Journal
FRONTIERS IN ONCOLOGY
Volume 8, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2018.00294
Keywords
machine learning; radiomics challenge; radiation oncology; head and neck; big data
Categories
Funding
- Andrew Sabin Family Foundation
- National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research [1R01DE025248-01/R56DE025248-01]
- National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant [NSF 1557679]
- NIH/National Cancer Institute (NCI) Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award [P50 CA097007-10]
- Paul Calabresi Clinical Oncology Program Award [K12 CA088084-06]
- Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant
- MD Anderson Institutional Research Grant (IRG) Program
- National Cancer Institute [U24 CA180927-03, U01 CA154601-06]
- Radiological Society of North America Education and Research Foundation Research Medical Student Grant Award [RSNA RMS1618]
- National Institutes of Health (NIH) [NCI-R01-CA214825, NCI-R01CA225190, NLM-R01LM012527]
- National Science Foundation (NSF) [CNS-1625941]
- Joseph and Bessie Feinberg Foundation
- Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant
- Elekta AB
- General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award
- Family of Paul W. Beach
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available