Zhibin Huang1, Kevin A Yuh1, Simon S Lo2, John C Grecula3, Steffen Sammet4, Christina L Sammet5, Guang Jia6, Michael V Knopp7, Qiang Wu8, Norman J Beauchamp9, William T C Yuh10, Roy Wang3, Nina A Mayr11. 1. Department of Radiation Oncology, East Carolina University, Greenville, NC; Department of Radiation Oncology, Ohio State University, Columbus, OH. 2. Department of Radiation Oncology, Case Western Reserve University, Cleveland, OH. 3. Department of Radiation Oncology, Ohio State University, Columbus, OH. 4. Department of Radiology, Ohio State University, Columbus, OH; Department of Radiology, University of Chicago, Chicago, Il. 5. Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Il. 6. Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA. 7. Department of Radiology, Ohio State University, Columbus, OH. 8. Department of Biostatistics, East Carolina University, Greenville, NC. 9. Department of Radiology, University of Washington, Seattle, WA. 10. Department of Radiology, Ohio State University, Columbus, OH; Department of Radiology, University of Washington, Seattle, WA. 11. Department of Radiation Oncology, Ohio State University, Columbus, OH; Department of Radiation Oncology, University of Washington, Seattle, WA. Electronic address: ninamayr@uw.edu.
Abstract
PURPOSE: To 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 METHOD: DCE-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. RESULTS: The 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. CONCLUSION: Optimal 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.
PURPOSE: To 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 METHOD:DCE-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. RESULTS: The 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. CONCLUSION: Optimal 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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