Unlocking artificial intelligence, machine learning and deep learning to combat therapeutic resistance in metastatic castration-resistant prostate cancer: a comprehensive review.

IF 1.3 Q4 ONCOLOGY

ecancermedicalscience Pub Date : 2025-07-29 eCollection Date: 2025-01-01 DOI:10.3332/ecancer.2025.1953

Zainab Haider Ejaz, Reyan Hussain Shaikh, Alizeh Sonia Fatimi, Saqib Raza Khan

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Abstract

Metastatic castration-resistant prostate cancer (mCRPC) remains a formidable clinical challenge despite advancements in therapy. This narrative review explores the role of artificial intelligence (AI), machine learning and deep learning in addressing therapeutic resistance in mCRPC. AI-driven approaches leverage integrated datasets encompassing genomics, proteomics and clinical parameters to uncover molecular mechanisms, predict treatment responses and identify biomarkers of resistance. These methodologies promise personalised treatment strategies tailored to individual patient profiles. However, data heterogeneity and regulatory considerations are challenges that hinder the translation of AI insights into clinical practice. By synthesising current literature, this review examines the progress, potential and limitations of AI applications in combating therapeutic resistance in mCRPC, highlighting implications for future research and clinical implementation.

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解锁人工智能、机器学习和深度学习以对抗转移性去势抵抗性前列腺癌的治疗耐药性：全面回顾。

转移性去势抵抗性前列腺癌（mCRPC）仍然是一个巨大的临床挑战，尽管治疗进展。本文探讨了人工智能（AI）、机器学习和深度学习在解决mCRPC治疗耐药中的作用。人工智能驱动的方法利用包括基因组学、蛋白质组学和临床参数在内的集成数据集，揭示分子机制，预测治疗反应并识别耐药性的生物标志物。这些方法保证了针对个别患者的个性化治疗策略。然而，数据异质性和监管方面的考虑是阻碍人工智能见解转化为临床实践的挑战。通过综合现有文献，本文综述了人工智能在对抗mCRPC治疗耐药方面的进展、潜力和局限性，强调了对未来研究和临床实施的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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