{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:44:41Z","timestamp":1780361081785,"version":"3.54.1"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"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><jats:title>Background<\/jats:title><jats:p>To develop and validate a new prognostic model using baseline PET parameters and clinical indicators for predicting early overall survival (OS) in Angioimmunoblastic T-cell lymphoma (AITL) patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a retrospective cohort study from December 2009 to December 2023 (n=124) at a single center. The model\u2019s predictors included baseline clinical characteristics, pathological indicators, laboratory metrics, and PET\/CT parameters. Independent prognostic factors were identified using Cox regression and presented as nomograms. The C-index assessed predictive accuracy, while calibration plots and decision curve analysis evaluated prediction accuracy and discrimination ability. The model\u2019s accuracy was compared with existing prognostic systems using C-index, NRI, ROC, and Kaplan-Meier survival curves.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>SUVmax, \u03b22MG, platelet, and albumin were identified as independent risk factors. The C-index for OS was 0.78 (95% CI: 0.70-0.85); for 1000 bootstrap samples, it was 0.76 (95% CI: 0.61-0.93). Calibration curves showed excellent agreement between predictions and actual observations. The AUC for 6-month and 1-year OS were 0.91(95% CI: 0.82-1.00) and 0.85 (95% CI: 0.77\u20130.94), respectively. The model outperformed PIAI, IPI, and PIT in predictive capacity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The new prediction model reliably estimates outcomes for AITL patients, demonstrating high discrimination and calibration.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1607177","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T04:13:15Z","timestamp":1752639195000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["PET\/CT-Based prognostic model enhances early survival prediction in angioimmunoblastic t-cell 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