Kostas Marias

Scientific Journals

  1. P. Behrenbruch, K. Marias, P.A. Armitage, M.Yam, N.R. Moore, R.E. English, J. Clarke, and M.J. Brady, “Fusion of contrast-enhanced breast MR and mammographic imaging data,” Medical image analysis, vol. 7, no. 3, pp. 311–340, Sep. 2003, England (1361-8415; 1361-8415).   http://doi.org/10.1016/S1361-8415(03)00015-X
  2. P. Behrenbruch, K. Marias, P.A. Armitage, M. Yam, N. R. Moore, R.E. English, P.J. Clarke, F.J. Leong, and M.J. Brady, “Fusion of contrast-enhanced breast MR and mammographic imaging data,” The British journal of radiology, 2004, 77 Spec No 2, (S201-8), England (0007-1285; 0007-1285). http://doi.org/10.1259/bjr/66587930
  3. Marias, C. Behrenbruch, R. Highnam, S. Parbhoo, A. Seifalian, and M. Brady, “A mammographic image analysis method to detect and measure changes in breast density,” Eur. J. Radiol., vol. 52, no. 3, pp. 276–282, Dec. 2004. http://doi.org/10.1016/j.ejrad.2004.02.014
  4. Marias, J. Ripoll, H. Meyer, V. Ntziachristos, and S. Orphanoudakis, “Image analysis for assessing molecular activity changes in time-dependent geometries,” IEEE Trans. Med. Imaging, vol. 24, no. 7, pp. 894–900, Jul. 2005. http://doi.org/10.1109/TMI.2005.848612
  5. Marias, C. Behrenbruch, S. Parbhoo, A. Seifalian, and M. Brady, “A registration framework for the comparison of mammogram sequences,” IEEE Trans. Med. Imaging, vol. 24, no. 6, pp. 782–790, Jun. 2005, (02780062). http://doi.org/10.1109/TMI.2005.848374
  6. G. Linguraru, K. Marias, R.E. English, and M.J. Brady, “A biologically inspired algorithm for microcalcification cluster detection,” Med. Image Anal., vol. 10, no. 6, pp. 850–862, Dec. 2006. http://doi.org/10.1016/j.media.2006.07.004
  7. Dimitriadis, K. Marias, and S.C. Orphanoudakis, “A multi-agent platform for content-based image retrieval,” Multimed. Tools Appl., Hingham, MA, USA: Kluwer Academic Publishers (1380-7501), vol. 33, no. 1, pp. 57–72, Mar. 2007. http://doi.org/10.1007/s11042-006-0095-2
  8. Darrell, H. Meyer, K. Marias, M. Brady, and J. Ripoll, “Weighted filtered backprojection for quantitative fluorescence optical projection tomography,” Phys. Med. Biol., vol. 53, no. 14, pp. 3863–3881, Jul. 2008. http://doi.org/10.1088/0031-9155/53/14/010
  9. Farmaki, K. Marias, V. Sakkalis, and N. Graf, “Spatially adaptive active contours: a semi-automatic tumor segmentation framework,” Int. J. Comput. Assist. Radiol. Surg., vol. 5, no. 4, pp. 369–384, Jul.2010. http://doi.org/10.1007/s11548-010-0477-9
  10. Skounakis, C. Farmaki, V. Sakkalis, A. Roniotis, K. Banitsas, N. Graf, and K. Marias,“DoctorEye: A Clinically Driven Multifunctional Platform, for Accurate Processing of Tumors in Medical Images,” Open Med. Inform. J., Special Issue: Intelligent signal and image processing in eHealth. The Open Medical Informatics Journal, vol. 4, no. 1, pp. 105–115, Jul. 2010. http://doi.org/10.2174/1874431101004010105
  11. Roniotis, K. Marias, V. Sakkalis, and M.E. Zervakis, “Diffusive Modelling of Glioma Evolution: A review,” Journal of Biomedical Science and Engineering, J. Biomed. Sci. Eng., vol. 03, no. 05, pp. 501–508, 2010. http://doi.org/10.4236/jbise.2010.35070
  12. Marias, D.D. Dionysiou, V. Sakkalis, N. Graf, R. Bohle, P.V. Coveney, S. Wan, A. Folarin, P. Büchler, M. Reyes, G. Clapworthy, E. Liu, J. Sabczynski, T. Bily, A. Roniotis, M.N. Tsiknakis, E. Kolokotroni, S. Gialiti, C. Veith, E. Messe, H. Stenzhom, Y. Kim, S. Zasada, A.N. Haidar, C. May, S. Bauer, T. Wang, Y. Zhao, M. Karasek, R. Grewer, A. Franz and G. Stamatakos, “Clinically-Driven Design of Multiscale Cancer Models: the Contra Cancrum Project Paradigm,” J.R. Soc Interface Focus., vol. 1, pp. 450-461, 2011. http://doi.org/10.1098/rsfs.2010.0037
  13. Roniotis, G.C. Manikis, V. Sakkalis, M.E. Zervakis, I. Karatzanis, and K. Marias, “High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 2, pp. 255–263, Mar. 2012. http://doi.org/10.1109/TITB.2011.2171190
  14. Roniotis, K. Marias, V. Sakkalis, G.C. Manikis, and M.E. Zervakis, “Simulating Radiotherapy Effect in High-Grade Glioma by Using Diffusive Modeling and Brain Atlases,” J. Biomed. Biotechnol, vol. 2012, pp. 1–9, 2012. http://doi.org/10.1155/2012/715812
  15. Roniotis, V. Sakkalis, I. Karatzanis, M.E. Zervakis, and K. Marias, “In-depth analysis and evaluation of diffusive glioma models,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 3, pp. 299–307, 2012. http://doi.org/10.1109/TITB.2012.2185704
  16. Stamatakos, D. Dionysiou, A. Lunzer, R. Belleman, E. Kolokotroni, E. Georgiadi, M. Erdt, J. Pukacki, S. Rueping, S. Giatili, A. d’Onofrio, S. Sfakianakis, K. Marias, C. Desmedt, M. Tsiknakis, and N. Graf, “The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling with Information Technology in the In Silico Oncology Context,” IEEE journal of biomedical and health informatics, vol. 18, no. 3, pp. 840–854, May 2014. http://doi.org/10.1109/JBHI.2013.2284276
  17. Johnson, S. McKeever, G. Stamatakos, D. Dionysiou, N. Graf, V. Sakkalis, K. Marias, Z. Wang, and T.S. Deisboeck, “Dealing with Diversity in Computational Cancer Modeling,” Cancer informatics, vol. 12, pp. 115-124, p. CIN.S11583, May 2013. http://doi.org/10.4137/CIN.S11583
  18. Genitsaridi, H. Kondylakis, L. Koumakis, K. Marias, and M.N. Tsiknakis, “Evaluation of personal health record systems through the lenses of EC research projects,” Computers in biology and medicine, vol. 59, pp. 175–185, Apr. 2015. http://doi.org/10.1016/j.compbiomed.2013.11.004
  19. Genitsaridi, H. Kondylakis, L. Koumakis, K. Marias, and M.N. Tsiknakis, “Towards Intelligent Personal Health Record Systems: Review, Criteria and Extensions,” Procedia Computer Science, vol. 21, pp. 327–334, 2013. http://doi.org/10.1016/j.procs.2013.09.043
  20. Kondylakis, E. Kazantzaki, L. Koumakis, I. Genitsaridi, K. Marias, A. Gorini, K. Mazzocco, G. Pravettoni, D. Burke, G. McVie and M.N. Tsiknakis, “Development of interactive empowerment services in support of personalised medicine,” eCancer Medical Science Journal, vol. 8, 400, Feb. 2014. http://doi.org/10.3332/ecancer.2014.400
  21. Sakkalis, S. Sfakianakis, E. Tzamali, K. Marias, G. Stamatakos, F. Misichroni, E. Ouzounoglou, E. Kolokotroni, D. Dionysiou, D Johnson, S. McKeever, and N. Graf, “Web-Based Workflow Planning Platform Supporting the Design and Execution of Complex Multiscale Cancer Models,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 3, pp. 824–831, May 2014. http://doi.org/10.1109/JBHI.2013.2297167
  22. Spanakis, V. Sakkalis, K. Marias, and A. Traganitis, “Cross Layer Interference Management in Wireless Biomedical Networks,” Entropy, vol. 16, no. 4, pp. 2085–2104, Apr. 2014. http://doi.org/10.3390/e16042085
  23. Tzamali, G. Grekas, K. Marias, and V. Sakkalis, “Exploring the Competition between Proliferative and Invasive Cancer Phenotypes in a Continuous Spatial Model,” PLoS One, vol. 9, no. 8, p. e103191, Aug. 2014. http://doi.org/10.1371/journal.pone.0103191
  24. Spanakis, and K. Marias, “In silico evaluation of gadofosveset pharmacokinetics in different population groups using the Simcyp® simulator platform,” In Silico Pharmacology, vol. 2, no. 1, pp. 1–9, Dec. 2014. http://doi.org/10.1186/s40203-014-0002-x
  25. Chourmouzi, E. Papadopoulou, K. Marias, and A. Drevelegas, “Imaging of Brain Tumors,” Surgical Oncology Clinics of North America, vol. 23, no. 4, pp. 629–684, Oct. 2014. http://doi.org/10.1016/j.soc.2014.07.004
  26. M.J. Lambregts, M.H. Martens, R.C.W. Quah, K. Nikiforaki, L.A. Heijnen, C.H.C. Dejong, G. L. Beets, K. Marias, N. Papanikolaou and R.G.H. Beets-Tan, “Whole-liver diffusion-weighted MRI histogram analysis: effect of the presence of colorectal hepatic metastases on the remaining liver parenchyma,” European Journal of Gastroenterology & Hepatology vol. 27, no. 4, pp. 399–404, Apr. 2015. http://doi.org/10.1097/MEG.0000000000000316
  27. Lagani, F. Chiarugi, D. Manousos, V. Verma, J. Fursse, K. Marias, and I. Tsamardinos, “Realization of a service for the long-term risk assessment of diabetes-related complications,” Journal of Diabetes and Its Complications, vol. 29, no. 5, pp. 691–698, Jul. 2015. http://doi.org/10.1016/j.jdiacomp.2015.03.011
  28. Müller, R. David, K. Marias, and N. Graf, “The Standardized Histogram Shift of T2 Magnetic Resonance Image (MRI) Signal Intensities of Nephroblastoma Does Not Predict Histopathological Diagnostic Information,” Cancer Informatics: Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes, vol. 14, Suppl. 1, pp. 1-5, Jan. 2015. http://doi.org/10.4137/CIN.S19340
  29. Roniotis, Μ.E. Oraiopoulou, E. Tzamali, E. Kontopodis, S. Van Cauter, V. Sakkalis, and K. Marias “A proposed paradigm shift in initializing cancer predictive models with DCE-MRI based PK parameters: A feasibility study,” Cancer Informatics: Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes, vol. 14, Suppl. 4, pp. 7–18, 2015. http://doi.org/10.4137/CIN.S19339
  30. Kontopodis, G. Kanli, G. C. Manikis, S. Van Cauter, and K. Marias, “Assessing Treatment Response through Generalized Pharmacokinetic Modeling of DCE-MRI Data,” Cancer Informatics: Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes, vol. 14s4, p. CIN.S19342, Jan. 2015. http://doi.org/10.4137/CIN.S19342
  31. Tzedakis, E. Tzamali, K. Marias, and V. Sakkalis, “The Importance of Neighborhood Scheme Selection in Agent-based Tumor Growth Modeling,” Cancer Inform.: Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes, vol. 14, Suppl. 4, pp. 67–81, p. CIN.S19343, Jan. 2015. http://doi.org/10.4137/CIN.S19343
  32. Johnson, J. Osborne, Z. Wang, and K. Marias, “Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes (Editorial)”, Cancer Informatics: Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes, vol. 14, suppl. 4, pp. 105–108, 2015. http://doi.org/10.4137/CIN.S37982
  33. Koumakis, K. Sigdel, G. A. Potamias, S. G. Sfakianakis, J. van Leeuwen, G. Zacharioudakis, V.A., Moustakis, M.E. Zervakis, A. Bucur, K. Marias, N. Graf, and M.N. Tsiknakis, “Bridging miRNAs and pathway analysis in clinical decision support; a case study in nephroblastoma,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 4, no. 1, p. 30, Dec. 2015. http://doi.org/10.1007/s13721-015-0102-5
  34. Sfakianaki, L. Koumakis, S.G. Sfakianakis, G. Iatraki, G. Zacharioudakis, N. Graf, K. Marias, and M.N. Tsiknakis, “Semantic biomedical resource discovery: a Natural Language Processing framework,” BMC Medical Informatics and Decision Making, vol. 15, no. 1, p. 77, Dec. 2015. http://doi.org/10.1186/s12911-015-0200-4
  35. H Martens, D.M.J. Lambregts, N. Papanikolaou, S. Alefantinou, M. Maas, G. C. Manikis, K. Marias, R. G. Riedl, G. L. Beets, and R. G. H. Beets-Tan, “Magnetization transfer imaging to assess tumour response after chemoradiotherapy in rectal cancer,” European Radiology, vol. 26, no. 2, pp. 390–397, Feb. 2016. http://doi.org/10.1007/s00330-015-3856-3
  36. G. Spanakis, S. Santana, M.N. Tsiknakis, K. Marias, V. Sakkalis, A. Teixeira, J. H Janssen, H. Jong and C. Tziraki, “Technology-Based Innovations to Foster Personalized Healthy Lifestyles and Well-Being: A Targeted Review,” Journal of Medical Internet Research, vol. 18, no. 6, p. e128, Jun. 2016. http://doi.org/10.2196/jmir.4863
  37. Andreu, F. Chiarugi, S. Colantonio, G. Giannakakis, G. Giorgi, P. Henriquez, E. Kazantzaki, D. Manousos, K. Marias, M.A. Matuszewski, BJ. Pascali, M. Pediaditis, G. Raccichini, and M.N. Tsiknakis, “Wize mirror – a smart, multisensory cardio-metabolic risk monitoring system,” Elsevier, Comput. Vis. Image Underst., vol. 148, pp. 3–22, Jul. 2016. http://doi.org/10.1016/j.cviu.2016.03.018
  38. Kondylakis , B. Claerhout, M. Keyur, L. Koumakis, J. van Leeuwen, K. Marias, D.Perez-Rey, K. De Schepper, M.N. Tsiknakis, and A. Bucur, “The INTEGRATE project: Delivering solutions for efficient multi-centric clinical research and trials,” Journal of Biomedical Informatics, vol. 62, pp. 32–47, Aug. 2016. http://doi.org/10.1016/j.jbi.2016.05.006
  39. Kartalis, G. Manikis, L. Loizou, N. Albiin, F. G Zöllner, M. Del Chiaro, K. Marias, and N. Papanikolaou, “Diffusion-weighted MR imaging of pancreatic cancer: A comparison of mono-exponential, bi-exponential and non-Gaussian kurtosis models,” European Journal of Radiology Open, vol. 3, pp. 79–85, 2016. http://doi.org/10.1016/j.ejro.2016.04.002
  40. Koumakis, A. Kanterakis, E. Kartsaki, M. Chatzimina, M. Zervakis, M. Tsiknakis, D. Vassou, D. Kafetzopoulos, K. Marias, V. Moustakis, and G. Potamias,“MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways,” PLOS Comput. Biol., vol. 12, no. 11, p. e1005187, Nov. 2016. http://doi.org/10.1371/journal.pcbi.1005187
  41. Spanakis, E. Mathioudakis, N. Kampanis, M. Tsiknakis, and K. Marias, “A Proposed Method for Improving Rigid Registration Robustness,” International Journal of Computer Science and Information Security, Pittsburgh, vol. 14, no. 5, pp. 1–11, Accessed: May 28, 2020.
  42. Spanakis, E. Kontopodis, S. Van Cauter, V. Sakkalis, and K. Marias, “Assessment of DCE–MRI parameters for brain tumors through implementation of physiologically–based pharmacokinetic model approaches for Gd-DOTA,” Springer, Journal of Pharmacokinetics and Pharmacodynamics, vol. 43, no. 5, pp. 529–547, 2016. http://doi.org/10.1007/s10928-016-9493-x
  43. Kondylakis, L. Koumakis, S. Hänold, I. Nwankwo, N. Forgó, K. Marias, M.N. Tsiknakis, and N. Graf, “Donor’s support tool: Enabling informed secondary use of patient’s biomaterial and personal data,” Int. J. Med. Inform., vol. 97, pp. 282–292, Jan. 2017. http://doi.org/10.1016/j.ijmedinf.2016.10.019
  44. Giannakakis, M. Pediaditis, D. Manousos, E. Kazantzaki, F. Chiarugi, P.G. Simos, K. Marias, and M.N. Tsiknakis, “Stress and anxiety detection using facial cues from videos,” Biomedical Signal Processing and Control, vol. 31, pp. 89–101, Jan. 2017. http://doi.org/10.1016/j.bspc.2016.06.020
  45. Nikiforaki, G.C. Manikis, T. Boursianis, K. Marias, A. Karantanas, and T.G. Maris, “The Impact of Spin Coupling Signal Loss on Fat Content Characterization in Multi-Echo multi echo acquisitions with different echo spacing,” Elsevier, Magnetic Resonance Imaging, vol. 38, pp. 6–12, May 2017. http://doi.org/10.1016/j.mri.2016.12.011
  46. Henriquez, B. J. Matuszewski, Y. Andreu-Cabedo, L. Bastiani, S. Colantonio, G. Coppini, M. D’Acunto, R. Favilla, D. Germanese, D. Giorgi, P. Marraccini, M. Martinelli, M.A. Morales, M.A. Pascali, M. Righi, O. Salvetti, M. Larsson, T. Stromberg, L. Randeberg, A. Bjorgan, G. Giannakakis, M. Pediaditis, F. Chiarugi, E. Christinaki, K. Marias, and M.N. Tsiknakis, “Mirror mirror on the wall… an unobtrusive intelligent multisensory mirror for well-being status self-assessment and visualization,” IEEE Transaction on Multimedia, vol. 19, no. 7, pp. 1467–1481, Jul. 2017. http://doi.org/10.1109/TMM.2017.2666545
  47. Pampouchidou, P. Simos, K. Marias, F. Meriaudeau, F. Yang, M. Pediaditis, and M.N. Tsiknakis, “Automatic Assessment of Depression Based on Visual Cues: A Systematic Review,” IEEE Transactions on Affective Computing, Institute of Electrical and Electronics Engineers Inc., vol. 10, no. 4. pp. 445–470, 2017. http://doi.org/10.1109/TAFFC.2017.2724035
  48. Pampouchidou, M. Pediaditis, A.Maridaki, M. Awais, C.M. Vazakopoulou, S. Sfakianakis, M.N. Tsiknakis, P. Simos, K. Marias, F. Yang, and F. Meriaudeau, “Quantitative comparison of motion history image variants for video-based depression assessment,” IEEE Transactions on Multimedia EURASIP J. Image Video Process., vol. 2017, no. 1, p. 64, Dec. 2017. http://doi.org/10.1186/s13640-017-0212-3
  49. G. Katehakis, H. Kondylakis, L. Koumakis, A. Kouroubali, and K. Marias, “Integrated Care Solutions for the Citizen: Personal Health Record Functional Models to Support Interoperability,” Eur. J. Biomed. Informatics, vol. 13, no. 1, 2017. http://doi.org/10.24105/ejbi.2017.13.1.8
  50. Z. Papadakis, S. Jha, T. Bhattacharyya, C. Millo, T.W. Tu, U. Bagci, K. Marias, A.H. Karantanas, and N. J Patronas, “18F-NaF PET/CT in Extensive Melorheostosis of the Axial and Appendicular Skeleton With Soft-Tissue Involvement,” Clin. Nucl. Med., vol. 42, no. 7, pp. 537–539, Jul. 2017. http://doi.org/10.1097/RLU.0000000000001647
  51. C. Manikis, K. Marias, D.M.J. Lambregts, K. Nikiforaki, M.M. van Heeswijk, F.C.H. Bakers, R.G.H. Beets-Tan, N. Papanikolaou, “Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models,” PloS one, vol. 12, no. 9, p. e0184197, Sep. 2017. http://doi.org/10.1371/journal.pone.0184197
  52. Venianaki, O. Salvetti, E. de Bree, T.G. Maris, A.H. Karantanas, E. Kontopodis, K. Nikiforaki, and K. Marias, “Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma,” Multimed. Tools Appl., vol. 77, no. 8, pp. 9417–9439, Apr. 2018. http://doi.org/10.1007/s11042-017-5046-6
  53. Iatraki, H. Kondylakis, L. Koumakis, M. Chatzimina, E. Kazantzaki, K. Marias, and M.N. Tsiknakis, “Personal Health Information Recommender: Impelenting A Tool for the Empowerment of Cancer Patients,” eCancer Medical Science, vol. 12, Jul. 2018. http://doi.org/10.3332/ecancer.2018.851
  54. Schera, M. Schäfer, A. Bucur, J. van Leeuwen, E. H. Ngantchjon, N. Graf, H. Kondylakis, L. Koumakis, K. Marias, and S. Kiefer, “iManageMyHealth and iSupportMyPatients: mobile decision support and health management apps for cancer patients and their doctors,” eCancer medical science, vol. 12, Jul. 2018. http://doi.org/10.3332/ecancer.2018.848
  55. S. Ioannidis, K. Marias, N. Galanakis, K. Perisinakis, A. Hatzidakis, D. Tsetis, A.H.Karantanas, and T.G. Maris, “A correlative study between diffusion and perfusion MR imaging parameters on peripheral arterial disease data,” Magnetic resonance imaging, Elsevier, vol. 55, pp. 26–35, Jan. 2019. http://doi.org/10.1016/j.mri.2018.08.006
  56. Kalyvianaki, A.A. Panagiotopoulos, P. Malamos, E. Moustou, M. Tzardi, E. N. Stathopoulos, G.S. Ioannidis, K. Marias, G. Notas, P. A. Theodoropoulos, E. Castanas, and M. Kampa, “Membrane androgen receptors (OXER1, GPRC6A AND ZIP9) in prostate and breast cancer: A comparative study of their expression,” Steroids, 2019, ISSN 0039-128X, vol. 142, pp. 100–108, Feb. 2019. http://doi.org/10.1016/j.steroids.2019.01.006
  57. S. Ioannidis, T.G. Maris, K. Nikiforaki, A.H. Karantanas, and K. Marias, “Investigating the Correlation of Ktrans with Semi-Quantitative MRI Parameters Towards More Robust and Reproducible Perfusion Imaging Biomarkers in Three Cancer Types,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 5, pp. 1855–1862, 2019. http://doi.org/10.1109/JBHI.2018.2888979
  58. Spanakis, E. Mathioudakis, N. Kampanis, M.N. Tsiknakis, and K. Marias, “Machine-learning regression in evolutionary algorithms and image registration,” IET Image Processing, vol. 13, no. 5, pp. 843–849, Apr. 2019. http://doi.org/10.1049/iet-ipr.2018.5389
  59. Nikiforaki, G.C. Manikis, E. Kontopodis, E. Lagoudaki, E. de Bree, K. Marias, A.H Karantanas, T.G Maris, “T2, T2* and spin coupling ratio as biomarkers for the study of lipomatous tumors,” Physica Medica, vol. 60, pp. 76–82, Apr. 2019. http://doi.org/10.1016/j.ejmp.2019.03.023
  60. Flavia Faccio, Chiara Renzi, Chiara Crico, Eleni Kazantzaki, Haridimos Kondylakis, Lefteris Koumakis, Kostas Marias and Gabriella Pravettoni, “Development of an eHealth tool for cancer patients: monitoring psychoemotional aspects with the family resilience (FaRe) questionnaire,” eCancer Medical Science, vol. 12, Jul. 2018 https://doi.org/10.3332/ecancer.2018.852
  61. Trivizakis, G.C. Manikis, K. Nikiforaki, K. Drevelegas, M. Constantinides, A. Drevelegas, and K. Marias, “Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification with Application to MRI Liver Tumor Differentiation,” Journal IEEE journal of biomedical and health informatics, vol. 23, no. 3, pp. 923–930, May 2019. doi: https://doi.org/10.1109/JBHI.2018.2886276
  62. Kontopodis, M. Venianaki, G.C. Manikis, K. Nikiforaki, O. Salvetti, E. Papadaki, G.Z. Papadakis, A.H. Karantanas and K. Marias, “Investigating the Role of Model-Based and Model-Free Imaging Biomarkers as Early Predictors of Neoadjuvant Breast Cancer Therapy Outcome,” Journal IEEE journal of biomedical and health informatics, vol. 23, no. 5, pp. 1834–1843, Sep. 2019. http://doi.org/10.1109/JBHI.2019.2895459
  63. Z. Papadakis, K. Marias, C. Millo, and A.H. Karantanas, “18F-NaF PET/CT imaging versus 99mTc-MDP scintigraphy in assessing metastatic bone disease in patients with prostate cancer,” Hellenic Journal οf Radiology, Volume 4, Issue 4, pp. 42-55, 2019. https://www.hjradiology.org/index.php/HJR/article/view/286
  64. Z. Papadakis, G.C. Manikis, A.H. Karantanas, P. Florenzano, U. Bagci, K. Marias, M.T. Collins, and A.M. Boyce, “F-18-NaF PET/CT imaging in fibrous dysplasia of bone,” J Bone Miner Res., vol. 34, no. 9, pp. 1619-1631, Sep. 2019. https://dx.doi.org/10.1002%2Fjbmr.3738
  65. Trivizakis, G.S. Ioannidis, V.D. Melissianos, G.Z. Papadakis, A. Tsatsakis, D.A. Spandidos, and K. Marias, “A novel deep learning architecture outperforming ‘off‑the‑shelf’ transfer learning and feature‑based methods in the automated assessment of mammographic breast density,” Oncol. Rep., vol. 42, no. 5, pp. 2009–2015, Oct. 2019. http://doi.org/10.3892/or.2019.7312
  66. M. Moreira, I. Santiago, J. Santinha, N. Figueiredo, K. Marias, M. Figueiredo, L. Vanneschi, and N. Papanikolaou, “Challenges and Promises of Radiomics for Rectal Cancer,” Current Colorectal Cancer Reports, vol. 15, no. 6, pp. 175–180, Dec. 2019. https://doi.org/10.1007/s11888-019-00446-y
  67. C. Manikis, K. Nikiforaki, E. Lagoudaki, E.de Bree, T.G. Maris, K. Marias, and A.H. Karantanas, “T2-based MRI radiomic features for discriminating tumour grading in soft tissues sarcomas,” Hellenic Journal of Radiology, Vol 4,2019. https://www.hjradiology.org/index.php/HJR/article/view/301/0
  68. I Kalaitzakis, E. Papadaki, E. Kavroulakis, T. Boursianis, K. Marias, and T.G. Maris, “Optimising T2 relaxation measurements on MS patients utilising a multi-component tissue mimicking phantom and different fitting algorithms in T2 calculations,” Hellenic Journal of Radiology, Vol 4, No 2, 2019. https://www.hjradiology.org/index.php/HJR/article/view/293/0
  69. Kouroubali, H. Kondylakis, E. Karadimas, G. Kavlentakis, A. Simos, R. María, Baños, Rocío, H. Camarano, G. Papagiannakis, P.Zikas, Y. Petrakis, A.J. Díaz, S. Hors-Fraile, K. Marias, and D.G. Katehakis, “Digital Health Tools for Preoperative Stress Reduction in Integrated Care,” European Journal for Biomedical Informatics, Vol.16, No 2, pp. 7-13, 2019. [Online]. https://www.ejbi.org/abstract/digital-health-tools-for-preoperative-stress-reduction-in-integrated-care-5987.html
  70. S. Kalemaki, A.H. Karantanas, D. Exarchos, E.T. Detorakis, O. Zoras, K. Marias, C. Millo, U. Bagci, I. Pallikaris, A. Stratis, I. Karatzanis, K. Perisinakis, P. Koutentakis, G.A. Kontadakis, D. Spandidos, A. Tsatsakis, and G.Z. Papadakis, “PET/CT and PET/MRI in ophthalmic oncology (Review),” International Journal of Oncology, pp. 417-429, Jan. 2020. http://doi.org/10.3892/ijo.2020.4955
  71. Kondylakis, A. Bucur, C. Crico, F. Dong, N. Graf, S. Hoffman, L. Koumakis, A. Manenti, K. Marias, K. Mazzocco, G. Pravettoni, C. Renzi, F. Schera, S. Triberti, M.N. Tsiknakis, and S. Kiefer, “Patient empowerment for cancer patients through a novel ICT infrastructure,” Journal of Biomedical Informatics, vol. 101, p. 103342, Jan. 2020. http://doi.org/10.1016/j.jbi.2019.103342
  72. Tsiknakis, E. Trivizakis, E. Vassalou, G. Papadakis, D. Spandidos, A. Tsatsakis, J. Sanchez‑Garcia, R. Lopez‑Gonzalez, N. Papanikolaou, A. Karantanas, and K. Marias, “Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays,” Experimental and Therapeutic Medicine, vol. 20, no. 2, pp. 727-735, May 2020. http://doi.org/10.3892/etm.2020.8797
  73. Pampouchidou, M. Pediaditis, E. Kazantzaki, S. Sfakianakis, I.A. Apostolaki, K. Argyraki, D. Manousos, F. Meriaudeau, K. Marias, F. Yang, M. Tsiknakis, M. Basta A. N. Vgontzas, and P. Simos, ”Automated facial video-based recognition of depression and anxiety symptom severity: cross-corpus validation,” Machine Vision and Applications, vol. 31, no. 4, p. 30, May 2020. http://doi.org/10.1007/s00138-020-01080-7  
  74. S. Ioannidis, K. Nikiforaki, G. Kalaitzakis, A.H. Karantanas, K. Marias, and T.G. Maris, “Inverse Laplace transform and multiexponential fitting analysis of T2 relaxometry data: a phantom study with aqueous and fat containing samples,” Eur. Radiol. Exp., vol. 4, no. 1, p. 28, May 2020, PMID: 32378090; PMCID: PMC7203287. http://doi.org/10.1186/s41747-020-00154-5
  75. Kalaitzakis, T. Boursianis, G. Gourzoulidis, S. Gourtsoyianni, G. Lymperopoulou, K. Marias, A.H. Karantanas, and T.G. Maris, “Apparent diffusion coefficient measurements on a novel diffusion weighted MRI phantom utilizing EPI and HASTE sequences. Phys. Med., Epub 2020 May 1, vol. 73, pp. 179-189, May 2020. http://doi.org/10.1016/j.ejmp.2020.04.024
  76. Z. Papadakis, G. Kochiadakis, G. Lazopoulos, K. Marias, N. Klapsinos, F. Hannah‑Shmouni, G. Igoumenaki, T.K. Nikolouzakis, S. Kteniadakis, D.A. Spandidos, and A.H. Karantanas, “Targeting vulnerable atherosclerotic plaque via PET‑tracers aiming at cell‑surface overexpression of somatostatin receptors,” Biomedical Reports, Reports, vol. 13, no.9, Jun. 2020. http://doi.org/10.3892/br.2020.1316
  77. Kontopodis, K. Marias, G.C. Manikis, K. Nikiforaki, M. Venianaki, T.G. Maris, V. Mastorodemos, G.Z. Papadakis, and E. Papadaki, “Extended perfusion protocol for MS lesion quantification,” Open Medicine, vol. 15, no. 1, pp. 520–530, Jun. 2020. http://doi.org/10.1515/med-2020-0100
  78. Karamanidou, P. Natsiavas, L. Koumakis, K. Marias, F. Schera, M. Schäfer, S. Payne, and C. Maramis, “Electronic Patient-Reported Outcome-Based Interventions for Palliative Cancer Care: A Systematic and Mapping Review,” JCO Clin Cancer Inform., no. 4, pp. 647–656, Sep. 2020, PMID: 32697604; PMCID: PMC7397776. http://doi.org/10.1200/CCI.20.00015
  79. E. Klontzas, G.Z. Papadakis, K. Marias, A.H. Karantanas, “Musculoskeletal trauma imaging in the era of novel molecular methods and artificial intelligence,” Injury, vol. 51, no. 12, pp. 2748–2756, Dec.2020, ISSN:0020-1383. http://doi.org/10.1016/j.injury.2020.09.019
  80. Trivizakis, N. Tsiknakis, E. Vassalou, G.Z.Papadakis, D. Spandidos, D. Sarigiannis, A. Tsatsakis, N. Papanikolaou, A.H. Karantanas, K. Marias, “Advancing Covid‑19 differentiation with a robust preprocessing and integration of multi‑institutional open‑repository computer tomography datasets for deep learning analysis,” Experimental and Therapeutic Medicine, vol. 20, no. 5, pp. 1–1, Sep. 2020. http://doi.org/10.3892/etm.2020.9210
  81. Genitsaridi, I. Flouri, D. Plexousakis, K. Marias, K. Boki, F. Skopouli, A. Drosos, G. Bertsias, D. Boumpas, and P. Sidiropoulos, “Rheumatoid arthritis patients on persistent moderate disease activity on biologics have adverse 5-year outcome compared to persistent low-remission status and represent a heterogeneous group,” Arthritis Res. Ther., vol. 22, no. 1, p. 226, Dec. 2020. http://doi.org/10.1186/s13075-020-02313-w
  82. Kondylakis, C. Axenie, D. Bastola, D. G. Katehakis, A. Kouroubali, D. Kurz, N. Larburu, I. Macía, R. Maguire, C. Maramis, K. Marias, P. Morrow, N. Muro, F Núñez-Benjumea, A. Rampun, O. Rivera-Romero, B. Scotney, G. Signorelli, H. Wang, M.N. Tsiknakis, and R. Zwiggelaar, “Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study,” J. Med. Internet Res., vol. 22, no. 12, p. e22034, Dec. 2020. http://doi.org/10.2196/22034
  83. Trivizakis, G.Z. Papadakis, I. Souglakos, N. Papanikolaou, L. Koumakis, D.A. Spandidos, A. Tsatsakis, A.H. Karantanas, and K. Marias, “Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review),” International Journal of Oncology, vol. 57, no. 1, pp. 43–53, 2020. http://doi.org/10.3892/ijo.2020.5063
  84. Nikiforaki, G.S. Ioannidis, E. Lagoudaki, G.C. Manikis, E. de Bree, A.H. Karantanas, T.G. Maris, and K. Marias, “Multiexponential T2 relaxometry of benign and malignant adipocytic tumours,” Eur Radiol Exp. vol. 4, no. 1, p. 45, Dec. 2020, PMID: 32743728; PMCID: PMC7396415. http://doi.org/10.1186/s41747-020-00175-0
  85. I. Korda, G. Giannakakis, E. Ventouras, P.A. Asvestas, N. Smyrnis, K. Marias, and G.K. Matsopoulos, “Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis,” Signals, vol. 2, no. 1, pp. 55–71, Jan. 2021. http://doi.org/10.3390/signals2010006
  86. E. Klontzas, G.A. Kakkos, G.Z. Papadakis, K. Marias and A.H. Karantanas, “Advanced clinical imaging for the evaluation of stem cell based therapies,” Expert Opinion on Biological Therapy, pp. 1–12, Feb. 2021. http://doi.org/10.1080/14712598.2021.1890711
  87. Skaramagkas, G. Giannakakis, E. Ktistakis, D. Manousos, I. Karatzanis, N. Tachos, E.E. Tripoliti, K. Marias, D.I. Fotiadis, and M.N. Tsiknakis, “Review of eye tracking metrics involved in emotional and cognitive processes,” IEEE Rev. Biomed. Eng., pp. 1–1, Mar. 2021. http://doi.org/10.1109/RBME.2021.3066072
  88. Kourou, G.C. Manikis, P. Poikonen-Saksela, K. Mazzocco, R. Pat-Horenczyk, B. Sousa, A.J. Oliveira-Maia, J. Mattson, I. Roziner, G. Pettini, H. Kondylakis, K. Marias, E. Karademas, P. Simos, and D.I. Fotiadis, “A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis: An initial effort to define resilience effects,” Computers in Biology and Medicine, vol. 131, p.104266,Apr.2021. http://doi.org/10.1016/j.compbiomed.2021.104266
  89. S. Ioannidis, E. Trivizakis, I. Metzakis, S. Papagiannakis, E. Lagoudaki, and K. Marias, “Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset,” Appl. Sci., vol. 11, no. 9, p. 3796, Apr. 2021. http://doi.org/10.3390/app11093796
  90. C. Manikis, K. Nikiforaki, E. Lagoudaki, E. de Bree, T. G. Maris, K. Marias, A.H. Karantanas “Differentiating low from high-grade soft tissue sarcomas using post-processed imaging parameters derived from multiple DWI models,” Eur. J. Radiol., vol. 138, p. 109660, May 2021. http://doi.org/10.1016/j.ejrad.2021.109660
  91. S. Ioannidis, S. Christensen, K. Nikiforaki, E. Trivizakis, K. Perisinakis, A. Hatzidakis, A. Karantanas, M. Reyes, M. Lansberg, K. Marias, “Cerebral CT Perfusion in Acute Stroke: The Effect of Lowering the Tube Load and Sampling Rate on the Reproducibility of Parametric Maps,” MDPI, Multidisciplinary Digital Publishing Institute, Diagnostics, vol. 11, issue 6, p. 1121 June 2021. http://doi.org/10.3390/diagnostics11061121
  92. Tsiknakis, D. Theodoropoulos, G. Manikis, E. Ktistakis, O. Boutsora, A. Berto, F. Scarpa, A. Scarpa, D. I. Fotiadis and K. Marias, “Deep Learning for Diabetic Retinopathy Detection and Classification Based on Fundus Images: A Review”, Computers in Biology and Medicine, 104599, 2021. http://doi.org/10.1016/j.compbiomed.2021.104599
  93. Giannakakis, M.R. Koujan, A. Roussos, and K. Marias, “Automatic stress analysis from facial videos based on deep facial action units recognition. Pattern Analysis & Applications, Volume 25, Issue 3, pp521–535, Aug 2022. https://doi.org/10.1007/s10044-021-01012-9
  94. Marias, “The constantly evolving role of medical image processing in oncology: From traditional medical image processing to imaging biomarkers and Radiomics “, Special Issue Advanced Computational Methods for Oncological Image Analysis, MDPI, Multidisciplinary Digital Publishing Institute, J. Imaging, vol. 7, issue 8, p. 124, July 2021, https://doi.org/10.3390/jimaging7080124
  95. Trivizakis, G.S. Ioannidis, I. Souglakos, A. H. Karantanas, M. Tzardi, K. Marias, “ A Neural Pathomics Framework for Classifying Colorectal Cancer Histopathology Images based on Wavelet Multi-Scale Texture Analysis”, Scientific reports, 11, 15546, 2021. https://doi.org/10.1038/s41598-021-94781-6
  96. Trivizakis, I. Souglakos, A. H. Karantanas, & K. Marias, “Deep Radiotranscriptomics of Non-Small Cell Lung Carcinoma for Assessing Molecular and Histology Subtypes with a Data-Driven Analysis”, MDPI, Multidisciplinary Digital Publishing Institute, Diagnostics, vol. 11(12), 2383. Dec. 2021. https://doi.org/10.3390/diagnostics11122383
  97. G, C. Manikis, G. S. Ioannidis, L. Siakallis, K. Nikiforaki, M. Iv, D. Vozlic, K. Surlan-Popovic, M. Wintermark, S. Bisdas, K. Marias, “Multicenter DSC-MRI based radiomics predict IDH mutation in gliomas”, MDPI, Multidisciplinary Digital Publishing Institute , Cancers, 13(16), 3965, 2021. https://doi.org/10.3390/cancers13163965
  98. Tsiknakis *, E. Savvidaki, S. Kafetzopoulos, G. Manikis, N. Vidakis, K. Marias, E. Alissandrakis, “Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1)”, MDPI, Multidisciplinary Digital Publishing Institute, Appl. Sci. 11, 6657, 2021. https://doi.org/10.3390/app11146657
  99. Boursianis1, G. Kalaitzakis, K. Nikiforaki, E. Kosteletou, D. Antypa, G. Gourzoulidis, A. Karantanas, E. Papadaki, P. Simos, T. G. Maris and K. Marias, “The significance of echo time in fMRI BOLD contrast: A clinical study during motor and visual activation tasks at 1.5T”, MDPI, Multidisciplinary Digital Publishing Institute, Tomography, 7(3), 333–343, 2021. https://doi.org/10.3390/tomography7030030
  100. Kontopodis, E. Papadaki, E. Trivizakis, T. G. Maris, P. Simos, G. Z. Papadakis, A. Tsatsakis, D. A. Spandidos, A. Karantanas and K. Marias, “Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients”, Experimental and Therapeutic Medicine, Spandidos Publications, 22(4), 1149, 2021. https://doi.org/10.3892/etm.2021.10583
  101. Tsiknakis, C. Spanakis, P. Tsompou, G. Karanasiou, G. Karanasiou, A. Sakellarios, G. Rigas, S. Kyriakidis, M. Papafaklis, S. Nikopoulos, F. Gijsen, L. Michalis, D. I. Fotiadis and K. Marias, “IVUS Longitudinal and Axial Registration for Atherosclerosis Progression Evaluation”, Diagnostics, MDPI, 11(8), 1513, June 2021. https://doi.org/10.3390/diagnostics11081513
  102. E. Klontzas, G.C. Manikis, K. Nikiforaki, E.E. Vassalou, K. Spanakis, I. Stathis, G.A. Kakkos, N. Matthaiou, A.H. Zibis, K. Marias, A.H. Karantanas, “Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip”, Diagnostics, MDPI, 11, no. 9: 1686, 2021. https://doi.org/10.3390/diagnostics11091686
  103. G. Chryssou, G.C. Manikis, G.S. Ioannidis, V. Chaniotis, T. Vrekoussis, T.G. Maris, K. Marias, A.H. Karantanas, “DiffusionWeighted Imaging in the Assessment of Tumor Grade in Endometrial Cancer Based on Intravoxel Incoherent Motion MRI”, Diagnostics, MDPI, vol. 12, p. 692, 2022. https://doi.org/10.3390/diagnostics12030692
  104. E. Vassalou, M.E. Klontzas, K. Marias, A.H. Karantanas, “Predicting long-term outcomes of ultrasound-guided percutaneous irrigation of calcific tendinopathy with the use of machine learning”, Skeletal Radiology, Springer Link, vol. 51, p. 417-422, August 2022. https://doi.org/10.1007/s00256-021-03893-7
  105. Pentari, G. Tzagkarakis, P. Tsakalides, P. Simos, G. Bertsias, E. Kavroulakis, K. Marias, N.J.Simos, E. Papadaki, “Changes in resting-state functional connectivity in neuropsychiatric lupus: A dynamic approach based on recurrence quantification analysis”, Biomedical Signal Processing and Control, ELSEVIER , vol. 72, p 103285, February 2022. https://doi.org/10.1016/j.bspc.2021.103285.
  106. S. Ioannidis, M. Goumenakis, I. Stefanis, A. Karantanas, K. Marias, “Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study”, Diagnostics, MDPI, vol. 12, p. 425, February 2022. https://doi.org/10.3390/diagnostics12020425
  107. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis, D. Fotiadis, “A smart cropping pipeline to improve prostate’s peripheral zone segmentation on MRI using deep learning”, EAI Endorsed Transactions on Bioengineering and Bioinformatics, EAI, vol. 1, p. 425, February 2022. https://doi.org/10.3390/diagnostics12020425
  108. G. Chryssou , G.C Manikis , G.S. Ioannidis 2, V.Chaniotis 3, Th. Vrekoussis , Th.G. Maris 2, K. Marias , A. Karantanas, “Diffusion Weighted Imaging in the Assessment of Tumor Grade in Endometrial Cancer Based on Intravoxel Incoherent Motion MRI”, Diagnostics, MDPI, vol. 12, issue 3, p. 692, March 2022. https://doi.org/10.3390/diagnostics12030692  
  109. N. Tsiknakis, E. Savvidaki, G. C. Manikis, P. Gotsiou, I. Remoundou, K. Marias, E. Alissandrakis, N. Vidakis, “Pollen Grain Classification Based on Ensemble Transfer Learning on the Cretan Pollen Dataset”, Plants, MDPI, vol. 29, issue 7, p. 919, March 2022 . https://doi.org/10.3390/plants11070919
  110. A. Triantafyllidis, H. Kondylakis , D. Katehakis , A. Kouroubali , L.Koumakis , K. Marias , A. Alexiadis , K. Votis , D. Tzovaras, “Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review”, JMIR Mhealth Uhealth, JMIR Publications Inc., vol. 10, issue 4, p. e32344, April 2022. https://doi.org/10.2196/32344
  111. Giannakakis, M. R. Koujan, A. Roussos, K. Marias, “Correction to: Automatic stress analysis from facial videos based on deep facial action units recognition”, Pattern Analysis and Applications, Springer London, vol. 25, issue 2, p. 487–488, May 2022.https://doi.org/10.1007/s10044-021-01012-9
  112. Kondylakis, S. Sfakianakis , V. Kalokyri , N. Tachos , D. Fotiadis, K. Marias , M Tsiknakis, “Data   Ingestion for AI in Prostate Cancer”, Challenges of Trustable AI and Added-Value on Health: Proceedings, IOS Press, vol. 25, p. 244-248, May 2022. https://doi.org/10.3233/SHTI220446
  113. E Klontzas, E. E. Vassalou, G. A. Kakkos, K. Spanakis, A. Zibis, K. Marias, A. Karantanas, “Differentiation between subchondral insufficiency fractures and advanced osteoarthritis of the knee using transfer learning and an ensemble of convolutional neural networks”, Injury, Elsevier, vol. 53, p. 2035-2040, June 2022. https://doi.org/10.1016/j.injury.2022.03.008
  114. P. Boaro, R. Biondi, N. Biondini, G. Collado, E. F.  JM, V. Pinto, N. Romano, V. Voi, G. B Ferrero, M. Casale, M. Cirillo, G. Palazzi, F. Cavalleri, G. L.Forni, G. Reggiani, S. Perrotta, M. Manu Pereira, S. Zazo, K. Marias, M. De Montalembert, P. Bartolucci, E. van Beers, F. Alvarez, F. Cremonesi, T. Sanavia, P. Fariselli, G. Castellani, R. Manara, R. Colombatti, “S265: Radiomics and Artificial intelligence for intelligence for identification and monitoring of silent cerebral infarcts in sicle cell disease: first analysis from the Genomed4All European project”, HemaSphere, LWW, vol. 6, p. 166-167, June 2022. https://doi.org/10.1097/01.HS9.0000843952.59228.1d
  115. Giannakakis, M.R. Koujan, A. Roussos, and K. Marias, “Automatic stress analysis from facial videos based on deep facial action units recognition”, Pattern Analysis and Applications, Springer London, vol. 25, pp .521- 535, 2022. https://doi.org/10.1007/s10044-021-01012-9
  116. Biondi, M. Boaro, N. Biondini, V. Pinto, N. Romano, G. Ferrero, M. Casale, M. Cirillo, G. Palazzi, F. Cavalleri, G. Forni, G. Reggiani, S. Perrotta, Manu Pereira, K. Marias, de Montalembert, P. Bartolucci, E. Vanbeers, F. Alvarez, F. Cremonesi, T. Sanavia, P. Fariselli, G. Castellani, R. Manara, and R. Colombatti, “ O-02: RADIOMICS AND ARTIFICIAL INTELLIGENCE FOR IDENTIFICATION AND MONITORING OF SILENT CEREBRAL INFARCTS IN SICKLE CELL DISEASE: FIRST ANALYSIS FROM THE GENOMED4ALL EUROPEAN PROJECT”,HemaSphere, LWW, vol. 6, p. 01-02,Aug.2022. https://doi.org/01.HS9.0000872816.60309.4c
  117. E. Klontzas, I. Stathis, K. Spanakis, A.H. Zibis, K. Marias, A.H. Karantanas, “Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip” ,Diagnostics, MDPI, vol. 12, issue 8, p. 1870, August 2022. https://doi.org/10.3390/diagnostics12081870
  118. Pentari, G. Tzagkarakis, K. Marias, P. Tsakalides, “Graph denoising of impulsive EEG signals and the effect of their graph representation”, Biomedical Signal Processing and Control, Elsevier, vol. 78, p. 103886, September 2022. https://doi.org/10.1016/j.bspc.2022.103886
  119. Stamoulou, C. Spanakis, G.C. Manikis, G. Karanasiou, G. Grigoriadis, T. Foukakis, M. Tsiknakis, D.I. Fotiadis, K. Marias, “Harmonization Strategies in Multicenter MRI-Based Radiomics”, Journal of Imaging, MDPI, vol. 8, issue 11, p. 303, November 2022. https://doi.org/10.3390/jimaging8110303
  120. Karanasiou, G. Grigoriadis, A. Alexandraki, A. Antoniades, C. Brown, A. Bucur, C. Cipolla, P. Economopoulou, T. Foukakis, J. Goossens, K. Keramida, L. Lakkas, K. Marias, K. Naka, A. Papakonstantinou, G. Pravettoni, D. Ribnikar, B. Šeruga, M. Zacharia, M. Tsiknakis, D.I. Fotiadis, “A multimodal approach for the management of co-morbid cardiotoxicity in the elderly breast cancer patients”, European Journal of Cancer, Elsevier, vol. 175, p. S40, November 2022. https://doi.org/10.1016/S0959-8049(22)01456-3.
  121. Dimitriadis, E. Trivizakis, N. Papanikolaou, M. Tsiknakis, K. Marias, “Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review”, Insights into Imaging, Springer Vienna, vol. 13, issue 1, p. 188, Dec. 2022https://doi.org/10.1186/s13244-022-01315-3
  122. Tsiknakis, C. Spanakis, P. Tsoumpou, G. Karanasiou, G. Karanasiou, A. Sakellarios, G. Rigas, S. Kyriakidis, M.I. Papafaklis, S. Nikopoulos, F. Gijsen, L. Michalis, D.I. Fotiadis, K. Marias, “OCT sequence registration before and after percutaneous coronary intervention (stent implantation)”, Biomedical Signal Processing and Control, Elsevier, vol. 79, p. 104251, January 2023. https://doi.org/10.1016/j.bspc.2022.104251
  123. I. Zaridis, E. Mylona, N. Tachos, V.C. Pezoulas, G. Grigoriadis, N. Tsiknakis, K. Marias, M. Tsiknakis, D.I. Fotiadis, “Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones”, Scientific Reports, Nature Publishing Group UK, vol. 13, issue 1, p. 714, Jan. 2023. https://doi.org/10.1038/s41598-023-27671-8
  124. Α. Dovrou, E. Bei, S. Sfakianakis, Marias, N. Papanikolaou, M. Zervakis, “Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study”, Diagnostics, MDPI, vol. 13, issue 4, p. 738, February 2023. https://doi.org/10.3390/diagnostics13040738
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  126. Kondylakis, V. Kalokyri, S. Sfakianakis, K. Marias, M. Tsiknakis, A. Jimenez-Pastor, E. Camacho-Ramos, I. Blanquer, J.D. Segrelles, S. López-Huguet, C. Barelle, M. Kogut-Czarkowska, G. Tsakou, N. Siopis, Z. Sakellariou, P. Bizopoulos, V. Drossou, A. Lalas, K. Votis, P. Mallol, L. Marti-Bonmati, L. Cerdá Alberich, K. Seymour, S. Boucher, E. Ciarrocchi, L. Fromont, J. Rambla, A. Harms, A. Gutierrez, M.P.A. Starmans, F. Prior, J.Ll. Gelpi, K. Lekadir, “Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects”, European Radiology Experimental, Springer Vienna, vol. 7, issue 1, p. 20, May 2023. https://doi.org/10.1186/s41747-023-00336-x
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