Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities
Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/12/2/289 |
| _version_ | 1797481288247017472 |
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| author | Huanye Li Chau Hung Lee David Chia Zhiping Lin Weimin Huang Cher Heng Tan |
| author_facet | Huanye Li Chau Hung Lee David Chia Zhiping Lin Weimin Huang Cher Heng Tan |
| author_sort | Huanye Li |
| collection | DOAJ |
| description | Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field. |
| first_indexed | 2024-03-09T22:12:43Z |
| format | Article |
| id | doaj.art-c55be45af25f43ce8d3c21ac34af25a4 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T22:12:43Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c55be45af25f43ce8d3c21ac34af25a42023-11-23T19:29:33ZengMDPI AGDiagnostics2075-44182022-01-0112228910.3390/diagnostics12020289Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future OpportunitiesHuanye Li0Chau Hung Lee1David Chia2Zhiping Lin3Weimin Huang4Cher Heng Tan5School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeDepartment of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, SingaporeDepartment of Radiation Oncology, National University Cancer Institute (NUH), Singapore 119074, SingaporeSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeInstitute for Infocomm Research, A*Star, Singapore 138632, SingaporeDepartment of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, SingaporeAdvances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.https://www.mdpi.com/2075-4418/12/2/289prostate MRIcancerdeep learningmachine learningPI-RADSsegmentation |
| spellingShingle | Huanye Li Chau Hung Lee David Chia Zhiping Lin Weimin Huang Cher Heng Tan Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities Diagnostics prostate MRI cancer deep learning machine learning PI-RADS segmentation |
| title | Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities |
| title_full | Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities |
| title_fullStr | Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities |
| title_full_unstemmed | Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities |
| title_short | Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities |
| title_sort | machine learning in prostate mri for prostate cancer current status and future opportunities |
| topic | prostate MRI cancer deep learning machine learning PI-RADS segmentation |
| url | https://www.mdpi.com/2075-4418/12/2/289 |
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