基于多模式数据融合的老年无手术性食道状细胞癌的个性化治疗决策模型
Yong Huang1, Xiaoyu Huang2, Anling Wang3
1Department of Medical Oncology, The Second People's Hospital of Hefei, Hefei, China.
BMC medical informatics and decision making
|October 23, 2023
概括
机器学习模型预测了老年食道癌症患者的治疗结果. 这些模型有助于针对不可手术病例做出个性化治疗决策,改善预后预测.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 医疗信息学 医疗信息学
背景情况:
- 食道状细胞癌 (ESCC) 在老年,不可手术的患者中带来了挑战.
- 个性化治疗对于优化这一人口群体的结果至关重要.
研究的目的:
- 开发一款用于个性化治疗决策的机器学习模型.
- 预测不可手术的老年ESCC患者的治疗疗效和预后.
主要方法:
- 使用多任务学习模型与多模式数据 (CT图像,临床,血液测试).
- 为预测客观响应率 (ORR) 和无进展生存率 (PFS) 构建的诺米克图.
- 将患者分为多层次的风险组,并进行生存分析.
主要成果:
- 确定ORR和PFS的关键风险因素,包括组织学等级,T阶段,BMI和放射性特征.
- 诺米图集这些因素作为独立的预测因素.
- 在低风险和高风险组之间观察到PFS的显著差异.
结论:
- 在不可手术的老年ESCC患者中开发了ORR和PFS的预测模型.
- 诺米图表促进了个性化的治疗策略,并指导了最佳的护理.
- 模型为治疗疗效和预后预测提供了宝贵的见解.
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