Pancreatic cancer AI for genomics and personalized Medicine

Informazioni relative al progetto

PANCAIM

ID dell’accordo di sovvenzione: 101016851

DOI 10.3030/101016851

Data della firma CE 4 Dicembre 2020

Data di avvio 1 Gennaio 2021

Data di completamento 31 Dicembre 2024

Finanziato da

• SOCIETAL CHALLENGES - Health, demographic change and well-being

Costo totale € 8 236 378,75

Contributo UE € 8 236 378,75

Coordinato da

STICHTING RADBOUD UNIVERSITAIR MEDISCH CENTRUM
Netherlands

Risultati finali

Websites, patent fillings, videos etc. (1)

Project webpage

Description of the PANCAIM webpage that will be prepared in the first months of the project

Other (1)

Open-source prediction algorithms beta-tested and made available in GitHub

Prediction algorithms tested by the end-user group, and the source codes of the algorithms made available for community use

Documents, reports (1)

Dissemination and communication strategy

The definition of the dissemination and communication strategy and tools to be used during the life of the project All possible dissemination routes for the obtained results will be identified and used during the whole course of the project

Pubblicazioni

Peer reviewed articles (4)

Artificial Intelligence in Pancreatic Ductal Adenocarcinoma Imaging: A Commentary on Potential Future Applications

Autori: Megan Schuurmans, Natália Alves, Pierpaolo Vendittelli, Henkjan Huisman, John Hermans, PANCAIM Consortium
Pubblicato in: Gastroenterology, Numero Volume 165, Numero 2, 2023, Pagina/e 309-316, ISSN 1528-0012
Editore: American Gastroenterological Association
DOI: 10.1053/j.gastro.2023.04.003

Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT

Autori: Natália Alves , Joeran S. Bosma, Kiran V. Venkadesh, Colin Jacobs, Zaigham Saghir, Maarten de Rooij, John Hermans, Henkjan Huisman
Pubblicato in: Radiology, Numero Volume 308, No. 3, 2023, ISSN 1527-1315
Editore: Radiological Society of North America (RSNA)
DOI: 10.1148/radiol.230275

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Autori: Natália Alves; Megan Schuurmans; Geke Litjens; Joeran S. Bosma; John Hermans; Henkjan J. Huisman
Pubblicato in: Cancers, Numero 20726694, 2022, ISSN 2072-6694
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers14020376

Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging

Autori: Megan Schuurmans; Natália Alves; Pierpaolo Vendittelli; Henkjan Huisman; John Hermans
Pubblicato in: Cancers, Numero 20726694, 2022, ISSN 2072-6694
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers14143498

Other (2)

Tutorial on survival modelling with omics data

Autori: Zhi Zhao, John Zobolas, Manuela Zucknick, Tero Aittokallio
Pubblicato in: 2023
Editore: Arxiv
DOI: 10.48550/arxiv.2302.12542

An example Machine Learning Survival Workflow

Autori: Zobolas, John
Pubblicato in: Numero 1, 2022
Editore: Zenodo
DOI: 10.5281/zenodo.8060668

Conference proceedings (1)

Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas.

Autori: Vendittelli, Pierpaolo; Smeets, Esther M. M.; Litjens, Geert
Pubblicato in: Proceedings of the SPIE, Volume 12039, 2022
Editore: SPIE
DOI: 10.1117/12.2611542