使用多相增强型CT图像预测晚期胆囊癌患者对全身治疗的治疗反应
Ji Wu1, Zhigang Zheng2, Jian Li3
1Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
European radiology
|May 9, 2025
概括
一个新的深度学习放射性临床模型准确地预测了接受全身治疗的晚期胆囊癌患者的治疗反应. 该工具有助于识别可能受益的患者,改善临床决策和资源分配.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 准确预测治疗反应对于指导晚期胆囊癌 (GBC) 系统治疗至关重要.
- 目前用于识别从全身疗法中受益的患者的临床工具有限.
研究的目的:
- 开发和外部验证一种模型,用于预测高级GBC系统治疗的治疗反应.
- 评估预测模型的临床效用和生存影响.
主要方法:
- 从多相增强CT图像开发了一个深度学习 (DL) 放射学签名.
- 通过将DL签名与显著的临床因素 (例如瘤大小) 结合起来,构建了一个DL放射性临床模型 (DLRSC).
- 使用曲线下的面积 (AUC) 和生存分析 (PFS,OS) 评估模型性能.
主要成果:
- DLRSC模型表现出强大的预测性能,AUC为0.86 (内部) 和0.84 (外部).
- 瘤大小被确定为治疗疗效的显著临床预测指标.
- 根据DLRSC模型分为低得分组的患者表现出更差的无进展和整体存活率.
结论:
- DLRSC模型有效地预测了接受全身治疗的高级GBC患者的治疗反应.
- 该模型为治疗选择提供了有价值的临床见解,可能减少不良事件并优化医疗保健资源配置.
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