A Combined Model of Ultrasound Viscoelasticity and Inflammatory Indices for Differentiating Benign and Malignant Breast Lesions

A Combined Model of Ultrasound Viscoelasticity and Inflammatory Indices for Differentiating Benign and Malignant Breast Lesions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Combined Model of Ultrasound Viscoelasticity and Inflammatory Indices for Differentiating Benign and Malignant Breast Lesions Zhilin Yang, XinZheng Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7715156/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted 10 You are reading this latest preprint version Abstract Background : Differentiating 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. Methods: This retrospective study enrolled 184 patients with 205 breast masses. All participants underwent preoperative ultrasound viscoelasticity examination (Shear Wave Elastography) 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. Results: Malignant lesions exhibited significantly higher viscoelastic and inflammatory parameters compared to benign lesions. The combined model achieved an area under the curve (AUC) of 0.95 (95% CI: 0.91-0.98), with a sensitivity of 86.96% and a specificity of 89.55%, significantly outperforming any single parameter. Conclusion: The integration of ultrasound viscoelasticity and systemic inflammatory indices provides a powerful non-invasive tool for distinguishing benign from malignant breast lesions, holding significant potential to optimize clinical decision-making and reduce unnecessary biopsies. Breast cancer Ultrasound viscoelasticity Shear wave elastography Inflammatory indices Diagnostic model BI-RADS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Breast cancer is the most common malignancy among women worldwide. According to data from the World Health Organization's International Agency for Research on Cancer (IARC), there were approximately 2.3 million new cases globally in 2022, ranking first in incidence among all cancers [ 1 ] . In China, the incidence of early-stage breast cancer has also been increasing annually [ 2 ] . As many patients are asymptomatic initially, tumors are often diagnosed at advanced stages. However, improvements in imaging technology and medical knowledge dissemination have enhanced the early diagnosis rate in recent years, accompanied by a subsequent decline in mortality. Early and accurate diagnosis is critical for improving patient outcomes. Conventional ultrasound, mammography, and magnetic resonance imaging (MRI) are primary screening tools, with ultrasound being widely used due to its accessibility and cost-effectiveness [ 3 ] . The Breast Imaging-Reporting and Data System (BI-RADS) classification standardizes reporting but faces challenges in differentiating indeterminate lesions, particularly in BI-RADS category 4A (low suspicion for malignancy), where the cancer probability is low (2–10%) yet biopsy is typically recommended, leading to many unnecessary invasive procedures [ 4 ] . Ultrasound elastography, especially Shear Wave Elastography (SWE), provides quantitative assessment of tissue stiffness, adding functional information to conventional morphological evaluation. Studies have shown that SWE can improve the characterization of breast masses and help reduce false-positive biopsies when reassessing BI-RADS 3 and 4A lesions [ 5 ] . However, the diagnostic performance of a single modality remains limited. Beyond local tissue properties, systemic inflammation plays a crucial role in cancer development and progression [ 6 ] . Inflammatory indices derived from routine blood tests, such as the Systemic Immune-Inflammation Index (SII), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Lymphocyte-to-Monocyte Ratio (LMR), are readily available biomarkers that reflect the host's immune response and have demonstrated prognostic value in various cancers, including breast cancer [ 7 , 8 ] . Despite the independent value of these local and systemic markers, integrated diagnostic models combining ultrasound viscoelastic parameters with systemic inflammatory indices are lacking. Therefore, this study aims to develop and validate a combined diagnostic model based on ultrasound viscoelastic parameters and inflammatory indices. The goal is to achieve a precise "local-systemic" integrated assessment for differentiating benign from malignant breast lesions, particularly those indeterminate on conventional ultrasound. Additionally, this study explores correlations between these parameters and clinicopathological data, potentially offering new insights for early diagnosis and prognosis evaluation. Materials and Methods 2.1. Study Population This retrospective study collected data from inpatients at the Second Hospital of Shanxi Medical University between January 2024 and September 2025. A total of 205 breast mass specimens from 184 patients were included. All patients underwent preoperative routine blood tests, conventional breast ultrasound, viscoelasticity measurement, and had complete postoperative pathological data. The study protocol was approved by the Hospital's Medical Ethics Committee (Approval No: 2025-YX-268). The requirement for informed consent was waived due to the retrospective nature of the study. Inclusion Criteria : First-time visiting patients with a breast mass; Complete postoperative pathological results; No history of preoperative chemotherapy, radiotherapy, or other anticancer treatments; Preoperative ultrasound viscoelasticity examination and routine blood tests performed within one week before surgery or biopsy. Exclusion Criteria : Previous history of chemotherapy or radiotherapy; Age < 18 years; Presence of other malignant tumors; Presence of severe systemic complications; Inability to cooperate with the examination. 2.2. Ultrasound Viscoelastic Examination Process All ultrasound examinations were performed using a Mindray Resona7 high-end color Doppler ultrasound system (Mindray, China) equipped with a linear array transducer (L11-3U, frequency range 3–11 MHz).Patients were placed in a supine position. The operator first performed conventional two-dimensional and Doppler ultrasound scans to observe the morphology, size, margins, internal echogenicity, and blood flow of the breast mass, identifying the target lesion for viscoelastic measurement. The probe was stabilized, and after clear visualization of the target area, Shear Wave Elastography (SWE) mode was activated to capture shear wave propagation in real-time. On the generated color-coded elastogram superimposed on the B-mode image, the operator manually placed a standardized quantitative measurement box (Q-Box), covering the entire lesion and a surrounding 2mm "shell" area. Light probe pressure was applied to avoid tissue deformation. Patients were instructed to hold their breath briefly (~ 5s), and measurements were taken consecutively 3–5 times in the same region to ensure stability. The instrument automatically calculated parameters like Young's Modulus for each measurement, and the average, maximum, minimum values, and standard deviation (SD) were recorded. Representative images of benign and malignant lesions were stored (Fig. 1 and Fig. 2 , respectively). The following viscoelastic parameters were automatically calculated: Elasticity (Young's modulus, E, in kPa) Maximum (Max), mean, and minimum (Min) values within the mass area (EAMax, EAmean, EAmin), the surrounding shell (ESMax, ESmean, ESmin), and the combined area (EA'Max, EA'mean, EA'min). Viscosity (V, in m/s) Maximum (Max), mean, and minimum (Min) values within the mass area (VAMax, VAmean, VAmin), the surrounding shell (VSMax, VSmean, VSmin), and the combined area (VA'Max, VA'mean, VA'min). 2.3. Inflammatory indices Peripheral venous blood samples were collected preoperatively. Routine blood tests were performed using a Sysmex XN-9000 automated hematology analyzer (Sysmex Corporation, Japan). The following inflammatory indices were calculated: Systemic Immune-Inflammation Index (SII) = (platelet count × neutrophil count) / lymphocyte count. Neutrophil-to-Lymphocyte Ratio (NLR) = neutrophil count / lymphocyte count. Platelet-to-Lymphocyte Ratio (PLR) = platelet count / lymphocyte count. Lymphocyte-to-Monocyte Ratio (LMR) = lymphocyte count / monocyte count. 2.4.Statistical Methods Statistical analysis was performed using SPSS software (version 27). Measurement data conforming to a normal distribution are expressed as Mean ± SD, while non-normally distributed data are expressed as Median (Interquartile Range, IQR). Independent samples t-test was used if normality and homogeneity of variance were met; otherwise, the Mann-Whitney U test was used. Comparisons of categorical variables were performed using the chi-square test or Fisher's exact test. The Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation was used for feature selection. Variables with non-zero coefficients were included in a multivariate logistic regression model to build the combined diagnostic model. Diagnostic performance was evaluated using ROC curve analysis. A nomogram was constructed based on the final model. Spearman correlation and ANOVA were used to assess associations with clinicopathological features. A two-sided P-value < 0.05 was considered statistically significant. Results 3.1. Pathological Classification of Breast Masses Based on postoperative pathology, the 205 breast lesions were classified into benign (n = 67) and malignant (n = 138) groups (Table 1 ). Breast adenosis (29.9%) and fibroadenoma (31.3%) were most common in the benign group. Invasive ductal carcinoma constituted the majority (87.7%) of malignant lesions.The detailed pathological classification is presented in Table 1 . Table 1 Pathological Classification of 205 Breast Masses Group Pathological Type Number of Cases Percentage (%) Benign (n = 67) Breast Adenosis 20 29.9 Fibroadenoma 21 31.3 Intraductal Papilloma 9 13.4 Mixed Benign Lesions 7 10.4 Ductal Adenoma 1 1.5 Fibroepithelial Tumor 2 3 Inflammatory Lesion 6 9 Hyperplasia 1 1.5 Malignant (n = 138) Invasive Ductal Carcinoma 121 87.7 Invasive Lobular Carcinoma 1 0.7 Invasive Papillary Carcinoma 4 2.9 Ductal Carcinoma In Situ 8 5.8 Lobular Carcinoma In Situ 1 0.7 Mucinous Carcinoma 1 0.7 Mixed Carcinoma 1 0.7 Borderline Phyllodes Tumor 1 0.7 3.2. Baseline Characteristics Patients with malignant lesions were significantly older and had larger tumors compared to those with benign lesions (P < 0.001). Malignant lesions more frequently exhibited irregular shape, irregular margins, hypoechoic/solid echogenicity, and higher BI-RADS categories (all P < 0.001). All four inflammatory indices (NLR, PLR, SII, LMR) showed significant differences between the groups (all P < 0.01), with malignant cases having higher NLR, PLR, SII, and lower LMR (Table 2 ). Table 2 Comparison of Baseline Characteristics, Ultrasound Features, and Inflammatory Indices Between Benign and Malignant Groups Parameter Category Benign (n = 67) Malignant (n = 138) t/Z P-value Age Group, n (%) ≤ 30years (Youth) 8 (11.9%) 4 (2.9%) 53.823 50 years(Elderly) 14 (20.9%) 102 (73.9%) Tumor Size, n (%) ≤ 2 cm 51 (76.1%) 76 (55.1%) 14.868 < 0.001 2 ~ 5 cm 16 (23.9%) 50 (36.2%) ≥ 5 cm 0 (0%) 12 (8.7%) Tumor Margin, n (%) Regular 45 (67.2%) 16 (11.6%) 66.634 < 0.001 Irregular 22 (32.8%) 122 (88.4%) Tumor Shape, n (%) Regular (Oval/Round) 50 (74.6%) 28 (20.3%) 56.494 < 0.001 Irregular 17 (25.4%) 110 (79.7%) Echogenicity, n (%) Anechoic/Cystic 57 (85.1%) 45 (32.6%) 58.888 < 0.001 Hypoechoic/Solid 10 (14.9%) 93 (67.4%) Blood Flow Grade, n (%) Grade0- I 62 (92.5%) 104(75.4%) 21.799 < 0.001 Grade II-III 5 (7.5%) 34 (24.6%) BI-RADS Category, n (%) 3-4a 60 (89.6%) 10 (7.2%) 135.874 < 0.001 4b-6 7 (10.4%) 128 (92.8%) Inflammatory Indices, Median (IQR) NLR 1.92 (1.63, 2.20) 2.57 (2.20, 3.29) -8.084 < 0.001 PLR 124.43(109.35, 155.41) 168.58(144.70, 208.34) -7.523 < 0.001 LMR 5.05 (4.36, 6.68) 4.39 (3.41, 5.57) -3.139 0.002 SII 457.02(357.35, 552.52) 709.74(579.18, 941.11) -8.607 < 0.001 3.3. Feature selection and diagnostic model LASSO regression identified nine non-zero coefficient predictors at the optimal lambda value. These variables were incorporated into a multivariate logistic regression model to establish the combined diagnostic model. The selected variables were: EAMax, EA'Max, EA'min, VAmean, VSMax, VSmin, NLR, PLR, and SII.The feature selection process is illustrated in Fig. 3 . Table 3 Diagnostic Performance of Individual Parameters and the Combined Model Parameter AUC Optimal Cut-off Value Sensitivity (%) Specificity (%) 95% CI Youden Index Combined Diagnosis 0.95 0.665 86.96 89.55 0.91–0.98 76.51 EAMax 0.74 63.62 56.52 82.09 0.67–0.81 38.61 EA'Max 0.74 77.53 59.42 77.61 0.67–0.81 37.03 EA'Min 0.6 5.17 71.01 55.22 0.52–0.69 26.23 VAmean 0.59 1.22 57.97 64.18 0.52–0.67 22.15 VSMax 0.7 4.1 76.81 55.22 0.63–0.78 32.03 VSmin 0.63 0.06 90.58 32.84 0.54–0.72 23.42 NLR 0.85 2.1 87.68 65.67 0.79–0.90 53.35 The diagnostic performance of the individual parameters and the combined model was evaluated using ROC analysis (Fig. 4 , Table 3 ). Strikingly, the combined model achieved an AUC of 0.95 (95% CI: 0.91–0.98), which was substantially higher than any single parameter. At the optimal cutoff value of 0.665, the model maintained high sensitivity (86.96%) while achieving excellent specificity (89.55%). The high specificity of the model carries significant clinical implications, as it indicates a strong capability for the accurate discrimination of benign breast lesions, which is crucial for reducing unnecessary biopsies. To facilitate clinical application, a nomogram was developed by integrating four key independent predictors: EAMax, VSMax, NLR, and SII (Fig. 5 ). This visual tool allows clinicians to estimate the individualized probability of malignancy for a given breast lesion. 3.4. Correlation with Clinicopathological Features Table 4 Associations between Viscoelastic/Inflammatory Parameters and Clinicopathological Features in Malignant Cases Parameter 1 Parameter 2 Analysis Method Correlation Coefficient/t/F P-value Significance Tumor Size NLR Spearman Analysis .277** 0.001 Tumor size positively correlates with NLR Tumor Size PLR Spearman Analysis .174* 0.042 Tumor size positively correlates with PLR Tumor Size LMR Spearman Analysis − .182* 0.033 Tumor size negatively correlates with LMR Tumor Size SII Spearman Analysis .203* 0.017 Tumor size positively correlates with SII Tumor Size EAMean Spearman Analysis .168* 0.05 Tumor size positively correlates with EAmean Tumor Size EAMax Spearman Analysis .225** 0.008 Tumor size positively correlates with EAmax Tumor Size ESMean Spearman Analysis .179* 0.036 Tumor size positively correlates with ESmean Tumor Size ESMax Spearman Analysis .205* 0.016 Tumor size positively correlates with ESMax Tumor Size EA'Max Spearman Analysis .197* 0.021 Tumor size positively correlates with EA'max Lymph Node Metastasis NLR Independent t-test -3.209 0.002 NLR negatively correlates with lymph node metastasis status Lymph Node Metastasis LMR Independent t-test 2.393 0.018 LMR positively correlates with lymph node metastasis status Lymph Node Metastasis SII Independent t-test -2.147 0.034 SII negatively correlates with lymph node metastasis status Molecular Subtype PLR One-way ANOVA 4.157 0.008 Inflammatory status (PLR) differs across molecular subtypes Molecular Subtype SII One-way ANOVA 2.856 0.04 Inflammatory status (SII) differs across molecular subtypes Histological Grade ESmean One-way ANOVA 3.151 0.046 Mean "shell" elasticity (ESmean) differs across histological grades We further explored the associations between the selected parameters and key clinicopathological features in malignant cases (Table 4 ). In malignant cases, larger tumor size was positively correlated with higher values of viscoelastic parameters (e.g., EAMax, ρ = 0.225, P = 0.008) and inflammatory indices (e.g., NLR, ρ = 0.277, P = 0.001). Significant differences in inflammatory indices (PLR, SII) were observed across different molecular subtypes. The mean elasticity of the surrounding shell (ESmean) varied significantly with histological grade.This suggests that both local tissue stiffness and systemic inflammation intensify as the tumor grows. Additionally, significant differences in inflammatory indices (PLR, SII) were observed across different molecular subtypes, and the mean elasticity of the surrounding shell (ESmean) varied with histological grade. These correlations indicate that the parameters incorporated into our model are not merely diagnostic markers but may also reflect underlying tumor biology and aggressiveness. Discussion This study developed a novel diagnostic model that integrates local tissue mechanical properties from ultrasound viscoelasticity with systemic inflammatory status. The combined model exhibited outstanding performance (AUC = 0.95) in differentiating benign from malignant breast lesions, significantly surpassing the diagnostic capability of individual parameters. The exceptional performance of our model, particularly its high specificity (89.55%), holds direct and significant clinical implications. Specifically for BI-RADS 4A lesions—which carry a low probability of malignancy yet currently mandate biopsy—a low score from our model could empower clinicians to confidently recommend short-term follow-up rather than immediate biopsy, potentially avoiding a substantial proportion of unnecessary procedures. Conversely, a high model score within this category could reinforce the decision to proceed with biopsy, ensuring timely diagnosis. Thus, our model acts as a powerful decision-support tool to optimize risk stratification within the diagnostic gray zone. Unlike conventional ultrasound, which primarily depicts anatomical morphology, ultrasound viscoelastic imaging quantitatively assesses tissue mechanical properties by measuring viscoelastic coefficients. This technique is sensitive to pathological alterations such as microcalcifications and increased fibrous or collagen content, thereby providing supplementary information for differentiating breast malignancies [ 9 ] . Consistent with previous reports, our study demonstrated that the incorporation of maximum elasticity values into the BI-RADS framework enhances diagnostic accuracy and aids in avoiding unnecessary biopsies [ 9 ] . In the present cohort, viscoelastic parameters—including EAMax, ESMax, EA'Max, and VAmean—were significantly elevated in malignant lesions compared to benign ones (all *p* < 0.05), indicating greater tissue stiffness and viscosity in cancers. This phenomenon may be attributed to the histopathological characteristics of malignant tumors, which typically exhibit denser fibrous stroma, increased cellularity, and reduced deformability relative to the looser architecture of benign lesions. Underlying mechanisms include tumor-induced angiogenesis, connective tissue proliferation, stromal invasion, and interstitial edema, collectively leading to decreased elasticity and increased overall hardness [ 10 , 11 ] —a finding that aligns with the palpable firmness characteristic of clinical breast examinations. Within the tumor microenvironment, systemic inflammation plays a pivotal role in oncogenesis, proliferation, and metastasis. Neutrophils have been shown to suppress the cytotoxic functions of lymphocytes, natural killer cells, and activated T cells, while monocytes differentiate into tumor-associated macrophages, and lymphocytes modulate tumor cell surveillance [ 12 ] . In line with this, inflammatory biomarkers such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII)—readily derived from routine blood tests—have emerged as accessible indicators with prognostic significance in various solid tumors [ 13 ] . These inflammatory mediators engage key molecular pathways—including the CXCL8/CXCR2 axis, NF-κB signaling, reactive oxygen species (ROS) production, and neutrophil extracellular trap (NET) formation—thereby influencing angiogenesis, invasion, and metastatic processes in breast cancer. The marked elevation of these indices in our malignant cohort further underscores the involvement of systemic inflammation in breast carcinogenesis. Moreover, the observed correlations between larger tumor size and elevated viscoelastic and inflammatory parameters suggest that both local tissue stiffness and systemic inflammatory responses intensify with disease progression. Breast cancers often exhibit a desmoplastic reaction characterized by abundant fibrous components, where cancer-associated fibroblasts (CAFs) promote tumor progression through mechanisms such as NLRP3 inflammasome activation and IL-1β secretion [ 14 ] . Additionally, elevated SII levels have been associated with axillary lymph node metastasis, supporting the prognostic relevance of inflammatory markers in breast cancer [ 15 ] . The associations identified in our study between these parameters and clinicopathological features—such as molecular subtype, histological grade, and lymph node status—suggest that they may reflect underlying tumor biology and aggressiveness, potentially offering valuable insights for diagnosis and risk stratification. Limitations: This study has several limitations, including its single-center retrospective design, potential operator-dependence of viscoelastic measurements, and limited sample size for certain rare molecular subtypes. Future multi-center prospective studies with larger cohorts are warranted to validate the model's generalizability. Conclusion In conclusion, the ‘local-systemic’ model developed in this study demonstrates high diagnostic accuracy. If validated prospectively, it could be integrated into clinical workflows to guide the management of BI-RADS 4A lesions, potentially reducing unnecessary biopsies while maintaining diagnostic sensitivity. Abbreviations AUC Area Under the Curve BI-RADS Breast Imaging-Reporting and Data System LASSO Least Absolute Shrinkage and Selection Operator LMR Lymphocyte-to-Monocyte Ratio NLR Neutrophil-to-Lymphocyte Ratio PLR Platelet-to-Lymphocyte Ratio ROC Receiver Operating Characteristic SII Systemic Immune-Inflammation Index SWE Shear Wave Elastography Declarations Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of The Second Hospital of Shanxi Medical University (Approval No: 2025-YX-268). This study was conducted in accordance with the Declaration of Helsinki.The requirement for informed consent was waived for this retrospective analysis. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This study received no specific funding. Author Contribution Yang Zhilin contributed to the study conception, design, data analysis, and drafting of the manuscript. Yang Zhilin was responsible for data collection, statistical analysis, and preparation of figures. Li Xinzheng contributed to literature review, interpretation of results, and critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660 . Huang DQ, Yang M, Xiong W, et al. Analysis of the disease burden and attributable risk factors of early-onset female breast cancer in China and globally from 1990 to 2021. Xiehe Yixue Zazhi. 2025;16(3):777–84. 10.12290/xhyxzz.2024-1083 . Spear G, Lee K, DePersia A, Lienhoop T, Saha P. Updates in breast cancer screening and diagnosis. Curr Treat Options Oncol. 2024;25(11):1451–60. 10.1007/s11864-024-01271-8 . Covington MF. Ultrasound elastography may better characterize BI-RADS 3 and BI-RADS 4A lesions to decrease false-positive breast biopsy rates and enable earlier detection of breast cancer. J Am Coll Radiol. 2022;19(5):635–6. 10.1016/j.jacr.2022.02.023 . Deng WY, Tang LN, Yang LC, et al. Value of a contrast-enhanced ultrasound prediction model in optimizing BI-RADS classification of breast lesions. Zhonghua Chaosheng Yingxiangxue Zazhi. 2018;27(4):318–22. 10.3760/cma.j.issn.1004-4477.2018.04.011 . Zhang Y, Song M, Yang Z, et al. Healthy lifestyles, systemic inflammation and breast cancer risk: a mediation analysis. BMC Cancer. 2024;24(1):208. 10.1186/s12885-024-11931-5 . Zhang S, Cheng T. Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis. Ann Med. 2024;56(1):2337729. 10.1080/07853890.2024.2337729 . Wen Y, Tang Y, Zou Q. Association between systemic immune-inflammatory biomarkers (SII, NLR, PLR, LMR) and breast cancer. J Cancer Res Clin Oncol. 2023;149(11):9365–75. 10.1007/s00432-023-04831-w . Li Y, Luan Y, Chang J, et al. Differential value of shear wave elastography parameters for breast masses and their correlation with clinicopathological characteristics [published in Chinese]. Oncoradiology. 2023;32(6):506–11. 10.19732/j.cnki.2096-6210.2023.06.004 . Zhao L, Li F, Du L, et al. Study on the additional diagnostic value of contrast-enhanced ultrasound and ultrasound elastography for non-palpable BI-RADS category 4 breast masses. Oncoradiology. 2023;32(6):512–20. 10.19732/j.cnki.2096-6210.2023.06.005 . Weismann C. Ultraschall-Elastographie-Techniken bei Brustkrebs [Ultrasound elastography techniques in breast cancer]. Radiologe. 2021;61(2):170–6. 10.1007/s00117-020-00799-8 . Wan C, Zhou L, Jin Y, Li F, Wang L, Yin W, et al. Strain ultrasonic elastography imaging features of locally advanced breast cancer: association with response to neoadjuvant chemotherapy and recurrence-free survival. BMC Med Imaging. 2023;23(1):216. 10.1186/s12880-023-01168-2 . Song D, Li X, Zhang X. Expression and prognostic value of ratios of platelet lymphocyte, neutrophil lymphocyte and lymphocyte monocyte in breast cancer patients. Am J Transl Res. 2022;14(5):3233–9. PMID: 35702097. Ershaid N, Sharon Y, Doron H, Raz Y, Shani O, Cohen N, et al. NLRP3 inflammasome in fibroblasts links tissue damage with inflammation in breast cancer progression and metastasis. Nat Commun. 2019;10(1):4375. 10.1038/s41467-019-12370-8 . Tong L, Wang S, Zhang R, Wu Y, Xu D, Chen L. High levels of SII and PIV are the risk factors of axillary lymph node metastases in breast cancer: a retrospective study. Int J Gen Med. 2023;16:2211–8. 10.2147/IJGM.S411592 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 18 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor invited by journal 30 Sep, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 25 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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14:20:34","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89936,"visible":true,"origin":"","legend":"","description":"","filename":"96dc72982d604eb3b02a22b0603422e21structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7715156/v1/544446a86275015a3b962057.xml"},{"id":93843557,"identity":"39417a39-dc46-457e-b90f-cb7c3b52fcdc","added_by":"auto","created_at":"2025-10-18 14:28:34","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98549,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7715156/v1/fa9f78a02daa11e145e8b89c.html"},{"id":93843740,"identity":"ba8fb2db-1497-4834-a7a4-bb88c4228954","added_by":"auto","created_at":"2025-10-18 14:36:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93756,"visible":true,"origin":"","legend":"\u003cp\u003eUltrasound and viscoelastic findings of a representative benign breast lesion.\u003c/p\u003e\n\u003cp\u003eNote:The viscoelasticity map (color-coded elastogram) overlaid on the B-mode image shows the lesion exhibiting predominantly green and blue colors, indicating low elasticity (soft consistency). The quantitative measurement box (Q-Box) is placed over the lesion.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7715156/v1/badb421a66e4bbdb20c33943.jpeg"},{"id":93842634,"identity":"4235db92-b631-4f53-81b0-ac7cc3fed1b7","added_by":"auto","created_at":"2025-10-18 14:20:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":474773,"visible":true,"origin":"","legend":"\u003ch4\u003eUltrasound and viscoelastic findings of a representative malignant breast lesion.\u003c/h4\u003e\n\u003cp\u003eNote:The viscoelasticity map shows the lesion exhibiting predominantly red and yellow colors, indicating high elasticity (stiff consistency). The Q-Box is placed over the lesion and the immediate surrounding tissue.3.Measurement Parameters and Statistical Analysis\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7715156/v1/b276263d9f46ff8625e300f0.png"},{"id":93843549,"identity":"01b3b67a-2fd8-405a-8e98-4ddf2f6c81a8","added_by":"auto","created_at":"2025-10-18 14:28:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13900,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model.Note: (A) LASSO coefficient profiles of the 22 candidate variables. (B) Selection of the optimal tuning parameter (λ) using 10-fold cross-validation. The optimal λ (lambda.min) was chosen where the cross-validated binomial deviance was minimized. The vertical dashed lines represent lambda.min and lambda.1se (the largest value of λ such that the error is within 1 standard error of the minimum).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7715156/v1/44333e20579fd53b2e27de2e.png"},{"id":93843742,"identity":"347821c0-2a2e-421b-bc1e-0b6aec26c0bf","added_by":"auto","created_at":"2025-10-18 14:36:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":131156,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for the differentiation of benign and malignant breast lesions.Note: EAMax, maximum elasticity within the mass; ESMax, maximum elasticity of the perilesional 2-mm shell; EA'max, maximum elasticity of the combined mass and shell region; EA'min, minimum elasticity of the combined mass and shell region; VAMean, mean viscosity within the mass; VSmax, maximum viscosity of the perilesional 2-mm shell; VSmin, minimum viscosity of the perilesional 2-mm shell; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammation index; AUC, area under the curve.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7715156/v1/e97038166caeb0a232783e4f.png"},{"id":93842637,"identity":"109a3bd1-81cc-4266-a928-d09397de888c","added_by":"auto","created_at":"2025-10-18 14:20:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46524,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the probability of malignancy in breast lesions.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7715156/v1/a07ebc8ece296a0c48da3420.png"},{"id":101153283,"identity":"913d8b80-fceb-4734-ad92-df4bbfa8c4d3","added_by":"auto","created_at":"2026-01-26 16:14:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1802818,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7715156/v1/aa20dd30-a242-4182-a00b-5040c62a64e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Combined Model of Ultrasound Viscoelasticity and Inflammatory Indices for Differentiating Benign and Malignant Breast Lesions","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer is the most common malignancy among women worldwide. According to data from the World Health Organization's International Agency for Research on Cancer (IARC), there were approximately 2.3\u0026nbsp;million new cases globally in 2022, ranking first in incidence among all cancers \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In China, the incidence of early-stage breast cancer has also been increasing annually\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. As many patients are asymptomatic initially, tumors are often diagnosed at advanced stages. However, improvements in imaging technology and medical knowledge dissemination have enhanced the early diagnosis rate in recent years, accompanied by a subsequent decline in mortality.\u003c/p\u003e\u003cp\u003eEarly and accurate diagnosis is critical for improving patient outcomes. Conventional ultrasound, mammography, and magnetic resonance imaging (MRI) are primary screening tools, with ultrasound being widely used due to its accessibility and cost-effectiveness\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The Breast Imaging-Reporting and Data System (BI-RADS) classification standardizes reporting but faces challenges in differentiating indeterminate lesions, particularly in BI-RADS category 4A (low suspicion for malignancy), where the cancer probability is low (2\u0026ndash;10%) yet biopsy is typically recommended, leading to many unnecessary invasive procedures \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUltrasound elastography, especially Shear Wave Elastography (SWE), provides quantitative assessment of tissue stiffness, adding functional information to conventional morphological evaluation. Studies have shown that SWE can improve the characterization of breast masses and help reduce false-positive biopsies when reassessing BI-RADS 3 and 4A lesions \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, the diagnostic performance of a single modality remains limited.\u003c/p\u003e\u003cp\u003eBeyond local tissue properties, systemic inflammation plays a crucial role in cancer development and progression \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Inflammatory indices derived from routine blood tests, such as the Systemic Immune-Inflammation Index (SII), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Lymphocyte-to-Monocyte Ratio (LMR), are readily available biomarkers that reflect the host's immune response and have demonstrated prognostic value in various cancers, including breast cancer \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Despite the independent value of these local and systemic markers, integrated diagnostic models combining ultrasound viscoelastic parameters with systemic inflammatory indices are lacking.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to develop and validate a combined diagnostic model based on ultrasound viscoelastic parameters and inflammatory indices. The goal is to achieve a precise \"local-systemic\" integrated assessment for differentiating benign from malignant breast lesions, particularly those indeterminate on conventional ultrasound. Additionally, this study explores correlations between these parameters and clinicopathological data, potentially offering new insights for early diagnosis and prognosis evaluation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Study Population\u003c/h2\u003e\n\u003cp\u003eThis retrospective study collected data from inpatients at the Second Hospital of Shanxi Medical University between January 2024 and September 2025. A total of 205 breast mass specimens from 184 patients were included. All patients underwent preoperative routine blood tests, conventional breast ultrasound, viscoelasticity measurement, and had complete postoperative pathological data. The study protocol was approved by the Hospital's Medical Ethics Committee (Approval No: 2025-YX-268). The requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFirst-time visiting patients with a breast mass;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eComplete postoperative pathological results;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNo history of preoperative chemotherapy, radiotherapy, or other anticancer treatments;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePreoperative ultrasound viscoelasticity examination and routine blood tests performed within one week before surgery or biopsy.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e:\u003cbr /\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ePrevious history of chemotherapy or radiotherapy;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;18 years;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePresence of other malignant tumors;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePresence of severe systemic complications;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInability to cooperate with the examination.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Ultrasound Viscoelastic Examination Process\u003c/h2\u003e\n\u003cp\u003eAll ultrasound examinations were performed using a Mindray Resona7 high-end color Doppler ultrasound system (Mindray, China) equipped with a linear array transducer (L11-3U, frequency range 3\u0026ndash;11 MHz).Patients were placed in a supine position. The operator first performed conventional two-dimensional and Doppler ultrasound scans to observe the morphology, size, margins, internal echogenicity, and blood flow of the breast mass, identifying the target lesion for viscoelastic measurement. The probe was stabilized, and after clear visualization of the target area, Shear Wave Elastography (SWE) mode was activated to capture shear wave propagation in real-time. On the generated color-coded elastogram superimposed on the B-mode image, the operator manually placed a standardized quantitative measurement box (Q-Box), covering the entire lesion and a surrounding 2mm \"shell\" area. Light probe pressure was applied to avoid tissue deformation. Patients were instructed to hold their breath briefly (~\u0026thinsp;5s), and measurements were taken consecutively 3\u0026ndash;5 times in the same region to ensure stability. The instrument automatically calculated parameters like Young's Modulus for each measurement, and the average, maximum, minimum values, and standard deviation (SD) were recorded. Representative images of benign and malignant lesions were stored (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively).\u003c/p\u003e\n\u003cp\u003eThe following viscoelastic parameters were automatically calculated:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElasticity (Young's modulus, E, in kPa)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaximum (Max), mean, and minimum (Min) values within the mass area (EAMax, EAmean, EAmin), the surrounding shell (ESMax, ESmean, ESmin), and the combined area (EA'Max, EA'mean, EA'min).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eViscosity (V, in m/s)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaximum (Max), mean, and minimum (Min) values within the mass area (VAMax, VAmean, VAmin), the surrounding shell (VSMax, VSmean, VSmin), and the combined area (VA'Max, VA'mean, VA'min).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Inflammatory indices\u003c/h2\u003e\n\u003cp\u003ePeripheral venous blood samples were collected preoperatively. Routine blood tests were performed using a Sysmex XN-9000 automated hematology analyzer (Sysmex Corporation, Japan). The following inflammatory indices were calculated:\u003c/p\u003e\n\u003cp\u003eSystemic Immune-Inflammation Index (SII) = (platelet count \u0026times; neutrophil count) / lymphocyte count.\u003c/p\u003e\n\u003cp\u003eNeutrophil-to-Lymphocyte Ratio (NLR)\u0026thinsp;=\u0026thinsp;neutrophil count / lymphocyte count.\u003c/p\u003e\n\u003cp\u003ePlatelet-to-Lymphocyte Ratio (PLR)\u0026thinsp;=\u0026thinsp;platelet count / lymphocyte count.\u003c/p\u003e\n\u003cp\u003eLymphocyte-to-Monocyte Ratio (LMR)\u0026thinsp;=\u0026thinsp;lymphocyte count / monocyte count.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4.Statistical Methods\u003c/h2\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS software (version 27). Measurement data conforming to a normal distribution are expressed as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, while non-normally distributed data are expressed as Median (Interquartile Range, IQR). Independent samples t-test was used if normality and homogeneity of variance were met; otherwise, the Mann-Whitney U test was used. Comparisons of categorical variables were performed using the chi-square test or Fisher's exact test. The Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation was used for feature selection. Variables with non-zero coefficients were included in a multivariate logistic regression model to build the combined diagnostic model. Diagnostic performance was evaluated using ROC curve analysis. A nomogram was constructed based on the final model. Spearman correlation and ANOVA were used to assess associations with clinicopathological features. A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Pathological Classification of Breast Masses\u003c/h2\u003e\u003cp\u003eBased on postoperative pathology, the 205 breast lesions were classified into benign (n\u0026thinsp;=\u0026thinsp;67) and malignant (n\u0026thinsp;=\u0026thinsp;138) groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Breast adenosis (29.9%) and fibroadenoma (31.3%) were most common in the benign group. Invasive ductal carcinoma constituted the majority (87.7%) of malignant lesions.The detailed pathological classification is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePathological Classification of 205 Breast Masses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePathological Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreast Adenosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFibroadenoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntraductal Papilloma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed Benign Lesions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuctal Adenoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFibroepithelial Tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInflammatory Lesion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHyperplasia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvasive Ductal Carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvasive Lobular Carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvasive Papillary Carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuctal Carcinoma In Situ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLobular Carcinoma In Situ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMucinous Carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed Carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBorderline Phyllodes Tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Baseline Characteristics\u003c/h2\u003e\u003cp\u003ePatients with malignant lesions were significantly older and had larger tumors compared to those with benign lesions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Malignant lesions more frequently exhibited irregular shape, irregular margins, hypoechoic/solid echogenicity, and higher BI-RADS categories (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All four inflammatory indices (NLR, PLR, SII, LMR) showed significant differences between the groups (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with malignant cases having higher NLR, PLR, SII, and lower LMR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Baseline Characteristics, Ultrasound Features, and Inflammatory Indices Between Benign and Malignant Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et/Z\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAge Group, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;30years (Youth)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (11.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e53.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u0026thinsp;~\u0026thinsp;50years(Middle-aged)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (67.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32 (23.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;50 years(Elderly)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102 (73.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTumor Size, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;2 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (76.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76 (55.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e14.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026thinsp;~\u0026thinsp;5 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTumor Margin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (67.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e66.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIrregular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (32.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e122 (88.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTumor Shape, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegular (Oval/Round)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (74.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e56.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIrregular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (25.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e110 (79.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEchogenicity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnechoic/Cystic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (85.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e58.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypoechoic/Solid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93 (67.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBlood Flow Grade, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade0- I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (92.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e104(75.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e21.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade II-III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBI-RADS Category, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3-4a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (89.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e135.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4b-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e128 (92.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eInflammatory Indices, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.92 (1.63, 2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.57 (2.20, 3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124.43(109.35, 155.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e168.58(144.70, 208.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.05 (4.36, 6.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.39 (3.41, 5.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e457.02(357.35, 552.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e709.74(579.18, 941.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Feature selection and diagnostic model\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLASSO regression identified nine non-zero coefficient predictors at the optimal lambda value. These variables were incorporated into a multivariate logistic regression model to establish the combined diagnostic model. The selected variables were: EAMax, EA'Max, EA'min, VAmean, VSMax, VSmin, NLR, PLR, and SII.The feature selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic Performance of Individual Parameters and the Combined Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOptimal Cut-off Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYouden Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.91\u0026ndash;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e76.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEAMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.67\u0026ndash;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e38.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEA'Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.67\u0026ndash;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e37.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEA'Min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.52\u0026ndash;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVAmean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.52\u0026ndash;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVSMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.63\u0026ndash;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVSmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.54\u0026ndash;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.79\u0026ndash;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e53.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe diagnostic performance of the individual parameters and the combined model was evaluated using ROC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Strikingly, the combined model achieved an AUC of 0.95 (95% CI: 0.91\u0026ndash;0.98), which was substantially higher than any single parameter. At the optimal cutoff value of 0.665, the model maintained high sensitivity (86.96%) while achieving excellent specificity (89.55%). The high specificity of the model carries significant clinical implications, as it indicates a strong capability for the accurate discrimination of benign breast lesions, which is crucial for reducing unnecessary biopsies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo facilitate clinical application, a nomogram was developed by integrating four key independent predictors: EAMax, VSMax, NLR, and SII (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This visual tool allows clinicians to estimate the individualized probability of malignancy for a given breast lesion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Correlation with Clinicopathological Features\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between Viscoelastic/Inflammatory Parameters and Clinicopathological Features in Malignant Cases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnalysis Method\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCorrelation Coefficient/t/F\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.277**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size positively correlates with NLR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.174*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size positively correlates with PLR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.182*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size negatively correlates with LMR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.203*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size positively correlates with SII\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.168*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size positively correlates with EAmean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.225**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size positively correlates with EAmax\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.179*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size positively correlates with ESmean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.205*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size positively correlates with ESMax\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEA'Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.197*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTumor size positively correlates with EA'max\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph Node Metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndependent t-test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNLR negatively correlates with lymph node metastasis status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph Node Metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndependent t-test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLMR positively correlates with lymph node metastasis status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph Node Metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndependent t-test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSII negatively correlates with lymph node metastasis status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular Subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne-way ANOVA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInflammatory status (PLR) differs across molecular subtypes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular Subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne-way ANOVA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInflammatory status (SII) differs across molecular subtypes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistological Grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESmean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne-way ANOVA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean \"shell\" elasticity (ESmean) differs across histological grades\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe further explored the associations between the selected parameters and key clinicopathological features in malignant cases (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In malignant cases, larger tumor size was positively correlated with higher values of viscoelastic parameters (e.g., EAMax, ρ\u0026thinsp;=\u0026thinsp;0.225, P\u0026thinsp;=\u0026thinsp;0.008) and inflammatory indices (e.g., NLR, ρ\u0026thinsp;=\u0026thinsp;0.277, P\u0026thinsp;=\u0026thinsp;0.001). Significant differences in inflammatory indices (PLR, SII) were observed across different molecular subtypes. The mean elasticity of the surrounding shell (ESmean) varied significantly with histological grade.This suggests that both local tissue stiffness and systemic inflammation intensify as the tumor grows. Additionally, significant differences in inflammatory indices (PLR, SII) were observed across different molecular subtypes, and the mean elasticity of the surrounding shell (ESmean) varied with histological grade. These correlations indicate that the parameters incorporated into our model are not merely diagnostic markers but may also reflect underlying tumor biology and aggressiveness.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed a novel diagnostic model that integrates local tissue mechanical properties from ultrasound viscoelasticity with systemic inflammatory status. The combined model exhibited outstanding performance (AUC\u0026thinsp;=\u0026thinsp;0.95) in differentiating benign from malignant breast lesions, significantly surpassing the diagnostic capability of individual parameters.\u003c/p\u003e\u003cp\u003eThe exceptional performance of our model, particularly its high specificity (89.55%), holds direct and significant clinical implications. Specifically for BI-RADS 4A lesions\u0026mdash;which carry a low probability of malignancy yet currently mandate biopsy\u0026mdash;a low score from our model could empower clinicians to confidently recommend short-term follow-up rather than immediate biopsy, potentially avoiding a substantial proportion of unnecessary procedures. Conversely, a high model score within this category could reinforce the decision to proceed with biopsy, ensuring timely diagnosis. Thus, our model acts as a powerful decision-support tool to optimize risk stratification within the diagnostic gray zone.\u003c/p\u003e\u003cp\u003eUnlike conventional ultrasound, which primarily depicts anatomical morphology, ultrasound viscoelastic imaging quantitatively assesses tissue mechanical properties by measuring viscoelastic coefficients. This technique is sensitive to pathological alterations such as microcalcifications and increased fibrous or collagen content, thereby providing supplementary information for differentiating breast malignancies \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Consistent with previous reports, our study demonstrated that the incorporation of maximum elasticity values into the BI-RADS framework enhances diagnostic accuracy and aids in avoiding unnecessary biopsies \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In the present cohort, viscoelastic parameters\u0026mdash;including EAMax, ESMax, EA'Max, and VAmean\u0026mdash;were significantly elevated in malignant lesions compared to benign ones (all *p* \u0026lt; 0.05), indicating greater tissue stiffness and viscosity in cancers. This phenomenon may be attributed to the histopathological characteristics of malignant tumors, which typically exhibit denser fibrous stroma, increased cellularity, and reduced deformability relative to the looser architecture of benign lesions. Underlying mechanisms include tumor-induced angiogenesis, connective tissue proliferation, stromal invasion, and interstitial edema, collectively leading to decreased elasticity and increased overall hardness \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;a finding that aligns with the palpable firmness characteristic of clinical breast examinations.\u003c/p\u003e\u003cp\u003eWithin the tumor microenvironment, systemic inflammation plays a pivotal role in oncogenesis, proliferation, and metastasis. Neutrophils have been shown to suppress the cytotoxic functions of lymphocytes, natural killer cells, and activated T cells, while monocytes differentiate into tumor-associated macrophages, and lymphocytes modulate tumor cell surveillance \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In line with this, inflammatory biomarkers such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII)\u0026mdash;readily derived from routine blood tests\u0026mdash;have emerged as accessible indicators with prognostic significance in various solid tumors \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. These inflammatory mediators engage key molecular pathways\u0026mdash;including the CXCL8/CXCR2 axis, NF-κB signaling, reactive oxygen species (ROS) production, and neutrophil extracellular trap (NET) formation\u0026mdash;thereby influencing angiogenesis, invasion, and metastatic processes in breast cancer. The marked elevation of these indices in our malignant cohort further underscores the involvement of systemic inflammation in breast carcinogenesis.\u003c/p\u003e\u003cp\u003eMoreover, the observed correlations between larger tumor size and elevated viscoelastic and inflammatory parameters suggest that both local tissue stiffness and systemic inflammatory responses intensify with disease progression. Breast cancers often exhibit a desmoplastic reaction characterized by abundant fibrous components, where cancer-associated fibroblasts (CAFs) promote tumor progression through mechanisms such as NLRP3 inflammasome activation and IL-1β secretion \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Additionally, elevated SII levels have been associated with axillary lymph node metastasis, supporting the prognostic relevance of inflammatory markers in breast cancer \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The associations identified in our study between these parameters and clinicopathological features\u0026mdash;such as molecular subtype, histological grade, and lymph node status\u0026mdash;suggest that they may reflect underlying tumor biology and aggressiveness, potentially offering valuable insights for diagnosis and risk stratification.\u003c/p\u003e\u003cp\u003eLimitations: This study has several limitations, including its single-center retrospective design, potential operator-dependence of viscoelastic measurements, and limited sample size for certain rare molecular subtypes. Future multi-center prospective studies with larger cohorts are warranted to validate the model's generalizability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the \u0026lsquo;local-systemic\u0026rsquo; model developed in this study demonstrates high diagnostic accuracy. If validated prospectively, it could be integrated into clinical workflows to guide the management of BI-RADS 4A lesions, potentially reducing unnecessary biopsies while maintaining diagnostic sensitivity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBI-RADS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBreast Imaging-Reporting and Data System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLymphocyte-to-Monocyte Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeutrophil-to-Lymphocyte Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet-to-Lymphocyte Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic Immune-Inflammation Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSWE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eShear Wave Elastography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study was approved by the Medical Ethics Committee of The Second Hospital of Shanxi Medical University (Approval No: 2025-YX-268). This study was conducted in accordance with the Declaration of Helsinki.The requirement for informed consent was waived for this retrospective analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study received no specific funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYang Zhilin contributed to the study conception, design, data analysis, and drafting of the manuscript. Yang Zhilin was responsible for data collection, statistical analysis, and preparation of figures. Li Xinzheng contributed to literature review, interpretation of results, and critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Int J Gen Med. 2023;16:2211\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/IJGM.S411592\u003c/span\u003e\u003cspan address=\"10.2147/IJGM.S411592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Ultrasound viscoelasticity, Shear wave elastography, Inflammatory indices, Diagnostic model, BI-RADS","lastPublishedDoi":"10.21203/rs.3.rs-7715156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7715156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground :\u003c/strong\u003eDifferentiating 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This retrospective study enrolled 184 patients with 205 breast masses. All participants underwent preoperative ultrasound viscoelasticity examination (Shear Wave Elastography) 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Malignant lesions exhibited significantly higher viscoelastic and inflammatory parameters compared to benign lesions. The combined model achieved an area under the curve (AUC) of 0.95 (95% CI: 0.91-0.98), with a sensitivity of 86.96% and a specificity of 89.55%, significantly outperforming any single parameter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The integration of ultrasound viscoelasticity and systemic inflammatory indices provides a powerful non-invasive tool for distinguishing benign from malignant breast lesions, holding significant potential to optimize clinical decision-making and reduce unnecessary biopsies.\u003c/p\u003e","manuscriptTitle":"A Combined Model of Ultrasound Viscoelasticity and Inflammatory Indices for Differentiating Benign and Malignant Breast Lesions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-18 14:20:29","doi":"10.21203/rs.3.rs-7715156/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-18T09:53:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T20:12:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-15T00:34:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92329307378523535574634273567581570641","date":"2025-10-07T15:12:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59776834724556268687359984025916671616","date":"2025-10-06T14:08:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T12:30:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-30T06:20:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T11:11:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T11:10:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-09-25T16:52:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38a9d8ca-0432-4023-8c3e-a6a7d1920c58","owner":[],"postedDate":"October 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:12:09+00:00","versionOfRecord":{"articleIdentity":"rs-7715156","link":"https://doi.org/10.1186/s12880-025-02117-x","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2026-01-23 15:58:23","publishedOnDateReadable":"January 23rd, 2026"},"versionCreatedAt":"2025-10-18 14:20:29","video":"","vorDoi":"10.1186/s12880-025-02117-x","vorDoiUrl":"https://doi.org/10.1186/s12880-025-02117-x","workflowStages":[]},"version":"v1","identity":"rs-7715156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7715156","identity":"rs-7715156","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}