Spectral Precision: The Added Value of Dual-Energy CT for Axillary Lymph Node Characterization in Breast Cancer

<b>Background/Objectives</b>: 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). <b>Methods</b>: 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. <b>Results</b>: On univariate testing, all DECT parameters and morphological criteria differed significantly between metastatic and benign nodes (<i>p</i> < 0.01). In multivariate analysis, water concentration emerged as the only independent DECT predictor (odds ratio = 0.97; <i>p</i> = 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; <i>p</i> = 0.63, not significant), with internal cross-validation confirming stable performance. <b>Conclusions</b>: 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.

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Publication Details

Journal
Cancers
Published
2026-01-23
DOI
https://doi.org/10.3390/cancers18030363
Primary Topic
Advanced X-ray and CT Imaging
Type
article
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article

Spectral Precision: The Added Value of Dual-Energy CT for Axillary Lymph Node Characterization in Breast Cancer

S. Guerrini, Alessandro Neri, Iacopo Capitoni, Nunzia Di Meglio et al.
Cancers
Advanced X-ray and CT Imaging
article

Spectral Precision: The Added Value of Dual-Energy CT for Axillary Lymph Node Characterization in Breast Cancer

S. Guerrini, Alessandro Neri, Iacopo Capitoni, Nunzia Di Meglio, Armando Perrella, Giulio Bagnacci, Donato Casella, Cecilia Zampieri, Maria Antonietta Mazzei, Paola Morrone, Chiara Esposito
article en

Abstract

<b>Background/Objectives</b>: 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). <b>Methods</b>: 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. <b>Results</b>: On univariate testing, all DECT parameters and morphological criteria differed significantly between metastatic and benign nodes (<i>p</i> < 0.01). In multivariate analysis, water concentration emerged as the only independent DECT predictor (odds ratio = 0.97; <i>p</i> = 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; <i>p</i> = 0.63, not significant), with internal cross-validation confirming stable performance. <b>Conclusions</b>: 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.

CancersVol. 18(3)
University of Siena (IT), Azienda Ospedaliera Universitaria Senese (IT)
Openalex Percentile: Top 80%
Advanced X-ray and CT Imaging
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