MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model
<b>Background/Objectives</b>: 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 rem...
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MDPI AG
2025-03-01
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| author | Mohammed S. Alshuhri Haitham F. Al-Mubarak Abdulrahman Qaisi Ahmad A. Alhulail Abdullah G. M. AlMansour Yahia Madkhali Sahal Alotaibi Manal Aljuhani Othman I. Alomair A. Almudayni F. Alablani |
| author_facet | Mohammed S. Alshuhri Haitham F. Al-Mubarak Abdulrahman Qaisi Ahmad A. Alhulail Abdullah G. M. AlMansour Yahia Madkhali Sahal Alotaibi Manal Aljuhani Othman I. Alomair A. Almudayni F. Alablani |
| author_sort | Mohammed S. Alshuhri |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: 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. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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. |
| format | Article |
| id | doaj-art-9722c3ea6bfb463bae7ce37f4fc0d4a9 |
| institution | OA Journals |
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| language | English |
| publishDate | 2025-03-01 |
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| series | Biomedicines |
| spelling | doaj-art-9722c3ea6bfb463bae7ce37f4fc0d4a92025-08-20T02:17:20ZengMDPI AGBiomedicines2227-90592025-03-0113481510.3390/biomedicines13040815MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse ModelMohammed S. Alshuhri0Haitham F. Al-Mubarak1Abdulrahman Qaisi2Ahmad A. Alhulail3Abdullah G. M. AlMansour4Yahia Madkhali5Sahal Alotaibi6Manal Aljuhani7Othman I. Alomair8A. Almudayni9F. Alablani10Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi ArabiaDepartment of Radiology, Northwestern University, Chicago, IL 60611, USADepartment of Radiology, Security Forces Hospital, Riyadh 11564, Saudi ArabiaRadiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi ArabiaRadiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi ArabiaDepartment of Diagnostic Radiography Technology, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi ArabiaRadiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi ArabiaRadiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi ArabiaRadiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi ArabiaRadiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi ArabiaRadiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia<b>Background/Objectives</b>: 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. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.https://www.mdpi.com/2227-9059/13/4/815delta radiomicsglioblastomaradiation therapyMRImachine learningtumor morphology and texture analysis |
| spellingShingle | Mohammed S. Alshuhri Haitham F. Al-Mubarak Abdulrahman Qaisi Ahmad A. Alhulail Abdullah G. M. AlMansour Yahia Madkhali Sahal Alotaibi Manal Aljuhani Othman I. Alomair A. Almudayni F. Alablani MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model Biomedicines delta radiomics glioblastoma radiation therapy MRI machine learning tumor morphology and texture analysis |
| title | MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model |
| title_full | MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model |
| title_fullStr | MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model |
| title_full_unstemmed | MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model |
| title_short | MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model |
| title_sort | mri delta radiomics to track early changes in tumor following radiation application in glioblastoma mouse model |
| topic | delta radiomics glioblastoma radiation therapy MRI machine learning tumor morphology and texture analysis |
| url | https://www.mdpi.com/2227-9059/13/4/815 |
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