RAMPART – RAdiomics and Modeling for ProstAte RadioTherapy
The general objective of the project is to generate new methods and tools allowing the exploitation of the very large amount of available heterogeneous data (mostly imaging, and also contextual and biological data) for toxicity and tumor recurrence prediction within the context of prostate cancer radiotherapy. “Radiomics” is a concept in which high-throughput extraction of quantitative imaging features is used aimed at creating mineable databases from radiological images, which might reveal quantitative predictive or prognostic associations between images and medical outcomes. Such an approach could also allow improving the predictive models of both tumor response and organs-at-risk (OAR) toxicity. The final objective is to better adapt radiotherapy for individuals suffering from prostate cancer, by fully taking advantage of the most recent irradiation techniques, such as intensity modulated and guided adaptive radiotherapy (IMRT, ART, IGRT).
A large amount of heterogeneous data from 2500 prospective patients coming from different observation modalities will be integrated: i) Image data such as computed tomography (CT), cone beam CT (CBCT), MRI and PET; ii) patient clinical history, presented toxicity and recurrence events; iii) Therapy records, such as dosimetric data (dose volume histogram and 3D dose distribution) and iv) biological markers of radio-induced lethality (apoptosis, genetic polymorphism) and inflammation. Hence, by integrating all the available data, the aim of the project is the establishment of new statistical predictive models of digestive and urinary toxicities and recurrence following prostate cancer radiotherapy.
The major breakthrough of the present project is the original integration of dosimetric/imaging and biological data within the models, which is currently rare in the literature, especially for large cohorts. The complex urinary toxicity will be particularly explored. The design of these models will also allow identifying highly sensitive sub-volumes within the whole urinary and digestive structures, as well as dominant intraprostatic tumor, and could be used for tailoring of personalized adaptive radiotherapy treatments . Personalized treatments are expected to have a strong impact on patients outcome by significantly decreasing side effects and simultaneously increasing curability following prostate cancer radiotherapy.
The RAMPART project involves two labs :
- LTSI (Renaud De Crevoisier, Oscar Acosta, Eugenia Mylona)
- LATIM (Mathieu Hatt)
