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We aimed to evaluate mathematical model structures capable of capturing the kinetics of lymphocyte concentrations after irradiation, with attention to the saturation effect observed during fractionated radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>MATERIALS AND METHODS<\/jats:title>\n                    <jats:p>\n                      A meta-analysis of aggregate data from patients with solid tumor treated with specific radiation doses was conducted. We extracted patient and treatment characteristics, lymphocyte counts, including tumor type\/location, radiation modality (X-rays\n                      <jats:italic toggle=\"yes\">v<\/jats:italic>\n                      protons), and baseline counts. Various models\u2014including exponential, extended exponential, exponential-quadratic, and saturation models\u2014were tested for their ability to predict lymphocyte kinetics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Data from 29 studies covering brain, nasopharyngeal, oropharyngeal, esophageal, non\u2013small cell lung, hepatocarcinoma, cervical, pancreatic, rectal cancers, and soft tissue sarcoma were analyzed. Lymphocyte depletion rates varied, with cervical cancer showing the highest reduction, followed by esophageal, hepatocarcinoma, and others. Lymphocyte recovery post-treatment depended heavily on baseline counts and time since radiotherapy completion. Saturation models best fit most cancer types, but for head and neck\/central nervous system cancer without nodal involvement, the exponential-quadratic model performed better, reflecting a unique early lymphocyte increase before RIL.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>In conclusion, the choice of lymphocyte depletion model should align with cancer type and location. Standardized prospective studies are needed to refine models and enhance radiotherapy strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-25-00144","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T20:00:24Z","timestamp":1761076824000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":3,"title":["Tumor Site\u2013Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9342-6464","authenticated-orcid":true,"given":"Thao-Nguyen","family":"Pham","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Caen Normandie, CNRS, Normandie Universit\u00e9, ISTCT UMR6030, GIP CYCERON, Caen, France"},{"name":"Laboratoire de Physique Corpusculaire UMR6534 IN2P3\/ENSICAEN, France\u2014Normandie Universit\u00e9, Caen, France"},{"name":"Department of Radiation Oncology, Centre Fran\u00e7ois Baclesse, Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie","family":"Coupey","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Caen Normandie, CNRS, Normandie Universit\u00e9, ISTCT UMR6030, GIP CYCERON, Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0355-0270","authenticated-orcid":true,"given":"Samuel","family":"Valable","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Caen Normandie, CNRS, Normandie Universit\u00e9, ISTCT UMR6030, GIP CYCERON, Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4755-496X","authenticated-orcid":true,"given":"Juliette","family":"Thariat","sequence":"additional","affiliation":[{"name":"Laboratoire de Physique Corpusculaire UMR6534 IN2P3\/ENSICAEN, France\u2014Normandie Universit\u00e9, Caen, France"},{"name":"Department of Radiation Oncology, Centre Fran\u00e7ois Baclesse, Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"233","reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1667\/RR15367.1"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1201500"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2021.07.1695"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.2307\/3577549"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.07.025"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2024.100593"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110406"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1097\/00004032-200110000-00009"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2022.100959"},{"key":"e_1_3_6_11_2","volume-title":"Nlme: Linear and Nonlinear Mixed Effects Models","author":"Pinheiro J","year":"2022","unstructured":"Pinheiro J, Bates D, R Core Team: Nlme: Linear and Nonlinear Mixed Effects Models. 2022. https:\/\/CRAN.R-project.org\/package=nlme"},{"key":"e_1_3_6_12_2","doi-asserted-by":"crossref","unstructured":"Pinheiro JC Bates DM: Mixed-Effects Models in S and S-PLUS. 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