解锁人工智能,机器学习和深度学习,以对抗转移性割抵抗性前列腺癌的治疗耐药性:综合性综述
Zainab Haider Ejaz1, Reyan Hussain Shaikh1, Alizeh Sonia Fatimi1
1Aga Khan University Hospital, Aga Khan Medical College, Sindh, Karachi 748000, Pakistan.
Ecancermedicalscience
|September 15, 2025
概括
人工智能 (AI) 提供了新的方法来对抗转移性割抵抗性前列腺癌 (mCRPC) 的治疗耐药性. 人工智能分析复杂的数据以个性化治疗和识别耐药性标志物,尽管临床使用仍然存在挑战.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物信息学是一种生物信息学.
背景情况:
- 转移性割抵抗性前列腺癌 (mCRPC) 提出了重大的治疗挑战.
- 现有的治疗方法由于获得或内在耐药性而面临局限性.
- 新的方法对于改善mCRPC患者的治疗结果至关重要.
研究的目的:
- 审查人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 在克服mCRPC治疗阻力方面的应用.
- 探索AI如何分析集成数据集以获得机械洞察力和生物标志物发现.
- 讨论AI在开发mCRPC个性化治疗策略方面的潜力和局限性.
主要方法:
- 叙事综述综合了mCRPC中关于AI的当前文献.
- 利用基因组学,蛋白质组学和临床数据对AI方法的分析.
- 检查专注于预测治疗反应和耐药机制的研究.
主要成果:
- 人工智能方法可以整合多学科和临床数据来识别耐药性驱动因素.
- 人工智能在预测治疗疗效和识别新生物标志物方面表现有前途.
- 使用人工智能洞察力,可以开发个性化治疗策略.
结论:
- 人工智能具有很大的潜力,可以推进针对mCRPC耐药性的治疗策略.
- 数据异质性和监管障碍是临床翻译的关键挑战.
- 需要进一步的研究才能在mCRPC临床实践中全面实施AI.
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