Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review
The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models t...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/13/3/552 |
| _version_ | 1797417019345207296 |
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| author | Ahmad Chaddad Michael J. Kucharczyk Abbas Cheddad Sharon E. Clarke Lama Hassan Shuxue Ding Saima Rathore Mingli Zhang Yousef Katib Boris Bahoric Gad Abikhzer Stephan Probst Tamim Niazi |
| author_facet | Ahmad Chaddad Michael J. Kucharczyk Abbas Cheddad Sharon E. Clarke Lama Hassan Shuxue Ding Saima Rathore Mingli Zhang Yousef Katib Boris Bahoric Gad Abikhzer Stephan Probst Tamim Niazi |
| author_sort | Ahmad Chaddad |
| collection | DOAJ |
| description | The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries. |
| first_indexed | 2024-03-09T06:12:47Z |
| format | Article |
| id | doaj.art-8bddea745bff4c6b997011be909c714d |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T06:12:47Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-8bddea745bff4c6b997011be909c714d2023-12-03T11:56:28ZengMDPI AGCancers2072-66942021-02-0113355210.3390/cancers13030552Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative ReviewAhmad Chaddad0Michael J. Kucharczyk1Abbas Cheddad2Sharon E. Clarke3Lama Hassan4Shuxue Ding5Saima Rathore6Mingli Zhang7Yousef Katib8Boris Bahoric9Gad Abikhzer10Stephan Probst11Tamim Niazi12School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, ChinaNova Scotia Cancer Centre, Dalhousie University, Halifax, NS B3H 1V7, CanadaDepartment of Computer Science, Blekinge Institute of Technology, SE-37179 Karlskrona, SwedenDepartment of Radiology, Dalhousie University, Halifax, NS B3H 1V7, CanadaSchool of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, ChinaCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USAMontreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, CanadaDepartment of Radiology, Taibah University, Al-Madinah 42353, Saudi ArabiaLady Davis Institute for Medical Research, McGill University, Montreal, QC H3S 1Y9, CanadaLady Davis Institute for Medical Research, McGill University, Montreal, QC H3S 1Y9, CanadaLady Davis Institute for Medical Research, McGill University, Montreal, QC H3S 1Y9, CanadaLady Davis Institute for Medical Research, McGill University, Montreal, QC H3S 1Y9, CanadaThe management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.https://www.mdpi.com/2072-6694/13/3/552artificial intelligenceradiomicsradiogenomicsprostate cancerGleason scoremagnetic resonance imaging |
| spellingShingle | Ahmad Chaddad Michael J. Kucharczyk Abbas Cheddad Sharon E. Clarke Lama Hassan Shuxue Ding Saima Rathore Mingli Zhang Yousef Katib Boris Bahoric Gad Abikhzer Stephan Probst Tamim Niazi Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review Cancers artificial intelligence radiomics radiogenomics prostate cancer Gleason score magnetic resonance imaging |
| title | Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review |
| title_full | Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review |
| title_fullStr | Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review |
| title_full_unstemmed | Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review |
| title_short | Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review |
| title_sort | magnetic resonance imaging based radiomic models of prostate cancer a narrative review |
| topic | artificial intelligence radiomics radiogenomics prostate cancer Gleason score magnetic resonance imaging |
| url | https://www.mdpi.com/2072-6694/13/3/552 |
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