{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T06:15:07Z","timestamp":1775542507262,"version":"3.50.1"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Severe radiation-induced lymphopenia (SRIL) is a poor prognostic factor in lung cancer. This study aimed to develop and validate normal tissue complication probability (NTCP) models for SRIL based on hematologic dose in patients receiving photon and proton radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and materials<\/jats:title>\n                    <jats:p>We retrospectively analyzed 131 lung cancer patients receiving curative-intent radiotherapy (94 with IMRT, 37 with IMPT) between 2022 and 2025. Whole-body blood dose-volume histograms were calculated by using the HEDOS framework. The Lyman-Kutcher-Burman NTCP model was adopted, with parameters optimized by maximum likelihood estimation. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and Brier score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The incidence of SRIL was 61.7% and 32.4% in the IMRT and IMPT cohorts, respectively. Blood generalized equivalent uniform dose was an independent predictor of SRIL in the IMRT cohort (OR = 4.682, p=0.002). The NTCP model demonstrated strong predictive power in both cohorts (IMRT: AUC = 0.82; IMPT: AUC = 0.80) Model parameters differed between modalities, particularly the volume-effect parameter (a = 19.85 for IMRT\n                      <jats:italic>vs<\/jats:italic>\n                      . 2.35 for IMPT). Cross-modality validation of the IMRT-derived model in the IMPT cohort revealed suboptimal calibration (calibration slope=0.54), indicating systematic overestimation of risk and reflecting the distinct blood dose distributions and parameter covariance between modalities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Modality-specific NTCP models are required to accurately predict SRIL in lung cancer radiotherapy. The IMRT- and IMPT-based models developed in this study demonstrated strong performance in their respective cohorts. Differences in model parameters underscore the influence of modality-dependent blood dose distributions, supporting the development of separate risk models for photon and proton therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1752785","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:34:33Z","timestamp":1775540073000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Normal tissue complication probability modeling of severe radiation-induced lymphopenia using blood dose for lung cancer patients treated with IMRT and IMPT"],"prefix":"10.3389","volume":"17","author":[{"given":"Shuting","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences","place":["Jinan, China"]},{"name":"Department of Radiation Oncology Physics and Technology, Shandong Cancer 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China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","place":["Jinan, China"]},{"name":"College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine","place":["Jinan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","place":["Jinan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruozheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Third Affiliated Teaching Hospital 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