Frontiers in Oncology (Aug 2018)

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

  • Hesham Elhalawani,
  • Timothy A. Lin,
  • Timothy A. Lin,
  • Stefania Volpe,
  • Stefania Volpe,
  • Abdallah S. R. Mohamed,
  • Abdallah S. R. Mohamed,
  • Aubrey L. White,
  • Aubrey L. White,
  • James Zafereo,
  • James Zafereo,
  • Andrew J. Wong,
  • Andrew J. Wong,
  • Joel E. Berends,
  • Joel E. Berends,
  • Shady AboHashem,
  • Shady AboHashem,
  • Bowman Williams,
  • Bowman Williams,
  • Jeremy M. Aymard,
  • Jeremy M. Aymard,
  • Aasheesh Kanwar,
  • Aasheesh Kanwar,
  • Subha Perni,
  • Subha Perni,
  • Crosby D. Rock,
  • Crosby D. Rock,
  • Luke Cooksey,
  • Luke Cooksey,
  • Shauna Campbell,
  • Shauna Campbell,
  • Pei Yang,
  • Pei Yang,
  • Khahn Nguyen,
  • Rachel B. Ger,
  • Rachel B. Ger,
  • Carlos E. Cardenas,
  • Carlos E. Cardenas,
  • Xenia J. Fave,
  • Carlo Sansone,
  • Gabriele Piantadosi,
  • Stefano Marrone,
  • Rongjie Liu,
  • Rongjie Liu,
  • Chao Huang,
  • Chao Huang,
  • Kaixian Yu,
  • Kaixian Yu,
  • Tengfei Li,
  • Tengfei Li,
  • Yang Yu,
  • Yang Yu,
  • Youyi Zhang,
  • Youyi Zhang,
  • Hongtu Zhu,
  • Hongtu Zhu,
  • Jeffrey S. Morris,
  • Jeffrey S. Morris,
  • Veerabhadran Baladandayuthapani,
  • Veerabhadran Baladandayuthapani,
  • John W. Shumway,
  • Alakonanda Ghosh,
  • Andrei Pöhlmann,
  • Hady A. Phoulady,
  • Vibhas Goyal,
  • Guadalupe Canahuate,
  • G. Elisabeta Marai,
  • David Vock,
  • Stephen Y. Lai,
  • Dennis S. Mackin,
  • Dennis S. Mackin,
  • Laurence E. Court,
  • Laurence E. Court,
  • John Freymann,
  • Keyvan Farahani,
  • Keyvan Farahani,
  • Jayashree Kaplathy-Cramer,
  • Clifton D. Fuller,
  • Clifton D. Fuller,
  • Clifton D. Fuller

DOI
https://doi.org/10.3389/fonc.2018.00294
Journal volume & issue
Vol. 8

Abstract

Read online

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

Keywords