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Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium & Dimitrios I. Fotiadis. Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences,  Insights Imaging 15, 265 (2024). https://doi.org/10.1186/s13244-024-01783-9

Jasper J. Twilt, Anindo Saha, Joeran S. Bosma, Bram van Ginneken, Anders Bjartell, Anwar R. Padhani, David Bonekamp, Geert Villeirs, Georg Salomon, Gianluca Giannarini, Jayashree Kalpathy-Cramer, Jelle Barentsz, Klaus H. Maier-Hein, Mirabela Rusu, Olivier Rouvière, Roderick van den Bergh, Valeria Panebianco, Veeru Kasivisvanathan, Nancy A. Obuchowski, Derya Yakar, Mattijs Elschot, Jeroen Veltman, Jurgen J. Fütterer, Henkjan Huisman Maarten de Rooij (2024). Evaluating Biparametric Versus Multiparametric Magnetic Resonance Imaging for Diagnosing Clinically Significant Prostate Cancer: An International, Paired, Noninferiority, Confirmatory Observer Study, European Urology. doi: https://doi.org/10.48550/arXiv.2309.12325

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D. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis and D. I. Fotiadis, “Fine-tuned feature selection to improve prostate segmentation via a fully connected meta-learner architecture,” 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece, 2022, pp. 01-04, doi: 10.1109/BHI56158.2022.9926929.

Avtantil Dimitriadis, Eleftherios Trivizakis, Nikolaos Papanikolaou, Manolis Tsiknakis & Kostas Marias (2022), “Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review”, Insights into Imaging 13(188), doi:10.1186/s13244-022-01315-3

Haridimos Kondylakis, , Esther Ciarrocchi, Leonor Cerda-Alberich, , Ioanna Chouvarda, , Lauren A. Fromont, Jose Manuel Garcia-Aznar, Varvara Kalokyri, Alexandra Kosvyra, Dawn Walker, Guang Yang, Emanuele Neri (2022), “Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks”, European Radiology Experimental 2022 Jul 1;6(1):29. doi: 10.1186/s41747-022-00281-1.

Eva Pachetti, Sara Colantonio and Maria Antonietta Pascali (2022), “On the Effectiveness of 3D Vision Transformers for the Prediction of Prostate Cancer Aggressiveness” , MEDTX – International Conference on Image Analysis and Processing ICIAP21 23-26/05/2022. doi: https://doi.org/10.1007/978-3-031-13324-4_27

João Santinha, Linda Bianchini, Mário Figueiredo, Celso Matos, Alessandro Lascialfari, Nikolaos Papanikolaou, Marta Cremonesi, Barbara A. Jereczek-Fossa, Francesca Botta and Daniela Origgi (2022), “Discrimination of Tumor Texture Based on MRI Radiomic Features: Is There a Volume Threshold? A Phantom Study”, https://doi.org/10.3390/app12115465, on line 27/05/2022 by MDPI in Applied Sciences

Luis Marti‑Bonmati, Dow‑Mu Koh, Katrine Riklund, Maciej Bobowicz, Yiannis Roussakis, Joan C. Vilanova, Jurgen J. Fütterer, Jordi Rimola, Pedro Mallol, Gloria Ribas, Ana Miguel, Manolis Tsiknakis, Karim Lekadir and Gianna Tsakou (2022), “Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper”, https://doi.org/10.1186/s13244-022-01220-9, Online 10/05/2022

Giovanni Maimone, Giulia Nicoletti, Simone Mazzetti, Daniele Regge, Valentina Giannini (2022), “Comparison of Machine and Deep Learning models for automatic segmentation of prostate cancers on multiparametric MRI”, ΙΕΕΕ–MeMea 2022, 22-24/06/22,  Taormina

Haridimos Kondylakis, Stelios Sfakianakis, Varvara Kalokyri, Nikolaos Tachos, Dimitrios Fotiadis, Kostas Marias, Manolis Tsiknakis (2022), “Data Ingestion for AI in Prostate Cancer”, MIE2022, 27-30/5/22, Nice, DOI: 10.3233/SHTI220446, ON LINE: 25/05/2022

Haridimos Kondylakis, Stelios Sfakianakis, Varvara Kalokyri, Alexandros Kanterakis, Lefteris Koumakis, Eugenia Mylona, , Nikolaos Tachos, Dimitrios Fotiadis, Kostas Marias, Manolis Tsiknakis (2022), “AI Passport – Traceability for Trustworthy AI ” EMBC 2022 11 – 15/7/22, Glasgow.

Saha, Anindo; Twilt, Jasper Jonathan; Bosma, Joeran Sander; van Ginneken, Bram; Yakar, Derya; Elschot, Mattijs; Veltman, Jeroen; Fütterer, Jurgen; de Rooij, Maarten; Huisman, Henkjan (2022) “Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)“, doi: 10.5281/zenodo.6522364 , On line 05/05/2022

Zaridis Dimitris; Mylona Eugenia; Tachos Nikolaos; Marias Kostas; Tsiknakis Manolis; Fotiadis Dimitrios (2022), “A smart cropping pipeline to improve prostate’s peripheral zone segmentation on MRI using Deep Learning” – EAI Endorsed Transactions on Bioengineering and Bioinformatics, https://eudl.eu/doi/10.4108/eai.24-2-2022.173546. Οnline 22/02/2022

Elena Bertelli, Laura Mercatelli, Chiara Marzi, Eva Pachetti , Michela Baccini, Andrea Barucci, Sara Colantonio, Luca Gherardini, Lorenzo Lattavo, Maria Antonietta Pascali, , Simone Agostini, and Vittorio Miele (2022), “Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI” – frontiersin https://doi.org/10.3389/fonc.2021.802964. Οnline 17/01/2022

Michela Gabelloni, Lorenzo Faggioni, Rita Borgheresi, Giuliana Restante, Jorge Shortrede, Lorenzo Tumminello, Camilla Scapicchio, Francesca Coppola, Dania Cioni, Ignacio Gómez‑Rico, Luis Martí‑Bonmatí, Emanuele Neri (2022), “Bridging gaps between images and data: a systematic update on imaging biobanks” – European Radiology https://doi.org/10.1007/s00330-021-08431-6 . Οnline 10/01/2022

Karim Lekadir, Richard Osuala, Catherine Gallin, Noussair Lazrak, Kaisar Kushibar, Gianna Tsakou, Susanna Aussó, Leonor Cerdá Alberich, Kostas Marias, Manolis Tsiknakis, Sara Colantonio, Nickolas Papanikolaou, Zohaib Salahuddin, Henry C Woodruff, Philippe Lambin, Luis Martí-Bonmatí (2021). FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. arXiv preprint arXiv:2109.09658.

Ana Rodrigues, João Santinha, Bernardo Galvão ,Celso Matos. Francisco M. Couto and Nickolas Papanikolaou (2021), “Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics” – Special Issue Radiomics/Radiogenomics in Cancer mdpi.com. Online 01/12/2021, https://doi.org/10.3390/cancers13236065

Eugenia Mylona, Dimitris Zaridis, Nikolaos Tachos, Dimitrios Fotiadis, Kostas Marias and Manolis Tsiknakis (2021), “A Deep Learning-based Cropping Technique to Improve Segmentation of Prostate’s Peripheral Zone” – 21st IEEE International Conference on BioInformatics and BioEngineering October 25-27, 2021, Kragujevac, Serbia *BEST STUDENT AWARD*

Daniela Condesso, Henrique Rodrigues, João Abrantes, João C. Costa (2021), RP Case Report nº 22: What is your diagnosis? “Case Report Quiz – Use of an MRI guided in-bore biopsy system for higher rates of cancer detection with real-time feedback with needle placement in the MRI system.”, Vol. 33 No. 1 (2021): Acta Radiológica Portuguesa, https://doi.org/10.25748/arp.24450

Daniela Condesso, Henrique Rodrigues, João Abrantes, João C. Costa (2021), ARP Case Report nº 22: Apical Anterior Prostate Lesion “Case Report Description – Use of an MRI guided in-bore biopsy system for higher rates of cancer detection with real-time feedback with needle placement in the MRI system“, Vol. 33 No. 2 (2021): Acta Radiológica Portuguesa, https://doi.org/10.25748/arp.25402

Anindo Saha, Joeran Bosma, Jasper Linmans, Matin Hosseinzadeh, Henkjan Huisman (2021), “Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI —Should Different Clinical Objectives Mandate Different Loss Functions?“, Medical Imaging Meets NeurIPS Workshop at 35th Conference on Neural Information Processing Systems (NeurIPS).

Valentina Giannini, Simone Mazzetti, Arianna Defeudis, Giuseppe Stranieri, Marco Calandri, Enrico Bollito, Martino Bosco, Francesco Porpiglia, Matteo Manfredi, Agostino De Pascale, Andrea Veltri, Filippo Russo and Daniele Regge (2021), “A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation“, Frontiers in Oncology, Online 01/10/2021. https://doi.org/10.3389/fonc.2021.718155

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A. Saha, M. Hosseinzadeh, H. Huisman (2021), “End-to-End Prostate Cancer Detection in bpMRI via 3D CNNs: Effect of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction“, MedIA: Medical Image Analysis, Online 29/06/2021. https://doi.org/10.1016/j.media.2021.102155

A. Saha, M. Hosseinzadeh, H. Huisman (2020), “Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI“, Medical Imaging Meets NeurIPS Workshop – 34th Conference on Neural Information Processing Systems (NeurIPS), Vancouever, Canada.

Dimitrios G. Zaridis, Eugenia Mylona, Nikolaos S. Tachos, Kostas Marias, Nikolaos Papanikolaou, Manolis Tsiknakis, Dimitrios I. Fotiadis (2021), “A new smart-cropping pipeline for prostate segmentation using deep learning networks“, arxiv.org 2021. Online  07/07/2021. https://doi.org/10.48550/arXiv.2107.02476

Jasper J.Twilt, Kicky G. van Leeuwen, Henkjan J. Huisman, Jurgen J. Fütterer and Maarten de Rooij (2021), “Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review“, Diagnostics 2021. Online  26 /05/2021. https://doi.org/10.3390/diagnostics11060959