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Publications

2024

  • Haueise T, Schick F, Stefan N, Machann J. Comparison of the accuracy of commercial two-point and multi-echo Dixon MRI for quantification of fat in liver, paravertebral muscles, and vertebral bone marrow. Eur J Radiol. 2024 Mar;172:111359. doi: 10.1016/j.ejrad.2024.111359. Epub 2024 Feb 5. PMID: 38325186
  • Kraus KM, Oreshko M, Schnabel JA, Bernhardt D, Combs SE, Peeken JC. Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation. Lung Cancer. 2024 Mar;189:107507. doi: 10.1016/j.lungcan.2024.107507. Epub 2024 Feb 17. PMID: 38394745
  • Lohmann P, Gutsche R, Werner JM, Shah NJ, Langen KJ, Galldiks N. AI-based decision support for amino acid PET - early prediction of suspicious brain tumor foci for patient management. 2024 J Nucl Med jnumed.123.267112. Advance online publication. doi: 10.2967/jnumed.123.267112
  • Rastogi A, Brugnara G, Foltyn-Dumitru M, Mahmutoglu MA, Preetha CJ, Kobler E, Pflüger I, Schell M, Deike-Hofmann K, Kessler T, van den Bent MJ, Idbaih A, Platten M, Brandes AA, Nabors B, Stupp R, Bernhardt D, Debus J, Abdollahi A, Gorlia T, Tonn JC, Weller M, Maier-Hein KH, Radbruch A, Wick W, Bendszus M, Meredig H, Kurz FT, Vollmuth P. Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study. Lancet Oncol. 2024 Mar;25(3):400-410. doi: 10.1016/S1470-2045(23)00641-1. PMID: 38423052
  • Voon CC, Wiltgen T, Wiestler B, Schlaeger S, Mühlau M. Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis. Neuroimage Clin. 2024 Mar 25;42:103598. doi: 10.1016/j.nicl.2024.103598. Epub ahead of print. PMID: 38582068

 

2023

  • Ambroladze, A. et al. (2023). CNN-based Whole Breast Segmentation in Longitudinal High-risk MRI Study. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_35
  • Fischer M, Küstner T, Pappa S, Niendorf T, Pischon T, Kröncke T, Bette S, Schramm S, Schmidt B, Haubold J, Nensa F, Nonnenmacher T, Palm V, Bamberg F, Kiefer L, Schick F, Yang B. Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study. BMC Med Imaging. 2023 Aug 8;23(1):104. doi: 10.1186/s12880-023-01056-9. PMID: 37553619
  • Gutsche R, Lowis C, Ziemons K, Kocher M, Ceccon G, Régio Brambilla, C, Shah NJ, Langen KJ, Galldiks N, Isensee F, Lohmann P. Automated brain tumor detection and segmentation for treatment response assessment using amino acid PET. J Nucl Med. 2023 Oct;64(10):1594-1602. doi: 10.2967/jnumed.123.265725. Epub 2023 Aug 10. PMID: 37562802
  • Haueise T, Schick F, Stefan N, Schlett CL, Weiss JB, Nattenmüller J, Göbel-Guéniot K, Norajitra T, Nonnenmacher T, Kauczor HU, Maier-Hein KH, Niendorf T, Pischon T, Jöckel KH, Umutlu L, Peters A, Rospleszcz S, Kröncke T, Hosten N, Völzke H, Krist L, Willich SN, Bamberg F, Machann J. Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort. Sci Adv. 2023 May 12;9(19):eadd0433. doi: 10.1126/sciadv.add0433. Epub 2023 May 12. PMID: 37172093
  • Haueise T, Stefan N, Schulz TJ, Schick F, Birkenfeld AL, Machann J. Automated shape-independent assessment of the spatial distribution of proton density fat fraction in vertebral bone marrow. Z Med Phys. 2023 Jan 30:S0939-3889(22)00137-4. doi: 10.1016/j.zemedi.2022.12.004. Epub ahead of print. PMID: 36725478
  • Laqua FC, Woznicki P, Bley TA, Schöneck M, Rinneburger M, Weisthoff M, Schmidt M, Persigehl T, Iuga AI, Baeßler B. Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer. Cancers (Basel). 2023 May 21;15(10):2850. doi: 10.3390/cancers15102850. PMID: 37345187
  • Lauerer M, Bussas M, Pongratz V, Berthele A, Kirschke JS, Wiestler B, Zimmer C, Hemmer B, Mühlau M. Percentage brain volume change in multiple sclerosis mainly reflects white matter and cortical volume. Ann Clin Transl Neurol. 2023 Jan;10(1):130-135. doi: 10.1002/acn3.51700. Epub 2022 Nov 25. PMID: 36427289
  • Meißner AK, Gutsche R, Galldiks N, Kocher M, Jünger ST, Eich ML, Nogova L, Araceli T, Schmidt NO, Ruge MI, Goldbrunner R, Proescholdt M, Grau S, Lohmann P. Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer. J Neurooncol. 2023 Jul;163(3):597-605. doi: 10.1007/s11060-023-04367-7. Epub 2023 Jun 29. PMID: 37382806
  • Pati S, Baid U, Edwards B, Sheller M, et al. Federated learning enables big data for rare cancer boundary detection. Nat Commun. 2022 Dec 5;13(1):7346. doi: 10.1038/s41467-022-33407-5. Erratum in: Nat Commun. 2023 Jan 26;14(1):436. PMID: 36470898
  • Prabhakar C, Li HB, Paetzold JC, Loehr T, Niu C, Mühlau M, Rueckert D, Wiestler B, Menze B (2023). Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. doi: 10.1007/978-3-031-43993-3_22
  • Rinneburger M, Carolus H, Iuga AI, Weisthoff M, Lennartz S, Hokamp NG, Caldeira L, Shahzad R, Maintz D, Laqua FC, Baeßler B, Klinder T, Persigehl T. Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network. Eur Radiol Exp. 2023 Jul 28;7(1):45. doi: 10.1186/s41747-023-00360-x. PMID: 37505296
  • Schlaeger S, Li HB, Baum T, Zimmer C, Moosbauer J, Byas S, Mühlau M, Wiestler B, Finck T. Longitudinal Assessment of Multiple Sclerosis Lesion Load With Synthetic Magnetic Resonance Imaging-A Multicenter Validation Study. Invest Radiol. 2023 May 1;58(5):320-326. doi: 10.1097/RLI.0000000000000938. Epub 2022 Nov 14. PMID: 36730638.
  • Ziller A, Erdur AC, Jungmann F, Rueckert D, Braren R, Kaissis G. Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients. IEEE ISBI 2023

2022

  • Albert S, Wichtmann B, Zhao W, Hesser J, Attenberger UI, Schad LR and Zöllner FG Comparison of Image Normalization Techniques for Rectal Cancer Segmentation in Multi-Center Data: Initial results. Proc. Int. Soc. Magn. Reson. Med., London UK, 2022, 31, p.619, index.mirasmart.com/ISMRM2022/PDFfiles/0619.html
  • 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) 2022;28(6):900-9. 10.1177/13524585211044957 PMID:34591698
  • Finck T, Li H, Schlaeger S, Grundl L, Sollmann N, Bender B, Bürkle E, Zimmer C, Kirschke J, Menze B, Mühlau M, Wiestler B. Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study. Frontiers in neuroscience 2022;16:889808. 10.3389/fnins.2022.889808 PMID:35557607
  • Föllmer B, Biavati F, Wald C, Stober S, Ma J, Dewey M, Samek W. Active multitask learning with uncertainty-weighted loss for coronary calcium scoring. Medical physics 10.1002/mp.15870 PMID:35861655
  • Galldiks N, Angenstein F, Werner JM, Bauer EK, Gutsche R, Fink GR, Langen KJ, Lohmann P. Use of advanced neuroimaging and artificial intelligence in meningiomas. Brain pathology (Zurich, Switzerland) 2022;32(2):e13015. 10.1111/bpa.13015 PMID:35213083
  • Gutsche R, Lohmann P, Hoevels M, Ruess D, Galldiks N, Visser-Vandewalle V, Treuer H, Ruge M, Kocher M. Radiomics outperforms semantic features for prediction of response to stereotactic radiosurgery in brain metastases. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2022;166:37-43. 10.1016/j.radonc.2021.11.010 PMID:34801629
  • Lohmann P, Franceschi E, Vollmuth P, Dhermain F, Weller M, Preusser M, Smits M, Galldiks N. Radiomics in neuro-oncological clinical trials. Lancet Digit Health. 2022 Nov;4(11):e841-e849. doi: 10.1016/S2589-7500(22)00144-3. Epub 2022 Sep 28. PMID: 36182633.
  • Maurovich-Horvat P, Bosserdt M, Kofoed KF, Rieckmann N, Benedek T, Donnelly P, et al.  CT or Invasive Coronary Angiography in Stable Chest Pain. The New England journal of medicine 2022;386(17):1591-602. 10.1056/NEJMoa2200963 PMID:35240010
  • Meißner AK, Gutsche R, Galldiks N, Kocher M, Jünger ST, Eich ML, Montesinos-Rongen M, Brunn A, Deckert M, Wendl C, Dietmaier W, Goldbrunner R, Ruge MI, Mauch C, Schmidt NO, Proescholdt M, Grau S, Lohmann P. Radiomics for the noninvasive prediction of the BRAF mutation status in patients with melanoma brain metastases. Neuro-oncology 2022;24(8):1331-40. 10.1093/neuonc/noab294 PMID:34935978
  • Mühlau M. T1/T2-weighted ratio is a surrogate marker of demyelination in multiple sclerosis: No. Multiple sclerosis (Houndmills, Basingstoke, England) 2022;28(3):355-6. 10.1177/13524585211063622 PMID:35067108
  • Müller M, Winz O, Gutsche R, Leijenaar RTH, Kocher M, Lerche C, Filss CP, Stoffels G, Steidl E, Hattingen E, Steinbach JP, Maurer GD, Heinzel A, Galldiks N, Mottaghy FM, Langen KJ, Lohmann P. Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression. Journal of neuro-oncology 10.1007/s11060-022-04089-2 PMID:35852737
  • Peisen F, Hänsch A, Hering A, Brendlin AS, Afat S, Nikolaou K, Gatidis S, Eigentler T, Amaral T, Moltz JH, Othman AE. Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy. Cancers 2022;14(12):2992. 10.3390/cancers14122992 PMID:35740659
  • Pflüger I, Wald T, Isensee F, Schell M, Meredig H, Schlamp K, Bernhardt D, Brugnara G, Heußel CP, Debus J, Wick W, Bendszus M, Maier-Hein KH, Vollmuth P. Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks. Neurooncol Adv. 2022 Aug 23;4(1):vdac138. doi: 10.1093/noajnl/vdac138. PMID: 36105388
  • Vollmuth P, Foltyn M, Huang RY, Galldiks N, Petersen J, Isensee F, van den Bent MJ, Barkhof F, Park JE, Park YW, Ahn SS, Brugnara G, Meredig H, Jain R, Smits M, Pope WB, Maier-Hein K, Weller M, Wen PY, Wick W, Bendszus M. AI-based decision support improves reproducibility of tumor response assessment in neuro-oncology: an international multi-reader study. Neuro-oncology 10.1093/neuonc/noac189 PMID:35917833
  • Wichtmann B.D. , Albert S, dos Santos B. Wichtmann, S. Albert, D. dos Santos, U. Attenberger and B. Baessler.  Test-retest repeatability of radiomic features derived from T2w MRI in prostate cancer patients. Proc. Int. Soc. Magn. Reson. Med., London, UK , 2022 31, p.2789, https://index.mirasmart.com/ISMRM2022/PDFfiles/2789.htm
  • Wichtmann BD, Albert S, Zhao W, Maurer A, Rödel C, Hofheinz RD, Hesser J, Zöllner FG, Attenberger UI. Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy. Diagnostics (Basel, Switzerland) 2022;12(7):1601. 10.3390/diagnostics12071601 PMID:35885506
  • Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, Pinto Dos Santos D, Baeßler B. Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging. Investigative radiology 10.1097/RLI.0000000000000921 PMID:36070524
  • Wuschek A, Bussas M, El Husseini M, Harabacz L, Pineker V, Pongratz V, Berthele A, Riederer I, Zimmer C, Hemmer B, Kirschke JS, Mühlau M. Somatosensory evoked potentials and magnetic resonance imaging of the central nervous system in early multiple sclerosis. J Neurol. 2023 Feb;270(2):824-830. doi: 10.1007/s00415-022-11407-1. Epub 2022 Oct 7. PMID: 36205793

2021

  • Almansour H, Afat S, Serna-Higuita LM, Amaral T, Schraag A, Peisen F, Brendlin A, Seith F, Klumpp B, Eigentler TK, Othman AE Early Tumor Size Reduction of at least 10% at the First Follow-Up Computed Tomography Can Predict Survival in the Setting of Advanced Melanoma and Immunotherapy. Acad Radiol. 2021 Jun 12:S1076-6332(21)00210-5. doi: 10.1016/j.acra.2021.04.015. Epub ahead of print. PMID: 34130924
  • 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
  • Dima A, Paetzold JC, Jungmann F, Lemke T, Raffler P, Kaissis G, Rueckert D, Braren R. Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging. InInternational Workshop on Machine Learning in Medical Imaging 2021 Sep 27 (pp. 596-605). Springer, Cham.
  • Galldiks, N., Zadeh, G., & Lohmann, P. (2021). Artificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology. Neurooncol Adv, 2(Suppl 4), iv1–iv2.
  • Grahl S, Bussas M, Wiestler B, Eichinger P, Gaser C, Kirschke J, Zimmer C, Berthele A, Hemmer B, Mühlau M. Differential Effects of Fingolimod and Natalizumab on Magnetic Resonance Imaging Measures in Relapsing-Remitting Multiple Sclerosis. Neurotherapeutics. 2021 Oct;18(4):2589-2597. doi: 10.1007/s13311-021-01118-2. Epub 2021 Sep 24. IF 6.088
  • 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, Kamal O, Kaissis GA, Heid I, Lohöfer FK, McTavish S, Van AT, Katemann C, Peeters JM, Karampinos DC, Makowski MR, Braren RF. Qualitative and Quantitative Comparison of Respiratory Triggered Reduced Field-of-View (FOV) Versus Full FOV Diffusion Weighted Imaging (DWI) in Pancreatic Pathologies. Acad Radiol. 2021 Nov;28 Suppl 1:S234-S243.
  • 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
  • Hering, A., Peisen, F., Amaral, T., Gatidis, S., Eigentler, T., Othman, A., & Moltz, J. H. (2021, August). Whole-Body Soft-Tissue Lesion Tracking and Segmentation in Longitu-dinal CT Imaging Studies. In Medical Imaging with Deep Learning (pp. 312-326). PMLR.
  • 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
  • Jungmann F, Kaissis GA, Ziegelmayer S, Harder F, Schilling C, Yen HY, Steiger K, Weichert W, Schirren R, Demir IE, Friess H, Makowski MR, Braren RF, Lohöfer FK. Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. Cancers (Basel). 2021 Apr 25;13(9):2069. doi: 10.3390/cancers13092069. PMID: 33922981
  • 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. doi.org/10.1093/noajnl/vdaa118

 

2020

  • 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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