Clinical-Injection Transformer with Domain-Adapted MAE for Lupus Nephritis Prognosis Prediction

TL;DR

This paper presents a novel multimodal deep learning framework combining clinical data and routine biopsy images to predict treatment response in pediatric lupus nephritis, achieving high accuracy and cost-effectiveness.

Contribution

It introduces a Clinical-Injection Transformer with domain-adapted MAE for integrated analysis of clinical and histopathological data in pediatric LN prognosis prediction.

Findings

Achieved 90.1% three-class accuracy

Attained 89.4% AUC in prognosis prediction

Demonstrated effectiveness with routine PAS-stained biopsies

Abstract

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.