Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review
Background: Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. T...
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MDPI AG
2024-12-01
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| author | Shailesh S. Nayak Saikiran Pendem Girish R. Menon Niranjana Sampathila Prakashini Koteshwar |
| author_facet | Shailesh S. Nayak Saikiran Pendem Girish R. Menon Niranjana Sampathila Prakashini Koteshwar |
| author_sort | Shailesh S. Nayak |
| collection | DOAJ |
| description | Background: Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and textural information imperceptible to the human eye. However, implementation in clinical practice is still distant, and concerns have been raised regarding the methodological quality of radiomic studies. Methodology: A systematic literature search was performed to identify original articles focused on the use of radiomics for brain tumors from 2015 based on the inclusion and exclusion criteria. The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. The present study systematically reviews the status of radiomic studies concerning brain tumors, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. Results: A systematic search of PubMed identified 300 articles, with 18 studies meeting the inclusion criteria for qualitative synthesis. These studies collectively demonstrate the potential of radiomics-based machine learning models in accurately distinguishing between glioma subtypes and grades. Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. Conclusions: The present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. Further validation and standardization efforts are warranted to facilitate the clinical integration of radiomics into routine practice, ultimately enhancing glioma management and patient outcomes. Open science practices: Machine learning using MRI radiomic features provides a simple, noninvasive, and cost-effective method for glioma classification, enhancing transparency, reproducibility, and collaboration within the scientific community. |
| format | Article |
| id | doaj-art-257ee67be7f044bd84b153a22a2dea6f |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-257ee67be7f044bd84b153a22a2dea6f2025-08-20T01:55:34ZengMDPI AGDiagnostics2075-44182024-12-011423274110.3390/diagnostics14232741Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic ReviewShailesh S. Nayak0Saikiran Pendem1Girish R. Menon2Niranjana Sampathila3Prakashini Koteshwar4Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaManipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaKasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaManipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaKasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaBackground: Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and textural information imperceptible to the human eye. However, implementation in clinical practice is still distant, and concerns have been raised regarding the methodological quality of radiomic studies. Methodology: A systematic literature search was performed to identify original articles focused on the use of radiomics for brain tumors from 2015 based on the inclusion and exclusion criteria. The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. The present study systematically reviews the status of radiomic studies concerning brain tumors, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. Results: A systematic search of PubMed identified 300 articles, with 18 studies meeting the inclusion criteria for qualitative synthesis. These studies collectively demonstrate the potential of radiomics-based machine learning models in accurately distinguishing between glioma subtypes and grades. Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. Conclusions: The present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. Further validation and standardization efforts are warranted to facilitate the clinical integration of radiomics into routine practice, ultimately enhancing glioma management and patient outcomes. Open science practices: Machine learning using MRI radiomic features provides a simple, noninvasive, and cost-effective method for glioma classification, enhancing transparency, reproducibility, and collaboration within the scientific community.https://www.mdpi.com/2075-4418/14/23/2741Gliomadifferential diagnosisradiomicsradiomics quality scoretexture analysis |
| spellingShingle | Shailesh S. Nayak Saikiran Pendem Girish R. Menon Niranjana Sampathila Prakashini Koteshwar Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review Diagnostics Glioma differential diagnosis radiomics radiomics quality score texture analysis |
| title | Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review |
| title_full | Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review |
| title_fullStr | Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review |
| title_full_unstemmed | Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review |
| title_short | Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review |
| title_sort | quality assessment of mri radiomics based machine learning methods in classification of brain tumors systematic review |
| topic | Glioma differential diagnosis radiomics radiomics quality score texture analysis |
| url | https://www.mdpi.com/2075-4418/14/23/2741 |
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