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Machine learning in neuro-oncology: toward novel development fields

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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.

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Data availability

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Oncology Department, AUSL Bologna, Bologna, Italy

    Vincenzo Di Nunno & Lidia Gatto

  2. Medical Statistics Unit, University of Campania “Luigi Vanvitelli”, Naples, Italy

    Mario Fordellone & Paolo Chiodini

  3. Radiation Oncology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy

    Giuseppe Minniti

  4. IRCCS Neuromed, Pozzilli, IS, Italy

    Giuseppe Minniti

  5. Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum University of Bologna, Bologna, Italy

    Sofia Asioli, Alfredo Conti & Diego Mazzatenta

  6. Programma Neurochirurgia Ipofisi- Pituitary Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy

    Sofia Asioli & Diego Mazzatenta

  7. Unit of Neurosurgery, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, Bologna, Italy

    Alfredo Conti

  8. Radiotherapy Department, AUSL Bologna, Bologna, Italy

    Damiano Balestrini

  9. Programma Neuroradiologia con Tecniche ad elevata complessità, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy

    Raffaele Agati

  10. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy

    Caterina Tonon & Raffaele Lodi

  11. Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche Di Bologna, Bologna, Italy

    Caterina Tonon & Raffaele Lodi

  12. Nervous System Medical Oncology Department, IRCCS Istituto delle Scienze Neurologiche di Bologna, Via Altura 3, Bologna, Italy

    Alicia Tosoni, Stefania Bartolini & Enrico Franceschi

Authors
  1. Vincenzo Di Nunno
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  2. Mario Fordellone
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  3. Giuseppe Minniti
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  14. Raffaele Lodi
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  15. Enrico Franceschi
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Contributions

All authors contributed to study conception, design, and review. The first draft of the article was written by all authors which also performed a revision of the first draft. All authors read and approved the final version of the manuscript.

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Correspondence to Enrico Franceschi.

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Di Nunno, V., Fordellone, M., Minniti, G. et al. Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 159, 333–346 (2022). https://doi.org/10.1007/s11060-022-04068-7

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