Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.
组织病理学全切片图像（WSI）通常被视为癌症诊断和预后的金标准。基于WSI的生存预测近年来受到广泛关注。然而，由于预测患者预后存在固有困难，且需从具有高度复杂千兆像素的WSI中有效提取与生存相关的信息表征，这仍是该领域的核心挑战。本研究提出了一种全自动细胞级双全局融合流程用于生存预测。具体而言，该方法首先描述WSI中不同细胞群的组成，随后生成降维的WSI嵌入图谱，从而实现对肿瘤微环境的高效分析。此外，我们引入了一种新颖的双全局融合网络，以整合细胞分布的全局特征与局部区域间特征，实现不同类型和位置细胞的充分融合。我们进一步利用癌症基因组图谱肺腺癌数据集验证了该流程的有效性。在五折交叉验证设置下，我们的模型取得了0.675（±0.05）的C指数，性能优于同类方法。此外，我们对嵌入图谱特征与生存概率进行了深入分析。这些实验结果证明了所提出流程在肺腺癌及其他恶性肿瘤WSI应用中的潜力。
Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis
肿瘤(癌症)患者之家