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INFN

National Institute for Nuclear Physics
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3 Projects, page 1 of 1
  • Funder: SNSF Project Code: 168556
    Funder Contribution: 94,310
    Partners: INFN
  • Funder: UKRI Project Code: EP/I017550/1
    Funder Contribution: 35,921 GBP
    Partners: QUB, CERN, INFN

    Radiation can cause cancer, but it can also be used to cure the disease. Indeed radiotherapy is more widely used than chemotherapy. It works by breaking DNA molecules in cells, which causes these cells to die. However, radiotherapy has a major problem: it isn't a very selective method and often damages healthy tissue, as well as killing the tumour. So a lot of work has gone into finding ways to minimise this damage, while making sure that the treatment still destroys the tumour.One way to improve the targeting of radiation is to use beams of ions (instead of x-rays, which are normally used). This works because ions do not lose much energy when they first enter the body (unlike x-rays which start depositing energy, and therefore causing damage, the moment they enter you). Instead they lose most of their energy at a precise distance into the body, at the so-called Bragg peak. The position of this Bragg peak depends on how fast the ions are travelling and what type of ions they are. So the position can be controlled such that it corresponds to the tumour. This enables the destructive power of the radiation to be focussed into the tumour, largely sparing surrounding, healthy tissues.Facilities which use hydrogen ions (protons) to treat cancer patients are in use in many countries worldwide. The results are impressive with improved treatment success and reduced side-effects. The NHS has recognised this potential and plans to build a new proton facility.However, the most modern facilities use ions from heavier elements such as carbon. It has even been suggested that ions of antimatter could be used. Although this sounds like something from a science fiction story, anti-protons can be made here on earth. They behave a lot like regular protons, passing through matter and depositing most of their energy at a Bragg peak. However, when an antiproton and a proton meet, they annihilate each other releasing even more energy. So they have the potential to be more effective than protons, because of this additional energy release.We have initiated a programme of experiments to compare how protons, carbon ions and antiprotons interact with living matter. We want to compare and contrast these different forms of radiation. In particular, we want to learn how they damage DNA in the cell. We have already learned quite a bit about how antiprotons damage cellular DNA. So we want to complete these experiments and extend them to protons and carbon ions. We will see if these types of radiation cause radical alterations to the chromosomes (the structures in cells which contain the DNA). We will see if the irradiated cells can repair their damaged DNA, and how fast they can do it. This is important because in radiotherapy we want to cause non-repairable damage. When irreparable damage occurs, cells often commit suicide in a special type of cell death called apoptosis. We will also look at the cells' chromosomes to see if any gross changes in structure have occurred.Although we can learn a lot from intact cells, they are sometimes just too complex. So we plan to use a special type of DNA molecule called plasmids because there is a straightforward method to see if these have been broken on one strand, both strands or in lots of places. We can also use this method to quantify the damage and find out how much radiation is required for a particular level of damage. So we should be able to compare the radiations.However, we can't do these experiments in the UK. There is only one source of antiprotons at sufficient energy in the world - at CERN in Geneva. Nor is there a source of carbon ions at clinically relevant energies. So for this we plan to travel to Catania (Italy) to do these experiments.The results will be of interest to oncologists looking at potential, novel cancer treatments, but also to a wide range of scientists who want to understand how radiation interacts with living matter.

  • Funder: CHIST-ERA Project Code: CHIST-ERA-19-XAI-009
    Partners: University of Liverpool, Sapienza University of Rome, University Politehnica of Bucharest, INFN, MedLea S.r.l.s., University of Sofia “St. Kl. Ohridski”

    Developing and testing methodologies that allow to interpret the predictions of the AI algorithms in terms of transparency, interpretability, and explainability has become today one of the most important open questions in AI. In this proposal we bring together researchers from different fields with complementary skills, essential to be able to understand the behaviour of the AI algorithms, that will be studied with an interesting set of multidisciplinary use-cases in which explainable AI can play a crucial role and that will be used to quantify strengths and highlight, and possible solve, weakness of the available explainable AI methods in different applicative contexts. One aspect hindering so far substantial progress towards explainability is the fact that several proposed solutions in explainable AI proved to be effective after being tailored to specific applications, and frequently not easily transferred to other domains. In this project, we will test the same array of techniques for explainability to use-cases intentionally chosen to be quite heterogeneous with respect to the types of data, learning tasks, scientific questions. The proposed use-cases range from High Energy Physics AI applications, to applied AI in medical imaging, to applied AI for the diagnosis of pulmonary, to tracheal and nasal airways, to machine-learning techniques of explainability used to improve analysis and modelling in neuroscience. For each use-case, the research project will consist of three phases. In the first part, we will apply state-of-the-art explainability techniques, properly chosen based on the requirements, to the case under consideration. In the second part, shortcomings of the techniques will be identified. Most notably, issues of scalability to high-dimensional and raw data, where noise can be prevalent compared to the signal of interest, will be taken into consideration, as long as the level of certifiability afforded by each algorithm. In the final phase, new algorithmic methodologies adequate to HEP, medical, and neuroscientific use cases will be designed, based on these considerations