{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:36:05Z","timestamp":1768563365327,"version":"3.49.0"},"posted":{"date-parts":[[2023,9,23]]},"group-title":"Radiology and Imaging","reference-count":23,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  Immunotherapies, especially the checkpoint inhibitors such as anti-PD-1 antibodies, have transformed cancer treatment by enhancing immune system\u2019s capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging.\n                  <jats:sup>18<\/jats:sup>\n                  F-AraG is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by non-invasive quantification of immune cell activity within tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of\n                  <jats:sup>18<\/jats:sup>\n                  F-AraG, as a potential quantitative biomarker for immune response evaluation.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>\n                    The study consisted of 90-min total-body dynamic scans of four healthy subjects and one non-small cell lung cancer (NSCLC) patient, scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection were employed to analyze tracer kinetics in various organs. Additionally, seven sub-regions of the primary lung tumor and four mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess reliability of kinetic parameter estimation. Correlations of SUVmean, SUVR (tissue-to-blood ratio), and Logan plot slope (\n                    <jats:italic>K<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>Logan<\/jats:italic>\n                    <\/jats:sub>\n                    ) with total volume-of-distribution (\n                    <jats:italic>\n                      V\n                      <jats:sub>T<\/jats:sub>\n                    <\/jats:italic>\n                    ) were calculated to identify potential surrogates for kinetic modeling.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    Strong correlations were observed between\n                    <jats:italic>K<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>Logan<\/jats:italic>\n                    <\/jats:sub>\n                    and SUVR values with\n                    <jats:italic>\n                      V\n                      <jats:sub>T<\/jats:sub>\n                    <\/jats:italic>\n                    , suggesting that they can be used as promising surrogates for\n                    <jats:italic>\n                      V\n                      <jats:sub>T<\/jats:sub>\n                    <\/jats:italic>\n                    , especially in organs with low blood-volume fraction. Moreover, the practical identifiability analysis suggests that the dynamic\n                    <jats:sup>18<\/jats:sup>\n                    F-AraG PET scans could potentially be shortened to 60 minutes, while maintaining quantification accuracy for all organs-of-interest. The study suggests that although\n                    <jats:sup>18<\/jats:sup>\n                    F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response post-therapy. While SUVmean showed variable changes in different sub-regions of the tumor post-therapy, the SUVR,\n                    <jats:italic>K<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>Logan<\/jats:italic>\n                    <\/jats:sub>\n                    , and\n                    <jats:italic>\n                      V\n                      <jats:sub>T<\/jats:sub>\n                    <\/jats:italic>\n                    showed consistent increasing trends in all analyzed sub-regions of the tumor with high practical identifiability.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>\n                    Our findings highlight the promise of\n                    <jats:sup>18<\/jats:sup>\n                    F-AraG dynamic imaging as a non-invasive biomarker for quantifying the immune response to immunotherapy in cancer patients. The promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2023.09.22.23295860","type":"posted-content","created":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T01:10:13Z","timestamp":1695517813000},"source":"Crossref","is-referenced-by-count":5,"title":["Total-body Dynamic Imaging and Kinetic Modeling of\n                  <sup>18<\/sup>\n                  F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7589-4745","authenticated-orcid":false,"given":"Negar","family":"Omidvari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jelena","family":"Levi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasser G","family":"Abdelhafez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiran","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"Nardo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Megan E","family":"Daly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guobao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon R","family":"Cherry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2023110408100707000_2023.09.22.23295860v2.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2017.07.025"},{"key":"2023110408100707000_2023.09.22.23295860v2.2","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-021-00347-z"},{"key":"2023110408100707000_2023.09.22.23295860v2.3","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s11307-021-01652-7","article-title":"ImmunoPET: harnessing antibodies for imaging immune cells","volume":"24","year":"2022","journal-title":"Mol Imaging Biol"},{"key":"2023110408100707000_2023.09.22.23295860v2.4","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.121.263293"},{"key":"2023110408100707000_2023.09.22.23295860v2.5","doi-asserted-by":"publisher","DOI":"10.1007\/s11307-010-0414-x"},{"key":"2023110408100707000_2023.09.22.23295860v2.6","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2006-11-056754"},{"key":"2023110408100707000_2023.09.22.23295860v2.7","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2006-0017"},{"key":"2023110408100707000_2023.09.22.23295860v2.8","doi-asserted-by":"publisher","DOI":"10.1158\/1538-7445.SABCS18-3455"},{"key":"2023110408100707000_2023.09.22.23295860v2.9","doi-asserted-by":"crossref","unstructured":"Levi J , Duan H , Yaghoubi S , et al. 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