Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Med Image Anal. 2024 Apr;93:103071. doi: 10.1016/j.media.2023.103071. Epub 2024 Jan 5.
Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%-4% and 7-8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models' performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations.
结直肠癌（CRC）是一个主要的全球健康问题，确定与 CRC 相关的分子途径、遗传亚型和突变对于精准医学至关重要。然而，传统的测量技术，如基因测序，既昂贵又耗时，而大多数为此任务提出的深度学习方法缺乏可解释性。本研究提供了一种新方法，通过整合细胞网络信息来增强用于分子途径和关键突变预测的最先进的深度学习方法。我们构建了以核为节点、核连接为网络边缘的细胞图，并利用社交网络分析（SNA）度量来提取抽象、可感知和可解释的特征，这些特征明确描述了图像中的细胞网络特征。我们的方法不依赖于精确的核分割或特征提取，计算效率高，易于扩展。在本研究中，我们利用 TCGA-CRC-DX 数据集，该数据集由 499 名患者和来自美国 36 个不同医疗中心的 502 个原发性结直肠肿瘤诊断幻灯片组成。通过在两个多实例学习框架中结合 SNA 特征和深度特征，我们展示了在染色体不稳定性（CIN）、高突变肿瘤（HM）、TP53 基因、BRAF 基因和微卫星不稳定性（MSI）状态预测任务中的性能提升（平均 AUROC 和 AUPRC 提高 2.4%-4%和 7-8.8%）。此外，我们的方法在外部 PAIP 数据集的 MSI 预测中取得了出色的表现（AUROC 为 99%，AUPRC 为 98%），证明了其泛化能力。我们的研究结果强调了 SNA 特征的辨别力以及它们如何有益于深度学习模型的性能，并深入了解细胞网络特征与分子途径和关键突变的相关性。
