具有领域适应MAE的临床注射变换器用于系统性红斑狼疮肾炎的预后预测
Clinical-Injection Transformer with Domain-Adapted MAE for Lupus Nephritis Prognosis Prediction
AI总结：
本研究提出了一种多模态计算病理学框架，利用临床数据和常规染色活检，通过临床注射变换器和领域适应MAE实现儿童系统性红斑狼疮肾炎预后预测的高准确率。
摘要：
系统性红斑狼疮肾炎（LN）是系统性红斑狼疮的严重并发症，影响儿童患者，其严重程度和肾功能恶化的程度显著高于成人。尽管有迫切的临床需求，但预测儿童LN预后在计算病理学中仍未被探索。此外，现有的基于组织病理学的LN方法依赖于多个昂贵的染色协议，并未能整合互补的临床数据。为了解决这些差距，我们提出了第一个多模态计算病理学框架，用于预测三种治疗反应（完全缓解、部分反应和无反应）在儿童LN中的三类治疗反应预测，仅使用常规PAS染色的活检和结构化临床数据。我们的框架引入了两种关键方法学创新。首先，临床注射变换器（CIT）将临床特征作为条件令牌嵌入到补丁级自注意力中，促进在统一的注意力空间内的隐含和双向跨模态交互。其次，我们设计了一种解耦的表示-知识适应策略，使用领域适应的掩码自动编码器（MAE）。该策略明确将自监督形态学特征学习与病理学知识提取分开。此外，我们引入了多粒度形态学类型注入机制，以连接提炼的分类知识与下游的预后预测，在实例和患者层面均实现。在71名儿童LN患者的队列上进行评估，我们的方法实现了三类准确率为90.1%和AUC为89.4%，证明了其作为高准确性和成本效益的预后工具的潜力。
英文摘要：
Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus that affects pediatric patients with significantly greater severity and worse renal outcomes compared to adults. Despite the urgent clinical need, predicting pediatric LN prognosis remains unexplored in computational pathology. Furthermore, the only existing histopathology-based approach for LN relies on multiple costly staining protocols and fails to integrate complementary clinical data. To address these gaps, we propose the first multimodal computational pathology framework for three-class treatment response prediction (complete remission, partial response, and no response) in pediatric LN, utilizing only routine PAS-stained biopsies and structured clinical data. Our framework introduces two key methodological innovations. First, a Clinical-Injection Transformer (CIT) embeds clinical features as condition tokens into patch-level self-attention, facilitating implicit and bidirectional cross-modal interactions within a unified attention space. Second, we design a decoupled representation-knowledge adaptation strategy using a domain-adapted Masked Autoencoder (MAE). This strategy explicitly separates self-supervised morphological feature learning from pathological knowledge extraction. Additionally, we introduce a multi-granularity morphological type injection mechanism to bridge distilled classification knowledge with downstream prognostic predictions at both the instance and patient levels. Evaluated on a cohort of 71 pediatric LN patients with KDIGO-standardized labels, our method achieves a three-class accuracy of 90.1% and an AUC of 89.4%, demonstrating its potential as a highly accurate and cost-effective prognostic tool.
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