PO.BCS01.06 · 生物信息与计算
基于AI的胰腺导管腺癌组织分析：研究肿瘤免疫生态系统并识别新型分类标志
AI-powered analysis of pancreatic ductal adenocarcinoma tissues to study the tumor immune ecosystem and identify novel classifiers
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：数字病理学和人工智能（AI）正逐渐成为免疫肿瘤学中的强大工具，可增强诊断和预后工作流程。当前趋势是将AI应用于组织病理学图像，以提取超越人类视觉感知的相关特征。胰腺导管腺癌（PDAC）是全球致死率最高的癌症之一，缺乏有效的生物标志物来对患者进行分层并评估治疗反应等级，这可能产生重大影响。目的：本项目提出一种整合机器学习与深度学习方法的计算流程，用于表征PDAC肿瘤免疫生态系统并提取具有潜在临床相关性的特征。
方法：分析了来自53例PDAC患者的全切片图像，包括接受和未接受新辅助化疗（NAT）的患者。切片分别采用H&E染色、天狼星红染色（检测纤维化）以及CD68抗体（检测巨噬细胞）。在H&E切片上，采用在结直肠癌上预训练的深度学习组织分类器对肿瘤区和间质区面积及其空间熵进行量化。基于QuPath的像素分类器对CD68+区域进行分割，以计算巨噬细胞丰度及由Morisita指数衡量的空间聚集程度，并量化天狼星红+区域以评估纤维化和胶原成熟度。将组织指标与免疫指标进行相关分析，并利用基础模型CTransPath提取的切片级嵌入向量投影至UMAP，以探索聚类模式。
结果：深度学习组织分类器在PDAC切片上取得了0.79的整体F1分数。高巨噬细胞丰度和间质熵与更差的总生存期相关。将免疫特征与肿瘤或间质特征相整合可改善患者分层。接受NAT治疗的患者表现出纤维化增加和巨噬细胞浸润减少，且间质熵仅在该组中具有预测价值。此外，CD68和天狼星红染色切片的CTransPath衍生嵌入向量根据NAT方案进行聚类。
结论：这一AI驱动的流程能够对PDAC免疫微环境进行定量空间分析，揭示具有预后和预测价值的可解释特征。我们的研究结果凸显了计算病理学在衍生具有临床意义的生物标志物和支持治疗反应评估方面的潜力。
查看英文原文 English abstract
Background : Digital pathology and artificial intelligence (AI) are emerging as powerful tools in immuno-oncology, enabling enhanced diagnostic and prognostic workflows. The current trend regards the application of AI on histopathological images to extract relevant features beyond human visual perception. Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, for which lack of efficient biomarkers to stratify patients and grade the response to therapy may play a significant impact. Aim : This project presents a computational pipeline integrating machine and deep learning approaches to characterize the PDAC tumor immune ecosystem and extract features with potential clinical relevance.
Methods : Whole-slide images from 53 PDAC patients, including those treated and untreated with neoadjuvant chemotherapy (NAT), were analyzed. Slides were stained with H&E, picrosirius red to detect fibrosis, and a CD68 antibody to detect macrophages. On H&E slides, a deep learning tissue classifier pretrained on colorectal cancer quantified Tumor and Stroma areas and their Spatial Entropy. QuPath-based pixel classifiers segmented CD68+ regions to compute macrophage abundance and spatial aggregation measured by the Morisita Index, and quantified picrosirius red+ areas to assess fibrosis and collagen maturity. Tissue and immune metrics were correlated, and slide-level embeddings extracted with the foundation model CTransPath were projected onto UMAP to explore clustering patterns.
Results : The deep learning tissue classifier achieved a global F1-score of 0.79 on PDAC slides. High macrophage abundance and Stroma Entropy were associated with worse overall survival. Integrating immune with tumor or stromal features improved patient stratification. NAT-treated patients showed increased fibrosis and reduced macrophage infiltration, with Stroma Entropy being predictive only in this group. Moreover, CTransPath-derived embeddings of CD68 and picrosirius red-stained slides clustered according to NAT regimen.
Conclusions : This AI-driven pipeline enables quantitative spatial profiling of the PDAC immune microenvironment, revealing interpretable features with prognostic and predictive value. Our findings highlight the potential of computational pathology to derive clinically meaningful biomarkers and support therapy response evaluation.
利益披露 Disclosure
R. Polidori, None..
M. Viatore, None..
A. R. Putignano, None..
G. Donisi, None..
C. Giovanni, None..
A. Bonometti, None..
S. Uccella, None..
S. Bozzarelli, None..
M. Locati, None..
F. Marchesi, None.