Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence

Management of metastatic prostate cancer (mPCa) poses significant challenges due to inherent tumor heterogeneity and therapeutic resistance. Advances in molecular imaging, liquid biopsies, and biomarkers are enabling precision oncology, while artificial intelligence (AI), including machine learning (ML) and deep learning (DL), integrates complex datasets to improve diagnostic accuracy, risk stratification, and treatment guidance. This review highlights AI's applications in mPCa, focusing on imaging, cell-free nucleic acids, circulating tumor cells, and genomic classifiers. We emphasize AI's role in enhancing diagnostics and personalizing treatments, with implications for improving clinical outcomes through better decision-making. Finally, we discuss opportunities and challenges in deploying AI systems, stressing multimodal integration and validation for real-world clinical impact.

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Publication Details

Journal
Technology in Cancer Research & Treatment
Published
2026-04-01
DOI
https://doi.org/10.1177/15330338261440434
Primary Topic
Prostate Cancer Treatment and Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence

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Technology in Cancer Research & Treatment
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Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence

Jingyi He, Tanziela Tanziela, Jonghoon Chang, Wensi Tao, Nai-Kei Wong, Shuhong Chen, Wesley Burrow, Luyuan Li, Rong-Jun Chen, R. Daniel Bonfil, Chang Liu, Jia-Le Zhuang
article en

Abstract

Management of metastatic prostate cancer (mPCa) poses significant challenges due to inherent tumor heterogeneity and therapeutic resistance. Advances in molecular imaging, liquid biopsies, and biomarkers are enabling precision oncology, while artificial intelligence (AI), including machine learning (ML) and deep learning (DL), integrates complex datasets to improve diagnostic accuracy, risk stratification, and treatment guidance. This review highlights AI's applications in mPCa, focusing on imaging, cell-free nucleic acids, circulating tumor cells, and genomic classifiers. We emphasize AI's role in enhancing diagnostics and personalizing treatments, with implications for improving clinical outcomes through better decision-making. Finally, we discuss opportunities and challenges in deploying AI systems, stressing multimodal integration and validation for real-world clinical impact.

Technology in Cancer Research & TreatmentVol. 25
Nova Southeastern University (US), Shantou University (CN), Guangzhou University (CN), Shantou University Medical College (CN), University of South Alabama Medical Center (US)
Shantou University, National Natural Science Foundation of China, Shantou University Medical College
Peace, Justice and strong institutions
Openalex Percentile: Top 29%
Prostate Cancer Treatment and Research
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