AI-driven precision in prostate brachytherapy: A systematic review of 70 studies

Artificial intelligence (AI) has transformed prostate brachytherapy by enhancing precision, efficiency, and personalization. This systematic review evaluates 70 peer-reviewed studies from PubMed, Embase, and Web of Science, focusing on the applications of AI in imaging, treatment planning, applicator reconstruction, and outcome prediction. Machine learning (ML) and deep learning (DL) techniques, including U-Net and deep reinforcement learning, demonstrate improvements in segmentation (84% sensitivity), dose optimization (20-30% time savings), and quality assurance (25% error reduction). Challenges include limited dataset diversity, generalizability, and clinical integration. This review highlights AI's potential to revolutionize prostate brachytherapy and identifies research gaps necessary for its clinical adoption.

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Publication Details

Journal
Journal of Cancer Research and Therapeutics
Published
2025-10-01
DOI
https://doi.org/10.4103/jcrt.jcrt_1996_25
Primary Topic
Prostate Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-driven precision in prostate brachytherapy: A systematic review of 70 studies

Vibhay Pareek, Nikunj Patil, Carlton Johnny, Bashir M. Bashir et al.
Journal of Cancer Research and Therapeutics
Prostate Cancer Diagnosis and Treatment
article

AI-driven precision in prostate brachytherapy: A systematic review of 70 studies

Vibhay Pareek, Nikunj Patil, Carlton Johnny, Bashir M. Bashir, Sheen Dube, Florence Mutua
article en

Abstract

Artificial intelligence (AI) has transformed prostate brachytherapy by enhancing precision, efficiency, and personalization. This systematic review evaluates 70 peer-reviewed studies from PubMed, Embase, and Web of Science, focusing on the applications of AI in imaging, treatment planning, applicator reconstruction, and outcome prediction. Machine learning (ML) and deep learning (DL) techniques, including U-Net and deep reinforcement learning, demonstrate improvements in segmentation (84% sensitivity), dose optimization (20-30% time savings), and quality assurance (25% error reduction). Challenges include limited dataset diversity, generalizability, and clinical integration. This review highlights AI's potential to revolutionize prostate brachytherapy and identifies research gaps necessary for its clinical adoption.

Journal of Cancer Research and TherapeuticsVol. 21(7)
CancerCare Manitoba (CA), The University of Winnipeg (CA)
Openalex Percentile: Top 27%
Prostate Cancer Diagnosis and Treatment
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