic, J Chapiro, K Bousabarah… - European …, 2020 - Springer
Objectives To investigate the predictive value of quantifiable imaging and inflammatory
biomarkers in patients with hepatocellular carcinoma (HCC) for the clinical outcome after drug-…
biomarkers in patients with hepatocellular carcinoma (HCC) for the clinical outcome after drug-…
Radiologic-pathologic analysis of contrast-enhanced and diffusion-weighted MR imaging in patients with HCC after TACE: diagnostic accuracy of 3D quantitative …
Purpose To evaluate the diagnostic performance of three-dimensional ( 3D three-dimensional
) quantitative enhancement-based and diffusion-weighted volumetric magnetic resonance …
) quantitative enhancement-based and diffusion-weighted volumetric magnetic resonance …
Interactive explainable deep learning model informs prostate cancer diagnosis at MRI
…, F Biessmann, NL Beetz, A Hartenstein, LJ Savic… - Radiology, 2023 - pubs.rsna.org
Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate
and efficient radiologic interpretation. Although artificial intelligence may assist in this task, …
and efficient radiologic interpretation. Although artificial intelligence may assist in this task, …
Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the …
Objectives To train a deep learning model to differentiate between pathologically proven
hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging …
hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging …