Pancreatic cancer AI for genomics and personalized Medicine

Informations projet

PANCAIM

N° de convention de subvention: 101016851

DOI 10.3030/101016851

Date de signature de la CE 4 Decembre 2020

Date de début 1 Janvier 2021

Date de fin 31 Decembre 2024

Financé au titre de

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

Coût total € 8 236 378,75

Contribution de l’UE € 8 236 378,75

Coordonné par

STICHTING RADBOUD UNIVERSITAIR MEDISCH CENTRUM
Netherlands

Livrables

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

Publications

Peer reviewed articles (4)

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

Auteurs: Megan Schuurmans, Natália Alves, Pierpaolo Vendittelli, Henkjan Huisman, John Hermans, PANCAIM Consortium
Publié dans: Gastroenterology, Numéro Volume 165, Numéro 2, 2023, Page(s) 309-316, ISSN 1528-0012
Éditeur: 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

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

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

Auteurs: Natália Alves; Megan Schuurmans; Geke Litjens; Joeran S. Bosma; John Hermans; Henkjan J. Huisman
Publié dans: Cancers, Numéro 20726694, 2022, ISSN 2072-6694
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers14020376

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

Auteurs: Megan Schuurmans; Natália Alves; Pierpaolo Vendittelli; Henkjan Huisman; John Hermans
Publié dans: Cancers, Numéro 20726694, 2022, ISSN 2072-6694
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers14143498

Other (2)

Tutorial on survival modelling with omics data

Auteurs: Zhi Zhao, John Zobolas, Manuela Zucknick, Tero Aittokallio
Publié dans: 2023
Éditeur: Arxiv
DOI: 10.48550/arxiv.2302.12542

An example Machine Learning Survival Workflow

Auteurs: Zobolas, John
Publié dans: Numéro 1, 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.8060668

Conference proceedings (1)

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

Auteurs: Vendittelli, Pierpaolo; Smeets, Esther M. M.; Litjens, Geert
Publié dans: Proceedings of the SPIE, Volume 12039, 2022
Éditeur: SPIE
DOI: 10.1117/12.2611542