一种轻量级多癌症肿瘤定位框架用于可部署的数字病理学
A Lightweight Multi-Cancer Tumor Localization Framework for Deployable Digital Pathology
AI总结：
本研究提出了一种轻量级多癌症肿瘤定位框架，通过跨癌症平衡训练实现高泛化能力，适用于数字病理学的可部署应用。
摘要：
准确从经苏木素和伊红染色的整张滑动图像中定位肿瘤区域对于包括空间分析、分子谱分析和组织架构研究在内的转化研究至关重要。然而，基于深度学习的肿瘤检测在特定癌症中训练后，当应用于不同肿瘤类型时可能表现出降低的鲁棒性。我们研究了在适度规模下跨癌症平衡训练是否能实现高性能并泛化到未见的肿瘤类型。使用DenseNet169的迁移学习，一个多癌症肿瘤定位模型（MuCTaL）在四种癌症（黑色素瘤、肝细胞癌、结直肠癌和非小细胞肺癌）的79984个非重叠切片上进行训练。该模型在四种训练癌症的验证数据上实现了0.97的切片级ROC-AUC，在独立的胰腺导管腺癌队列上达到了0.71。构建了可扩展的推理工作流以生成与现有数字病理学工具兼容的空间肿瘤概率热图。代码和模型可在https://github.com/AivaraX-AI/MuCTaL上公开获取。
英文摘要：
Accurate localization of tumor regions from hematoxylin and eosin-stained whole-slide images is fundamental for translational research including spatial analysis, molecular profiling, and tissue architecture investigation. However, deep learning-based tumor detection trained within specific cancers may exhibit reduced robustness when applied across different tumor types. We investigated whether balanced training across cancers at modest scale can achieve high performance and generalize to unseen tumor types. A multi-cancer tumor localization model (MuCTaL) was trained on 79,984 non-overlapping tiles from four cancers (melanoma, hepatocellular carcinoma, colorectal cancer, and non-small cell lung cancer) using transfer learning with DenseNet169. The model achieved a tile-level ROC-AUC of 0.97 in validation data from the four training cancers, and 0.71 on an independent pancreatic ductal adenocarcinoma cohort. A scalable inference workflow was built to generate spatial tumor probability heatmaps compatible with existing digital pathology tools. Code and models are publicly available at https://github.com/AivaraX-AI/MuCTaL.
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