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  • Armanious K,  Abdulatif S, Shi W, Hepp T, Gatidis S, Yang B, "Uncertainty-Based Biological Age Estimation of Brain MRI Scans," ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 1100-1104, doi: 10.1109/ICASSP39728.2021.9414112.

  • Armanious K, Abdulatif, S, Shi W, Salian S, Küstner T, Weiskopf D, Hepp T, Gatidis S, Yang B. (2021). Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation. IEEE Transactions on Medical Imaging. PP. 1-1. 10.1109/TMI.2021.3066857.

  • Brendlin AS, Peisen F, Almansour H, Afat S, Eigentler T, Amaral T, Faby S, Calvarons AF, Nikolaou K, Othman AE. A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma. Journal for immunotherapy of cancer 2021;9(11):e003261. 10.1136/jitc-2021-003261 PMID:34795006

  • Bussas M, Grahl S, Pongratz V, Berthele A, Gasperi C, Andlauer T, Gaser C, Kirschke JS, Wiestler B, Zimmer C, Hemmer B, Mühlau M. Gray matter atrophy in relapsing-remitting multiple sclerosis is associated with white matter lesions in connecting fibers. Multiple sclerosis (Houndmills, Basingstoke, England) [published online: September 30, 2021]. 10.1177/13524585211044957 PMID:34591698

  • Gutsche R, Scheins J, Kocher M, Bousabarah K, Fink GR, Shah NJ, Langen KJ, Galldiks N, Lohmann P. Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients. Cancers (Basel). 2021 Feb 5;13(4):647. doi: 10.3390/cancers13040647.

  • Harder FN, Jungmann F, Kaissis GA, Lohöfer FK, Ziegelmayer S, Havel D, Quante M, Reichert M, Schmid RM, Demir IE, Friess H, Wildgruber M, Siveke J, Muckenhuber A, Steiger K, Weichert W, Rauscher I, Eiber M, Makowski MR, Braren RF. [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. EJNMMI research 2021;11(1):70. 10.1186/s13550-021-00808-4 PMID:34322781

  • Hepp T, Blum D, Armanious K, Schölkopf B, Stern D, Yang B, Gatidis S. Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 2021;92:101967. 10.1016/j.compmedimag.2021.101967 PMID:34392229

  • Iuga AI, Carolus H, Höink AJ, Brosch T, Klinder T, Maintz D, Persigehl T, Baeßler B, Püsken M. Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Med Imaging. 2021 Apr 13;21(1):69. doi: 10.1186/s12880-021-00599-z.

  • Iuga AI, Lossau T, Caldeira LL, Rinneburger M, Lennartz S, Große Hokamp N, Püsken M, Carolus H, Maintz D, Klinder T, Persigehl T. Automated mapping and N-Staging of thoracic lymph nodes in contrast-enhanced CT scans of the chest using a fully convolutional neural network. Eur J Radiol. 2021 Jun;139:109718. doi: 10.1016/j.ejrad.2021.109718.

  • Jayachandran Preetha C, Meredig H, Brugnara G, Mahmutoglu MA, Foltyn M, Isensee F, Kessler T, Pflüger I, Schell M, Neuberger U, Petersen J, Wick A, Heiland S, Debus J, Platten M, Idbaih A, Brandes AA, Winkler F, van den Bent MJ, Nabors B, Stupp R, Maier-Hein KH, Gorlia T, Tonn JC, Weller M, Wick W, Bendszus M, Vollmuth P. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. The Lancet. Digital health 2021;3(12):e784-e794. 10.1016/S2589-7500(21)00205-3 PMID:34688602

  • Kart T, Fischer M, Küstner T, Hepp T, Bamberg F, Winzeck S, Glocker B, Rueckert D, Gatidis S. Deep Learning-Based Automated Abdominal Organ Segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies. Investigative radiology 2021;56(6):401-8. 10.1097/RLI.0000000000000755 PMID:33930003

  • Knolle M, Kaissis G, Jungmann F, Ziegelmayer S, Sasse D, Makowski M, Rueckert D, Braren R. Efficient, high-performance semantic segmentation using multi-scale feature extraction. PloS one 2021;16(8):e0255397. 10.1371/journal.pone.0255397 PMID:34411138

  • Lennartz S, Täger P, Zopfs D, Iuga AI, Reimer RP, Zäske C, Große Hokamp N, Maintz D, Heidenreich A, Drzezga A, Kobe C, Persigehl T. Lymph Node Assessment in Prostate Cancer: Evaluation of Iodine Quantification With Spectral Detector CT in Correlation to PSMA PET/CT. Clin Nucl Med. 2021 Apr 1;46(4):303-309. doi: 10.1097/RLU.0000000000003496.

  • Lohmann, P., Meißner, A. K., Kocher, M., Bauer, E. K., Werner, J. M., Fink, G. R., Shah, N. J., Langen, K. J.,  Galldiks, N. (2021). Feature-based PET/MRI radiomics in patients with brain tumors. Neuro-oncology advances, 2(Suppl 4), iv15–iv21. https://doi.org/10.1093/noajnl/vdaa118.




  • Finck T, Li H, Grundl L, Eichinger P, Bussas M, Mühlau M, et al. Deep-Learning Generated Synthetic Double Inversion Recovery Images Improve Multiple Sclerosis Lesion Detection. Invest Radiol. 2020 05;55(5):318-23.

  • Kaissis GA, Jungmann F, Ziegelmayer S, Lohöfer FK, Harder FN, Schlitter AM, Muckenhuber A, Steiger K, Schirren R, Friess H, Schmid R, Weichert W, Makowski MR, Braren RF. Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters. J Clin Med. 2020 Apr25;9(5):1250. doi: 10.3390/jcm9051250.

  • Kaissis GA, Ziegelmayer S, Lohöfer FK, Harder FN, Jungmann F, Sasse D, Muckenhuber A, Yen HY, Steiger K, Siveke J, Friess H, Schmid R, Weichert W, Makowski MR, Braren RF. Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. J Clin Med. 2020 Mar 7;9(3):724. doi: 10.3390/jcm9030724.

  • Kickingereder P, Brugnara G, Hansen MB, Nowosielski M, Pflüger I, Schell M, Isensee F, Foltyn M, Neuberger U, Kessler T, Sahm F, Wick A, Heiland S, Weller M, Platten M, von Deimling A, Maier-Hein KH, Østergaard L, van den Bent MJ, Gorlia T, Wick W, Bendszus M. Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial. Radiology. 2020 Jul 28:200978.

  • Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol. 2020 Oct;196(10):856-867. doi: 10.1007/s00066-020-01626-8. Epub 2020 May 11.

  • Küstner T, Hepp T, Fischer M, Schwartz M, Fritsche A, Häring HU, Nikolaou K, Bamberg F, Yang B, Schick F, Gatidis S, Machann J. Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies. Radiol Artif Intell. 2020 Oct 28;2(6):e200010. doi: 10.1148/ryai.2020200010.

  • Lohmann P, Bousabarah K, Hoevels M, Treuer H. Radiomics in radiation oncology-basics, methods, and limitations. Strahlenther Onkol. 2020 Oct;196(10):848-855. doi: 10.1007/s00066-020-01663-3. Epub 2020 Jul 9.

  • Lohmann P, Elahmadawy MA, Gutsche R, et al. FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation. Cancers (Basel). 2020;12(12):3835. Published 2020 Dec 18. doi:10.3390/cancers12123835.

  • Lohmann P, Galldiks N, Kocher M, Heinzel A, Filss CP, Stegmayr C, Mottaghy FM, Fink GR, Jon Shah N, Langen KJ. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods. 2021 Apr;188:112-121. doi: 10.1016/j.ymeth.2020.06.003. Epub 2020 Jun 6.

  • Lohmann, P., Kocher, M., Ruge, M. I., Visser-Vandewalle, V., Shah, N. J., Fink, G. R., Langen, K. J., Galldiks, N. (2020). PET/MRI Radiomics in Patients With Brain Metastases. Frontiers in neurology, 11, 1. https://doi.org/10.3389/fneur.2020.00001

  • Schell M, Pflüger I, Brugnara G, Isensee F, Neuberger U, Foltyn M, Kessler T, Sahm F, Wick A, Nowosielski M, Heiland S, Weller M, Platten M, Maier-Hein KH, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Kickingereder P. Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial. Neuro Oncol. 2020 May 11

  • Ziegelmayer S, Kaissis G, Harder F, Jungmann F, Müller T, Makowski M, et al. Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP). J Clin Med. 2020 Dec 11;9(12):E4013.





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