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Spectral Precision: The Added Value of Dual-Energy CT for Axillary Lymph Node Characterization in Breast Cancer
by
, , , ,
Susanna Guerrini
1,*,†
,
Giulio Bagnacci
1,2,†,
Paola Morrone
1,2,
Cecilia Zampieri
1,2,
Chiara Esposito
1,2,
Iacopo Capitoni
1,2,
Nunzia Di Meglio
1,2,
Armando Perrella
1,2,
Francesco Gentili
1,
Alessandro Neri
3,
Donato Casella
3 and
Maria Antonietta Mazzei
1,2
1
Diagnostic Imaging Unit, Department of Medical Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
2
Department of Medical, Surgical and Neuro Sciences and of Medical Sciences, University of Siena, 53100 Siena, Italy
3
Breast Cancer Surgery Unit, Department of Women’s and Children’s Health, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2026, 18(3), 363; https://doi.org/10.3390/cancers18030363
Submission received: 5 November 2025
/
Revised: 5 January 2026
/
Accepted: 13 January 2026
/
Published: 23 January 2026
(This article belongs to the Special Issue Enhancing Precision in Cancer Treatment: AI-Driven Innovations in Imaging)
Simple Summary
Accurate assessment of lymph node involvement is crucial for tailoring breast cancer treatment. This study explores an advanced imaging approach that combines dual-energy CT (DECT) with morphological features to distinguish benign from cancer-affected lymph nodes. By analyzing both the physical appearance of nodes and their chemical composition after contrast injection, our model can support non-invasive identification of metastatic nodes. Although iodine concentration remains informative, our findings show that water concentration provides complementary diagnostic information. Morphologic features, however, remain the cornerstone for nodal assessment. This integrated imaging strategy can enhance surgical planning, reduce unnecessary procedures and improve patient outcomes. Future large-scale studies are needed to standardize protocols and confirm these findings across diverse patient populations.
Abstract
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 < 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 (ΔAUC = 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.
