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The rationale of radiotherapy has always been a trade-off between tumour control and healthy organ toxicity, where healthy organs are in many cases very sensitive to radiation. However, after recent developments in FLASH radiotherapy, this paradigm has been challenged due to a significant reduction in the toxicity of healthy organs. “FLASH” is based on very high dose-rate irradiation (dose rate ≥50 Gy/s), short beam-on times (≤100 ms) and large single doses (≥10 Gy) per beam. Recent clinical implementation of FLASH has been mostly focused on delivering a single beam with FLASH to treat cancers on the skin or in the extremities (hands and legs) with electron or proton beams. At the moment the clinical application of FLASH with proton beams is still being studied. In particular, it is still unknown how to perform FLASH with multi-beams, since single beam doses above 10Gy to 12Gy are not common in standard radiotherapy, because of the very high risk of radiation toxicity. In this project, the optimal ion beams for FLASH RT will be investigated.

A computational simulation of the initiation and progress of breast cancer, reaching different stage levels, according to TNM and histologic classification, will be developed and validated. This initial work involves the analysis of computer tomography (CT) images and histologic information from biopsies and the adjustment of the simulation parameters to particular patient stages. After the validation of the simulation phase, it will be possible to introduce the different therapies, from gamma radiation to proton therapy. First, breast cancer cell lines will be used to characterize, in a quantitative way, the effects of radiotherapy through irradiation of cells in different environmental conditions to characterize radiation biological effects. The simulation of interaction of radiation with the biologic material will be done using the GEANT4/TOPAS package, complemented with nBios. The simulation conditions will be adjusted to the experimental ones. The use of patient-derived organoids (PDO), that are able to mimetize basic features of primary tumors, including histological complexity, will finally allow us to compare the cancer treatment clinical output with simulation data through a personalized cancer treatment approach. At the end of the work plan, it will be possible to simulate the effect of proton therapy in breast cancer, in particular using the PDO model, first with a dedicated simulation and ultimately with a proton beam, requested through the Inspire Project.

In this proposal, the student will work on a benchtop, portable and high-performance PET prototype for 3D quality assessment of proton beam delivery in Proton Therapy Centers. In Proton Therapy, the highly conformal dose irradiation of the tumours is one of the aspects that makes this technique so powerful, resulting in better treatment outcomes, increased survival rates, chances of recovery and overall quality of life of cancer patients. Besides the physical advantages of Proton Therapy when compared with typical photon-based radiotherapy, its greatest strength, namely the depth-dose profile (DDP), known as the Bragg-Peak, the dose profile with a sharp edge at the end, is also a great concern for clinical usage, demanding an extremely precise beam control since the dose profile is much more sensitive to spatial uncertainties than in conventional radiotherapy, potentially delivering unwanted doses to healthy tissues if the treatment is not properly planned and controlled. To measure the beam profile during a proton therapy procedure, imaging techniques based on the detection of secondary particles originated by the interaction of the beam particles in the tissues have been proposed, with Positron Emission Tomography (PET) imaging being widely studied for clinical practice and real time assessment of the beam interaction in the body. easyPET is a cost-effective benchtop PET system technology using a innovative and patented acquisition method, able to achieve very high spatial resolution (below 1 mm) and sampling with a much smaller number of cells than other PET imaging technologies. Besides its high performance the major advantages are its high compactness and portability, contrary to typically bulky PET scanners (important for mobility and integration in different locations within the treatment facilities) and its innovative scanning method, which allows to produce high quality images with lower material budget. The student will assess through in-beam tests at a Proton Therapy Center of excellence in Europe (Paul Scherrer Institut, Switzerland), the feasibility of adapting and integrating easyPET technology in proton therapy usage, and will make the needed implementations for system optimization. The benefits of using this technology in future generation PET scanners in proton therapy facilities for guided precision particle therapy will be investigated. This project answers to the great interest of the community in these devices due to the increasing number of particle therapy facilities installed worldwide (also in the Portuguese strategy for the near future), the need for more clinical evidences of the benefits of proton therapy in more types of cancer and the need to master the complexity of these treatments to unlock new possibilities and breakthroughs in cancer treatment.

Unexpected late effects of proton therapy (PT) have been observed over the last few years as the number of patients treated with PT has increased. These side effects could be related to the uncertainties in the relative high biological effectiveness (RBE) of protons stopping in organs at risk (OAR).RBE values across the spread-out Bragg peak (SOBP) of protons range can generally vary from 0.9 – 1.7 (Paganetti). The use of a constant RBE value of 1.1, which is still the accepted clinical practice, disregards experimental evidence that the RBE depends on many factors, including - dose per fraction (higher for smaller dose fractions) - Tissue type (higher for late-response tissue, in particular in the brain); - LET and depth (LET increases with depth reaching a maximum on the distal edge of the Bragg peak); - biological endpoint (higher for neurological late-response endpoints, including brainstem necrosis and blindness). DNA damage generated by particle tracks with higher LET are thought to be more complex and more difficult to repair. This effect will increase the magnitude of RBE in critical healthy tissues when protons are stopped in them. Moreover, the effect of fractionation certainly plays a role. There is evidence that RBE is higher with fractionation, further amplifying the high-LET effect of stopping protons.

Proton and carbon ions are characterized by physical and biological features that allow a high radiation dose to tumors, minimizing irradiation to adjacent normal tissues. For this reason, radioresistant tumors and tumors located near highly radiosensitive critical organs, such as brain tumors, represent the best target for this kind of therapy [1]. The potential side effects of proton therapy can include acute and late effects. Among the late events, the most severe is radiation-induced cerebral necrosis (RICN). In the literature, the definition of RICN varies across studies, and in addition, wide variation in the reporting may be also attributable to improved quality and frequency of diagnostic imaging, increased awareness within the oncology community and length of follow-up [2]. The incidence of RICN is poorly known [3] Some reports indicate that depending on the treatment protocol the actual incidence could be anywhere from 3% to 24% [4] and has a latency of 3 month up to 13 year after treatment [5]. Although the expected incidence of RICN may be low, it is a severe and potentially lethal late effect that can extremely impact these patients quality of life. In particular for proton therapy, a few retrospective clinical studies from adult populations have demonstrated its occurrence with a 2-year rate of 4.6% (for temporal lobe necrosis) [6]. Importantly, in the pediatric population, it has been described that radiation necrosis induced by proton beam radiotherapy tends to occurs early after treatment and away from the main lesion, which is concerning for recurrence [7]. Little is known about the mechanism by which RICN occurs. Recent retrospective studies showed that some of potential risk factors for RICN, includes: total dose given, the total volume irradiated, fraction size, the linear energy transfer (LET), treatment modality, among others [2, 5, 8, 9, 10]. Bearing this, the main aim of this thesis is to understand how RICN is affected by some of the described physical factors related to proton therapy, in a multidisciplinary study taking advantage of advanced brain cellular models. The student will have the opportunity to work at Centro de Ciências e Tecnologias Nucleares (C2TN/IST) and at the Cyclotron Centre Bronowice (CCB)/Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN) in Krakow, Poland. The student will also profit from collaboration with researchers from the iBB-Institute for Bioengineering and Biosciences from Instituto Superior Técnico, Universidade de Lisboa, which will contribute with their expertise in development of innovative advanced models of healthy and diseased human tissues from induced pluripotent stem cells (hiPSCs) and their applications for personalized medicine. REFERENCES [1] Paganetti et al Roadmap: proton therapy physics and biology Published 26 February 2021 •Physics in Medicine & Biology, Volume 66, Number 5 [2] Vellayappan, Balamurugan et al. “Diagnosis and Management of Radiation Necrosis in Patients With Brain Metastases.” Frontiers in oncology vol. 8 395. 28 Sep. 2018, doi:10.3389/fonc.2018.00395. [3] Chao, S.T et al. “Challenges with the diagnosis and treatment of cerebral radiation necrosis”. Int. J. Radiat. Oncol. Biol. Phys. 2013, 87, 449–457. [4] Ruben, J.D. et al “Cerebral radiation necrosis: Incidence, outcomes, and risk factors with emphasis on radiation parameters and chemotherapy”. Int. J. Radiat. Oncol. Biol. Phys. 2006, 65, 499–508. [5] Fink, J. et al “Radiation necrosis: Relevance with respect to treatment of primary and secondary brain tumors". Curr. Neurol. Neurosci. Rep. 2012, 12, 276–285. [6] Kitpanit S, et al “Temporal Lobe Necrosis in Head and Neck Cancer Patients after Proton Therapy to the Skull Base”. Int J Part Ther. 2020 Spring;6(4):17-28. doi: 10.14338/IJPT-20-00014.1. [7] Davanzo, J. et al Radiation Necrosis Following Proton Beam Therapy in the Pediatric Population: a Case Series. Cureus, 2017 9(10), e1785. https://doi.org/10.7759/cureus.1785. [8] Lee, D.-S et al. Radiation-induced brain injury: Retrospective analysis of twelve pathologically proven cases. Radiat. Oncol. J. 2011, 29, 147–155. [9] Lawrence, Y.R. et al “Radiation dose-volume effects in the brain”. Int. J. Radiat. Oncol. Biol. Phys. 2010, 76, S20–S27. [10] Haas-Kogan D et al National Cancer Institute Workshop on Proton Therapy for Children: Considerations Regarding Brainstem Injury. Int J Radiat Oncol Biol Phys. 2018 May 1;101(1):152-168. doi: 10.1016/j.ijrobp.2018.01.013. PMID: 29619963; PMCID: PMC5903576.

Proton radiotherapy is recognized as an effective method to treat cancers, allowing to better cover the tumor compared to conventional photon therapy while better sparing the surrounding healthy tissues. Further improvements in proton therapy can be achieved with advanced treatment planning methods offering fast and precise dose optimization and calculation in clinical routine. A better quantification, understanding, and modelling of the physical and biological uncertainties in patients will probably reduce the risk of complications resulting from proton therapy, such as development of secondary tumors or necrosis in about 10% of patients. The state-of-the-art of advanced treatment planning techniques are based on Monte Carlo methods. They allow to precisely calculate dose distributions in a patient. Monte Carlo methods exploiting graphic cards (GPU) are nowadays being explored since they significantly improve the time of calculations compared to processor-based (CPU) Monte Carlo engines. The GPU-accelerated Monte Carlo code FRED, developed by our research partners from the Sapienza University of Rome, has been recently used at CCB Krakow and Maastricht proton therapy facilities for treatment planning studies and patient quality assurance, as well as detector research. In the framework of this project, new treatment plan optimization methods and planning strategies will be developed, including robust optimization of treatment plans taking into account range, delineation, setup, organ motion uncertainties as well as biological uncertainties. For this purpose FRED MC code and clinical data from the two involved proton facilities will be exploited. The student will have the opportunity to work at Centro de Ciências e Tecnologias Nucleares (C2TN/IST), at Cyclotron Centre Bronowice (CCB)/Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN) in Krakow, Poland and at the Maastro Clinic in Maastricht, Netherlands.

Prostate cancer (PCa) is a major health burden among aging men. Most newly-diagnosed cases of PCa are adenocarcinomas, originating in the glandular epithelial tissue of the prostate. Recently, proton therapy has been applied in the treatment of advanced PCa. The success of this approach is consequence of the precision of proton therapy, which is able to target the cancerous lesion with accuracy. Nevertheless, to take full advantage of proton therapy it is important to correlate the correct size of the tumor with the image obtained by MRI, and to correctly assess prostate cancer malignancy. The current clinical management of PCa enables its detection at early organ-confined stages by combining regular screening and patient classification in risk groups. Although these tumors do not usually pose a threat to the patient, the majority of PCa cases are prescribed a radical treatment after diagnosis (e.g., surgery or radiotherapy). The implementation of proton therapy in Portugal will contribute to diminishing the prevalence of surgeries. However, the limited individualization of the clinical management beyond risk-group definition will still lead to significant overtreatment and undertreatment rates, which may adversely impact the patients’ lives and life expectancy, respectively. Thus, PCa is a paradigmatic condition in which an individualized predictive technology could make a crucial difference in clinical practice. Recently, mechanistic and reproducible computational methods to simulate cancer growth and treatment have enabled the personalized prediction of clinical outcomes and the design of optimal therapies, e.g., for breast and brain cancers. This new approach, termed computational oncology, combines the use of mathematical models accounting for key physical and biological mechanisms involved in tumor growth with computer simulations to forecast tumor growth. Personalization of these predictions relies on the parameterization of the model with longitudinal clinical and imaging data from each patient and on simulating tumor growth over the actual anatomy of the patient’s affected organ. In particular, multiparametric magnetic resonance (mpMR) imaging has been providing a wealth of data to describe the tumor morphology, architecture and behavior. Given the increasing use mpMRI data to inform clinical decision-making in PCa, and given the facility of analyzing removed prostates, it is timely to validate the predictive capability of state-of-the-art tumor forecasting models for PCa cases against longitudinal patient-specific data. Here, we propose to design and validate a personalized, organ-scale mathematical model able to forecast PCa growth and assist physicians in diagnosis, clinical-decision making and treatment with proton therapy. Our approach relies on 4 key strategies. First, we will leverage phase-field (PF) modeling, a robust and flexible modeling framework extensively used to successfully describe tumor growth phenomena. Second, the resolution of an inverse problem will provide a personalized parameterization of model equations using longitudinal mpMR imaging and clinical data of PCa patients. Third, model validation will be performed against the corresponding histopathological data extracted from each patient’s prostate surgical specimen, which is the current gold standard in PCa research. Finally, we will use the parameterized model to simulate the tumor growth after proton therapy. The student will integrate an international high-productivity interdisciplinary collaboration with researchers from Portugal (University of Coimbra, University of Porto), Italy (University of Pavia) and USA (University of Texas Austin), which include Physicists, Mathematicians, Engineers, Pathologists and Medical Doctors. This will provide an invaluable learning opportunity to the student and will generate a vast range of international contacts guaranteeing the success of the student’s professional future.

Owing to the favorable physical properties of interaction of protons with matter, application of proton beams in radiation therapy for highly selective cancer treatment is rapidly spreading worldwide. To date, over 90 ion therapy facilities are operational, predominantly with proton beams, and about the same amount is under construction or planning. Over the last decades, considerable improvements have been achieved in accelerator technology, beam delivery and medical physics to enhance conformation of the dose delivery to complex shaped tumor volumes, with excellent sparing of surrounding healthy tissue and critical organs. Nevertheless, full clinical exploitation of the proton beam advantages is still challenged, especially by uncertainties in the knowledge of the beam range in the actual patient anatomy during the fractionated course of treatment, thus calling for continued multidisciplinary research in this rapidly emerging field. To date, there are two main physical channels that may be explored for in-vivo monitoring of the proton therapy treatments. The first one is imaging beta+ decays via dedicated or commercial positron emission tomographs (PET), and the second is prompt gamma (PG) imaging. Both beta+ decaying nuclei or PG emission are produced by nuclear reactions between the penetrating protons and the nuclei of the patient being irradiated. In order to provide valuable feedback to the fractionated treatment, both methods need to compare the measured PET and/or PG distribution with the computed predictions. Studies that our teams have performed show that using off-the-shell Monte Carlo simulation codes such as Geant4 is too time consuming for daily clinical applications. Timing is still unacceptable even when typical high performance computing clusters available at some clinical centers are utilized. Hence, in this thesis we propose to develop an approach (and realize it in a custom software) that accelerates the simulation of beta+ creation and decay during clinical proton irradiation. The software must take into account the computed tomogram (CT) of the patient and the beam fluency on a voxel-by-voxel basis and estimate the production of beta+ activity based on the stored look-up values determined previously by validated, detailed Monte Carlo simulations. Development of such fast simulation approaches is crucial to fully realize the potential of the PET-based range monitoring systems, including those to be eventually operated at the planned proton facility at C2TN in Sacavém, Portugal. In addition, the same code will be utilized in the task of monitoring proton treatments at the MDACC (MD Anderson Cancer Center) in Houston, Texas, USA, under the ongoing TPPT (time-of-flight PET for monitoring proton therapy) consortium (between LIP, ICNAS-UC, PETsys electronics, The University of Texas at Austin, and the MDACC).

The knowledge of radiation effects is still incomplete even more than one century of its use for clinical purposes. The ability to measure the effects of radiation on the healthy tissue and cancer cells at the micrometric scale is fundamental and still presents an enormous challenge. There are already some instruments that can make such measurements, but most of the existing instruments are bigger than the size of a cell, and the measured dose is integrated over a small volume. These instruments are unable to produce microscopic descriptions about how the energy is being distributed on the cells by knock-on electrons and other secondary particles. The development of instruments that are able to make these measurements directly are the scope of this thesis. In this project, it is purposed the development of materials (micrometric optical fibres and corundum crystals) able to describe the interactions of particles at the micrometric scale, namely the interactions of hadrons used in the treatment of cancer. Based on micrometric Scintillating Plastic Optical Fibers (SPOF) it is proposed the development of active dosimeters with micrometric sizes. Using electrospinning technology the production of SPOF at the micrometric scale is now a possibility, two orders of magnitude smaller than the ones commercially available. With this dosimeter, the real-time detection of the energy distribution at the cell level can be a reality. Within the project, we foresee the optimization of the fabrication procedure in particular on what concerns the achievement of the best mechanical properties necessary to have orderly aligned structures able to be layered and produce volumes with 10ths to 100ths of micrometres able to be read by standard photodetectors. Corundum crystals (Al2O3) have been used to achieve a more detailed description of the energy deposition of particles and possibly identify particles. This is a consequence of using specific dopants in the crystals. For instance, while using carbon doping one produces OSL (optically stimulated luminescence) dosimeters, but using carbon and magnesium doping one produces FNTD (fluorescent neutral tracking detectors) dosimeters. The dosimetric crystals with FNTD characteristics are produced using the flux method. The optimization of their characteristics for low mass particles and neutrons can be accomplished by changing the doping elements. The development of these materials in situ is an upgrade in the country capabilities in this field. The use of GEANT4/TOPAS simulation will be a natural and essential tool to use and to follow the experimental developments and to produce the adequate translation of the experimental data into physical quantities.