ArticleBMC medical imaging2026
A combined model of ultrasound viscoelasticity and inflammatory indices for differentiating benign and malignant breast lesions.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundDifferentiating breast lesions relies on imaging and pathological biopsy. Ultrasound viscoelastic imaging quantitatively assesses tissue stiffness, while systemic inflammatory parameters reflect the host’s immune status. This study aimed to develop and validate a combined model utilizing both viscoelastic and inflammatory parameters to improve diagnostic accuracy.
methodsThis retrospective study enrolled 184 patients with 205 breast masses. All participants underwent preoperative ultrasound viscoelasticity examination and blood tests. Viscoelastic parameters (Young’s modulus, viscosity) and inflammatory indices (SII, NLR, PLR, LMR) were analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection, and a multivariate logistic regression model was constructed. Diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) analysis.
resultsMalignant lesions exhibited significantly elevated viscoelastic and inflammatory parameters compared to benign lesions. The combined model demonstrated superior diagnostic performance, with an area under the curve (AUC) of 0.934 (95% CI: 0.90–0.97), sensitivity of 84.78%, and specificity of 89.55%. DeLong’s test confirmed that the AUC of the combined model was significantly higher than that of all single-parameter approaches (all P < 0.001), representing an incremental gain of ΔAUC ≈ 0.19 compared to the best single elasticity parameter (EAMax, AUC = 0.740).
conclusionThe integration of ultrasound viscoelasticity and systemic inflammatory indices represents a promising non-invasive approach for distinguishing benign from malignant breast lesions. This combined model holds potential to optimize clinical decision-making and reduce unnecessary biopsies, pending further validation.
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