{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T21:48:58Z","timestamp":1769464138390,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: To develop and validate a predictive model that combines morphological features and dual-energy CT (DECT) parameters to non-invasively distinguish metastatic from benign axillary lymph nodes in patients with breast cancer (BC). Methods: In this retrospective study, 117 patients (median age, 65 years; 111 women and 6 men) who underwent DECT followed by axillary lymphadenectomy between April 2015 and July 2023, were analyzed. A total of 375 lymph nodes (180 metastatic, 195 benign) were evaluated. Two radiologists recorded morphological criteria (adipose hilum status, cortical appearance, extranodal extension, and short-axis diameter) and placed regions of interest to measure dual-energy parameters: attenuation at 40 and 70 keV, iodine concentration, water concentration and spectral slope. Normalized iodine concentration was calculated using the aorta as reference. Univariate analysis identified variables associated with metastasis. Multivariate logistic regression with cross-validation was used to construct two models: one based solely on morphological features and one integrating water concentration. Results: On univariate testing, all DECT parameters and morphological criteria differed significantly between metastatic and benign nodes (p &lt; 0.01). In multivariate analysis, water concentration emerged as the only independent DECT predictor (odds ratio = 0.97; p = 0.002) alongside cortical abnormality, absence of adipose hilum, extranodal extension and short-axis diameter. The morphologic model achieved an area under the receiver operating characteristic curve (AUC) of 0.871. Increasing water concentration increased the AUC to 0.883 (\u0394AUC = 0.012; p = 0.63, not significant), with internal cross-validation confirming stable performance. Conclusions: A model combining standard morphologic criteria with water concentration quantification on DECT accurately differentiates metastatic from benign axillary nodes in BC patients. Although iodine-based metrics remain valuable indicators of perfusion, water concentration offers additional tissue composition information. Future multicenter prospective studies with standardized imaging protocols are warranted to refine parameter thresholds and validate this approach for routine clinical use.<\/jats:p>","DOI":"10.3390\/cancers18030363","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T17:52:38Z","timestamp":1769190758000},"page":"363","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spectral Precision: The Added Value of Dual-Energy CT for Axillary Lymph Node Characterization in Breast Cancer"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7875-3342","authenticated-orcid":false,"given":"Susanna","family":"Guerrini","sequence":"first","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giulio","family":"Bagnacci","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"},{"name":"Department of Medical, Surgical and Neuro Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paola","family":"Morrone","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"},{"name":"Department of Medical, Surgical and Neuro Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cecilia","family":"Zampieri","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"},{"name":"Department of Medical, Surgical and Neuro Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiara","family":"Esposito","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"},{"name":"Department of Medical, Surgical and Neuro Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9640-8770","authenticated-orcid":false,"given":"Iacopo","family":"Capitoni","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"},{"name":"Department of Medical, Surgical and Neuro Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6019-8541","authenticated-orcid":false,"given":"Nunzia","family":"Di Meglio","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"},{"name":"Department of Medical, Surgical and Neuro Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0888-5187","authenticated-orcid":false,"given":"Armando","family":"Perrella","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"},{"name":"Department of Medical, Surgical and Neuro Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Gentili","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Neri","sequence":"additional","affiliation":[{"name":"Breast Cancer Surgery Unit, Department of Women\u2019s and Children\u2019s Health, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donato","family":"Casella","sequence":"additional","affiliation":[{"name":"Breast Cancer Surgery Unit, Department of Women\u2019s and Children\u2019s Health, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6778-6894","authenticated-orcid":false,"given":"Maria Antonietta","family":"Mazzei","sequence":"additional","affiliation":[{"name":"Diagnostic Imaging Unit, Department of Medical Sciences, Azienda Ospedaliero Universitaria Senese, 53100 Siena, Italy"},{"name":"Department of Medical, Surgical and Neuro Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. 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