Skip to main content

TCGA-OV-RADIOGENOMICS

The Cancer Imaging Archive

TCGA-OV-Radiogenomics | Imaging Features, and Correlations with Genomic and Clinical Data from the TCGA Ovarian Radiology Research Group

DOI: 10.7937/K9/TCIA.2016.PSJOXM47 | Data Citation Required | Analysis Result

Cancer Types Location Subjects Related Collections Size Supporting Data Updated
High grade serous ovarian cancer Ovary 93 13.92GB Radiologist assessments of image features, genomic subtypes 2016/08/02

Summary

This study was a multi-reader, multi-institutional, IRB-approved retrospective analysis of 93 HGSOC patients with abdominal and pelvic CT scans prior to primary debulking that were available through The Cancer Imaging Archive (TCIA). Eight radiologists from The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) Imaging Research Group developed and subsequently independently recorded the following CT features in each patient: primary ovarian mass(es) characteristics (if present), presence and distribution of peritoneal tumor spread, lymphadenopathy, and distant metastases. Inter-observer agreement for the CT features was assessed, as were associations of these features with time-to-disease progression (TTP) and CLOVAR subtypes and abilities of combinations of these features to predict TTP and CLOVAR subtypes. Results of analyzing this data are published in a manuscript titled Radiogenomics of High-Grade Serous Ovarian Cancer: Multi- Reader Multi-Institutional Study from The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) Imaging Research Group.

Data Access

Version 1: Updated 2016/08/02

Title Data Type Format Access Points Subjects Studies Series Images License
Images CT, MR, OT DICOM
Download requires NBIA Data Retriever
92 147 462 26,570 CC BY 3.0
Full Radiologist Assessments of Image Features Classification CSV 93 CC BY 3.0
Consensus Radiologist Assessments of Image Features Classification CSV 93 CC BY 3.0
Clinical data Follow-Up, Measurement CSV CC BY 3.0
Genomic sub-type data Classification, Measurement CSV CC BY 3.0

Collections Used In This Analysis Result

Related Collections
Related Datasets
TCGA-OV
No related Analysis Results found: Submit your proposal!
Legend: Collections| Analysis Results

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

Vargas, A., Huang, E., Lakhman, Y., Ippolito, J., Bhosale, P., Mellnick, V., Shinagare, A., Anello, M., Kirby, J., Fevrier-Sullivan, B., Freymann, J., Jaffe, C., & Sala, E. (2016). Imaging Features, and Correlations with Genomic and Clinical Data from the TCGA Ovarian Radiology Research Group. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.PSJOXM47

Related Publications

Publications by the Dataset Authors

No publications by dataset authors were found.

Publication Citation

Vargas, H. A., Huang, E. P., Lakhman, Y., Ippolito, J. E., Bhosale, P., Mellnick, V., Shinagare, A. B., Anello, M., Kirby, J., Fevrier-Sullivan, B., Freymann, J., Jaffe, C. C., & Sala, E. (2017). Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group. Radiology, 285(2), 482-492. https://doi.org/10.1148/radiol.2017161870

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 the TCIA Helpdesk.

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 the TCIA Helpdesk.

Publication Citation

Vargas, H. A., Huang, E. P., Lakhman, Y., Ippolito, J. E., Bhosale, P., Mellnick, V., Shinagare, A. B., Anello, M., Kirby, J., Fevrier-Sullivan, B., Freymann, J., Jaffe, C. C., & Sala, E. (2017). Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group. Radiology, 285(2), 482-492. https://doi.org/10.1148/radiol.2017161870