利用机器学习来预测前列腺癌生存率:一篇评论
Sungun Bang1, Young Jin Ahn1, Kyo Chul Koo1
1Department of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Frontiers in oncology
|January 27, 2025
概括
机器学习 (ML) 通过分析复杂的患者数据来提高前列腺癌 (PCa) 存活率预测. 本综述探讨了在PCa护理中为量身定制的治疗和改进的精确医学的ML应用.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 前列腺癌 (PCa) 治疗决策在很大程度上依赖于准确的生存结果预测.
- 机器学习 (ML) 方法对于癌症预后越来越重要,因为复杂的,非线性PCa景观.
- ML集成促进了个性化治疗策略和精准医学,旨在改善PCa患者的生存率.
研究的目的:
- 审查ML在预测前列腺癌生存结果中的演变作用.
- 专注于ML在预测无生化复发,无割抵抗,无转移和整体存活中的应用.
- 确定未来的研究领域,以提高临床PCa预后和治疗优化中的ML实用性.
主要方法:
- 对使用ML用于PCa生存预测的最近研究的综述.
- 分析各种数据集,包括患者/瘤特征,放射数据和人口数据库.
- 专注于在前列腺癌中应用于特定生存终点的ML方法.
主要成果:
- 越来越多的研究使用ML来使用复杂数据集预测PCa生存率.
- 在预测各种生存结果方面,ML显示出前景,包括无复发和整体生存.
- ML的应用正在扩展到PCa预后的不同方面.
结论:
- ML在预测前列腺癌生存结果方面发挥着至关重要的和不断扩大的作用.
- 需要进一步的研究来将ML的进步转化为用于PCa预后和治疗的临床可用的工具.
- 增强的ML实用性可以导致更精确的治疗优化和改善前列腺癌患者的生存率.
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