Skip to main content

Spine-Mets-CT-SEG

The Cancer Imaging Archive

Spine-Mets-CT-SEG | Spine metastatic bone cancer: pre and post radiotherapy CT

DOI: 10.7937/kh36-ds04 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Bone Human 55 Demographic, Follow-Up, Classification, CT, SEG Metastatic disease, Bladder Cancer, Breast Cancer, Colon Cancer, Kidney Cancer, Lung Cancer, Prostate Cancer, Soft-tissue Sarcoma, Skin Cancer 20.4GB Clinical Public, Complete 2024/09/25

Summary

We provide an annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with a large range of primary cancers and corresponding bone metastatic lesions obtained for patients with metastatic spine disease. The subjects of the study planned for radiotherapy were simulated at the Radiation Oncology Department, Brigham and Women's Hospital, Boston, MA, using 1) Siemens SOMATOM Confidence (Siemens Healthcare GmbH, Erlangen, Germany) and 2) GE Lightspeed (General Electric Medical System, Waukesha, WI) CT scanner. Simulation scan parameters are detailed in Table 1.

 Table 1. CT image acquisition parameters.

Radiotherapy 

CT scanner

 

Siemens SOMATOM Confidence

GE Lightspeed

Protocol parameters

SBRT

All Others

SBRT

All Others

kVp 

120

120

120

120

mA 

Variable

Variable

300

300

FOV 

A*, B**

A*, B**

A*, B**

A*, B**

Slice Thickness 

0.5mm

1.5mm

1.25mm

1.25mm

In-Plane Pixel Size (mm)

0.31x0.31

0.31x0.31

0.31x0.31

0.31x0.31

Gantry rotation 

1s

1s

1s

1s

Gating 

None

None

None

None

Breath Hold 

None

None

None

None

      

*A: 16cm field of view, **B: Skin-to-Skin field of view 

The dataset includes: 

  • Vertebral metastatic bone lesion classifications performed by RNA (20 years experience in biomechanics and image analysis of pathologic spines) and DBH (49 years experience in evaluating clinical imaging of pathologic spines) using standard radiological criteria as defined by the SINS protocol (J Clin Oncol, Aug 1;29(22):3072-7,.2011. doi:10.1200/ JCO. 2010 .34.3897). Specifically, vertebrae were classified as either Normal, Osteolytic, Osteosclerotic or having mixed lesions. 
  • Manual segmentation labels for the vertebral levels was performed and verified by RNA in 3Dslicer (https://www.slicer.org/
  • Deep-learning spinal segmentation model: Our automated segmentation model uses nnU-Net, a freely available open-source framework for deep learning in healthcare imaging, and is made publicly available. 
  • Accompanying clinical data for each patient in the dataset includes demographics, primary cancer type lesion classification and vertebral involvement.

Data Access

Version 1: Updated 2024/09/25

Title Data Type Format Access Points Subjects Studies Series Images License
Images CT, SEG DICOM
Download requires NBIA Data Retriever
55 55 110 35,582 CC BY 4.0
Spine Lesion Classifications and Demographics Demographic, Follow-Up, Classification XLSX 55 CC BY 4.0
Related Datasets
No related Analysis Results found: Submit your proposal! No related Collections found
Legend: Analysis Results| Collections

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Pieper, S., Haouchine, N., Hackney, D.B., Wells, W.M. Sanhinova, M., Balboni, T., Spektor, A., Huynh, M., Tanguturi, S., Kim, E., Guenette, J.P., Kozono, D.E., Czajkowski, B., Caplan, S., Doyle, P., Kang, H., Alkalay, R.N. (2024) Spine metastatic bone cancer: pre and post radiotherapy CT (Spine-Mets-CT-SEG) [Dataset] (Version 1). The Cancer Imaging Archive. https://doi.org/10.7937/kh36-ds04

Acknowledgements

This data set is supported by funding from 

  • NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES , grant # 3R01AR075964-03S1
  • NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES, grant # R01AR075964

 

Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 from NCI.

The SAIC Metadata Content Development Team identified existing or created new caDSR Common Data elements (CDEs) to describe the harmonized components of this dataset.  The Metadata Content Development Team is supported by CBIIT under Task Order 140D0421F0008 from NCI.

 

Related Publications

Publications by the Dataset Authors

The authors recommended this paper as the best source of additional information about this dataset:

The Collection authors suggest the below will give context to this dataset:

  • Fourney, D. R., Frangou, E. M., et al. (2011). Spinal Instability Neoplastic Score: An Analysis of Reliability and Validity From the Spine Oncology Study Group. In Journal of Clinical Oncology (Vol. 29, Issue 22, pp. 3072–3077). American Society of Clinical Oncology (ASCO). https://doi.org/10.1200/jco.2010.34.3897

 

Research Community Publications

TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Additional Publications Related to this Work

The Collection authors suggest the below will give context to this dataset:

  • Fourney, D. R., Frangou, E. M., et al. (2011). Spinal Instability Neoplastic Score: An Analysis of Reliability and Validity From the Spine Oncology Study Group. In Journal of Clinical Oncology (Vol. 29, Issue 22, pp. 3072–3077). American Society of Clinical Oncology (ASCO). https://doi.org/10.1200/jco.2010.34.3897

 

Other Publications Using this Data

TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.