AUTHOR=Indovina Luca , Scolozzi Valentina , Capotosti Amedeo , Sestini Stelvio , Taralli Silvia , Cusumano Davide , Giancipoli Romina Grazia , Ciasca Gabriele , Cardillo Giuseppe , Calcagni Maria Lucia 

TITLE=Short 2-[18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer

JOURNAL=Frontiers in Medicine

VOLUME=8

YEAR=2021

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.725387

DOI=10.3389/fmed.2021.725387

ISSN=2296-858X

ABSTRACT=<p><bold>Purpose:</bold> To test a short 2-[<sup>18</sup>F]Fluoro-2-deoxy-D-glucose (2-[<sup>18</sup>F]FDG) PET dynamic acquisition protocol to calculate K<sub>i</sub> using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC).</p><p><bold>Methods:</bold> 24 patients with NSCLC who underwent standard dynamic 2-[<sup>18</sup>F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (<italic>n</italic> = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (<italic>n</italic> = 14), K<sub>i</sub> was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain K<sub>i,s</sub> using IDIF<sub>Fitted</sub> curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIF<sub>Fitted</sub> curves were modeled to assume the value of 2-[<sup>18</sup>F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between K<sub>i</sub> and K<sub>i,s</sub>. Finally, K<sub>i,s</sub> was obtained with our method in a separate group of patients (group 3, <italic>n</italic> = 8) that perform two short dynamic acquisitions.</p><p><bold>Results:</bold> Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: <italic>a</italic> = 9.684, <italic>b</italic> = 16.410, and <italic>c</italic> = 0.068 min<sup>−1</sup>. The LOOCV error was 0.4%. In patients of group 2, the mean values of K<sub>i</sub> and K<sub>i,s</sub> were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (<italic>R</italic><sup>2</sup> = 0.9970). The Passing–Bablok regression for comparison between K<sub>i</sub> and K<sub>i,s</sub> showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011).</p><p><bold>Conclusions:</bold> Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[<sup>18</sup>F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional K<sub>i</sub> estimation.</p>