Vincenzo Di Nunno1, Mario Fordellone2, Giuseppe Minniti3,4, Sofia Asioli5,6, Alfredo Conti5,7, Diego Mazzatenta5,6, Damiano Balestrini8, Paolo Chiodini2, Raffaele Agati9, Caterina Tonon10,11, Alicia Tosoni12, Lidia Gatto1, Stefania Bartolini12, Raffaele Lodi10,11, Enrico Franceschi13. 1. Oncology Department, AUSL Bologna, Bologna, Italy. 2. Medical Statistics Unit, University of Campania "Luigi Vanvitelli", Naples, Italy. 3. Radiation Oncology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy. 4. IRCCS Neuromed, Pozzilli, IS, Italy. 5. Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum University of Bologna, Bologna, Italy. 6. Programma Neurochirurgia Ipofisi- Pituitary Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy. 7. Unit of Neurosurgery, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, Bologna, Italy. 8. Radiotherapy Department, AUSL Bologna, Bologna, Italy. 9. Programma Neuroradiologia con Tecniche ad elevata complessità, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy. 10. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy. 11. Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche Di Bologna, Bologna, Italy. 12. Nervous System Medical Oncology Department, IRCCS Istituto delle Scienze Neurologiche di Bologna, Via Altura 3, Bologna, Italy. 13. Nervous System Medical Oncology Department, IRCCS Istituto delle Scienze Neurologiche di Bologna, Via Altura 3, Bologna, Italy. enricofra@yahoo.it.
Abstract
PURPOSE: Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS: We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS: Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION: It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
PURPOSE: Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS: We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS: Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION: It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
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