Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges

Hesham Elhalawani, Abdallah S.R. Mohamed, Aubrey L. White, James Zafereo, Andrew J. Wong, Joel E. Berends, Shady AboHashem, Bowman Williams, Jeremy M. Aymard, Aasheesh Kanwar, Subha Perni, Crosby D. Rock, Luke Cooksey, Shauna Campbell, Yao Ding, Stephen Y. Lai, Elisabeta G. Marai, David Vock, Guadalupe M. Canahuate, John FreymannKeyvan Farahani, Jayashree Kalpathy-Cramer, Clifton D. Fuller

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that â € radiomics', or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes. This data descriptor defines a repository for head and neck radiomic challenges, executed via a Kaggle in Class platform, in partnership with the MICCAI society 2016 annual meeting.These public challenges were designed to leverage radiomics and/or machine learning workflows to discriminate HPV phenotype in one challenge (HPV status challenge) and to identify patients who will develop a local recurrence in the primary tumor volume in the second one (Local recurrence prediction challenge) in a segmented, clinically curated anonymized oropharyngeal cancer (OPC) data set.

Original languageEnglish (US)
Article number170077
JournalScientific Data
StatePublished - Jul 4 2017

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© 2017 The Author(s).


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