Machine learning in prostate MRI for prostate cancer: current status and future opportunities
Open accessAbstract
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.
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Many AI efforts in prostate cancer aim to classify clinically significant disease and quantify features such as prostate gland segmentation and volume.
“There have been many AI efforts in this domain, including those aimed at directly classifying clinically significant prostate cancer and approaches to quantify clinically relevant features such as segmenting and measuring the volume of the prostate gland (35).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [35]
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References 141 · Cited by 74
Research works printed but not linked: 4
- [45]Motamed S. Gujrathi I. Deniffel D. Oentoro A. Haider M.A. Khalvati F. Transfer Learning for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI arXiv 2020 1909.09541
- [53]Simpson A.L. Antonelli M. Bakas S. Bilello M. Farahani K. Van Ginneken B. Kopp-Schneider A. Landman B.A. Litjens G. Menze B. A large annotated medical image dataset for the development and evaluation of segmentation algorithms arXiv 2019 1902.09063
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- Authors
- Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W., & Tan, C. H.
- Affiliations
- Nanyang Technological University (SG)
- Tan Tock Seng Hospital (SG)
- National University Cancer Institute, Singapore (SG)
- Agency for Science, Technology and Research (SG)
- Corresponding author
- Tan, Cher Heng
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2022-01-24
- Volume
- 12
- Issue
- 2
- Pages
- 289
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC8870978
- OpenAlex
- W4210697829
- Cited by
- 74
- References
- 141
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 24 · 2023: 24 · 2022: 3
- FWCI
- 2.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- prostate cancer, prostate MRI, machine learning, PI-RADS, image fusion, automated lesion segmentation, clinically significant prostate cancer, targeted biopsy, risk stratification
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