通过融合组织空间关系的图神经网络，提升三阴性乳腺癌新辅助化疗响应预测准确率。
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer
- 构建肿瘤微环境空间图，结合纹理与社交网络特征表示组织块
- 在105例患者数据上达到90%准确率，灵敏度达96%
- 适合临床精准医疗、病理图像分析与癌症个性化治疗研究者
三阴性乳腺癌（TNBC）新辅助化疗（NAC）响应预测面临挑战，需理解肿瘤微环境（TME）内复杂的组织学交互。数字全切片图像（WSIs）虽包含详细组织信息，但其吉比特级尺寸要求基于多实例学习的计算方法，传统方法通常仅分析孤立的小图像块，缺乏空间上下文。为解决此问题并融入TME的空间组织学交互，我们提出一种组织学上下文感知的Transformer图卷积网络（NACNet）。该方法从WSIs中识别单个图像块的组织病理学标签，构建空间TME图，每个节点用组织纹理与社交网络分析特征表示，并利用增强图同构网络层的Transformer图卷积模型预测NAC响应。我们在一个包含105例TNBC患者的队列上评估该方法，与多种先进机器学习和深度学习模型对比。NACNet在八折交叉验证下取得90.0%准确率、96.0%敏感度、88.0%特异度和0.82 AUC，优于基线模型。实验结果表明，NACNet在分层TNBC患者NAC响应方面具有强潜力，有助于避免过度治疗、改善生活质量、降低治疗成本并提升临床结局，是实现个性化乳腺癌治疗的重要进展。
原文摘要 · Abstract (English)
Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga-pixel size necessitates computational methods based on multiple instance learning, which typically analyze small, isolated image tiles without the spatial context of the TME. To address this limitation and incorporate TME spatial histology interactions in predicting NAC response for TNBC patients, we developed a histology context-aware transformer graph convolution network (NACNet). Our deep learning method identifies the histopathological labels on individual image tiles from WSIs, constructs a spatial TME graph, and represents each node with features derived from tissue texture and social network analysis. It predicts NAC response using a transformer graph convolution network model enhanced with graph isomorphism network layers. We evaluate our method with WSIs of a cohort of TNBC patient (N=105) and compared its performance with multiple state-of-the-art machine learning and deep learning models, including both graph and non-graph approaches. Our NACNet achieves 90.0% accuracy, 96.0% sensitivity, 88.0% specificity, and an AUC of 0.82, through eight-fold cross-validation, outperforming baseline models. These comprehensive experimental results suggest that NACNet holds strong potential for stratifying TNBC patients by NAC response, thereby helping to prevent overtreatment, improve patient quality of life, reduce treatment cost, and enhance clinical outcomes, marking an important advancement toward personalized breast cancer treatment.
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