EPISODE · Jul 14, 2026 · 20 MIN
1278-深度学习与影像组学结合的肺腺癌分级模型
from 聊聊Sci
这项研究开发并验证了一种可解释的集成学习模型，旨在提升浸润性肺腺癌（IPA）病理分级的准确性。该研究整合了计算机断层扫描（CT）影像学与全切片图像（WSI）病理组学的多尺度特征，其诊断效能达到或超过了资深病理学家的水平。通过对配对的影像、病理及RNA测序数据进行多组学分析，研究团队识别出23个关键基因及数百条与肿瘤分级相关的生物通路。研究进一步揭示了影像学表型背后的生物学基础，涵盖信号传导、细胞增殖和代谢重塑等关键生理过程。这种融合宏观与微观特征的AI方法，不仅缓解了病理诊断的主观性差异，还为肺癌的个体化精准治疗提供了稳健的生物学支撑。References:Yang Z, Li F, Han Q, et al. Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images[J]. NPJ Precision Oncology, 2025.前往小宇宙评论区与主播互动
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1278-深度学习与影像组学结合的肺腺癌分级模型
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