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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Abstract 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.
AbstractList Simple SummaryRadiomics and radiogenomics offer new insight into high-grade glioma biology, as well as into glioma behavior in response to standard therapies. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the role of radiomics in providing more accurate diagnoses, prognostication, and surveillance of patients with high-grade glioma, and on the potential application of radiomics in clinical practice, with the overarching goal of advancing precision medicine for optimal patient care.AbstractMachine 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.
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.
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.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.
Author Davatzikos, Christos
Fathi Kazerooni, Anahita
Nabavizadeh, Ali
Akbari, Hamed
Saxena, Sanjay
Bakas, Spyridon
Chawla, Sanjeev
Guo, Jun
Mohan, Suyash
Bagheri, Sina
Nasrallah, MacLean P.
Bagley, Stephen J.
AuthorAffiliation 1 Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; Anahita.Kazerooni@pennmedicine.upenn.edu (A.F.K.); Hamed.Akbari@pennmedicine.upenn.edu (H.A.); Sanjay.Saxena@Pennmedicine.upenn.edu (S.S.); Jun.Guo@Pennmedicine.upenn.edu (J.G.); Ali.Nabavizadeh@pennmedicine.upenn.edu (A.N.); Suyash.Mohan@pennmedicine.upenn.edu (S.M.); Spyridon.Bakas@pennmedicine.upenn.edu (S.B.); Christos.Davatzikos@pennmedicine.upenn.edu (C.D.)
4 Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
3 Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; sbagley@pennmedicine.upenn.edu
2 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Sina.Bagheri@Pennmedicine.upenn.edu (S.B.); Sanjeev.Chawla@pennmedicine.upenn.edu (S.C.)
5 Department of Pathology & Laboratory Med
AuthorAffiliation_xml – name: 1 Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; Anahita.Kazerooni@pennmedicine.upenn.edu (A.F.K.); Hamed.Akbari@pennmedicine.upenn.edu (H.A.); Sanjay.Saxena@Pennmedicine.upenn.edu (S.S.); Jun.Guo@Pennmedicine.upenn.edu (J.G.); Ali.Nabavizadeh@pennmedicine.upenn.edu (A.N.); Suyash.Mohan@pennmedicine.upenn.edu (S.M.); Spyridon.Bakas@pennmedicine.upenn.edu (S.B.); Christos.Davatzikos@pennmedicine.upenn.edu (C.D.)
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Snippet Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and...
Simple SummaryRadiomics and radiogenomics offer new insight into high-grade glioma biology, as well as into glioma behavior in response to standard therapies....
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SubjectTerms 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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Title Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine
URI https://www.proquest.com/docview/2608069215
https://www.proquest.com/docview/2608533947
https://pubmed.ncbi.nlm.nih.gov/PMC8656630
Volume 13
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