SU‐F‐J‐224: Impact of 4D PET/CT On PERCIST Classification of Lung and Liver Metastases in NSLC and Colorectal Cancer

Abstract

Purpose:To quantify the impact of 4D PET/CT on PERCIST metrics in lung and liver tumors in NSCLC and colorectal cancer patients.Methods:32 patients presenting lung or liver tumors of 1–3 cm size affected by respiratory motion were scanned on a GE Discovery 690 PET/CT. The bed position with lesion(s) affected by motion was acquired in a 12 minute PET LIST mode and unlisted into 8 bins with respiratory gating. Three different CT maps were used for attenuation correction: a clinical helical CT (CT_clin), an average CT (CT_ave), and an 8‐phase 4D CINE CT (CT_cine). All reconstructions were 3D OSEM, 2 iterations, 24 subsets, 6.4 Gaussian filtration, 192×192 matrix, non‐TOF, and non‐PSF. Reconstructions using CT_clin and CT_ave used only 3 out of the 12 minutes of the data (clinical protocol); all 12 minutes were used for the CT_cine reconstruction. The percent change of SUVbw_peak and SUVbw_max was calculated between PET_CTclin and PET_CTave. The same percent change was also calculated between PET_CTclin and PET_CTcine in each of the 8 bins and in the average of all bins. A 30% difference from PET_CTclin classified lesions as progressive metabolic disease (PMD) using maximum bin value and the average of eight bin values.Results:30 lesions in 25 patients were evaluated. Using the bin with maximum SUVbw_peak and SUVbw_max difference, 4 and 13 lesions were classified as PMD, respectively. Using the average bin values for SUVbw_peak and SUVbw_max, 3 and 6 lesions were classified as PMD, respectively. Using PET_CTave values for SUVbw_peak and SUVbw_max, 4 and 3 lesions were classified as PMD, respectively.Conclusion:These results suggest that response evaluation in 4D PET/CT is dependent on SUV measurement (SUVpeak vs. SUVmax), number of bins (single or average), and the CT map used for attenuation correction.