PathBench：面向精准肿瘤学的病理学基础模型综合比较基准
PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology
AI总结：
PathBench提出首个综合病理学基础模型基准，基于多中心大规模数据覆盖诊断至预后全谱任务，严格防泄漏并自动化评估，发现Virchow2和H-Optimus-1表现最佳。
摘要：
病理学基础模型的出现彻底改变了计算病理学领域，使得高精度、泛化能力强的全切片图像分析成为可能，从而改善了癌症诊断和预后评估。尽管这些模型在癌症诊断和预后方面展现出巨大潜力，但其临床转化面临关键挑战，包括不同癌症类型间最优模型的差异性、评估中潜在的数据泄漏以及缺乏标准化基准。没有严格、无偏的评估，即使最先进的基础模型也可能仅限于研究环境，从而延迟其挽救生命的应用。现有的基准测试工作仍受限于癌症类型覆盖狭窄、预训练数据可能重叠或任务覆盖不完整等问题。我们提出了PathBench，这是首个通过以下方式解决这些差距的综合基准：涵盖常见癌症的多中心院内数据集并严格防止泄漏，评估从诊断到预后的完整临床谱系，以及用于持续模型评估的自动化排行榜系统。我们的框架整合了大规模数据，能够客观比较基础模型，同时反映真实世界的临床复杂性。所有评估数据均来自私人医疗提供者，严格排除任何预训练使用以避免数据泄漏风险。我们收集了来自10家医院8549名患者的15888张全切片图像，涵盖超过64项诊断和预后任务。目前，我们对19个基础模型的评估显示，Virchow2和H-Optimus-1是整体上最有效的模型。这项工作为研究人员提供了稳健的模型开发平台，并为临床医生提供了关于基础模型在不同临床场景中性能的可操作见解，最终加速这些变革性技术向常规病理实践的转化。
英文摘要：
The emergence of pathology foundation models has revolutionized computational histopathology, enabling highly accurate, generalized whole-slide image analysis for improved cancer diagnosis, and prognosis assessment. While these models show remarkable potential across cancer diagnostics and prognostics, their clinical translation faces critical challenges including variability in optimal model across cancer types, potential data leakage in evaluation, and lack of standardized benchmarks. Without rigorous, unbiased evaluation, even the most advanced PFMs risk remaining confined to research settings, delaying their life-saving applications. Existing benchmarking efforts remain limited by narrow cancer-type focus, potential pretraining data overlaps, or incomplete task coverage. We present PathBench, the first comprehensive benchmark addressing these gaps through: multi-center in-hourse datasets spanning common cancers with rigorous leakage prevention, evaluation across the full clinical spectrum from diagnosis to prognosis, and an automated leaderboard system for continuous model assessment. Our framework incorporates large-scale data, enabling objective comparison of PFMs while reflecting real-world clinical complexity. All evaluation data comes from private medical providers, with strict exclusion of any pretraining usage to avoid data leakage risks. We have collected 15,888 WSIs from 8,549 patients across 10 hospitals, encompassing over 64 diagnosis and prognosis tasks. Currently, our evaluation of 19 PFMs shows that Virchow2 and H-Optimus-1 are the most effective models overall. This work provides researchers with a robust platform for model development and offers clinicians actionable insights into PFM performance across diverse clinical scenarios, ultimately accelerating the translation of these transformative technologies into routine pathology practice.
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