  Recently, intelligent analysis of lung nodules with the assistant of computer
aided detection (CAD) techniques can improve the accuracy rate of lung cancer
diagnosis. However, existing CAD systems and pulmonary datasets mainly focus on
Computed Tomography (CT) images from one single period, while ignoring the
cross spatio-temporal features associated with the progression of nodules
contained in imaging data from various captured periods of lung cancer. If the
evolution patterns of nodules across various periods in the patients' CT
sequences can be explored, it will play a crucial role in guiding the precise
screening identification of lung cancer. Therefore, a cross spatio-temporal
lung nodule dataset with pathological information for nodule identification and
diagnosis is constructed, which contains 328 CT sequences and 362 annotated
nodules from 109 patients. This comprehensive database is intended to drive
research in the field of CAD towards more practical and robust methods, and
also contribute to the further exploration of precision medicine related field.
To ensure patient confidentiality, we have removed sensitive information from
the dataset.
