Recent deep learning frameworks in histopathology, particularly multiple instance learning (MIL) combined with pathology foundational models (PFMs), have shown strong performance. However, PFMs exhibit limitations on certain cancer or specimen types due to domain shifts - these cancer types were rarely used for pretraining or specimens contain tissue-based artifacts rarely seen within the pretraining population. Such is the case for transurethral resection of bladder tumor (TURBT), which are essential for diagnosing muscle-invasive bladder cancer (MIBC), but contain fragmented tissue chips and electrocautery artifacts and were not widely used in publicly available PFMs. To address this, we propose a simple yet effective domain-adaptive self-supervised adaptor (DA-SSL) that realigns pretrained PFM features to the TURBT domain without fine-tuning the foundational model itself. We pilot this framework for predicting treatment response in TURBT, where histomorphological features are currently underutilized and identifying patients who will benefit from neoadjuvant chemotherapy (NAC) is challenging. In our multi-center study, DA-SSL achieved an AUC of 0.77+/-0.04 in five-fold cross-validation and an external test accuracy of 0.84, sensitivity of 0.71, and specificity of 0.91 using majority voting. Our results demonstrate that lightweight domain adaptation with self-supervision can effectively enhance PFM-based MIL pipelines for clinically challenging histopathology tasks. Code is Available at https://github.com/zhanghaoyue/DA_SSL_TURBT.

翻译：近期组织病理学中的深度学习框架，特别是多实例学习（MIL）与病理学基础模型（PFMs）的结合，已展现出强大的性能。然而，由于领域偏移，PFMs在某些癌症或标本类型上存在局限性——这些癌症类型在预训练中很少使用，或者标本包含预训练数据集中罕见的组织相关伪影。经尿道膀胱肿瘤切除术（TURBT）标本即属此类情况，这些标本对于诊断肌层浸润性膀胱癌（MIBC）至关重要，但包含碎片化的组织芯片和电灼伪影，且未在公开可用的PFMs中被广泛使用。为解决此问题，我们提出了一种简单而有效的领域自适应自监督适配器（DA-SSL），该适配器可将预训练的PFM特征重新对齐至TURBT领域，而无需微调基础模型本身。我们率先将该框架应用于TURBT标本的治疗反应预测，其中组织形态学特征目前尚未被充分利用，且识别可能受益于新辅助化疗（NAC）的患者具有挑战性。在我们的多中心研究中，DA-SSL在五折交叉验证中实现了0.77+/-0.04的AUC，并在外部测试中通过多数投票获得了0.84的准确率、0.71的敏感性和0.91的特异性。我们的结果表明，结合自监督的轻量级领域适应方法能有效增强基于PFM的MIL流程，以应对临床中具有挑战性的组织病理学任务。代码发布于 https://github.com/zhanghaoyue/DA_SSL_TURBT。