多模态时空深度学习框架,预测乳腺癌对新辅助系统治疗的反应
Monu Verma1, Leila Abdelrahman2, Fernando Collado-Mesa3
1Department of Electrical and Computer Engineering, University of Miami, Miami, FL 33146, USA.
Diagnostics (Basel, Switzerland)
|July 14, 2023
概括
在接受新辅助全身疗法 (NST) 的乳腺癌患者中,准确预测病理完整反应 (pCR) 是至关重要的. 一个新的深度学习框架,深度NST,有效地预测pCR早期使用多式联络数据,改善生存结果.
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:
- 新辅助全身疗法 (NST) 是一种关键的乳腺癌治疗.
- 病理完整反应 (pCR) 是NST的关键疗效指标.
- 早期预测pCR对于治疗优化和患者生存至关重要.
研究的目的:
- 开发一种自动化框架,用于在接受NST的乳腺癌患者中早期预测pCR.
- 通过准确识别响应者来提高治疗疗效和降低毒性.
主要方法:
- 提出了一个端到端的多式联络空间时间深度学习框架 (deep-NST).
- 集成成像,分子和人口统计数据用于预测.
- 在ISPY-1数据集上验证了框架.
主要成果:
- 深度NST框架在预测pCR方面表现优异.
- 实现了高精度和曲线下的面积 (AUC).
- 废弃性研究证实了各个框架组件的有效性.
结论:
- 深度NST框架为NST中早期PCR预测提供了一种精确有效的方法.
- 这种工具可以帮助个性化治疗决策,并改善乳腺癌患者的治疗结果.
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