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PET and MRI Guided Irradiation of a Glioblastoma Rat Model Using a Micro-irradiator
Published on: December 28, 2017
MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model
Mohammed S Alshuhri1, Haitham F Al-Mubarak2, Abdulrahman Qaisi3
1Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia.
Abstract:
Background/Objectives: Glioblastoma (GBM) is an aggressive and lethal primary brain tumor with a poor prognosis, with a 5-year survival rate of approximately 5%. Despite advances in oncologic treatments, including surgery, radiotherapy, and chemotherapy, survival outcomes have remained stagnant, largely due to the failure of conventional therapies to address the tumor's inherent heterogeneity. Radiomics, a rapidly emerging field, provides an opportunity to extract features from MRI scans, offering new insights into tumor biology and treatment response. This study evaluates the potential of delta radiomics, the study of changes in radiomic features over time in response to treatment or disease progression, exploring the potential of delta radiomics to track temporal radiation changes in tumor morphology and microstructure. Methods: A cohort of 50 female CD1 nude mice was injected intracranially with G7 glioblastoma cells and divided into irradiated (IR) and non-irradiated (non-IR) groups. MRI scans were performed at baseline (week 11) and post-radiation (weeks 12 and 14), and radiomic features, including shape, histogram, and texture parameters, were extracted and analyzed to capture radiation-induced changes. The most robust features were those identified through intra-observer reproducibility assessment, ensuring reliability in feature selection. A machine learning model was developed to classify irradiated tumors based on delta radiomic features, and statistical analyses were conducted to evaluate feature feasibility, stability, and predictive performance. Results: Our findings demonstrate that delta radiomics effectively captured significant temporal variations in tumor characteristics. Delta radiomics features exhibited distinct patterns across different time points in the IR group, enabling machine learning models to achieve a high accuracy. Conclusions: Delta radiomics offers a robust, non-invasive method for monitoring the treatment of glioblastoma (GBM) following radiation therapy. Future research should prioritize the application of MRI delta radiomics to effectively capture short-term changes resulting from intratumoral radiation effects. This advancement has the potential to significantly enhance treatment monitoring and facilitate the development of personalized therapeutic strategies.
Insights
Delta radiomics effectively tracks radiation changes in glioblastoma (GBM) tumors using MRI. This non-invasive method enhances treatment monitoring and personalized therapy development for aggressive brain cancers.
Area of Science:
- Neuro-oncology
- Medical Imaging Analysis
- Radiomics and Machine Learning
Background:
- Glioblastoma (GBM) is a lethal brain tumor with poor prognosis, resistant to conventional therapies due to tumor heterogeneity.
- Radiomics offers insights into tumor biology and treatment response by extracting features from MRI scans.
- Delta radiomics, analyzing temporal changes in radiomic features, shows promise for monitoring treatment effects.
Purpose of the Study:
- To evaluate the potential of delta radiomics in tracking temporal radiation-induced changes in glioblastoma (GBM) tumor morphology and microstructure.
- To develop a machine learning model for classifying irradiated tumors based on delta radiomic features.
Main Methods:
- 50 female CD1 nude mice with intracranial G7 glioblastoma were divided into irradiated (IR) and non-irradiated groups.
- MRI scans were acquired at baseline and post-radiation (weeks 12 and 14).
- Radiomic features (shape, histogram, texture) were extracted, analyzed for temporal changes, and used to train a machine learning classification model.
Main Results:
- Delta radiomics successfully captured significant temporal variations in tumor characteristics post-radiation.
- Distinct patterns in delta radiomics features were observed in the IR group, enabling high accuracy in machine learning classification.
- Feature selection was refined using intra-observer reproducibility assessment for reliability.
Conclusions:
- Delta radiomics provides a robust, non-invasive method for monitoring glioblastoma (GBM) treatment response after radiation therapy.
- Future research should focus on MRI delta radiomics for capturing short-term intratumoral radiation effects.
- This approach can significantly improve treatment monitoring and personalized therapeutic strategies for GBM.

