GOLDMARK：受控的成果关联诊断模型评估参考套件
GOLDMARK: Governed Outcome-Linked Diagnostic Model Assessment Reference Kit
AI总结：
GOLDMARK通过标准化框架和临床可操作的生物标记物标签，为计算病理学提供可重复的基准测试和方法比较，提升模型在不同数据集和模型间的可比性。
摘要：
计算生物标记物（CBs）是通过人工智能（AI）从苏木素-伊红（H&E）全切片图像（WSIs）中提取的组织病理学衍生模式，用于预测治疗反应或预后。最近，基于病理基础模型（PFMs）的切片级多实例学习（MIL）已成为CB开发的标准基线。尽管这些方法提高了预测性能，但计算病理学缺乏标准化的中间数据格式、溯源跟踪、检查点约定和可重复的评估指标，这些是临床级部署所必需的。我们引入GOLDMARK（https://artificialintelligencepathology.org），一个基于精选TCGA队列的标准化基准框架，该队列具有临床可操作的OncoKB等级1-3生物标记物标签。GOLDMARK发布结构化的中间表示，包括切片坐标图、来自标准PFMs的每张切片特征嵌入、质量控制元数据、预定义的患者级分割、训练好的切片级模型和评估输出。模型在TCGA上训练，并在独立的MSKCC队列上进行评估，采用互为测试的方法。在33种肿瘤-生物标记物任务中，平均AUROC为0.689（TCGA）和0.630（MSKCC）。限制在八个表现最好的任务中，平均AUROCs分别为0.831和0.801。这些任务对应于已建立的形态-基因组关联（例如，LGG IDH1、COAD MSI/BRAF、THCA BRAF/NRAS、BLCA FGFR3、UCEC PTEN）并且在跨站点表现上最稳定。标准编码器之间的差异相对任务特定的变异性较小。GOLDMARK建立了一个共享的实验基质，使计算病理学能够实现可重复的基准测试和不同数据集和模型之间的直接比较。
英文摘要：
Computational biomarkers (CBs) are histopathology-derived patterns extracted from hematoxylin-eosin (H&E) whole-slide images (WSIs) using artificial intelligence (AI) to predict therapeutic response or prognosis. Recently, slide-level multiple-instance learning (MIL) with pathology foundation models (PFMs) has become the standard baseline for CB development. While these methods have improved predictive performance, computational pathology lacks standardized intermediate data formats, provenance tracking, checkpointing conventions, and reproducible evaluation metrics required for clinical-grade deployment. We introduce GOLDMARK (https://artificialintelligencepathology.org), a standardized benchmarking framework built on a curated TCGA cohort with clinically actionable OncoKB level 1-3 biomarker labels. GOLDMARK releases structured intermediate representations, including tile coordinate maps, per-slide feature embeddings from canonical PFMs, quality-control metadata, predefined patient-level splits, trained slide-level models, and evaluation outputs. Models are trained on TCGA and evaluated on an independent MSKCC cohort with reciprocal testing. Across 33 tumor-biomarker tasks, mean AUROC was 0.689 (TCGA) and 0.630 (MSKCC). Restricting to the eight highest-performing tasks yielded mean AUROCs of 0.831 and 0.801, respectively. These tasks correspond to established morphologic-genomic associations (e.g., LGG IDH1, COAD MSI/BRAF, THCA BRAF/NRAS, BLCA FGFR3, UCEC PTEN) and showed the most stable cross-site performance. Differences between canonical encoders were modest relative to task-specific variability. GOLDMARK establishes a shared experimental substrate for computational pathology, enabling reproducible benchmarking and direct comparison of methods across datasets and models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
