LUCQUE：魁北克肺癌筛查数据集，支持可靠的 AI 评测
LUCQUE: A lung cancer screening dataset from Québec for reliable AI evaluation
搞肺癌筛查 AI 的朋友可以看看：魁北克团队放出了 1,678 例 LDCT 扫描加专家标注的公开数据集，标注量比现有公开数据集都大。亮点是 model-to-data 访问机制，数据不出门，代码上门跑，做模型对比评测方便多了。
加拿大团队发布 LUCQUE，一个基于魁北克低剂量 CT（LDCT）肺癌筛查项目构建的大规模标注数据集，目前包含 1,678 例基线扫描，配套 voxel 级专家肺结节与囊肿分割，以及基于 Lung-RADS 的病灶特征标注。
数据托管在 PARADIM 平台上，该平台负责 DICOM 数据去标识化、标注、长期存储与分析，共标注 1,776 个肺结节和 60 个囊肿，结节体积中位数为 89 mm³。团队称其为目前可获取的最大标注真实世界肺癌筛查数据集，此前该领域评测多依赖私有数据或碎片化公开数据，临床信息和专家标注不一致，影响可复现性。
访问采用 model-to-data 模式：用户代码在平台上执行，数据不出本地，从而在保护隐私的同时支持可复现的独立评测。对开发肺癌筛查 AI 工具的团队来说，这意味着有了统一基准来比较不同模型的表现。
LUCQUE: A lung cancer screening dataset from Québec for reliable AI evaluation
BACKGROUND: AI-based tools are expected to play a key role in lung cancer screening (LCS). The evaluation of these tools often relied on private datasets or fragmented public datasets with inconsistent availability of clinical data and expert nodule annotations. This has hindered reproducibility, comparability, and independent assessment of AI tools. To address these gaps, we present LUCQUE, a novel, large, comprehensively annotated dataset derived from LDCT lung cancer screening in Québec, which is accessible for reliable AI evaluation. METHODS: LUCQUE was curated and is hosted on PARADIM, a digital infrastructure supporting DICOM data de-identification, annotation, long-term storage and analysis. LDCT scans and clinical information were combined to a curated collection and complemented by voxel-level, expert-derived pulmonary nodule and cyst segmentations, and Lung-RADS-based lesion characteristics. Key dataset characteristics and statistics were reported, including participant demographics, imaging parameters, radiological findings, and segmented lesion characteristics. Dataset analyses were conducted according to PARADIM's model-to-data approach, in which user code is executed on the platform such that data remains on premise. RESULTS: LUCQUE currently comprises 1,678 LDCT baseline studies from 1,678 participants. Most studies were classified as Lung-RADS 2. A total of 1,776 pulmonary nodules and 60 cysts were annotated, with a median (interquartile range) volume of $89\,(56,\,159)\,\mathrm{mm}^3$ and $1,384\,(589,\,3793)\,\mathrm{mm}^3$ respectively. CONCLUSIONS: Compared to publicly available LCS datasets, LUCQUE currently represents the largest accessible annotated real-world LCS dataset. Access to LUCQUE is provided via a model-to-data approach, enabling privacy-preserving, reproducible, and independent real-world evaluation of AI tools in LCS.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：LUCQUE、肺癌筛查、医学影像、数据集，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Johanna Topalis, Yannick Lemaréchal, Gabriel Couture, Hanife Ersay, Loran Nas, Balthasar Schachtner, Bastian Oliver Sabel, Michael Ingrisch, Philippe Després, Katharina Jeblick），经 AITOP 多维评分管线结构化提炼。