Validation of optimal DCE-MRI perfusion threshold to classify at-risk tumor imaging voxels in heterogeneous cervical cancer for outcome prediction |
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Authors: | Zhibin Huang Kevin A. Yuh Simon S. Lo John C. Grecula Steffen Sammet Christina L. Sammet Guang Jia Michael V. Knopp Qiang Wu Norman J. Beauchamp III William T.C. Yuh Roy Wang Nina A. Mayr |
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Affiliation: | 1. Department of Radiation Oncology, East Carolina University, Greenville, NC;2. Department of Radiation Oncology, Ohio State University, Columbus, OH;3. Department of Radiation Oncology, Case Western Reserve University, Cleveland, OH;4. Department of Radiology, Ohio State University, Columbus, OH;5. Department of Radiology, University of Chicago, Chicago, Il;6. Department of Medical Imaging, Ann & Robert H. Lurie Children''s Hospital of Chicago, Chicago, Il;g Department of Biostatistics, East Carolina University, Greenville, NC;h Department of Radiology, University of Washington, Seattle, WA;i Department of Radiation Oncology, University of Washington, Seattle, WA;j Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA |
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Abstract: | PurposeTo classify tumor imaging voxels at-risk for treatment failure within the heterogeneous cervical cancer using DCE MRI and determine optimal voxel's DCE threshold values at different treatment time points for early prediction of treatment failure.Material and MethodDCE-MRI from 102 patients with stage IB2–IVB cervical cancer was obtained at 3 different treatment time points: before (MRI 1) and during treatment (MRI 2 at 2–2.5 weeks and MRI 3 at 4–5 weeks). For each tumor voxel, the plateau signal intensity (SI) was derived from its time-SI curve from the DCE MRI. The optimal SI thresholds to classify the at-risk tumor voxels was determined by the maximal area under the curve using ROC analysis when varies SI value from 1.0 to 3.0 and correlates with treatment outcome.ResultsThe optimal SI thresholds for MRI 1, 2 and 3 were 2.2, 2.2 and 2.1 for significant differentiation between local recurrence/control, respectively, and 1.8, 2.1 and 2.2 for death/survival, respectively.ConclusionOptimal SI thresholds are clinically validated to quantify at-risk tumor voxels which vary with time. A single universal threshold (SI = 1.9) was identified for all 3 treatment time points and remained significant for the early prediction of treatment failure. |
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Keywords: | Perfusion imaging Image analysis Microcirculation Resistant tumor cell Cervical cancer Radiation therapy |
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