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This study aims to utilize machine learning to select optimal patient populations for PEG-IFN therapy and construct the nomogram model for predicting interferon response (IR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Between April 2020 and October 2023, we collected data from CHB patients treated with PEG-INF\u03b1-2b at the Affiliated Hospital of Xuzhou Medical University. Based on HBeAg status, IR is defined as follows: For HBeAg-positive patients, an IR is defined as achieving HBeAg negativity, HBV DNA negativity, and HBsAg levels below 100\u00a0IU\/mL after 48\u00a0weeks of treatment with pegylated interferon; For HBeAg-negative patients, an IR is achieved when HBV DNA becomes undetectable and HBsAg is negative after 48\u00a0weeks of pegylated interferon treatment. Through a combination of LASSO COX regression, Random Survival Forests, and CoxBoost, we identified optimal patient populations. A multivariable COX regression was then employed to construct the nomogram model. The performance of the nomogram model was assessed using Receiver Operating Characteristic (ROC) curves, decision curves, and calibration curves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Utilizing machine learning, we pinpointed baseline HBsAg, \u25b3HBsAg, and \u25b3ALT as predictors of IR. Leveraging these key predictors, we developed a nomogram model that showcased robust accuracy in predicting IR at 48\u00a0weeks across different datasets: the training cohort, validation cohort, and RNA cohort, with AUROC scores of 0.922 (95% CI: 0.88\u20130.96), 0.938 (95% CI: 0.87\u20131.00), and 0.933 (95% CI: 0.87\u20131.00), respectively. The results of the calibration and decision curves show the model has a good consistency and precision. Furthermore, Kaplan\u2013Meier analysis identified that CHB patients with baseline HBsAg\u2009&lt;\u20092.43 lg IU\/mL, \u25b3HBsAg\u2009&gt;\u20091.26 lg IU\/mL, and \u25b3ALT\u2009&gt;\u20098.00 U\/L were more likely to achieve IR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The nomogram model can evaluate the efficacy of PEG-IFN in the treatment of CHB patients, and is helpful for clinical decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12876-026-04813-6","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T09:41:30Z","timestamp":1775814090000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging machine learning to identify optimal patient populations for PEG-IFN therapy in CHB and constructing nomogram models of interferon response: a 48-week follow-up study"],"prefix":"10.1186","volume":"26","author":[{"given":"Deyang","family":"Xi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuebing","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9200-6208","authenticated-orcid":false,"given":"Chunyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"issue":"10381","key":"4813_CR1","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1016\/S0140-6736(22)01468-4","volume":"401","author":"WJ Jeng","year":"2023","unstructured":"Jeng WJ, Papatheodoridis GV, Lok A. 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