Personalized Planning in Radio Therapy Through Integrative Modeling of Local Dose Effect and New Dosimetric Constraints
In this project FRRB finances the Partner 3: IRCCS San Raffaele (OSR). The Principal Investigator responsible of the project is Dr. Claudio Fiorino.
| Pathology of interest: | Prostate Cancer |
| Area of research: | Oncology/Radiotheraphy |
| Start date: | 1st April 2021 |
| End date: | 31st March 2024 |
| Funding: | € 356.600,00 |
| Project partners: | - Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM 1099, Université de Rennes 1 - Centre de Lutte contre le Cancer Eugène Marquis (CEM) - Fondazione IRCCS Istituto Nazionale dei Tumori (INT) - IRCCS San Raffaele (OSR) - Universidad Carlos III de Madrid (UC3M) - Clínica Universidad de Navarra (CUN) |
PROJECT SUMMARY
Radiotherapy for prostate cancer (PC) involves irradiation not only of the target volume but also of portions of healthy neighboring Organs at Risk (OaR) such as bladder, rectum or penile bulb.
RT-induced morbidity of sexual, urinary, or rectal nature can arise, impacting Quality of life (QoL). Predicting toxicities to devise personalized treatments with reduced RT-induced morbidity and maximized local control is a crucial question in RT.RT protocols are currently optimized on the former assumptions that the radio-sensitivity and the functionality are uniform within the same OaR.
Image mining of 3D dose distribution in low spatial scales, by means of voxel-based methods, has highlighted the existence of radiosensitive sub-regions (SRR) responsible of radio-induced toxicity.
Modern RT protocols have not yet incorporated these findings due to the lack of
- extensive validation;
- dosimetric constraints for plan optimization;
- automated methods to contour these patient-specific SRR for quick generation of accurate and robust treatment plans.
The goal of PerPlanRT is to devise innovative decision-making tools aimed at proposing integrated and feasible strategies for personalized RT in PC with reduced RT-induced toxicities (rectal, urinary, sexual) aimed at improving QoL.
Multivariable spatially accurate predictive models derived from large set of cohorts prospectively collected will be applied to different RT scenarios (IMRT/VMAT, post-prostatectomy, hypofractionated, proton treatments, MRI-based RT) to explore their patient-specific benefits in depth.
The application of these models to the clinical practice will be performed through the generation of dose distributions adapted to patient-specific anatomies. Guidelines will be proposed for designing prospective clinical trials.
This translational approach will enable the transfer, for the first time, of innovative tools to the clinics and the implementation of validated finding from 3D voxel-wise analysis.