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2. Differentiating Well-Differentiated from Poorly-Differentiated HCC: The Potential and the Limitation of Gd-EOB-DTPA in the Presence of Liver Cirrhosis.

Author

Goetz, Andrea, Verloh, Niklas, Utpatel, Kirsten, Fellner, Claudia, Rennert, Janine, Einspieler, Ingo, Doppler, Michael, Luerken, Lukas, Alizadeh, Leona S., Uller, Wibke, Stroszczynski, Christian, and Haimerl, Michael

Subjects
MAGNETIC resonance imaging, CIRRHOSIS of the liver, CONTRAST media, HEPATOCELLULAR carcinoma, GADOLINIUM
Abstract

This study uses magnetic resonance imaging (MRI) to investigate the potential of the hepatospecific contrast agent gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) in distinguishing G1- from G2/G3-differentiated hepatocellular carcinoma (HCC). Our approach involved analyzing the dynamic behavior of the contrast agent in different phases of imaging by signal intensity (SI) and lesion contrast (C), to surrounding liver parenchyma, and comparing it across distinct groups of patients differentiated based on the histopathological grading of their HCC lesions and the presence of liver cirrhosis. Our results highlighted a significant contrast between well- and poorly-differentiated lesions regarding the lesion contrast in the arterial and late arterial phases. Furthermore, the hepatobiliary phase showed limited diagnostic value in cirrhotic liver parenchyma due to altered pharmacokinetics. Ultimately, our findings underscore the potential of Gd-EOB-DTPA-enhanced MRI as a tool for improving preoperative diagnosis and treatment selection for HCC while emphasizing the need for continued research to overcome the diagnostic complexities posed by the disease. [ABSTRACT FROM AUTHOR]

Published
2024
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3. SARS-CoV2 evokes structural brain changes resulting in declined executive function

Author

Deuter, Daniel, primary, Hense, Katharina, additional, Kunkel, Kevin, additional, Vollmayr, Johanna, additional, Schachinger, Sebastian, additional, Wendl, Christina, additional, Schicho, Andreas, additional, Fellner, Claudia, additional, Salzberger, Bernd, additional, Hitzenbichler, Florian, additional, Zeller, Judith, additional, Vielsmeier, Veronika, additional, Dodoo-Schittko, Frank, additional, Schmidt, Nils Ole, additional, and Rosengarth, Katharina, additional

Published
2024
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6. Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI.

Author

Hammer, Simone, Nunes, Danilo Weber, Hammer, Michael, Zeman, Florian, Akers, Michael, Götz, Andrea, Balla, Annika, Doppler, Michael Christian, Fellner, Claudia, Platz Batista da Silva, Natascha, Thurn, Sylvia, Verloh, Niklas, Stroszczynski, Christian, Wohlgemuth, Walter Alexander, Palm, Christoph, and Uller, Wibke

Subjects
CONVOLUTIONAL neural networks, HUMAN abnormalities, MAGNETIC resonance imaging, RARE diseases
Abstract

BACKGROUND: Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease. OBJECTIVE: A convolutional neural network (CNN) was evaluated for differentiation of peripheral vascular malformations (VMs) on T2-weighted short tau inversion recovery (STIR) MRI. METHODS: 527 MRIs (386 low-flow and 141 high-flow VMs) were randomly divided into training, validation and test set for this single-center study. 1) Results of the CNN's diagnostic performance were compared with that of two expert and four junior radiologists. 2) The influence of CNN's prediction on the radiologists' performance and diagnostic certainty was evaluated. 3) Junior radiologists' performance after self-training was compared with that of the CNN. RESULTS: Compared with the expert radiologists the CNN achieved similar accuracy (92% vs. 97%, p = 0.11), sensitivity (80% vs. 93%, p = 0.16) and specificity (97% vs. 100%, p = 0.50). In comparison to the junior radiologists, the CNN had a higher specificity and accuracy (97% vs. 80%, p < 0.001; 92% vs. 77%, p < 0.001). CNN assistance had no significant influence on their diagnostic performance and certainty. After self-training, the junior radiologists' specificity and accuracy improved and were comparable to that of the CNN. CONCLUSIONS: Diagnostic performance of the CNN for differentiating high-flow from low-flow VM was comparable to that of expert radiologists. CNN did not significantly improve the simulated daily practice of junior radiologists, self-training was more effective. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Volume-Assisted Estimation of Remnant Liver Function Based on Gd-EOB-DTPA Enhanced MR Relaxometry: A Prospective Observational Trial

Author

Verloh, Niklas, primary, Rio Bartulos, Carolina, additional, Utpatel, Kirsten, additional, Brennfleck, Frank, additional, Goetz, Andrea, additional, Schicho, Andreas, additional, Fellner, Claudia, additional, Nickel, Dominik, additional, Zeman, Florian, additional, Steinmann, Johannes F., additional, Uller, Wibke, additional, Stroszczynski, Christian, additional, Schlitt, Hans-Jürgen, additional, Wiggermann, Phillip, additional, and Haimerl, Michael, additional

Published
2023
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17. The impact of fMRI on multimodal navigation in surgery of cerebral lesions: four years clinical experience

Author

Wurm, Gabriele, Schnizer, Mathilde, and Fellner, Claudia

Abstract

Abstract: Object: Neuronavigation with display of intraoperative structures, instrument locations, orientation and relationships to nearby structures can increase anatomic precision while enhancing the surgeon’s confidence and his/her perception of safety. Combination of neuronavigation with functional imaging provides multimodal guidance for surgery of cerebral lesions. We evaluated the impact of functional MRI (fMRI) on surgical decision making and outcome. Materials and methods: A neuronavigational device (StealthStation (tm), Medtronic Inc.) was used as platform to merge fMRI data with anatomic images, and to implement intraoperative multimodal guidance. In a 52-month period, where 977 surgical procedures were performed with the aid of neuronavigation, 88 patients underwent image-guided procedures using multimodal guidance. Patient, surgical and outcome data of this series was prospectively collected. Results: Evaluation of 88 procedures on cerebral lesions in complex regions where fMRI data were integrated using the navigation system demonstrated that the additional information was presented in a user-friendly way. Computer assisted fMRI integration was found to be especially helpful in planning the best approach, in assessing alternative approaches, and in defining the extent of the surgical exposure. Furthermore, the surgeons found it more effective to interpret fMRI information when shown in a navigation system as compared to the traditional display on a light board or monitor. Conclusion: Multimodal navigation enhanced by fMRI was judged useful for optimization of surgery of cerebral lesions, especially in and around eloquent regions by experienced neurosurgeons.

Published
2024
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22. Application of A U-Net for Map-Like Segmentation and Classification of Discontinuous Fibrosis Distribution in Gd-EOB-DTPA-Enhanced Liver MRI

Author

Strotzer, Quirin David, Winther, Hinrich, Utpatel, Kirsten, Scheiter, Alexander, Fellner, Claudia, Doppler, Michael Christian, Ringe, Kristina Imeen, Raab, Florian, Haimerl, Michael, Uller, Wibke, Stroszczynski, Christian, Luerken, Lukas, and Verloh, Niklas

Subjects
liver fibrosis, cirrhosis, segmentation, Artificial Intelligence, U-Net, convolutional neural network, ddc:610, Clinical Biochemistry, 610 Medizin
Abstract

We aimed to evaluate whether U-shaped convolutional neuronal networks can be used to segment liver parenchyma and indicate the degree of liver fibrosis/cirrhosis at the voxel level using contrast-enhanced magnetic resonance imaging. This retrospective study included 112 examinations with histologically determined liver fibrosis/cirrhosis grade (Ishak score) as the ground truth. The T1-weighted volume-interpolated breath-hold examination sequences of native, arterial, late arterial, portal venous, and hepatobiliary phases were semi-automatically segmented and co-registered. The segmentations were assigned the corresponding Ishak score. In a nested cross-validation procedure, five models of a convolutional neural network with U-Net architecture (nnU-Net) were trained, with the dataset being divided into stratified training/validation (n = 89/90) and holdout test datasets (n = 23/22). The trained models precisely segmented the test data (mean dice similarity coefficient = 0.938) and assigned separate fibrosis scores to each voxel, allowing localization-dependent determination of the degree of fibrosis. The per voxel results were evaluated by the histologically determined fibrosis score. The micro-average area under the receiver operating characteristic curve of this seven-class classification problem (Ishak score 0 to 6) was 0.752 for the test data. The top-three-accuracy-score was 0.750. We conclude that determining fibrosis grade or cirrhosis based on multiphase Gd-EOB-DTPA-enhanced liver MRI seems feasible using a 2D U-Net. Prospective studies with localized biopsies are needed to evaluate the reliability of this model in a clinical setting.

Published
2022

23. Application of A U-Net for Map-like Segmentation and Classification of Discontinuous Fibrosis Distribution in Gd-EOB-DTPA-Enhanced Liver MRI

Author

Strotzer, Quirin David, primary, Winther, Hinrich, additional, Utpatel, Kirsten, additional, Scheiter, Alexander, additional, Fellner, Claudia, additional, Doppler, Michael Christian, additional, Ringe, Kristina Imeen, additional, Raab, Florian, additional, Haimerl, Michael, additional, Uller, Wibke, additional, Stroszczynski, Christian, additional, Luerken, Lukas, additional, and Verloh, Niklas, additional

Published
2022
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28. Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Author

Bumes, Elisabeth, primary, Fellner, Claudia, additional, Fellner, Franz A., additional, Fleischanderl, Karin, additional, Häckl, Martina, additional, Lenz, Stefan, additional, Linker, Ralf, additional, Mirus, Tim, additional, Oefner, Peter J., additional, Paar, Christian, additional, Proescholdt, Martin Andreas, additional, Riemenschneider, Markus J., additional, Rosengarth, Katharina, additional, Weis, Serge, additional, Wendl, Christina, additional, Wimmer, Sibylle, additional, Hau, Peter, additional, Gronwald, Wolfram, additional, and Hutterer, Markus, additional

Published
2022
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30. Assessment of Physiological Rat Kidney Ageing—Implications for the Evaluation of Allograft Quality Prior to Renal Transplantation

Author

Baumgartner, Andreas, Reichelt-Wurm, Simone, Gronwald, Wolfram, Samol, Claudia, Schröder, Josef A., Fellner, Claudia, Holler, Kathrin, Steege, Andreas, Putz, Franz Josef, Oefner, Peter J., Banas, Bernhard, and Banas, Miriam C.

Subjects
ddc:610, Endocrinology, Diabetes and Metabolism, 610 Medizin, kidney, ageing, transplantation, inflammation, urinary metabolites, Molecular Biology, Biochemistry
Abstract

Due to organ shortage and rising life expectancy the age of organ donors and recipients is increasing. Reliable biomarkers of organ quality that predict successful long-term transplantation outcomes are poorly defined. The aim of this study was the identification of age-related markers of kidney function that might accurately reflect donor organ quality. Histomorphometric, biochemical and molecular parameters were measured in young (3-month-old) and old (24-month-old) male Sprague Dawley rats. In addition to conventional methods, we used urine metabolomics by NMR spectroscopy and gene expression analysis by quantitative RT-PCR to identify markers of ageing relevant to allograft survival. Beside known markers of kidney ageing like albuminuria, changes in the concentration of urine metabolites such as trimethylamine-N-oxide, trigonelline, 2-oxoglutarate, citrate, hippurate, glutamine, acetoacetate, valine and 1-methyl-histidine were identified in association with ageing. In addition, expression of several genes of the toll-like receptor (TLR) pathway, known for their implication in inflammaging, were upregulated in the kidneys of old rats. This study led to the identification of age-related markers of biological allograft age potentially relevant for allograft survival in the future. Among those, urine metabolites and markers of immunity and inflammation, which are highly relevant to immunosuppression in transplant recipients, are promising and deserve further investigation in humans.

Published
2022

34. Quantitative analysis of liver function: 3D variable-flip-angle versus Look-Locker T1 relaxometry in hepatocyte-specific contrast-enhanced liver MRI

Author

Verloh, Niklas, primary, Fuhrmann, Irene, additional, Fellner, Claudia, additional, Nickel, Dominik, additional, Zeman, Florian, additional, Kandulski, Arne, additional, Hornung, Matthias, additional, Stroszczynski, Christian, additional, Wiggermann, Philipp, additional, and Haimerl, Michael, additional

Published
2022
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35. Assessment of Physiological Rat Kidney Ageing—Implications for the Evaluation of Allograft Quality Prior to Renal Transplantation

Author

Baumgartner, Andreas, primary, Reichelt-Wurm, Simone, additional, Gronwald, Wolfram, additional, Samol, Claudia, additional, Schröder, Josef A., additional, Fellner, Claudia, additional, Holler, Kathrin, additional, Steege, Andreas, additional, Putz, Franz Josef, additional, Oefner, Peter J., additional, Banas, Bernhard, additional, and Banas, Miriam C., additional

Published
2022
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49. Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1 H-Magnetic Resonance Spectroscopy and Machine Learning.

Author

Bumes, Elisabeth, Fellner, Claudia, Fellner, Franz A., Fleischanderl, Karin, Häckl, Martina, Lenz, Stefan, Linker, Ralf, Mirus, Tim, Oefner, Peter J., Paar, Christian, Proescholdt, Martin Andreas, Riemenschneider, Markus J., Rosengarth, Katharina, Weis, Serge, Wendl, Christina, Wimmer, Sibylle, Hau, Peter, Gronwald, Wolfram, and Hutterer, Markus

Subjects
*SUPPORT vector machines, *GENETIC mutation, *IN vivo studies, *CONFIDENCE intervals, *PROTON magnetic resonance spectroscopy, *RESEARCH methodology, *GLIOMAS, *MACHINE learning, *RETROSPECTIVE studies, *ACQUISITION of data, *CANCER patients, *DESCRIPTIVE statistics, *MEDICAL records, *OXIDOREDUCTASES, *COMPUTER-aided diagnosis, *PREDICTION models, *SENSITIVITY & specificity (Statistics)
Abstract

Simple Summary: The enzyme isocitrate dehydrogenase (IDH) affects glioma cell metabolism in multiple ways. Mutation of IDH is not only indicative of the presence of astrocytoma or oligodendroglioma but it also comes with a better prognosis and constitutes a promising therapeutic target. Therefore, determination of IDH mutation status is essential in clinical practice. In most patients, tissue can be obtained by resection or biopsy to determine IDH status histologically. However, in some cases, this is not possible for technical reasons. We recently showed in a small cohort of patients that non-invasive determination of IDH mutation status using proton magnetic resonance spectroscopy (1H-MRS) at 3.0 Tesla (T) together with machine learning techniques is feasible in a standard clinical setting and with acceptable effort. Here, we demonstrate that our approach showed comparably good results in sensitivity (82.6%) and specificity (72.7%) in a larger validation cohort employing 1H-MRS at 1.5 T in a retrospective, distinct setting. We concluded that our method works well regardless of the magnetic field strength and scanner used, and thus, may improve patient care. The isocitrate dehydrogenase (IDH) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of IDH mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of IDH mutation status employing proton magnetic resonance spectroscopy (1H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which 1H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2–95.1%) and a specificity of 72.7% (95% CI, 57.2–85.0%) could be achieved. We concluded that our 1H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining IDH status that is well-applicable in an everyday clinical setting. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Author

Bumes, Elisabeth, Wirtz, Fro-Philip, Fellner, Claudia, Grosse, Jirka, Hellwig, Dirk, Oefner, Peter J., Häckl, Martina, Linker, Ralf A., Proescholdt, Martin A., Schmidt, Nils Ole, Riemenschneider, Markus J., Samol, Claudia, Rosengarth, Katharina, Wendl, Christina M., Hau, Peter, Gronwald, Wolfram, and Hutterer, Markus

Subjects
D-2-hydroxyglutarate, ddc:610, myo-inositol, glioma, IDH mutation, 18F-FET, 1H-MRS, glycine, linear support vector machine, 610 Medizin, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282
Abstract

Isocitrate dehydrogenase (IDH)-1 mutation is an important prognostic factor and a potential therapeutic target in glioma. Immunohistological and molecular diagnosis of IDH mutation status is invasive. To avoid tumor biopsy, dedicated spectroscopic techniques have been proposed to detect D-2-hydroxyglutarate (2-HG), the main metabolite of IDH, directly in vivo. However, these methods are technically challenging and not broadly available. Therefore, we explored the use of machine learning for the non-invasive, inexpensive and fast diagnosis of IDH status in standard 1H-magnetic resonance spectroscopy (1H-MRS). To this end, 30 of 34 consecutive patients with known or suspected glioma WHO grade II-IV were subjected to metabolic positron emission tomography (PET) imaging with O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) for optimized voxel placement in 1H-MRS. Routine 1H-magnetic resonance (1H-MR) spectra of tumor and contralateral healthy brain regions were acquired on a 3 Tesla magnetic resonance (3T-MR) scanner, prior to surgical tumor resection and molecular analysis of IDH status. Since 2-HG spectral signals were too overlapped for reliable discrimination of IDH mutated (IDHmut) and IDH wild-type (IDHwt) glioma, we used a nested cross-validation approach, whereby we trained a linear support vector machine (SVM) on the complete spectral information of the 1H-MRS data to predict IDH status. Using this approach, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2&ndash, 99.9%) and a specificity of 75.0% (95% CI, 42.9&ndash, 94.5%), respectively. The area under the curve (AUC) amounted to 0.83. Subsequent ex vivo 1H-nuclear magnetic resonance (1H-NMR) measurements performed on metabolite extracts of resected tumor material (eight specimens) revealed myo-inositol (M-ins) and glycine (Gly) to be the major discriminators of IDH status. We conclude that our approach allows a reliable, non-invasive, fast and cost-effective prediction of IDH status in a standard clinical setting.

Published
2020
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