RIDER Lung CT | Coffee-break lung CT collection with scan images reconstructed at multiple imaging parameters

DOI: 10.7937/k9/tcia.2015.u1x8a5nr | Data Citation Required | Image Collection

Summary

Quantitative imaging biomarkers (QIB) are increasingly used in clinical research to advance precision medicine approaches in oncology. Unlike biopsy-based biomarkers, QIBs are non-invasive and can estimate the spatial and temporal heterogeneity of total tumor burden. Computed tomography (CT) is a modality of choice for cancer diagnosis, prognosis, and response assessment due to its reliability and global accessibility. 

In recent years, despite overwhelmingly increased awareness of the reproducibility and robustness in quantitative imaging studies, lack of precious clinical image data limits our investigation and algorithm development. Here, we contribute to the cancer imaging community with our investigator-initiated, same-day repeat CT scan images of 32 non–small cell lung cancer (NSCLC) patients, along with radiologist’s annotated lesion contours as the reference standard. Each scan was reconstructed into 6 image settings using various combinations of three slice thicknesses (1.25 mm, 2.5 mm, 5 mm) and two reconstruction kernels (lung, standard; GE CT equipment), which spans a wide range of CT imaging reconstruction parameters commonly used in lung cancer clinical practice and clinical trials. One of the 6-settings, i.e., the setting of 1.25mm slice thickness and lung reconstruction (1.25L), was published as part of the Reference Image Database to Evaluate Therapy Response (RIDER) project in 2012. 

We believe that this entire dataset, comprising CT lung cancer images reconstructed on the same day at six different image settings, holds considerable value for advancing the development of robust Artificial Intelligence (AI) and machine learning (ML) methods. Additionally, it provides a valuable resource for comparing QIBs derived from a wide range of CT imaging parameter settings, for investigating data harmonization approaches, and for identifying specific CT imaging parameters most suitable for studying radiomics in lung cancer.

Data Access

Version 3: Updated 2024/06/25

Added new CT and SEG series for all patients.

Additional Resources for this Dataset

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.

• Imaging Data Commons (IDC) (Imaging Data)

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