从使用半监督深度学习的多模式临床和图像数据来预测癌症免疫治疗反应
Xi Wang1, Yuming Jiang2, Hao Chen3
1Department of Radiation Oncology, Stanford University School of Medicine, Stanford 94305, CA, USA; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; Zhejiang Lab, Hangzhou, China.
概括
一个新的深度学习模型使用临床和CT成像数据预测胃癌患者的免疫治疗反应. 这种方法对识别可能从治疗中受益的患者充满希望,改善了个性化医疗策略.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 免疫疗法是癌症的关键治疗方法,但由于缺乏可靠的生物标志物,预测患者的反应仍然具有挑战性.
- 深度学习已经推进了癌症检测,但在预测治疗反应方面取得了有限的成功.
研究的目的:
- 使用常规可用的临床和计算机断层扫描 (CT) 影像数据,预测胃癌患者的免疫疗法反应.
- 开发一种多模式的深度学习放射学方法,以提高预测准确度.
主要方法:
- 使用临床数据和来自168名接受免疫治疗的晚期胃癌患者的CT图像开发了一种多模式的深度学习放射学模型.
- 一个半监督框架使用了2,029名患者 (非免疫治疗) 的额外数据集来学习内在成像表型,解决了小型训练数据的局限性.
- 模型的性能在两个独立的队列中得到验证,其中包括81名接受免疫治疗的患者.
主要成果:
- 深度学习模型在内部验证队列中实现了接收器操作特征曲线 (AUC) 下的面积为0.791,在外部验证队列中为0.812.
- 将PD-L1表达数据与模型集成,进一步提高了绝对值的预测准确性4-7%.
结论:
- 深度学习模型在使用标准临床和成像数据预测免疫疗法反应方面表现出有希望的表现.
- 多模式方法具有适应性,可以通过结合额外的相关数据来改进免疫治疗反应预测来增强.
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