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 Freymann & 3 others Keyvan Farahani, Jayashree Kalpathy-Cramer, Clifton D. Fuller

Research output: Research - peer-reviewArticle

Abstract

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.

LanguageEnglish (US)
Article number170077
JournalScientific Data
Volume4
DOIs
StatePublished - Jul 4 2017
Externally publishedYes

Fingerprint

Computed Tomography
Cancer
Segmentation
Demographics
Computed tomography
Tomography
Tumors
cancer
segmentation
Recurrence
Phenotype
Tumor
Chemoradiotherapy
Image texture
Learning systems
Texture Analysis
Radiotherapy
Medical Image
Image Analysis
Leverage

ASJC Scopus subject areas

  • Education
  • Library and Information Sciences
  • Computer Science Applications
  • Information Systems
  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

Elhalawani, H., Mohamed, A. S. R., White, A. L., Zafereo, J., Wong, A. J., Berends, J. E., ... Fuller, C. D. (2017). Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. Scientific Data, 4, [170077]. DOI: 10.1038/sdata.2017.77

Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. / Elhalawani, Hesham; Mohamed, Abdallah S.R.; White, Aubrey L.; Zafereo, James; Wong, Andrew J.; Berends, Joel E.; AboHashem, Shady; Williams, Bowman; Aymard, Jeremy M.; Kanwar, Aasheesh; Perni, Subha; Rock, Crosby D.; Cooksey, Luke; Campbell, Shauna; Ding, Yao; Lai, Stephen Y.; Marai, Elisabeta G.; Vock, David; Canahuate, Guadalupe M.; Freymann, John; Farahani, Keyvan; Kalpathy-Cramer, Jayashree; Fuller, Clifton D.

In: Scientific Data, Vol. 4, 170077, 04.07.2017.

Research output: Research - peer-reviewArticle

Elhalawani, H, Mohamed, ASR, White, AL, Zafereo, J, Wong, AJ, Berends, JE, AboHashem, S, Williams, B, Aymard, JM, Kanwar, A, Perni, S, Rock, CD, Cooksey, L, Campbell, S, Ding, Y, Lai, SY, Marai, EG, Vock, D, Canahuate, GM, Freymann, J, Farahani, K, Kalpathy-Cramer, J & Fuller, CD 2017, 'Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges' Scientific Data, vol 4, 170077. DOI: 10.1038/sdata.2017.77
Elhalawani H, Mohamed ASR, White AL, Zafereo J, Wong AJ, Berends JE et al. Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. Scientific Data. 2017 Jul 4;4. 170077. Available from, DOI: 10.1038/sdata.2017.77
Elhalawani, Hesham ; Mohamed, Abdallah S.R. ; White, Aubrey L. ; Zafereo, James ; Wong, Andrew J. ; Berends, Joel E. ; AboHashem, Shady ; Williams, Bowman ; Aymard, Jeremy M. ; Kanwar, Aasheesh ; Perni, Subha ; Rock, Crosby D. ; Cooksey, Luke ; Campbell, Shauna ; Ding, Yao ; Lai, Stephen Y. ; Marai, Elisabeta G. ; Vock, David ; Canahuate, Guadalupe M. ; Freymann, John ; Farahani, Keyvan ; Kalpathy-Cramer, Jayashree ; Fuller, Clifton D./ Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. In: Scientific Data. 2017 ; Vol. 4.
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