在前列腺癌患者中自动推瘤板的机器学习算法
Marcus Sondermann1, Hannah Glaser2, Anke Rentsch3
1Department of Urology University Hospital Carl Gustav Carus, TU Dresden Dresden Germany.
BJUI compass
|August 20, 2025
概括
机器学习模型显示出自动化多学科瘤委员会 (MTBs) 的前列腺癌治疗建议的潜力. 虽然对于局部疗法是准确的,但对于更广泛的临床用途需要进一步优化.
科学领域:
- 癌症学
- 医疗信息学
- 机器学习
背景情况:
- 多学科瘤委员会对于治疗前列腺癌至关重要,但需要大量的时间.
- 自动化MTB建议可以提高可访问性和效率.
研究的目的:
- 评估机器学习 (ML) 算法,以自动化前列腺癌患者的诊断和治疗建议.
- 评估决策树,随机森林和K-Nearest Neighbours (KNN) 算法在预测MTB决策中的性能.
主要方法:
- 使用了1929个MTB建议 (2020-2024) 的回顾性数据集.
- 三个ML算法被训练来预测PSMA-PET,常规成像,主动监测和局部治疗的建议.
- 使用准确性,精度,回忆和F1分数来评估模型的性能.
主要成果:
- 随机森林模型获得了最高的整体精度 (66.3%).
- 观察到局部治疗预测的高准确性 (F1分数: 0. 99).
- 由于类不平衡和指导方针的变化,对较少的建议 (PSMA- PET,主动监测) 的表现较低.
结论:
- 在前列腺癌中复制MTB决策模式.
- 目前的模型需要进一步优化用于临床应用,作为概念证明.
- 未来的研究应侧重于多机构数据,前性验证和适应不断变化的指导方针.
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