PRE-ACT: Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification

Project Information

PRE-ACT

Grant agreement ID: 101057746

DOI

10.3030/101057746

EC signature date 23 May 2022

Start date 1 October 2022

End date 30 September 2027

Funded under

• Health

Total cost

€ 4 596 615,50

EU contribution

€ 4 592 114,75

Coordinated by

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER
Greece

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Documents, reports (6)

Report on panels & templates

Report on membership of the scientific advisory and patient advisory groups, and on production and distribution of internal reporting templates

Context analysis

Report on context analysis of AI-assisted development of therapeutic strategies for cancer patients

Plan for exploitation & dissemination

Material for the website and plan for exploitation and dissemination

Co-design framework

Report on dedicated co-design framework for trustworthy AI decision support systems

User requirements

Report on user requirements from different stakeholders

Operational ethics and fairness

Report on operational ethics and fairness metrics

Websites, patent fillings, videos etc. (1)

Project website launched

Project website is active with all relevant material

Other (1)

Data management plan

Publications

Peer reviewed articles (5)

Co-design of Human-centered, Explainable AI for Clinical Decision Support

Author(s): C. Panigutti, A. Beretta, D. Fadda, F. Giannotti, D. Pedreschi, A. Perotti and S. Rinzivillo
Published in: ACM Transactions on Interactive Intelligent Systems, Issue Volume 13Issue 4Article No.: 21pp 1–35, 2023, ISSN 2160-6455
Publisher: Association for Computing Machinery (ACM)
DOI: 10.1145/3587271

Breast

Author(s): Yuqin Liang, Yuedan Zhou, Ruud Houben, Karolien Verhoeven, Sofia Rivera, Liesbeth J. Boersma
Published in: The Breast, Issue 78, 2024, ISSN 0960-9776
Publisher: Churchill Livingstone
DOI: 10.1016/J.BREAST.2024.103812

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Author(s): J.M. Górriz; I. Álvarez-Illán; A. Álvarez-Marquina; J.E. Arco; M. Atzmueller; F. Ballarini; E. Barakova; G. Bologna; P. Bonomini; G. Castellanos-Dominguez; D. Castillo-Barnes; S.B. Cho; R. Contreras; J.M. Cuadra; E. Domínguez; F. Domínguez-Mateos; R.J. Du
Published in: Information Fusion, Issue 100, 2023, ISSN 1566-2535
Publisher: Elsevier
DOI: 10.1016/j.inffus.2023.101945

Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models

Author(s): Guido Bologna; Jean-Marc Boutay; Damian Boquete; Quentin Leblanc; Deniz Köprülü; Ludovic Pfeiffer
Published in: Algorithms, Issue vol.18, no.3, 2025, ISSN 0000-0000
Publisher: MDPI
DOI: 10.20944/PREPRINTS202501.1536.V1

Information (Switzerland)

Author(s): Guido Bologna
Published in: MDPI Information, Issue Volume 14, no.2, 2023, ISSN 2078-2489
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/INFO14020089

Conference proceedings (13)

Size-adaptive Hypothesis Testing for Fairness

Author(s): • A. Ferrara, F. Cozzi, A. Perotti, A. Panisson, F. Bonchi
Published in: The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), 2025
Publisher: NeurIPS
DOI: 10.48550/ARXIV.2506.10586

Value is in the Eye of the Beholder: A Framework for an Equitable Graph Data Evaluation

Author(s): Francesco Paolo Nerini; Paolo Bajardi; André Panisson
Published in: The 2024 ACM Conference on Fairness Accountability and Transparency, 2024
Publisher: ACM
DOI: 10.1145/3630106.3658919

PRE-ACT: Prediction of Radiotherapy Side Effects using Explainable AI for Patient Communication and Treatment Modification

Author(s): I. Koutsopoulos
Published in: Proceedings Of the Leading and Management in the Digital Era (LMDE), (extended abstract), Syros, Greece, 2023, 2023
Publisher: Springer

Research Challenges in Trustworthy Artificial Intelligence and Computing for Health: The Case of the PRE-ACT project

Author(s): F. Charalampakos, T. Tsouparopoulos, I. Papageorgiou, G. Bologna, A. Panisson, A. Perotti and I. Koutsopoulos
Published in: Proceedings of the Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), IEEE, Gothenburg, Sweden, 2023, 2023, ISSN 2575-4912
Publisher: IEEE
DOI: 10.1109/EuCNC/6GSummit58263.2023.10188239

Auditing Fairness and Explainability in Chest X-Ray Image Classifiers

Author(s): G. B. Bordes and A. Perotti
Published in: International Conference on the Interplay between Natural and Artificial Computation (IWINAC’24), 2024, ISSN 2184-433X
Publisher: SCITEPRESS

Explainability and Continual Learning meet Federated Learning at the Network Edge

Author(s): T. Tsouparopoulos, I. Koutsopoulos
Published in: International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), 2025
Publisher: IEEE
DOI: 10.23919/WIOPT66569.2025.11123219

Fidex: an Algorithm for the Explainability of Ensembles and SVMs

Author(s): G. Bologna, J-M. Boutay, Q. Leblanc and D. Boquete
Published in: International Conference on the Interplay between Natural and Artificial Computation (IWINAC’24), 2024, 2024, ISBN 978-3-031-61137-7
Publisher: ACM

Joint Explainability-Performance Optimization with Surrogate Models for AI-Driven Edge Services

Author(s): • F. Charalampakos, T. Tsouparopoulos, I. Koutsopoulos
Published in: IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), 2025
Publisher: IEEE
DOI: 10.1109/ICMLCN64995.2025.11140577

HES-XPLAIN - An Open Platform for Accelerating the Development of eXplainable AI Systems

Author(s): • A. Babey, J.M. Boutay, R. Marquis, D.B. Costa, G. Bologna, M. Graf and C.A. C.A. Peña-Reyes
Published in: AI days Geneva, Switzerland, 2025, 2025
Publisher: AI days HES-SO '25

Collaborative Split Federated Learning with Parallel Training and Aggregation

Author(s): • Y. Papageorgiou, Y. Thomas, A. Filippakopoulos, R. Khalili, and I. Koutsopoulos
Published in: International Conference on Artificial Intelligence and Applications and Innovations (AIAI), 2025
Publisher: Springer
DOI: 10.1007/978-3-031-96228-8_7

Lecture Notes in Computer Science

Author(s): T. Tsouparopoulos and I. Koutsopoulos
Published in: 1st Workshop on Advancements in Federated Learning (WAFL) of the ECML/PKDD, Turin, Italy, 2023, 2023, ISSN 0302-9743
Publisher: Springer PKDD WAFL workshop

Exploring Multi-Task Learning for Explainability

Author(s): F. Charalampakos and I. Koutsopoulos
Published in: 3rd International Workshop on Explainable and Interpretable Machine Learning (XI-ML) of the 26th European Conference on Artificial Intelligence (ECAI), Krakow, Poland, 2023, 2024, ISBN 978-3-031-50395-5
Publisher: Springer

Explaining CNN Classifications Using Small Patches

Author(s): J-M. Boutay, Q. Leblanc, B.D. Boquete, D. Köprülü, L. Pfeiffer, G. Bologna
Published in: International Workshop on Advanced Neuro-Symbolic Applications (ANSyA), Co-located with the European Conference of AI (ECAI) 2025, 2025
Publisher: ECAI

Other (9)

Development of an explainable AI prediction model for arm lymphoedema following breast cancer surgery and radiotherapy

Author(s): • T. Rattay, G. Bologna, A. Bombezin-Domino, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, A. Panisson, A. Perotti, B.L.T. Ramaekers, S. Rivera, A. Romita, C. Roumen, J. van Soest, A. Traverso, F. Tohidinezhad, K. Verhoeven, A.J. Webb, I. Koutsopoulos
Published in: 14th European Breast Cancer Conference, 2024
Publisher: Elsevier
DOI: 10.1016/J.EJCA.2024.113624

Breast cancer patients’ communication needs and wishes for an explainable Artificial Intelligence prediction model for lymphedema

Author(s): • C. Roumen , J. Rainbird , K. Verhoeven , G. Bologna , A. Bombezin-Domino , T. Rattay , J. van Soest, A. Dekker , M. Joore , A. Panisson, A. Perotti, B.L.T. Ramaekers , S. Rivera A. Romita , A. Traverso, A.J. Webb, I. Koutsopoulos, C.J. Talbot, G. Cortel
Published in: 14th European Breast Cancer Conference, 2024
Publisher: Elsevier
DOI: 10.1016/J.EJCA.2024.113820

Impact of Missing Data on AI Fairness for Breast Cancer Radiotherapy: Insights from the PRE-ACT Project

Author(s): • F. Cozzi, A. Panisson, A. Perotti, A. Ferrara, G. Bologna, S. Rivera, M. Bergeaud, C. Gaudin, G. Auzac, T. Sarrade, I. Vaz Luis, T. Rattay, A. Romita, K. Verhoeven, Y. Liang, J. Rainbird, M. Balia, B. Ramaekers, W. Witlox, C. Roumen, G. Cortellessa, F.
Published in: European Society for Radiation Oncology (ESTRO) 2025, 2025
Publisher: ESTRO

PRE-ACT: Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification

Author(s): • Y. Liang, P. Bajardi, G. Bologna, F. Bonchi, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, N. Paragios, T. Rattay, S. Rivera, C. Roumen, J. van Soest, A. Traverso, K. Verhoeven, I. Koutsopoulos, C. J. Talbot, PRE-ACT study consortium
Published in: European Cancer Summit 2024, 2024
Publisher: European Cancer Summit

Physicians’ Views on Explainable Artificial Intelligence Models to Predict the Risk of Toxicity Following Breast Radiotherapy

Author(s): • Y. Liang, J. Rainbird, G. Cortellessa, M. Balia, G. Bologna, I. Koutsopoulos, A. Pannison, A. Perotti, B. L.T. Ramaekers, T. Rattay, S. Rivera, A. Romita, C. Roumen, C. J Talbot, K. Verhoeven, M. Bergeaud, F. Fracasso
Published in: American Society for Radiation Oncology (ASTRO), 2024
Publisher: ASTRO

Development of an AI prediction model for arm lymphoedema following breast cancer surgery and radiotherapy

Author(s): • T. Rattay, G. Bologna, A. Bombezin-Domino, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, A. Panisson, A. Perotti, B. L.T. Ramaekers, S. Rivera, A. Romita, C. Roumen, J. van Soest, H. Stobart, F. Tohidinezhad, A. Traverso, K. Verhoeven, A. J. Webb, I
Published in: European Journal of Surgical Oncology 50, 2024
Publisher: EJSO
DOI: 10.1016/J.EJSO.2024.108216

Multi-Institutional Qualitative Evaluation of Automatic and Manual Segmentations of Organs at Risk on PRE ACT Breast Cancer Cohorts

Author(s): • K. Verhoeven, T. Brion, W.R. Green, M. Balia, A. Webb, T. Rattay, Y. Liang, L.G. Assia, I. Hafsa, S. Romdhani, R. Iandolo, A. Bombezin-Domino, K. Teo, R. McBeth, I. Koutsopoulos, C. Talbot, N. Paragios, S. Rivera
Published in: American Society for Radiation Oncology (ASTRO), 2024, 2024
Publisher: ASTRO
DOI: 10.1016/J.IJROBP.2024.07.1450

PRE-ACT-01: The Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification-01 Trial

Author(s): • T. Rattay, K. Verhoeven, M. Bergeaud, G. Bologna, G. Cortellessa, A. Dekker, F. Fracasso, M. Joore, I. Koutsopoulos, Y. Liang, A. Panisson, A. Perotti, B. Ramaekers, C. Roumen, J. van Soest, H. Stobart, C. Talbot, A. Webb, and S. Rivera
Published in: 19th St. Gallen Breast Cancer Conference, 2025
Publisher: Elsevier

Multi-cost analyses of artificial intelligence diagnostics in radiotherapy

Author(s): • T. Holly, B.M. Sugden, W.J.A. Witlox, B.L.T Ramaekers
Published in: CAPHRI research day 2025, 2025
Publisher: CAPHRI