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Oncology: Basic, Translational & Therapy: Technical Advances & QuantificationTechnical Advances & Quantification Posters |
1 Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 2 Johns Hopkins University, Baltimore, MD
Abstract No. 1773
Objectives: To compare inter-rater agreement of anatomic and metabolic response assessments when quantitative measurements of tumor size and metabolism are determined by the same vs. different readers pre- and post-therapy.
Methods: 52 tumors (25 pts) were evaluated by FDG PET/CT pre- and post-therapy. Maximum single-pixel SUVbw (SUV) and CT size measurements were determined for each tumor independently pre- and post-therapy by 4 PET and 4 CT readers. Percent changes in SUV and CT size between pre- and post-therapy scans were calculated using 1) the same reader pre- and post-therapy and 2) 12 combinations of readers pre- and post-therapy (i.e., reader 1 (R1) pre-therapy and R2 post therapy, etc). Metabolic and anatomic responses for each tumor were assigned according to PERCIST or RECIST 1.1, respectively for all percent change determinations. Kappa statistics describe inter-rater agreement in response classifications.
Results: The means (±sd) of the average percent change in tumor size for all tumors with the same vs. different readers pre- and post-therapy were not statistically different (-20±27% vs. -14±34%, p=0.27), similar to previously reported results for means of average percent change in SUV with the same vs. different readers (-45±34% vs. -43±38%, p=0.87). Inter-rater agreement in anatomic response classification was better for the same (kappa 0.77) vs. different (kappa 0.61) CT readers compared to less variable results for metabolic response classifications determined by the same vs. different readers (kappa 0.95 vs. 0.92).
Conclusions: Anatomic response classification is more variable when multiple CT readers perform assessments at different time points compared to a single CT reader. This contrasts highly reproducible results for PET metabolic response classifications with one or multiple readers, further supporting SUV as a robust and generalizable parameter for monitoring tumor response to therapy
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