在数字病理学中使用基于注意力的卷积神经网络增强乳腺癌病理完整反应的早期预测
Maria Colomba Comes1, Andrea Lupo1, Arianna Bozzi1
1Laboratorio di Biostatistica e Bioinformatica, IRCCS Istituto Tumori "Giovanni Paolo II," Bari, Italy.
Digital health
|February 2, 2026
概括
本研究引入了基于注意力的CNN管道,用于预测乳腺癌中使用全幻灯片图像 (WSI) 对新辅助化疗 (NAC) 的病理完整反应 (pCR). 该模型在不同的队列和放大值中显示出强大的性能,增强数字病理学以获得更好的临床实用性.
科学领域:
- 数字病理学数字病理学
- 计算病理学计算病理学
- 在瘤学瘤学.
背景情况:
- 对新辅助化疗 (NAC) 的病理完整反应 (pCR) 的准确预测对于乳腺癌治疗至关重要.
- 全幻灯片图像 (WSI) 分析提供了改进预测的潜力,但需要先进的计算方法.
- 在WSI分析中,解释性和特征选择仍然是临床应用的挑战.
研究的目的:
- 开发基于注意力的卷积神经网络 (CNN) 管道,用于早期预测乳腺癌中pCR到NAC的情况.
- 用注意力机制在WSI分析中增强特征选择和可解释性.
- 验证模型在独立队列和不同分辨率上的性能.
主要方法:
- 一个回顾性分析的384,076从122个WSIs跨三个队伍 (调查,验证,外部验证).
- 应用小批次C-模糊K-Means用于过非信息区域.
- 利用一个具有卷积块注意模块的CNN来优先考虑组织学特征和PCR预测的关键.
主要成果:
- 美国有线电视新闻管道在各个队列中取得了强的表现:调查队列 (AUC 81.4%),验证队列 (AUC 80.9%) 和外部验证队列 (AUC 76.2%).
- 该模型证明了对不同WSI分辨率 (20x与40x放大) 的稳定性.
- 报告了高精度,特异性和灵敏度,表明可靠的pCR预测.
结论:
- 开发的基于注意力的CNN管道显著改善了乳腺癌中PCR到NAC的早期预测.
- 这种创新方法通过提高预测准确性和可解释性来提高数字病理学的临床实用性.
- 该模型对分辨率变化的稳定性支持其广泛临床采用的潜力.
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