一个基于机器学习的分析,用于定义癌脏活检的最佳脏活检
F Belladelli1, F De Cobelli2, C Piccolo1
1URI - Urological Research Institute, Department of Urology, Division of Experimental Oncology, IRCCS San Raffaele Hospital, Milan, Italy; Department of Urology, IRCCS San Raffaele Hospital, Milan, Italy.
Urologic oncology
|November 8, 2024
概括
瘤活检 (RTB) 可以准确诊断癌,与外科病理相比,差异很小. 机器学习通过指导精确癌症表征所需的组织核心数量来优化RTB.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 在瘤学瘤学.
- 病理学 病理学 病理学
背景情况:
- 瘤活检 (RTB) 有助于癌治疗计划.
- 对于组织学和分级的RTB准确性的局限性限制了其使用.
- 关于RTB和外科病理学一致性的数据很少.
研究的目的:
- 开发一种用于优化RTB技术的机器学习算法.
- 评估RTB和外科病理之间的一致率.
主要方法:
- 一个机器学习模型 (K-Nearest Neighbors) 分析了RTB方法,核数 (NoC) 和组织长度 (LoC).
- 基于这些参数来评估诊断结果.
- 将RTB结果与最终的外科病理报告进行了比较.
主要成果:
- 197名患者接受了RTB,其中89.8%的组织学信息和44.7%的分级信息.
- RTB和外科病理之间的差异率很低:组织学为3.6%,分级为5.0%.
- 机器学习确定了最佳策略:至少2个核心,总组织0.8厘米,或至少3个核心,没有最小长度.
结论:
- 脏瘤活检显示,与最终的手术病理学差异很小.
- 一个优化的RTB策略涉及至少2个核心和0.8厘米的组织,或3个核心没有最小长度.
- 机器学习提高了癌诊断和治疗的RTB准确性.
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