期刊
SCIENTIFIC REPORTS
卷 8, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-017-14687-0
关键词
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资金
- Andrew Sabin Family Foundation
- National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) [1R01DE025248-01/R56DE025248-01]
- National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) [NSF 1557679]
- NIH National Cancer Institute/Big Data to Knowledge (BD2K) [1R01CA214825-01]
- NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award [P50CA097007-10]
- NCI Paul Calabresi Clinical Oncology Program Award [K12 CA088084-06]
- General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging
- Elekta AB/MD Anderson Department of Radiation Oncology
- Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center
- MD Anderson Institutional Research Grant (IRG)
- Elekta AB
- Radiological Society of North America (RSNA) Education and Research Foundation Research Medical Student Grant Award [RSNA RMS1618]
- University of Texas MD Anderson Cancer Center
- Stiefel Oropharyngeal Research Fund as part of the programmatic efforts of the Charles and Daneen Stiefel Center for Head and Neck Cancer
- NIH/NCI Cancer Center [CA016672, P30 CA016672]
- [NIH-NCI-R01CA225190]
- NATIONAL CANCER INSTITUTE [R01CA214825, R01CA225190, P30CA016672, P50CA097007, K12CA088084] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DENTAL & CRANIOFACIAL RESEARCH [R56DE025248, R01DE025248] Funding Source: NIH RePORTER
Radiomics is one such big data approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a noninvasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo) radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between favorable and unfavorable clusters were noted.
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