Biomedicines (Dec 2022)

Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology

  • Lidia Gatto,
  • Enrico Franceschi,
  • Alicia Tosoni,
  • Vincenzo Di Nunno,
  • Caterina Tonon,
  • Raffaele Lodi,
  • Raffaele Agati,
  • Stefania Bartolini,
  • Alba Ariela Brandes

DOI
https://doi.org/10.3390/biomedicines10123205
Journal volume & issue
Vol. 10, no. 12
p. 3205

Abstract

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Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators’ efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.

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