User profiles for L.J. Savic

Lynn Jeanette Savic

Clinician 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 …

Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI

CA Hamm, CJ Wang, LJ Savic, M Ferrante… - European …, 2019 - Springer
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 …

[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 „…

Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning—an artificial intelligence concept

A Abajian, N Murali, LJ Savic, FM Laage-Gaupp… - Journal of Vascular and …, 2018 - Elsevier
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 …

Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features

CJ Wang, CA Hamm, LJ Savic, M Ferrante… - European …, 2019 - Springer
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 …

MR elastography in cancer

J Guo, LJ Savic, KH Hillebrandt, I Sack - Investigative Radiology, 2023 - journals.lww.com
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 …

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-…

Radiologic-pathologic analysis of contrast-enhanced and diffusion-weighted MR imaging in patients with HCC after TACE: diagnostic accuracy of 3D quantitative …

…, V Charu, R Schernthaner, Z Wang, V Tacher, LJ Savic… - Radiology, 2014 - pubs.rsna.org
Purpose To evaluate the diagnostic performance of three-dimensional ( 3D three-dimensional
) 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, …

Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the …

PM Oestmann, CJ Wang, LJ Savic, CA Hamm… - European …, 2021 - Springer
Objectives To train a deep learning model to differentiate between pathologically proven
hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging …