Protonterap-IA: Protonterap-IA. Developement of decision support tools for selecting protontherapy patients – Iniciative of a pilot study at the "Centro de protonterapia de Galicia."
Radiotherapy, or treatment with radiation beams, is one of the most effective treatments against cancer. It contributes to the cure of more than 40% of cancer patients and almost 70% benefit from these treatments throughout their illness.
Proton therapy, a radiotherapy technique that uses proton beams, is one of the most promising forms of radiotherapy. This technique has recently emerged thanks to technological advances in equipment and research into the radiobiological characteristics and advantages of these highly ionising particles.
These advances have made it possible to set up modern proton therapy units in many countries, accessible to certain cancer patients for whom it is very important to minimise the possible toxicities of radiation. This is the case with paediatric patients or patients with certain comorbidities or risks, and also for those patients in whom it is necessary to re-irradiate or administer very high doses of irradiation to the tumour without damaging nearby critical organs.
Which patients are most likely to benefit from proton therapy (PT) compared to conventional photon radiotherapy (RT)? The main motivation of this project is to answer this question using predictive techniques based on artificial intelligence.
Within the framework of the future Proton Therapy Centre of Galicia (under construction in Santiago), in this project we intend to carry out a pilot study to develop AI methodology and tools to obtain predictive models of the probability of complications in healthy tissue (in RT, applicable to PT) based on specific patient data.
Objectives
The objective of this project is to develop an objective methodology to select, from a group of patients, those who are most likely to benefit from proton therapy, and to obtain a tool to support decision-making in personalised precision medicine, in a way that is quick, easy to implement in clinical practice and exportable.
A secondary objective is to determine the minimum number of clinical and patient-related variables that are significant for the decision of selecting the type of treatment.
The working hypothesis is that it is possible to train a machine learning model based on specific disease characteristics in a group of patients, which can predict which patients can benefit most from proton radiotherapy.
We aim to provide evidence that for optimising the RT/PT process, it is possible to stratify patients based on AI tools and currently available digital medical record systems.
Project
/research/projects/desenvolvemento-de-ferramentas-de-apoio-a-toma-de-decisions-para-a-seleccion-de-pacientes-en-protonterapia--iniciativa-de-estudio-piloto-no-centro-de-protonterapia-de-galicia
<p>Radiotherapy, or treatment with radiation beams, is one of the most effective treatments against cancer. It contributes to the cure of more than 40% of cancer patients and almost 70% benefit from these treatments throughout their illness. </p><p>Proton therapy, a radiotherapy technique that uses proton beams, is one of the most promising forms of radiotherapy. This technique has recently emerged thanks to technological advances in equipment and research into the radiobiological characteristics and advantages of these highly ionising particles. </p><p>These advances have made it possible to set up modern proton therapy units in many countries, accessible to certain cancer patients for whom it is very important to minimise the possible toxicities of radiation. This is the case with paediatric patients or patients with certain comorbidities or risks, and also for those patients in whom it is necessary to re-irradiate or administer very high doses of irradiation to the tumour without damaging nearby critical organs.</p><p>Which patients are most likely to benefit from proton therapy (PT) compared to conventional photon radiotherapy (RT)? The main motivation of this project is to answer this question using predictive techniques based on artificial intelligence. </p><p>Within the framework of the future Proton Therapy Centre of Galicia (under construction in Santiago), in this project we intend to carry out a pilot study to develop AI methodology and tools to obtain predictive models of the probability of complications in healthy tissue (in RT, applicable to PT) based on specific patient data. </p><p>The objective of this project is to develop an objective methodology to select, from a group of patients, those who are most likely to benefit from proton therapy, and to obtain a tool to support decision-making in personalised precision medicine, in a way that is quick, easy to implement in clinical practice and exportable.</p><p>A secondary objective is to determine the minimum number of clinical and patient-related variables that are significant for the decision of selecting the type of treatment.</p><p>The working hypothesis is that it is possible to train a machine learning model based on specific disease characteristics in a group of patients, which can predict which patients can benefit most from proton radiotherapy. </p><p>We aim to provide evidence that for optimising the RT/PT process, it is possible to stratify patients based on AI tools and currently available digital medical record systems.</p> - PI24/00795 - Manuel Mucientes Molina, Alberto José Bugarín Diz
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