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

Informations projet

PRE-ACT

N° de convention de subvention: 101057746

DOI

10.3030/101057746

Date de signature de la CE 23 Mai 2022

Date de début 1 Octobre 2022

Date de fin 30 Septembre 2027

Financé au titre de

• Health

Coût total

€ 4 596 615,50

Contribution de l’UE

€ 4 592 114,75

Coordonné par

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER
Greece

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

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

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

Breast

Auteurs: Yuqin Liang, Yuedan Zhou, Ruud Houben, Karolien Verhoeven, Sofia Rivera, Liesbeth J. Boersma
Publié dans: The Breast, Numéro 78, 2024, ISSN 0960-9776
Éditeur: Churchill Livingstone
DOI: 10.1016/J.BREAST.2024.103812

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

Auteurs: 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
Publié dans: Information Fusion, Numéro 100, 2023, ISSN 1566-2535
Éditeur: Elsevier
DOI: 10.1016/j.inffus.2023.101945

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

Auteurs: Guido Bologna; Jean-Marc Boutay; Damian Boquete; Quentin Leblanc; Deniz Köprülü; Ludovic Pfeiffer
Publié dans: Algorithms, Numéro vol.18, no.3, 2025, ISSN 0000-0000
Éditeur: MDPI
DOI: 10.20944/PREPRINTS202501.1536.V1

Information (Switzerland)

Auteurs: Guido Bologna
Publié dans: MDPI Information, Numéro Volume 14, no.2, 2023, ISSN 2078-2489
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/INFO14020089

Conference proceedings (13)

Size-adaptive Hypothesis Testing for Fairness

Auteurs: • A. Ferrara, F. Cozzi, A. Perotti, A. Panisson, F. Bonchi
Publié dans: The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), 2025
Éditeur: NeurIPS
DOI: 10.48550/ARXIV.2506.10586

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

Auteurs: Francesco Paolo Nerini; Paolo Bajardi; André Panisson
Publié dans: The 2024 ACM Conference on Fairness Accountability and Transparency, 2024
Éditeur: ACM
DOI: 10.1145/3630106.3658919

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

Auteurs: I. Koutsopoulos
Publié dans: Proceedings Of the Leading and Management in the Digital Era (LMDE), (extended abstract), Syros, Greece, 2023, 2023
Éditeur: Springer

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

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

Auditing Fairness and Explainability in Chest X-Ray Image Classifiers

Auteurs: G. B. Bordes and A. Perotti
Publié dans: International Conference on the Interplay between Natural and Artificial Computation (IWINAC’24), 2024, ISSN 2184-433X
Éditeur: SCITEPRESS

Explainability and Continual Learning meet Federated Learning at the Network Edge

Auteurs: T. Tsouparopoulos, I. Koutsopoulos
Publié dans: International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), 2025
Éditeur: IEEE
DOI: 10.23919/WIOPT66569.2025.11123219

Fidex: an Algorithm for the Explainability of Ensembles and SVMs

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

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

Auteurs: • F. Charalampakos, T. Tsouparopoulos, I. Koutsopoulos
Publié dans: IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), 2025
Éditeur: IEEE
DOI: 10.1109/ICMLCN64995.2025.11140577

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

Auteurs: • A. Babey, J.M. Boutay, R. Marquis, D.B. Costa, G. Bologna, M. Graf and C.A. C.A. Peña-Reyes
Publié dans: AI days Geneva, Switzerland, 2025, 2025
Éditeur: AI days HES-SO '25

Collaborative Split Federated Learning with Parallel Training and Aggregation

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

Lecture Notes in Computer Science

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

Exploring Multi-Task Learning for Explainability

Auteurs: F. Charalampakos and I. Koutsopoulos
Publié dans: 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
Éditeur: Springer

Explaining CNN Classifications Using Small Patches

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

Other (9)

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

Auteurs: • 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
Publié dans: 14th European Breast Cancer Conference, 2024
Éditeur: Elsevier
DOI: 10.1016/J.EJCA.2024.113624

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

Auteurs: • 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
Publié dans: 14th European Breast Cancer Conference, 2024
Éditeur: 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

Auteurs: • 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.
Publié dans: European Society for Radiation Oncology (ESTRO) 2025, 2025
Éditeur: ESTRO

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

Auteurs: • 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
Publié dans: European Cancer Summit 2024, 2024
Éditeur: European Cancer Summit

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

Auteurs: • 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
Publié dans: American Society for Radiation Oncology (ASTRO), 2024
Éditeur: ASTRO

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

Auteurs: • 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
Publié dans: European Journal of Surgical Oncology 50, 2024
Éditeur: 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

Auteurs: • 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
Publié dans: American Society for Radiation Oncology (ASTRO), 2024, 2024
Éditeur: 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

Auteurs: • 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
Publié dans: 19th St. Gallen Breast Cancer Conference, 2025
Éditeur: Elsevier

Multi-cost analyses of artificial intelligence diagnostics in radiotherapy

Auteurs: • T. Holly, B.M. Sugden, W.J.A. Witlox, B.L.T Ramaekers
Publié dans: CAPHRI research day 2025, 2025
Éditeur: CAPHRI