The LUNOVO26 Challenge: Debug Phase Dataset

The LUNOVO26 (Lung Nodule Volumetry 2026) challenge is a public benchmarking challenge organized by EIBALL (European Imaging Biomarkers Alliance), QMIC (Quantitative Medical Imaging Coalition) and Radboud University Medical Center. LUNOVO26 is supported and co-funded by the SOLACE project. LUNOVO26 aims to provide an objective framework for the evaluation of AI algorithms for volumetric measurement of pulmonary nodules in lung cancer screening. LUNOVO26 has the following objectives: Provide an objective framework for transparent validation and comparison of the accuracy, variability and reproducibility of both academic and commercial AI algorithms for volumetric measurement of pulmonary nodules. Attract attention of the community to the task of volumetric measurement of pulmonary nodules, a crucial component for running an effective lung cancer screening program. Provide a good basis for reliable quality assurance and quality control of AI algorithms for volumetric measurement of pulmonary nodules. This dataset includes the data used for the Debug Phase of the LUNOVO26 challenge and is a derivative of the original LIDC-IDRI dataset which is publicly available and can be downloaded from the TCIA website. In this phase, the AI algorithms will be executed on 5 LIDC-IDRI cases to debug and test the implementation of their algorithms, and can be used for debugging the algorithm inference Dockers that are submitted to the GrandChallenge platform.. The original LIDC-IDRI dataset is provided under a CC-BY 3.0 license: Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C. , Qing, D. P. Y. , Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SXLUNOVO26（肺结节体积测量2026，Lung Nodule Volumetry 2026）挑战赛是由EIBALL（欧洲影像生物标志物联盟，European Imaging Biomarkers Alliance）、QMIC（定量医学影像联盟，Quantitative Medical Imaging Coalition）与拉德堡德大学医学中心联合举办的公开基准测试挑战赛。LUNOVO26得到SOLACE项目的支持与联合资助。 LUNOVO26旨在为评估肺癌筛查场景下肺结节体积测量的AI算法提供客观框架，其具体目标如下： 1. 构建客观评估框架，以透明化验证并对比学术与商用AI算法在肺结节体积测量任务中的准确性、变异性与可重复性； 2. 吸引学界关注肺结节体积测量任务——该任务是开展高效肺癌筛查项目的核心环节； 3. 为肺结节体积测量AI算法的可靠质量保证与质量控制提供坚实基础。 本数据集包含LUNOVO26挑战赛调试阶段所用数据，其衍生自公开可从TCIA（癌症影像档案，The Cancer Imaging Archive）网站下载的原始LIDC-IDRI数据集。在该调试阶段，参赛AI算法将在5例LIDC-IDRI病例上运行，以调试并测试算法实现效果，同时可用于调试提交至GrandChallenge平台的算法推理Docker容器。 原始LIDC-IDRI数据集采用CC-BY 3.0许可协议发布：Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C. , Qing, D. P. Y. , Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., Clarke, L. P. (2015). LIDC-IDRI数据集[数据集]. 癌症影像档案. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX