Journal article
Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges
Frontiers in oncology, Vol.8, pp.294-294
08/17/2018
DOI: 10.3389/fonc.2018.00294
PMCID: PMC6107800
PMID: 30175071
Abstract
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.
Details
- Title: Subtitle
- Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges
- Creators
- Hesham Elhalawani - University of Texas MD Anderson Cancer CenterChao Huang - Baylor College of MedicineGuadalupe Canahuate - University of IowaTimothy A Lin - Baylor College of MedicineStefania Volpe - University of MilanAbdallah S. R Mohamed - Alexandria UniversityAubrey L White - University of Texas at AustinJames Zafereo - University of Texas at AustinAndrew J Wong - University of Texas Health Science Center at San AntonioJoel E Berends - University of Texas Health Science Center at San AntonioShady AboHashem - Harvard UniversityBowman Williams - Furman UniversityJeremy M Aymard - Abilene Christian UniversityAasheesh Kanwar - Oregon Health & Science UniversitySubha Perni - Memorial Sloan Kettering Cancer CenterCrosby D Rock - Texas Tech University Health Sciences Center at El PasoLuke Cooksey - University of North Texas Health Science CenterShauna Campbell - Cleveland ClinicPei Yang - Baylor College of MedicineRachel B Ger - University of Texas MD Anderson Cancer CenterKhahn Nguyen - Colgate UniversityCarlos E Cardenas - University of Texas MD Anderson Cancer CenterXenia J Fave - University of California, Los AngelesCarlo Sansone - University of Naples Federico IIGabriele Piantadosi - University of Naples Federico IIStefano Marrone - University of Naples Federico IIRongjie Liu - Baylor College of MedicineKaixian Yu - Baylor College of MedicineTengfei Li - Baylor College of MedicineYang Yu - Baylor College of MedicineYouyi Zhang - Baylor College of MedicineHongtu Zhu - Baylor College of MedicineJeffrey S Morris - Baylor College of MedicineVeerabhadran Baladandayuthapani - Baylor College of MedicineJohn W Shumway - University of Texas MD Anderson Cancer CenterAlakonanda Ghosh - University of Texas MD Anderson Cancer CenterAndrei Pöhlmann - Fraunhofer-Institut für Fabrikbetrieb und Automatisierung (IFF), Magdeburg, Germany.Hady A Phoulady - University of Southern MaineVibhas Goyal - Indian Institute of Technology HyderabadG. Elisabeta Marai - University of Illinois ChicagoDavid Vock - University of MinnesotaStephen Y Lai - University of Texas MD Anderson Cancer CenterDennis S Mackin - Colgate UniversityLaurence E Court - Colgate UniversityJohn Freymann - Frederick National Laboratory for Cancer ResearchKeyvan Farahani - Johns Hopkins UniversityJayashree Kaplathy-Cramer - Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, MGH/Harvard Medical SchoolClifton D Fuller - Baylor College of MedicineMICCAI/M.D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group
- Resource Type
- Journal article
- Publication Details
- Frontiers in oncology, Vol.8, pp.294-294
- DOI
- 10.3389/fonc.2018.00294
- PMID
- 30175071
- PMCID
- PMC6107800
- NLM abbreviation
- Front Oncol
- ISSN
- 2234-943X
- eISSN
- 2234-943X
- Publisher
- Frontiers Media S.A
- Language
- English
- Date published
- 08/17/2018
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197454702771
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