Pancreatic cancer AI for genomics and personalized Medicine

Información del proyecto

PANCAIM

Identificador del acuerdo de subvención: 101016851

DOI 10.3030/101016851

Fecha de la firma de la CE 4 Diciembre 2020

Fecha de inicio 1 Enero 2021

Fecha de finalización 31 Diciembre 2024

Financiado con arreglo a

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

Coste total € 8 236 378,75

Aportación de la UE € 8 236 378,75

Coordinado por

STICHTING RADBOUD UNIVERSITAIR MEDISCH CENTRUM
Netherlands

Resultado final

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

Publicaciones

Peer reviewed articles (4)

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

Autores: Megan Schuurmans, Natália Alves, Pierpaolo Vendittelli, Henkjan Huisman, John Hermans, PANCAIM Consortium
Publicado en: Gastroenterology, Edición Volume 165, Edición 2, 2023, Página(s) 309-316, ISSN 1528-0012
Editor: 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

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

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

Autores: Natália Alves; Megan Schuurmans; Geke Litjens; Joeran S. Bosma; John Hermans; Henkjan J. Huisman
Publicado en: Cancers, Edición 20726694, 2022, ISSN 2072-6694
Editor: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers14020376

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

Autores: Megan Schuurmans; Natália Alves; Pierpaolo Vendittelli; Henkjan Huisman; John Hermans
Publicado en: Cancers, Edición 20726694, 2022, ISSN 2072-6694
Editor: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers14143498

Other (2)

Tutorial on survival modelling with omics data

Autores: Zhi Zhao, John Zobolas, Manuela Zucknick, Tero Aittokallio
Publicado en: 2023
Editor: Arxiv
DOI: 10.48550/arxiv.2302.12542

An example Machine Learning Survival Workflow

Autores: Zobolas, John
Publicado en: Edición 1, 2022
Editor: Zenodo
DOI: 10.5281/zenodo.8060668

Conference proceedings (1)

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

Autores: Vendittelli, Pierpaolo; Smeets, Esther M. M.; Litjens, Geert
Publicado en: Proceedings of the SPIE, Volume 12039, 2022
Editor: SPIE
DOI: 10.1117/12.2611542