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Published on: December 28, 2017
PET-based Radiomics Analysis for Predicting Prognosis and Differentiation Treatment-Related Changes in Glioma: A
Mahsa Shakeri1,2, Azadeh Amraee3, Seyyed Mohammad Hosseini1,2
1Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Journal of Biomedical Physics & Engineering
|August 12, 2026
Summary
Positron Emission Tomography (PET)-based radiomics models effectively distinguish treatment effects from tumor recurrence in glioma. These advanced models improve glioma prognosis prediction and aid in personalized treatment strategies.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Glioma treatment management requires accurate assessment of treatment-induced changes and prognosis.
- Positron Emission Tomography (PET) imaging and radiomics offer insights into therapeutic response.
Purpose of the Study:
- To systematically review the performance of PET-based radiomics models.
- To evaluate their ability in distinguishing treatment-related changes and predicting glioma prognosis.
Main Methods:
- Systematic literature search across major databases (Web of Science, MEDLINE, PubMed, EMBASE).
- Inclusion of studies using keywords related to PET, glioma, radiomics, AI, and treatment outcomes.
- Review and abstraction by independent reviewers, with PRISMA checklist for quality assessment.
Main Results:
- PET-based radiomics models surpassed conventional PET parameters in differentiating post-treatment changes and predicting prognosis.
- Integrated models combining radiomics and conventional PET parameters showed superior diagnostic performance.
Conclusions:
- PET-based radiomics models enhance the differentiation of tumor recurrence from treatment-related changes.
- Implementation can lead to personalized treatment plans, improved survival, and quality of life.
