在小鼠瘤模型中的超声波弹性图像中预测化学免疫疗法的反应的卷积注意力模型
Chrysovalantis Voutouri1, Demetris Englezos2, Constantinos Zamboglou3,4,5
1Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus. cvouto01@ucy.ac.cy.
Communications medicine
|October 17, 2024
概括
预测癌症治疗反应是个性化医学的关键. 这项研究使用剪波弹性学 (SWE) 和深度学习来准确预测瘤对化疗免疫疗法的反应,帮助治疗决策.
科学领域:
- 在瘤学瘤学.
- 生物医学工程 生物医学工程
- 医疗成像医学成像
背景情况:
- 瘤异质性影响个性化癌症治疗的疗效.
- 带有密集和硬的筋膜的瘤构成治疗挑战.
- 机械疗法可以调节瘤硬度.
研究的目的:
- 使用非侵入性生物标志物预测瘤对化疗免疫疗法的反应.
- 探索瘤力学和医学成像在预测治疗结果方面的潜力.
- 评估一种深度学习模型,用于预测脱质瘤的治疗反应.
主要方法:
- 开发了一个使用卷积神经网络 (CNN) 和注意力模块的深度学习模型.
- 利用来自630个瘤的1365个样本的剪切波弹性图像 (SWE).
- 评估了模型预测瘤对治疗后机械疗法反应 (响应,稳定,不响应) 的能力.
主要成果:
- 证明将SWE衍生的生物标志物与自动瘤细分相结合,可以准确预测治疗结果.
- 展示了剪切波弹性学和深度学习在瘤检测和响应预测方面的潜力.
- 提供了强有力的证据,证明了开发的方法的有效性.
结论:
- 这种方法为个性化癌症治疗提供了非侵入性和可靠的预测.
- 通过预测患者对化疗免疫疗法的反应,增强治疗决策.
- 突出了瘤机制作为预测生物标志物的作用.
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