自监督模型跨域适配，用活检样本精准预测胸膜瘤分型与生存期
Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies
- 在切除标本上训练的自监督模型直接用于小活检样本
- 可准确预测患者生存期并分类肿瘤亚型
- 为临床真实场景下的病理诊断提供可行AI工具
胸膜瘤的精确分型和预后预测对指导治疗和患者管理至关重要。目前多数计算病理模型基于大型切除标本的组织图像训练，限制了其在以小活检为主的临床实际场景中的应用。本文表明，一种在切除标本上训练的自监督编码器可有效应用于活检样本，捕捉有意义的形态学模式。利用这些模式，模型能够预测患者生存期并分类肿瘤亚型。该方法展示了人工智能驱动工具在胸膜瘤诊断与治疗规划中的潜力。
原文摘要 · Abstract (English)
Accurate subtype classification and outcome prediction in mesothelioma are essential for guiding therapy and patient care. Most computational pathology models are trained on large tissue images from resection specimens, limiting their use in real-world settings where small biopsies are common. We show that a self-supervised encoder trained on resection tissue can be applied to biopsy material, capturing meaningful morphological patterns. Using these patterns, the model can predict patient survival and classify tumor subtypes. This approach demonstrates the potential of AI-driven tools to support diagnosis and treatment planning in mesothelioma.
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