通过深度学习从常规组织病理学切片推断胰腺癌的临床相关分子亚型
Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning
AI总结：
PanSubNet通过深度学习从常规H&E染色切片预测胰腺癌分子亚型，提供可解释的临床应用工具。
摘要：
胰腺导管癌（PDAC）的分子分型分为基底样和经典亚型已确立了预后和预测价值。然而，其在临床实践中的应用受到成本、周转时间和组织要求的限制，从而限制了其在PDAC管理中的应用。我们介绍了PanSubNet，一个可解释的深度学习框架，该框架可直接从标准的H&E染色的石蜡组织切片（WSIs）预测与治疗相关的分子亚型。PanSubNet是在来自两个多机构队列（PANCAN，n=846；TCGA，n=209）的1055名患者数据上开发的，这些数据包含配对的组织学和RNA测序数据。真实标签是通过经过验证的Moffitt 50基因签名并经过GATA6表达优化后得到的。该模型采用双尺度架构，融合细胞水平的形态学和组织水平的结构，利用注意力机制进行多尺度表示学习，并具有透明的特征归因。在PANCAN内部验证中使用五折交叉验证，PanSubNet实现了平均AUC为88.5%，具有平衡的灵敏度和特异性。在不进行微调的独立TCGA队列上的外部验证显示了强大的泛化能力（AUC 84.0%）。PanSubNet在转移性疾病中保持并加强了比RNA测序标签更明确的预后分层。预测不确定性与中间转录状态相关，而非分类噪声。模型预测与已建立的转录组程序、分化标记和DNA损伤修复签名一致。通过使从常规H&E染色切片中快速、低成本地进行分子分层成为可能，PanSubNet提供了一种在临床中可部署且可解释的工具用于基因亚型分型。我们正在从两家机构收集数据以验证和评估实际性能，支持将其整合到数字病理学工作流程中，并推动PDAC的精准肿瘤学发展。
英文摘要：
Molecular subtyping of PDAC into basal-like and classical has established prognostic and predictive value. However, its use in clinical practice is limited by cost, turnaround time, and tissue requirements, thereby restricting its application in the management of PDAC. We introduce PanSubNet, an interpretable deep learning framework that predicts therapy-relevant molecular subtypes directly from standard H&E-stained WSIs. PanSubNet was developed using data from 1,055 patients across two multi-institutional cohorts (PANCAN, n=846; TCGA, n=209) with paired histology and RNA-seq data. Ground-truth labels were derived using the validated Moffitt 50-gene signature refined by GATA6 expression. The model employs dual-scale architecture that fuses cellular-level morphology with tissue-level architecture, leveraging attention mechanisms for multi-scale representation learning and transparent feature attribution. On internal validation within PANCAN using five-fold cross-validation, PanSubNet achieved mean AUC of 88.5% with balanced sensitivity and specificity. External validation on the independent TCGA cohort without fine-tuning demonstrated robust generalizability (AUC 84.0%). PanSubNet preserved and, in metastatic disease, strengthened prognostic stratification compared to RNA-seq based labels. Prediction uncertainty linked to intermediate transcriptional states, not classification noise. Model predictions are aligned with established transcriptomic programs, differentiation markers, and DNA damage repair signatures. By enabling rapid, cost-effective molecular stratification from routine H&E-stained slides, PanSubNet offers a clinically deployable and interpretable tool for genetic subtyping. We are gathering data from two institutions to validate and assess real-world performance, supporting integration into digital pathology workflows and advancing precision oncology for PDAC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
