基于临床患者和瘤特征的乳腺癌新辅助治疗结果预测:一个横截面研究
Eva Brenner1, Luka Bulić2, Marija Milković-Periša3
1School of Medicine, University of Zagreb, 10000 Zagreb, Croatia.
Current problems in cancer
|May 13, 2025
概括
这项研究确定了影响乳腺癌新辅助治疗结果的关键患者和瘤特征. 使用这些因素的预测模型实现了80%的准确性,帮助个性化治疗策略.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 乳腺癌是全球女性死亡的主要原因之一.
- 许多影响乳腺癌发展和预后的因素正在调查中.
- 新辅助疗法是乳腺癌的关键治疗方式.
研究的目的:
- 确定影响乳腺癌患者新辅助治疗结果的重要因素.
- 开发乳腺癌新辅助治疗反应的预测模型.
- 通过预测结果来增强个性化的治疗策略.
主要方法:
- 对2018-2022年患者数据的回顾性分析.
- 用RCB指数对患者/瘤特征进行统计关联分析 (斯皮尔曼,曼-惠特尼U,ANOVA,克鲁斯卡尔-瓦利斯).
- 开发一种使用显著特征的随机森林机器学习模型.
主要成果:
- 患者因素 (年龄,BMI,先前的恶性瘤) 和瘤特征 (焦点,等级,免疫类型,受体状态,Ki-67,淋巴血管入侵) 与RCB指数显著相关.
- 一个预测模型实现了80%的准确性和0.83的ROC-AUC.
- 鉴定的因素与现有的乳腺癌研究一致.
结论:
- 确定的患者和瘤特征对于预测新辅助疗法反应至关重要.
- 开发的预测模型显示了个性化乳腺癌治疗的前景.
- 需要对更大的数据集进行进一步的研究,以改进预测模型并改善治疗结果.
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