Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence,...

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Published inCancers Vol. 13; no. 23; p. 5921
Main Authors Fathi Kazerooni, Anahita, Bagley, Stephen J., Akbari, Hamed, Saxena, Sanjay, Bagheri, Sina, Guo, Jun, Chawla, Sanjeev, Nabavizadeh, Ali, Mohan, Suyash, Bakas, Spyridon, Davatzikos, Christos, Nasrallah, MacLean P.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 25.11.2021
MDPI
Subjects
Biology
Biomarkers
Brain cancer
Brain tumors
Clinical decision making
Clinical outcomes
Clinical trials
Genomics
Glioma
Learning algorithms
Machine learning
Magnetic resonance imaging
Medical imaging
Medical prognosis
Metastases
Oncology
Pathophysiology
Patients
Precision medicine
Predictions
Radiomics
Surgery
Tumors
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Summary:Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.
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ISSN:2072-6694
2072-6694
DOI:10.3390/cancers13235921