International Journal of Radiation Oncology*Biology*Physics
Poster Viewing Q&A SessionRadiomics Prediction of Radiation Treatment Outcomes in Oropharyngeal Cancer: A Clinical and Image Repository in Concert with the Cancer Imaging Archive (TCIA)
Section snippets
Purpose/Objective(s)
There is an unmet need for integrating quantitative imaging biomarkers into current risk stratification tools. To explore the correlation between radiomics features – alone or in combination with clinical prognosticators- and tumor outcome, we retrieved clinical meta-data and matched baseline contrast-enhanced computed tomography (CECT) scans from a single institution, institutional review board-approved cohort of 495 oropharyngeal cancer (OPC) patients. We opted to publicly share this large
Materials/Methods
Diagnostic CECT images were acquired at our institution between 2005 and 2012 for 495 OPC patients (prior to any active intervention) in Digital Imaging and Communications in Medicine (DICOM) format . Expert radiation oncologists manually segmented primary and nodal disease gross volumes (GTVp & GTVn). Structure sets were named per the American Association of Physicists in Medicine (AAPM) TG-263 recommendations, then retrieved in DICOM RTSTRUCT format. Matched patient, disease, treatment and
Results
Anonymized data for 495 OPC patients will be made publicly available from TCIA as downloadable DICOM files (N=**), clinical data, and processed outputs, i.e. tumor segmentations and extracted radiomics features. All data attributes can be cross-referenced via the same anonymized subject IDs. We prepared a data dictionary that specifies clinical data attributes, definitions and possible variables per North American Association of Central Cancer Registries (NAACCR) guidelines.
Conclusion
Large-scale data curation-anonymization-transfer workflows, as well as advanced image registration algorithms and common ontology data dictionaries, are unmet needs for joint machine-learning/radiomics research projects. If these resources are paired with large, curated data sets, like those provided via open-access mega-data repositories, like TCIA, the potential benefits include identification of imaging-derived radiomics signatures associated with treatment outcomes and normal-tissue
References (0)
Cited by (0)
Author Disclosure: H. Elhalawani: Employee; MD Anderson Cancer Center. A.S. Mohamed: None. S. Mulder: None. A. Grossberg: None. K.E. Smith: Research Grant; University of Arkansas for Medical Sciences. G.B. Gunn: MD Anderson Cancer Center - Proton Therapy. S.J. Frank: Research Grant; C4 Imaging, ELEKTA, U19. Founder and Director; C4 Imaging. Honoraria; ELEKTA, Varian Medican Systems, Inc. Consultant; Varian Medican Systems, Inc. Advisory Board; Varian Medican Systems, Inc. Stock; C4 Imaging. Royalty; C4 Imaging. Patent/License Fees/Copyright; C4 Imaging; North America Skull Base Society. Chair - Head. D.I. Rosenthal: None. A.S. Garden: None. C.D. Fuller: Research Grant; National Institutes of Health, National Science Foundation, Elekta AB, National Institutes of Health. Grant funding; Elekta AB. Honoraria; Nederlandse Organisatie voor Wetenschappelijk Onde. Consultant; Elekta AB, Nederlandse Organisatie voor Wetenschappelijk Onde. Travel Expenses; Elekta AB, Nederlandse Organisatie voor We.