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.

翻译：间皮瘤的准确亚型分类与预后预测对于指导治疗和患者护理至关重要。大多数计算病理学模型基于切除标本的大组织图像进行训练，这限制了其在常见小活检样本的真实场景中的应用。本研究证明，在切除组织上训练的自监督编码器可应用于活检材料，并捕获有意义的形态学模式。利用这些模式，该模型能够预测患者生存期并对肿瘤亚型进行分类。该方法展示了人工智能驱动工具在支持间皮瘤诊断与治疗规划方面的潜力。