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The relationships between quantitative LD-DE&amp;PCT-derived parameters and the efficacy of TACE-LEN-ICIs were analyzed using logistic regression analysis. A nomogram incorporating the independent predictors was constructed, and its predictive performance was evaluated by the area under the receiver operating characteristic curve (AUROC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 125 lesions from 71 uHCC patients were enrolled, with 71 lesions (56.8%) classified as the objective response (ObR) group and 54 lesions (43.2%) as the non-response (NR) group. Univariate analysis revealed significant differences in tumor size, corona enhancement, tumor location, iodine concentration in the arterial phase (IC-AP), normalized iodine concentration in the arterial phase (NIC-AP), effective atomic number in the arterial phase (Z\n                      <jats:sub>eff<\/jats:sub>\n                      -AP), slope of spectral HU curve in the arterial phase (\u03bb\n                      <jats:sub>HU<\/jats:sub>\n                      -AP), and permeability surface area product (PS) between ObR and NR groups. Among these, NIC-AP exhibited the highest predictive value (AUROC\u2009=\u20090.770; 95% confidence interval [CI]: 0.682\u20120.858). Multivariate analysis identified tumor size, NIC-AP, and PS as independent predictors. The nomogram showed excellent performance (AUROC\u2009\u2009=\u20090.913; 95% CI: 0.858\u20130.968). The total radiation dose was 19.02\u2009\u00b1\u20095.39\u2009mSv.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The LD-DE&amp;PCT-based nomogram can accurately predict the response to TACE-LEN-ICIs in uHCC patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>Low-dose one-stop dual-energy and perfusion CT provides a noninvasive method to predict response to TACE combined with lenvatinib and immune checkpoint inhibitors in unresectable HCC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Predicting response to TACE-LEN-ICIs in uHCC helps treatment decision-making.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>NIC-AP and PS from LD-DE&amp;PCT, and tumor size were independent predictive biomarkers.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>NIC-AP was the best parameter for predicting response to TACE-LEN-ICIs in uHCC.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-025-00669-9","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:01:45Z","timestamp":1768809705000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-energy CT biomarkers for predicting the efficacy of TACE combined with lenvatinib and immune checkpoint inhibitors in unresectable HCC"],"prefix":"10.1186","volume":"10","author":[{"given":"Jingwen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taoming","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingxuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1274-5179","authenticated-orcid":false,"given":"Liqin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"669_CR1","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.jhep.2021.11.018","volume":"76","author":"M Reig","year":"2022","unstructured":"Reig M, Forner A, Rimola J et al (2022) BCLC strategy for prognosis prediction and treatment recommendation: the 2022 update. 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