Machine learning in neuro-oncology: toward novel development fields

Nunno, Vincenzo Di; Fordellone, Mario; Minniti, Giuseppe; Asioli, Sofia; Conti, Alfredo; Mazzatenta, Diego; Balestrini, Damiano; Chiodini, Paolo; Agati, R.; Tonon, Caterina; Tosoni, Alicia; Gatto, Lidia; Bartolini, Stefania; Lodi, Raffaele; Franceschi, Enrico

doi:10.1007/s11060-022-04068-7

Cited by 5 publications

(1 citation statement)

References 98 publications

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“…With the incremental inclusion of artificial intelligence [ 149 ], large-scale data sharing, and CNNs combined with clinical data, neuroradiologists’ performances are likely to improve, allowing more precise, impactful diagnoses.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning

Zhou,

Ji,

Peng

et al. 2023

Eur Radiol

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Artificial intelligence in neuro-oncology

Nakhate,

Gonzalez Castro

2023

Front. Neurosci.

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Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.

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Machine learning in neuro-oncology: toward novel development fields

Cited by 5 publications

References 98 publications

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

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

Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning

Artificial intelligence in neuro-oncology

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