Building Data Rich Clinical Trials

Livrables

[This Deliverable is co-lead by UCAM/KI]Technical specifications for non-invasive molecular monitoring approaches, including decision making thresholds for specific clinical trial scenarios.

Quality assurance guidelines and procedures

A set of quality assurance scientific and clinical principles to be applied in all work to be developed during the project

Imaging response

[This Deliverable is co-lead by UCAM/KI]Definition of new imaging and molecular markers, based on single or combination of measurements, for patient stratification and as early readouts for tumour progression and drug response.

Imaging standardization

[this Deliverable is co-lead by UCAM/KI]Development of a pipeline for multi-scanner image data standardization, processing and radiomics feature extraction.

Project website

Creation of a public project website

Initial workshop

Convene Workshop 1 between academic / pharma / regulatory stakeholders and publish workshop report.

BoB biorepository of data (front end)

[This Deliverable is co-lead by KI/HYVE]Deployment of a cBioPortal instance to navigate the generated data adapted to the CCE-DART clinical needs.

Publications

Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune evasion mechanisms

Auteurs: Orre et al
Publié dans: Nature Cancer, Numéro 26621347, 2021, Page(s) 1224-1242, ISSN 2662-1347
Éditeur: Springer Nature
DOI: 10.1038/s43018-021-00259-9

Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making

Auteurs: Oskar Wysocki; Jessica Katharine Davies; Markel Vigo; Anne Caroline Armstrong; Dónal Landers; Rebecca Lee; André Freitas
Publié dans: Wysocki , O , Davies , J K , Vigo , M , Armstrong , A C , Landers , D , Lee , R & Freitas , A 2023 , ' Assessing the communication gap between AI models and healthcare professionals : Explainability, utility and trust in AI-driven clinical decision-making ' , Artificial Intelligence , vol. 316 , 103839 . https://doi.org/10.1016/j.artint.2022.103839, Numéro 4, 2023, ISSN 0004-3702
Éditeur: Elsevier BV
DOI: 10.1016/j.artint.2022.103839

Public and patient involvement: a survey on knowledge, experience and opinions among researchers within a precision oncology European project

Auteurs: Paola Mosconi; Cinzia Colombo; Pasquale Paletta; Laura Gangeri; Chiara Pellegrini; Elena Garralda; Rosalba Miceli; Cinzia Brunelli; Irene Braña; Jordi Rodon; Guillermo Villacampa; Anna Pedrola; Rodrigo Dienstmann; Bianca Pont; Júlia Lostes; Alejandro Piris; Elena Chavarria; Xenia Villalobos; Berta Colldeforns; Raquel Pérez-López; Paolo Nuciforo; David Tamborero; Janne Lehtiö; Ali Razzak; Mari
Publié dans: BMC Cancer, Vol 23, Iss 1, Pp 1-10 (2023), Numéro 7, 2023, ISSN 1471-2407
Éditeur: BioMed Central
DOI: 10.1186/s12885-023-11262-x

Engaging European society at the forefront of cancer research and care

Auteurs: Celis, Julio E; Ringborg, Ulrik; heitor, manuel; Berns, Anton; Albreht, Tit; GARRALDA, Elena; Baumann, Michael
Publié dans: Scientia, Numéro 2, 2023, ISSN 1574-7891
Éditeur: Elsevier BV
DOI: 10.1002/1878-0261.13423

The Porto European cancer research summit 2021

Auteurs: Ulrik Ringborg , Anton Berns , Julio E Celis , Manuel Heitor , Josep Tabernero , Joachim Schüz , Michael Baumann , Rui Henrique , Matti Aapro , Partha Basu 8, Regina Beets-Tan , Benjamin Besse , Fátima Cardoso , Fátima Carneiro , Guy van den Eede , Alexander Eggermont , Stefan Fröhling , Susan Galbraith , Elena Garralda , Douglas Hanahan , Thomas Hofmarc
Publié dans: Molecular Oncology, Numéro 15747891, 2021, ISSN 1574-7891
Éditeur: Elsevier BV
DOI: 10.1002/1878-0261.13078

The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology

Auteurs: Tamborero et al
Publié dans: Nature Cancer, Numéro 26621347, 2022, ISSN 2662-1347
Éditeur: Springer Nature
DOI: 10.1038/s43018-022-00332-x

Early phase clinical trials in oncology: Realising the potential of seamless designs

Auteurs: Jaki, Thomas; Burdon, Abigail; Chen, Xijin; Mozgunov, Pavel; Zheng, Haiyan; Baird, Richard
Publié dans: Crossref, Numéro 189, 2023, ISSN 0959-8049
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.ejca.2023.05.005

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

Auteurs: Wysocka, Magdalena; Wysocki, Oskar; Zufferey, Marie; Landers, Dónal; Freitas, André
Publié dans: BMC Bioinformatics, Vol 24, Iss 1, Pp 1-31 (2023), Numéro 1, 2023, ISSN 1471-2105
Éditeur: BioMed Central
DOI: 10.21203/rs.3.rs-1970036/v1

Transformers and the Representation of Biomedical Background Knowledge

Auteurs: Wysocki, Oskar; Zhou, Zili; O'Regan, Paul; Ferreira, Deborah; Wysocka, Magdalena; Landers, Dónal; Freitas, André
Publié dans: Crossref, Numéro 2, 2023, ISSN 0891-2017
Éditeur: MIT Press
DOI: 10.1162/coli_a_00462

Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach

Auteurs: Delmas, Maxime; Wysocka, Magdalena; Freitas, André
Publié dans: arXiv, Numéro 3, 2023, ISSN 2331-8422
Éditeur: Cornell Tech
DOI: 10.48550/arxiv.2311.06364

Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery

Auteurs: Wysocka, Magdalena; Wysocki, Oskar; Delmas, Maxime; Mutel, Vincent; Freitas, Andre
Publié dans: Crossref, Numéro 2, 2023, ISSN 2331-8422
Éditeur: Cornell Tech
DOI: 10.48550/arxiv.2305.17819

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