Glioma
Conditions
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Centro di Riferimento Oncologico - Aviano
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Patients with a histopathological diagnosis of glioma for whom it is possible to have cryopreserved or fixed in formalin and embedded in paraffin biological material. Specimens may result from incisional biopsy and/or surgical resection and/or blood. Whole blood is taken for germinal analysis; 2. Age \>=18 years; 3. Patients must understand and provide written informed consent; 4. Life expectancy \>3 months; 5. Presence of biological material from resection and blood considered sufficient by quality and quantity to proceed to molecular characterisation in the opinion of the Investigator Principal Investigator; 6. Presence of available and accessible clinical and histopathological data.
Exclusion criteria
1. Refusal of informed consent; 2. Uncooperative patients; 3. Presence of other neoplastic diseases in the last 5 years; 4. Pregnant and/or breastfeeding women
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| proportion of patients, out of total patients number, with molecular characteristics susceptible to potential drug therapies ('druggable') identified using an AI model | 24 months | proportion of patients, out of total patients number, with molecular characteristics susceptible to potential drug therapies ('druggable') identified using an AI model |
Countries
Italy
Contacts
Primary ContactGiuseppe Toffoli
Outcome results
None listed