Current role of machine learning and radiogenomics in precision neuro-oncology - Exploration of Targeted Anti-tumor Therapy - Open Access Review

Aug 5, 2026

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Artificial intelligence increasingly supports medical workflows, with neuro-oncology particularly benefiting from machine learning (ML) and radiogenomics. ML develops algorithms that learn from available medical data to perform specific automated tasks, while radiogenomics links tumor genetics with imaging features to reveal relationships not evident to clinicians. Together, ML and radiogenomics can support treatment tailoring, a core requirement for personalized neuro-oncology. This review summarizes current and potential future applications of ML and radiomics across neuro-oncology.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Artificial intelligence and machine learning

Deep learning and neural networks

Radiogenomics and imaging genomics

Brain gliomas

Incidence, grading, and molecular markers

Purpose of the review

Exploration of Targeted Anti-tumor Therapy Open Access Review Current role of machine learning and radiogenomics in precision neuro-oncology Teresa Perillo1* , Marco de Giorgi2, Umberto Maria Papace2, Antonietta Serino1, Renato Cuocolo3, Andrea Manto1 1Department of Neuroradiology,“Umberto I” Hospital, 84014 Norcera Inferiore, Italy 2Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy 3Department of Medicine, Surgery, and Dentistry, University of Salerno, 84084 Fisciano, Italy *Correspondence: Teresa Perillo, Department of Neuroradiology,“Umberto I” Hospital, 84014 Norcera Inferiore, Italy. [tperillo3@gmail.com](tperillo3@gmail.com) Academic Editor: Valerio Nardone, University of Campania “L. Vanvitelli”, Italy Received: December 20, 2022 Accepted: April 20, 2023 Published: July 19, 2023 Cite this article: Perillo T, de Giorgi M, Papace UM, Serino A, Cuocolo R, Manto A. Current role of machine learning and radiogenomics in precision neuro-oncology. Explor Target Antitumor Ther. 2023;4:545–55. [https://doi.org/10.37349/etat](https://doi.org/10.37349/etat). 2023.00151 Abstract In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology. Keywords Artificial intelligence, machine learning, radiogenomics, neuro-oncology, glioblastoma, meningioma Introduction Artificial intelligence (AI) consists of algorithms that are developed to automatically analyze large amounts of data in order to make high-level abstractions [1]. Recently, it has been increasingly used to create tools to enhance medical workflow in multiple fields, though it has proved extremely useful in precision oncology, as it may identify features hidden in the human eye which can guide therapy [2] . Machine learning (ML) is a subfield of AI that can be used to automatically analyze large amounts of medical data to solve different problems and it does not require prior explicit programming [3] . As a matter of fact, ML algorithms can learn using different approaches [4] . Supervised (or active) learning is the most used type of learning, and it is based on an external known standard (Figure 1) . Unsupervised learning © The Author(s) 2023. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Explor Target Antitumor Ther. 2023;4:545–55 | [https://doi.org/10.37349/etat.2023.00151](https://doi.org/10.37349/etat.2023.00151) Page 545 automatically identifies hidden structures present in large amounts, without needing an external ground truth (Figure 2) [5]. Finally, reinforcement learning uses a trial-and-error process through external positive or negative reinforcement. These different types of learning paradigms may be used in combination [6] . Nowadays, lots of ML algorithms are used in medicine,

FAQs

How does machine learning contribute to precision neuro-oncology?

Machine learning can train algorithms on medical data to automatically perform specific tasks, helping clinicians uncover patterns relevant to therapy planning.

What is radiogenomics and why is it important?

Radiogenomics (imaging genomics) identifies relationships between tumor genetic profiles and imaging phenotypes that are not visible to the human eye, offering new biological insights.

What is the primary goal of this review?

The review aims to describe current roles and possible future applications of machine learning and radiogenomics in precision neuro-oncology.

Aug 5, 2026

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