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

Informazioni relative al progetto

PRE-ACT

ID dell’accordo di sovvenzione: 101057746

DOI

10.3030/101057746

Data della firma CE 23 Maggio 2022

Data di avvio 1 Ottobre 2022

Data di completamento 30 Settembre 2027

Finanziato da

• Health

Costo totale

€ 4 596 615,50

Contributo UE

€ 4 592 114,75

Coordinato da

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER
Greece

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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

Pubblicazioni

Peer reviewed articles (5)

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

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

Breast

Autori: Yuqin Liang, Yuedan Zhou, Ruud Houben, Karolien Verhoeven, Sofia Rivera, Liesbeth J. Boersma
Pubblicato in: The Breast, Numero 78, 2024, ISSN 0960-9776
Editore: Churchill Livingstone
DOI: 10.1016/J.BREAST.2024.103812

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

Autori: 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
Pubblicato in: Information Fusion, Numero 100, 2023, ISSN 1566-2535
Editore: Elsevier
DOI: 10.1016/j.inffus.2023.101945

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

Autori: Guido Bologna; Jean-Marc Boutay; Damian Boquete; Quentin Leblanc; Deniz Köprülü; Ludovic Pfeiffer
Pubblicato in: Algorithms, Numero vol.18, no.3, 2025, ISSN 0000-0000
Editore: MDPI
DOI: 10.20944/PREPRINTS202501.1536.V1

Information (Switzerland)

Autori: Guido Bologna
Pubblicato in: MDPI Information, Numero Volume 14, no.2, 2023, ISSN 2078-2489
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/INFO14020089

Conference proceedings (13)

Size-adaptive Hypothesis Testing for Fairness

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

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

Autori: Francesco Paolo Nerini; Paolo Bajardi; André Panisson
Pubblicato in: The 2024 ACM Conference on Fairness Accountability and Transparency, 2024
Editore: ACM
DOI: 10.1145/3630106.3658919

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

Autori: I. Koutsopoulos
Pubblicato in: Proceedings Of the Leading and Management in the Digital Era (LMDE), (extended abstract), Syros, Greece, 2023, 2023
Editore: Springer

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

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

Auditing Fairness and Explainability in Chest X-Ray Image Classifiers

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

Explainability and Continual Learning meet Federated Learning at the Network Edge

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

Fidex: an Algorithm for the Explainability of Ensembles and SVMs

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

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

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

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

Autori: • A. Babey, J.M. Boutay, R. Marquis, D.B. Costa, G. Bologna, M. Graf and C.A. C.A. Peña-Reyes
Pubblicato in: AI days Geneva, Switzerland, 2025, 2025
Editore: AI days HES-SO '25

Collaborative Split Federated Learning with Parallel Training and Aggregation

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

Lecture Notes in Computer Science

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

Exploring Multi-Task Learning for Explainability

Autori: F. Charalampakos and I. Koutsopoulos
Pubblicato 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
Editore: Springer

Explaining CNN Classifications Using Small Patches

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

Other (9)

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

Autori: • 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
Pubblicato in: 14th European Breast Cancer Conference, 2024
Editore: Elsevier
DOI: 10.1016/J.EJCA.2024.113624

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

Autori: • 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
Pubblicato in: 14th European Breast Cancer Conference, 2024
Editore: 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

Autori: • 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.
Pubblicato in: European Society for Radiation Oncology (ESTRO) 2025, 2025
Editore: ESTRO

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

Autori: • 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
Pubblicato in: European Cancer Summit 2024, 2024
Editore: European Cancer Summit

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

Autori: • 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
Pubblicato in: American Society for Radiation Oncology (ASTRO), 2024
Editore: ASTRO

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

Autori: • 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
Pubblicato in: European Journal of Surgical Oncology 50, 2024
Editore: 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

Autori: • 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
Pubblicato in: American Society for Radiation Oncology (ASTRO), 2024, 2024
Editore: 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

Autori: • 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
Pubblicato in: 19th St. Gallen Breast Cancer Conference, 2025
Editore: Elsevier

Multi-cost analyses of artificial intelligence diagnostics in radiotherapy

Autori: • T. Holly, B.M. Sugden, W.J.A. Witlox, B.L.T Ramaekers
Pubblicato in: CAPHRI research day 2025, 2025
Editore: CAPHRI