基于深度学习的病理图像分析的进步引发了对癌症预测的巨大见解。尽管如此，缺乏可解释性仍然是临床应用的重大障碍。我们建立了肝内胆管癌（iCCA）的综合预后神经网络，以对全幻灯片图像的结构和细粒度信息进行综合评估。然后，利用多模态数据，我们对模型进行了广泛的询问方法，以提取和可视化与临床结果和潜在分子改变最相关的形态特征。这些模型是针对我们中心的 373 名 iCCA 患者开发和优化的在内部 (n = 213) 和外部 (n = 168) 队列中表现出一致的准确性和稳健性。闭塞敏感性图显示三级淋巴结构的分布、浸润边缘的几何特征、肿瘤实质和间质的相对组成、坏死范围、播散灶的存在以及肿瘤邻近的微血管是影响预后的决定性架构特征。 CellProfiler 提取的可量化形态学向量表明，来自高危患者的肿瘤细胞核表现出明显更大的尺寸、更扭曲的形状，但核膜和纹理对比度不太明显。多组学数据（n = 187）进一步揭示了网络可能参与的关键分子改变留下的形态印记，包括糖酵解、缺氧、顶端连接、mTORC1信号传导和免疫浸润。我们提出了一个可解释的深度学习框架深入了解 iCCA 的生物学行为。网络感知到的大多数重要形态预测因子都是人类思维可以理解的。© 2024。作者。

The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application.We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations.The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration.We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.© 2024. The Author(s).