{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:59:59Z","timestamp":1790315999488,"version":"4.1.0"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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>Purpose<\/jats:title>\n                    <jats:p>This study aimed to evaluate the value of systemic inflammatory prognostic markers and early clinical toxicity in the risk assessment of radiation pneumonitis (RP) after radiotherapy in lung cancer patients. We developed and validated an explainable, auxiliary nomogram integrating multiple features and SHAP analysis to optimize early risk monitoring and clinical decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A least absolute shrinkage and selection operator (LASSO) penalized Cox model was used for feature selection to build a multivariable Cox proportional hazards model, which was visualized as a nomogram. A Fine-Gray competing risks model was used for sensitivity analysis. Model performance was evaluated using the C-index, 6-month time-dependent AUC, calibration, continuous NRI\/IDI, and decision curve analysis (DCA). Internal validation using bootstrap resampling. Finally, SHAP analysis was performed to interpret the final Cox model through beeswarm and waterfall plots.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This study retrospectively collected data on 726 patients with lung cancer who received radiotherapy. The primary endpoint was grade \u22652 RP, with non-RP-related death treated as a competing risk. Multivariable Cox regression, validated by Fine-Gray competing risk analysis, identified alcohol history, radiotherapy modality, baseline lactate dehydrogenase (LDH), systemic inflammation score (SIS), and acute radiation esophagitis (RE) as independent factors (all P &amp;lt; 0.05). Among the three sequential models, the integrated model (Model 3) achieved the highest C-index (0.688) and 6-month AUC (0.713). Compared to the baseline model (Model 1), Model 3 improved risk reclassification (continuous NRI\u00a0=\u00a00.274, IDI\u00a0=\u00a00.037; both P &amp;lt; 0.05). Model 3 demonstrated optimal calibration and yielded the highest net clinical benefit within the 10%-15% threshold probability range in DCA. Furthermore, SHAP analysis enhanced model interpretability, indicating a potential association between a baseline systemic inflammation score of zero (SIS\u00a0=\u00a00) and higher RP risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed and validated a nomogram that integrates fundamental clinical factors with inflammatory indicators (LDH and SIS) and early acute radiation esophagitis (RE). This model serves as a multidimensional, auxiliary screening tool for early risk assessment of radiation pneumonitis (RP) in lung cancer patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1910553","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:33:16Z","timestamp":1790314396000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An explainable nomogram incorporating systemic inflammatory indicators and radiation esophagitis for risk assessment of radiation pneumonitis in lung cancer"],"prefix":"10.3389","volume":"17","author":[{"given":"Jiayi","family":"Li","sequence":"first","affiliation":[{"name":"School of Public Health, Jilin University","place":["Changchun, China"]},{"name":"National Health Commission (NHC) Key Laboratory of Radiobiology, School of Public Health, Jilin University","place":["Changchun, 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