Artificial intelligence (AI) undoubtedly represents the main tool currently available in the definition of complex algorithms and its use in the medical field is becoming increasingly strategic.As reported in the literature, it is increasingly difficult to find new therapeutic strategies for neoplasms, especially neurological ones. Molecular characterisation is therefore increasingly essential, as is the use of new predictive methods. With this in mind, the aim of this study is to assess, by means of AI algorithms applied to genomic data, in what percentage molecular alterations are susceptible to potential drug therapies, compared to the literature data that does not consider AI algorithms for this purpose.
Artificial intelligence (AI) undoubtedly represents the main tool currently available in the definition of complex algorithms and its use in the medical field is becoming increasingly strategic.As reported in the literature, it is increasingly difficult to find new therapeutic strategies for neoplasms, especially neurological ones. Molecular characterisation is therefore increasingly essential, as is the use of new predictive methods. With this in mind, the aim of this study is to assess, by means of AI algorithms applied to genomic data, in what percentage molecular alterations are susceptible to potential drug therapies, compared to the literature data that does not consider AI algorithms for this purpose.
Study Type
OBSERVATIONAL
Enrollment
164
Centro di Riferimento Oncologico (CRO) di Aviano - IRCCS
Aviano, Pordenone, Italy
RECRUITINGIOV
Padova, Italy
RECRUITINGAzienda Sanitaria Universitaria Friuli Centrale (ASUFC
Udine, Italy
RECRUITINGproportion of patients, out of total patients number, with molecular characteristics susceptible to potential drug therapies ('druggable') identified using an AI model
proportion of patients, out of total patients number, with molecular characteristics susceptible to potential drug therapies ('druggable') identified using an AI model
Time frame: 24 months
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