Explore the words cloud of the PREDICT project. It provides you a very rough idea of what is the project "PREDICT" about.
The following table provides information about the project.
Coordinator |
UNIVERSITEIT MAASTRICHT
Organization address contact info |
Coordinator Country | Netherlands [NL] |
Total cost | 3˙860˙059 € |
EC max contribution | 3˙860˙059 € (100%) |
Programme |
1. H2020-EU.1.3.1. (Fostering new skills by means of excellent initial training of researchers) |
Code Call | H2020-MSCA-ITN-2017 |
Funding Scheme | MSCA-ITN-ETN |
Starting year | 2017 |
Duration (year-month-day) | from 2017-10-01 to 2021-09-30 |
Take a look of project's partnership.
The high degree of tumour (genomic and phenotypic) heterogeneity influences patient’s response to therapy and hampers wide deployment of personalised medicine for cancer treatment. Thus, there is an imperative need for new technologies that can accurately detect tumour heterogeneity, allow for patient stratification and assist clinicians in providing the right diagnosis and treatment for the right patient. PREDICT’s mission is to address this huge unmet need. Radiomics, a newly emerging field that uses high-throughput extraction of large amounts of features from radiographic images, can boost the field of personalised medicine. The analysis of medical images taken as standard-of-care allows Radiomics to capture tumour heterogeneity and to generate ‘tumour-specific’ signatures in a non-invasive way, without the need of assessing the patient’s genetic profile. Thus, Radiomics, if linked to Big- data and decision support systems (DSS), can be used as diagnostic tool for patient stratification, for prediction of treatment response and for guidance, involving the patient, of clinical decisions in oncology. However, researchers that understand cancer biology, advanced imaging and big data analytics are virtually absent. Even more challenging is to translate the outcomes into actual clinical tools involving the patient. PREDICT will train 15 highly promising researchers in the emerging field of Radiomics and Big data. These ESRs will be trained to implement the automatic exploitation of large amounts of imaging data to drive decision-making algorithms that will guide diagnosis and treatment of different types of cancer and to develop ‘tumour-specific’ signatures integrated in multifactorial DSS. The ESRs will become experts and innovators in Radiomics, Big Data and DSS, which will allow them to bring unique solutions towards the clinic. PREDICT builds upon a strong consortium with 8 academic and 10 non-academic partners that are all pioneers in their respective field.
year | authors and title | journal | last update |
---|---|---|---|
2019 |
(2) Janna E. van Timmeren, Wouter van Elmpt, Ralph T.H.Leijenaar, Bart Reymen, René Monshouwer, Johan Bussink, Leen Paelinck, Evelien Bogaert, Carlos De Wagter, Elamin Elhaseen, Yolande Lievens, Olfred Hanseneg, Carsten Brink, Philippe Lambin Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence published pages: , ISSN: 0167-8140, DOI: |
Radiotherapy and Oncology 136 | 2020-01-29 |
2019 |
A. Ibrahim, H. C. Woodruff, R, Miclea, A. Jochems, R. van Joep. O. Morin, P. Lambin Radiomics-based malignancy prediction in renal cystic lesions, Research and Patient Safety - Poster published pages: , ISSN: , DOI: |
ECR | 2020-01-29 |
2018 |
A Jochems, RTH Leijenaar, M Bogowicz, FJP Hoebers, F Wesseling, SH Huang, B Chan, JN Waldron, B O\'Sullivan, D Rietveld Combining deep learning and radiomics to predict HPV status in oropharyngeal squamous cell carcinoma published pages: , ISSN: 0167-8140, DOI: |
Radiotherapy and Oncology 127 | 2020-01-29 |
2019 |
R. Da-ano, F. Lucia, M. Vallières, P. Bonaffini, I. Masson, A. Mervoyer, C. Reinhold, U. Schick, D. Visvikis, M. Hatt Harmonization strategies based on ComBat for mutlicentric radiomics studies published pages: , ISSN: , DOI: |
2020-01-29 | |
2017 |
RTH Leijenaar, M Bogowicz, A Jochems, FJP Hoebers, FWR Wesseling, Sophie H Huang, Biu Chan, John N Waldron, Brian O\'Sullivan, Derek Rietveld, C Rene Leemans, Ruud H Brakenhoff, Oliver Riesterer, Stephanie Tanadini-Lang, Matthias Guckenberger, Kristian Ikenberg, Philippe Lambin Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study published pages: , ISSN: 0007-1285, DOI: |
The British journal of radiology 91 (1086) | 2020-01-29 |
2019 |
A. Iantsen, F. Lucia, M. Ferreira, P. Bonaffini, I. Masson, A. Mervoyer, C. Reinhold, P. Lovinfosse, R. Hustinx, M. De Cuypere, F. Kridelka, U. Schick, D. Visvikis, M. Hatt Automated Cervical Primary Tumor Functional Volume Segmentation in PET Images published pages: , ISSN: , DOI: |
2020-01-29 | |
2019 |
Y. van Wijk, I. Halilaj, E. van Limbergen, S. Walsh, L. Lutgens, P. Lambin, and B. G. L. Vanneste Decision Support Systems in Prostate Cancer Treatment: An Overview published pages: , ISSN: 2314-6141, DOI: |
Biomed Research International | 2019-12-17 |
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The information about "PREDICT" are provided by the European Opendata Portal: CORDIS opendata.
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