Development and validation of a nomogram incorporating three-dimensional shear wave elastography and clinicopathological factors for predicting axillary lymph node metastasis in breast cancer

Development and validation of a nomogram incorporating three-dimensional shear wave elastography and clinicopathological factors for predicting axillary lymph node metastasis in breast cancer | 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 Development and validation of a nomogram incorporating three-dimensional shear wave elastography and clinicopathological factors for predicting axillary lymph node metastasis in breast cancer Fei-Yi Sun, Lin Liu, Ti Zhao, Xiu-Qun Cao, Wei-Peng Lv, Xiao-Fang Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9022552/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Preoperative assessment of axillary lymph node metastasis (ALNM) is critical for tailoring surgical and systemic treatment in patients with breast cancer. We aimed to develop and validate a prediction model incorporating three-dimensional shear wave elastography (3D-SWE), conventional ultrasound features, and clinicopathological factors for individualized estimation of ALNM risk. Methods This retrospective single-center study included 329 consecutive women with pathologically confirmed breast cancer. All patients underwent conventional ultrasound and 3D-SWE before surgery. Clinical and pathological data were retrieved from medical records. Patients were randomly assigned to a training cohort (70%) and a validation cohort (30%). Independent predictors were identified using multivariable logistic regression, and a nomogram was constructed. Model performance was assessed using ROC analysis, calibration curves, and decision curve analysis. Results Five variables were independently associated with ALNM: calcifications, HER2 positivity, Ki-67 positivity, sagittal SWE Eratio, and coronal SWE Emax. The nomogram showed good discrimination, with an AUC of 0.89 in the training cohort and 0.83 in the validation cohort. Calibration analysis demonstrated satisfactory agreement between predicted and observed outcomes. Decision curve analysis suggested a meaningful net clinical benefit across a range of threshold probabilities. Conclusions This nomogram, integrating quantitative elastography parameters with conventional ultrasound and clinicopathological factors, provides a practical tool for estimating the probability of ALNM before surgery in patients with breast cancer. Breast cancer Axillary lymph node metastasis Three-dimensional shear wave elastography Ultrasound Nomogram Prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Breast cancer remains the most commonly diagnosed malignancy among women worldwide and continues to be a leading cause of cancer-related mortality despite advances in screening and systemic treatment [ 1 ]. Axillary lymph node status is a major determinant of prognosis and plays a central role in guiding surgical management and adjuvant therapy [ 2 ]. Accurate preoperative identification of axillary lymph node metastasis (ALNM) is therefore essential for individualized treatment planning and for avoiding unnecessary axillary intervention. Several imaging modalities, including ultrasound, magnetic resonance imaging, and positron emission tomography, are used to evaluate axillary lymph nodes. Among these, ultrasound remains the most widely applied method because of its accessibility, cost-effectiveness, and real-time capability [ 3 ]. However, conventional ultrasound assessment is largely based on morphological criteria and is inherently operator-dependent, which may limit sensitivity, particularly in early or low-volume nodal disease. These limitations have prompted increasing interest in quantitative imaging biomarkers and multiparametric imaging approaches, reflecting a broader transition from purely morphologic assessment toward data-driven characterization of tumor biology [ 4 ]. Shear-wave elastography (SWE) provides quantitative measurement of tissue stiffness and has been increasingly incorporated into breast imaging practice. Tissue stiffness may reflect underlying tumor biology, including stromal remodeling and desmoplastic reaction associated with malignant progression [ 5 ]. Previous studies have demonstrated that SWE parameters can enhance lesion characterization when combined with conventional imaging [ 6 ]. Nevertheless, the specific contribution of quantitative elastographic metrics to preoperative prediction of ALNM—especially when integrated with established clinicopathological factors—remains incompletely defined. In recent years, predictive models and nomograms integrating imaging and clinical variables have gained attention as tools for individualized risk assessment. Multimodal ultrasonographic features have been incorporated into nomograms for early prediction of ALNM with encouraging results [ 7 ]. Radiomics- and deep learning–based approaches have further highlighted the value of combining imaging features with clinical information for axillary risk stratification [ 8 , 9 ]. However, most existing models do not include three-dimensional SWE (3D-SWE), which allows assessment of tissue stiffness across multiple planes and may better reflect intratumoral heterogeneity. Against this background, we hypothesized that integrating quantitative 3D-SWE parameters with conventional ultrasound findings and clinicopathological markers could improve preoperative prediction of ALNM. The aim of this study was therefore to develop and internally validate a nomogram based on these combined variables to support individualized axillary risk assessment in patients with breast cancer. Methods Study design and participants This retrospective single-center study was conducted at the Central Hospital of Dalian University of Technology between June 2019 and June 2023. A total of 329 consecutive women with pathologically confirmed primary breast cancer were included. Among them, 101 patients had axillary lymph node metastasis (ALNM) confirmed by postoperative histopathology. The mean age was 56.05 ± 11.75 years (range, 21–84 years). Inclusion criteria Patients were eligible if they: Had pathologically confirmed primary breast cancer; Underwent preoperative conventional ultrasound and three-dimensional shear wave elastography (3D-SWE) at our institution; Had complete imaging and clinicopathological data available. Exclusion criteria Patients were excluded if they: Had incomplete imaging or clinicopathological information; Received prior breast cancer treatment (including radiotherapy, chemotherapy, or neoadjuvant therapy); Had a history of other malignancies. Ultrasound examination and 3D-SWE assessment All ultrasound examinations were performed using the Aixplorer ultrasound system (Supersonic Imagine, Aix-en-Provence, France). Conventional ultrasound Conventional ultrasonography was performed using a high-frequency linear transducer (4–15 MHz). Patients were examined in the supine position with both breasts and axillae fully exposed. The primary breast lesion was evaluated according to established sonographic criteria. Three-dimensional shear wave elastography (3D-SWE) 3D-SWE is based on acoustic radiation force impulse technology and provides quantitative assessment of tissue stiffness [10,11]. Imaging was performed using a convex array probe (5–16 MHz). The transducer was placed lightly on the skin without compression to avoid artificially increasing stiffness measurements. Three orthogonal planes (transverse, longitudinal, and coronal) were obtained simultaneously. Quantitative elasticity parameters were analyzed using Q-BOX™ software. A 2-mm region of interest (ROI) was positioned over the stiffest area of the lesion. The following parameters were recorded: Maximum elasticity (Emax), Elasticity ratio (Eratio), Standard deviation of elasticity (Esd). Each measurement was repeated three times, and the mean value was used for analysis. All examinations were performed by sonographers with at least 5 years of clinical experience. A representative example of ultrasound and 3D-SWE findings in a patient with ALNM is shown in Figure 1. Clinicopathological variables Clinicopathological data were retrieved from medical records. The following variables were collected: age, histological type, histological grade, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, Ki-67 expression, molecular subtype, and ALNM status. Immunohistochemical results were interpreted according to National Comprehensive Cancer Network (NCCN) guidelines [12]. ER and PR were considered positive when more than 1% of tumor cells demonstrated nuclear staining. Ki-67 expression was categorized as low (≤14%) or high (>14%). HER2 expression was scored as 0, +, ++, or +++. Cases scored as ++ underwent fluorescence in situ hybridization (FISH), and HER2 amplification was considered positive when gene amplification was detected. Model development and statistical analysis Patients were randomly assigned to a training cohort (n = 230, 70%) and a validation cohort (n = 99, 30%) using computer-generated random numbers. Variable selection In the training cohort, categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate. Variables with P < 0.05 in univariable analysis were entered into multivariable logistic regression to identify independent predictors of ALNM. Multivariable logistic regression modeling followed established methodological principles for prediction research [13]. Multicollinearity among candidate variables was assessed before model construction. Model construction A multivariable logistic regression model was developed in the training cohort. Based on the regression coefficients, a nomogram was generated using the rms package (version 4.4.0) in R [14]. Model performance evaluation Model discrimination was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Calibration was assessed using calibration plots. Clinical utility was examined using decision curve analysis (DCA) as originally described by Vickers and Elkin [15]. All statistical tests were two-sided, and a P value < 0.05 was considered statistically significant. Results Patient characteristics A total of 329 patients were included in the analysis, of whom 101 had histopathologically confirmed ALNM. The training cohort comprised 230 patients and the validation cohort 99 patients. Baseline demographic, imaging, and clinicopathological characteristics are presented in Table 1. There were no statistically significant differences between the training and validation cohorts (all P > 0.05), suggesting that the two datasets were comparable (Table 2). Univariable and multivariable analyses In the training cohort, univariable analysis showed that maximum tumor diameter, calcifications, TNM stage, histological grade, Ki-67 status, HER2 status, stiff rim sign, and multiple 3D-SWE parameters (including axial Emax, sagittal Emax and Eratio, and coronal Emax, Esd, and Eratio) were associated with ALNM (all P < 0.05; Table 3). Variables meeting the predefined significance threshold were entered into multivariable logistic regression analysis. Five factors remained independently associated with ALNM: Calcifications (OR = 7.66, 95% CI: 2.61–22.52, P < 0.001), HER2 positivity (OR = 2.94, 95% CI: 1.34–6.48, P = 0.007), Ki-67 positivity (OR = 9.25, 95% CI: 3.90–21.98, P < 0.001), 3D sagittal SWE Eratio (OR = 1.07, 95% CI: 1.02–1.12, P = 0.009), 3D coronal SWE Emax (OR = 1.01, 95% CI: 1.01–1.02, P = 0.039). (Table 4). Nomogram development and validation A nomogram was constructed based on these five independent predictors to estimate the probability of ALNM (Figure 2). In the training cohort, the model achieved an AUC of 0.89 (95% CI, 0.85–0.93). In the validation cohort, the AUC was 0.83 (95% CI, 0.75–0.91), indicating stable performance across datasets (Figure 3). Calibration plots demonstrated satisfactory agreement between predicted and observed probabilities in both cohorts (Figure 4). Decision curve analysis showed that the nomogram provided a net clinical benefit across a range of clinically relevant threshold probabilities in both the training and validation cohorts (Figure 5). Discussion In this study, we developed and internally validated a multivariable nomogram integrating quantitative three-dimensional shear wave elastography (3D-SWE), conventional ultrasound findings, and clinicopathological markers for preoperative prediction of axillary lymph node metastasis (ALNM) in breast cancer. The model demonstrated good discrimination and stable performance in both the training and validation cohorts, supporting its potential utility in individualized axillary risk stratification. Biological predictors and nodal metastasis Among the retained predictors, HER2 positivity and elevated Ki-67 expression were independently associated with ALNM. These findings are consistent with established evidence linking high proliferative activity and HER2-driven tumor biology to increased metastatic potential [16-18]. Ki-67 reflects tumor cell proliferation, and higher indices have been correlated with nodal involvement and adverse prognosis. Similarly, HER2 overexpression has long been associated with aggressive tumor behavior and lymphatic dissemination. Calcifications within the primary lesion also emerged as an independent predictor. Previous imaging–pathology correlation studies have suggested that microcalcifications may be associated with higher tumor grade and increased likelihood of nodal metastasis [19]. The presence of calcifications may reflect underlying biological heterogeneity and necrotic or proliferative changes within the tumor microenvironment. Role of quantitative elastography Importantly, sagittal SWE Eratio and coronal SWE Emax remained independently associated with ALNM after adjustment for clinicopathological factors. Tumor stiffness assessed by shear-wave elastography has been increasingly recognized as a surrogate marker of stromal remodeling, extracellular matrix deposition, and tumor–stroma interaction [20,21]. These biomechanical alterations are closely linked to invasive potential and metastatic spread. Recent studies have expanded the application of SWE beyond lesion characterization. A 2024 multimodal ultrasonographic study demonstrated that integrating B-mode features and SWE parameters improved ALNM prediction compared with single-modality models (AUC 0.803) [22]. Similarly, a predictive nomogram incorporating SWE for axillary assessment reported promising diagnostic performance in patients with suspicious lymph nodes [23]. More recently, artificial intelligence–based dual-modality fusion models combining B-mode ultrasound and SWE achieved improved performance for ALNM prediction, with AUC values exceeding 0.83 [24,25]. These findings support the growing role of elastography-derived stiffness metrics in axillary risk assessment. Compared with two-dimensional elastography, 3D-SWE enables evaluation of lesion stiffness across orthogonal planes and may better reflect intratumoral heterogeneity. In our analysis, Eratio—by normalizing lesion stiffness relative to adjacent tissue—may mitigate variability caused by probe pressure, while Emax captures focal regions of maximal rigidity that may correspond to invasive fronts. The persistence of these parameters in multivariable analysis underscores their incremental value. Comparison with existing nomograms Several nomograms for predicting ALNM have been proposed using conventional ultrasound, radiomics, or multimodal ultrasound features [22,26,27]. While many models demonstrate high AUC values in development cohorts, quantitative elastography is not uniformly incorporated, and external validation remains limited. Our model differs by integrating quantitative 3D-SWE parameters with molecular markers and conventional morphology in a logistic regression framework. Although deep learning–based approaches show promise [24,25], they often require substantial computational infrastructure and large annotated datasets. In contrast, our nomogram relies on routinely available variables, potentially facilitating clinical implementation. Clinical implications Axillary management in breast cancer has evolved toward de-escalation strategies in selected patient populations. Accurate preoperative risk stratification is therefore increasingly important. A noninvasive model that integrates tumor biology and biomechanical characteristics may assist in refining surgical planning and multidisciplinary decision-making [12,16]. While such a model cannot replace pathological staging, it may complement existing imaging and biopsy strategies. Limitations This study has several limitations. First, it was a retrospective single-center study, which may introduce selection bias. Second, external validation was not performed, and the generalizability of the model to other populations remains to be confirmed. Third, although internal validation demonstrated stable performance, prospective multicenter studies are warranted to further evaluate clinical utility. Conclusions In conclusion, we developed and validated a nomogram integrating 3D-SWE parameters, conventional ultrasound features, and clinicopathological factors for predicting axillary lymph node metastasis in breast cancer. The model demonstrated good discrimination, calibration, and clinical utility, and may serve as a practical tool for individualized preoperative risk assessment. Declarations Ethics approval and consent to participate This retrospective study was conducted in accordance with the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board (IRB) of the Central Hospital of Dalian University of Technology. Due to the retrospective and anonymized nature of the study, the requirement for written informed consent was waived. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Dalian Municipal Life and Health Field Guidance Program (Grant No. 2022ZXYG07). Authors’ contributions F.Y.S. and X.F.P. designed the study. L.L. and T.Z. collected the clinical and imaging data. X.Q.C. and W.P.L. performed the pathological evaluation and data verification. F.Y.S. conducted the statistical analysis and drafted the manuscript. X.F.P. and W.P.L. supervised the study and revised the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank the staff of the Central Hospital of Dalian University of Technology for their assistance in data collection and patient management. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Choi JS, Han BK, Ko EY, Ko ES, Hahn SY, Shin JH. Development and validation of a nomogram for predicting axillary lymph node metastasis in breast cancer. Eur Radiol. 2019;29(8):4030–4039. Yao J, Zhou W, Zhu Y, Zhou J, Chen X, Zhan W. Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early-stage invasive breast cancer. Oncol Lett. 2024;27(3):95. doi:10.3892/ol.2024.14228. Tables Tables 1 to 4 are available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 23 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 11 Mar, 2026 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9022552","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628033089,"identity":"212c01e3-a28b-458d-9a4e-84c793c2e876","order_by":0,"name":"Fei-Yi Sun","email":"","orcid":"","institution":"Central Hospital of Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Fei-Yi","middleName":"","lastName":"Sun","suffix":""},{"id":628033090,"identity":"d876f9c0-9ec4-4c9b-9cd5-ac3a2ffccb5b","order_by":1,"name":"Lin Liu","email":"","orcid":"","institution":"Central Hospital of Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liu","suffix":""},{"id":628033093,"identity":"66b342be-3404-43f6-8788-b47acf861e27","order_by":2,"name":"Ti Zhao","email":"","orcid":"","institution":"Central Hospital of Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ti","middleName":"","lastName":"Zhao","suffix":""},{"id":628033094,"identity":"e08ccfbd-f450-46ce-92e6-60fe7551cc81","order_by":3,"name":"Xiu-Qun Cao","email":"","orcid":"","institution":"Central Hospital of Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiu-Qun","middleName":"","lastName":"Cao","suffix":""},{"id":628033095,"identity":"1e5c1374-dc33-4b51-915c-3732912249a2","order_by":4,"name":"Wei-Peng Lv","email":"","orcid":"","institution":"Affiliated Zhongshan Hospital of Dalian University","correspondingAuthor":false,"prefix":"","firstName":"Wei-Peng","middleName":"","lastName":"Lv","suffix":""},{"id":628033096,"identity":"decf79a4-1033-4482-9b40-2e9bd8960450","order_by":5,"name":"Xiao-Fang Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie3QsQrCMBCA4QuFuqR2TZHqKxwEXBSfpaWgo06dnTIVXQu+TErAsa5CHSp9gY4OClY7iCA2bg75p9zwcVwATKZ/zJKkDHBKNs/J1iF2YGG9mhMBlj6xvbRWPxDcO4pTPPiCLSXUsQJ3t/5OPNEPK4oFFywCkuYK2El+J+4o4c2WInwQyxEKkAUdp9h0PKCYt+SmQ9yGeCnKlhAd0twSYY0RF7TCLMkXlB07SPNjWRlcZ/62F57LSzwZumkHAaCvp3wfdYjJZDKZPnYHNPU9nY3dIywAAAAASUVORK5CYII=","orcid":"","institution":"Central Hospital of Dalian University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Xiao-Fang","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2026-03-03 17:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9022552/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9022552/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707569,"identity":"8abb5fb7-f812-4596-ba34-c4a0fbffc28a","added_by":"auto","created_at":"2026-04-24 09:20:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":255802,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Axillary lymph node metastasis of breast cancer with HER-2 positive, Ki-67 positive and calcification. (B) 3D SWE showed Emax: 192.6, Esd：26.5, Eratio: 13.0 on axial plane. (C) 3D SWE showed Emax: 216.6, Esd: 20.1, Eratio: 18.1 on sagittal plane. (D) 3D SWE showed Emax: 189.8, Esd：49.1, Eratio: 29.0 on coronal plane.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9022552/v1/30506d2d81eab58b4b962d97.png"},{"id":107635984,"identity":"f2d55a6a-0630-4b15-97b4-09c5831e74ad","added_by":"auto","created_at":"2026-04-23 12:37:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119450,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting axillary lymph node metastasis in breast cancer. The prediction model was developed with the training dataset, which incorporated the Calcifications, HER2, Ki67, 3D Sagittal SWE Eratio, 3D Coronal SWE Emax, and presented as a nomogram. The total score is plotted on the Total Points axis and corresponds to the risk of axillary lymph node metastasis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9022552/v1/b6c9ec96b361268014384013.png"},{"id":107635986,"identity":"3a742848-7fc1-40e8-9391-bb916a15962f","added_by":"auto","created_at":"2026-04-23 12:37:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51430,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the nomogram in the training and validation cohorts.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9022552/v1/98b1cf2e057b2e6f0686bbd0.png"},{"id":107706570,"identity":"302e21eb-1b18-4a93-beb0-4057f24205de","added_by":"auto","created_at":"2026-04-24 09:18:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52325,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots for histological findings between the nomogram prediction model and real observations in the (A) training cohort and (B) validation cohort. The calibration plots presented excellent agreement in the training cohort and good agreement in the validation cohort between the nomogram prediction and real observations made for the histological findings. The Hosmer and Lemeshow Test were 0.516 for the training cohort and 0.471 for the validation cohort.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9022552/v1/a3be8ca0e538108982c00d3d.png"},{"id":107635985,"identity":"4fa3e21f-2eab-4548-af9d-2757c6530518","added_by":"auto","created_at":"2026-04-23 12:37:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36947,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) derived from the training and validation cohorts.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9022552/v1/7d7be061d29928784142b303.png"},{"id":107709286,"identity":"8b68271e-24a8-44d8-b536-ca0dbfe10b8a","added_by":"auto","created_at":"2026-04-24 09:35:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":654408,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9022552/v1/f1d0c520-5ddf-4934-9290-4aaa146dd774.pdf"},{"id":107635982,"identity":"8ca58ee1-499a-4363-aafc-06755e2f0c3a","added_by":"auto","created_at":"2026-04-23 12:37:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":60615,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9022552/v1/f671f66ce6e59a3cb381d1c5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a nomogram incorporating three-dimensional shear wave elastography and clinicopathological factors for predicting axillary lymph node metastasis in breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer remains the most commonly diagnosed malignancy among women worldwide and continues to be a leading cause of cancer-related mortality despite advances in screening and systemic treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Axillary lymph node status is a major determinant of prognosis and plays a central role in guiding surgical management and adjuvant therapy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accurate preoperative identification of axillary lymph node metastasis (ALNM) is therefore essential for individualized treatment planning and for avoiding unnecessary axillary intervention.\u003c/p\u003e \u003cp\u003eSeveral imaging modalities, including ultrasound, magnetic resonance imaging, and positron emission tomography, are used to evaluate axillary lymph nodes. Among these, ultrasound remains the most widely applied method because of its accessibility, cost-effectiveness, and real-time capability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, conventional ultrasound assessment is largely based on morphological criteria and is inherently operator-dependent, which may limit sensitivity, particularly in early or low-volume nodal disease. These limitations have prompted increasing interest in quantitative imaging biomarkers and multiparametric imaging approaches, reflecting a broader transition from purely morphologic assessment toward data-driven characterization of tumor biology [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eShear-wave elastography (SWE) provides quantitative measurement of tissue stiffness and has been increasingly incorporated into breast imaging practice. Tissue stiffness may reflect underlying tumor biology, including stromal remodeling and desmoplastic reaction associated with malignant progression [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies have demonstrated that SWE parameters can enhance lesion characterization when combined with conventional imaging [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Nevertheless, the specific contribution of quantitative elastographic metrics to preoperative prediction of ALNM\u0026mdash;especially when integrated with established clinicopathological factors\u0026mdash;remains incompletely defined.\u003c/p\u003e \u003cp\u003eIn recent years, predictive models and nomograms integrating imaging and clinical variables have gained attention as tools for individualized risk assessment. Multimodal ultrasonographic features have been incorporated into nomograms for early prediction of ALNM with encouraging results [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Radiomics- and deep learning\u0026ndash;based approaches have further highlighted the value of combining imaging features with clinical information for axillary risk stratification [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, most existing models do not include three-dimensional SWE (3D-SWE), which allows assessment of tissue stiffness across multiple planes and may better reflect intratumoral heterogeneity.\u003c/p\u003e \u003cp\u003eAgainst this background, we hypothesized that integrating quantitative 3D-SWE parameters with conventional ultrasound findings and clinicopathological markers could improve preoperative prediction of ALNM. The aim of this study was therefore to develop and internally validate a nomogram based on these combined variables to support individualized axillary risk assessment in patients with breast cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective single-center study was conducted at the Central Hospital of Dalian University of Technology between June 2019 and June 2023. A total of 329 consecutive women with pathologically confirmed primary breast cancer were included. Among them, 101 patients had axillary lymph node metastasis (ALNM) confirmed by postoperative histopathology. The mean age was 56.05 ± 11.75 years (range, 21–84 years).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were eligible if they: Had pathologically confirmed primary breast cancer; Underwent preoperative conventional ultrasound and three-dimensional shear wave elastography (3D-SWE) at our institution; Had complete imaging and clinicopathological data available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were excluded if they: Had incomplete imaging or clinicopathological information; Received prior breast cancer treatment (including radiotherapy, chemotherapy, or neoadjuvant therapy); Had a history of other malignancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUltrasound examination and 3D-SWE assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll ultrasound examinations were performed using the Aixplorer ultrasound system (Supersonic Imagine, Aix-en-Provence, France).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConventional ultrasound\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConventional ultrasonography was performed using a high-frequency linear transducer (4–15 MHz). Patients were examined in the supine position with both breasts and axillae fully exposed. The primary breast lesion was evaluated according to established sonographic criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThree-dimensional shear wave elastography (3D-SWE)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3D-SWE is based on acoustic radiation force impulse technology and provides quantitative assessment of tissue stiffness [10,11]. Imaging was performed using a convex array probe (5–16 MHz). The transducer was placed lightly on the skin without compression to avoid artificially increasing stiffness measurements. Three orthogonal planes (transverse, longitudinal, and coronal) were obtained simultaneously.\u003c/p\u003e\n\u003cp\u003eQuantitative elasticity parameters were analyzed using Q-BOX™ software. A 2-mm region of interest (ROI) was positioned over the stiffest area of the lesion. The following parameters were recorded: Maximum elasticity (Emax), Elasticity ratio (Eratio), Standard deviation of elasticity (Esd).\u003c/p\u003e\n\u003cp\u003eEach measurement was repeated three times, and the mean value was used for analysis. All examinations were performed by sonographers with at least 5 years of clinical experience.\u003cbr\u003e\u0026nbsp;A representative example of ultrasound and 3D-SWE findings in a patient with ALNM is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinicopathological variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinicopathological data were retrieved from medical records. The following variables were collected: age, histological type, histological grade, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, Ki-67 expression, molecular subtype, and ALNM status.\u003c/p\u003e\n\u003cp\u003eImmunohistochemical results were interpreted according to National Comprehensive Cancer Network (NCCN) guidelines [12]. ER and PR were considered positive when more than 1% of tumor cells demonstrated nuclear staining. Ki-67 expression was categorized as low (≤14%) or high (\u0026gt;14%). HER2 expression was scored as 0, +, ++, or +++. Cases scored as ++ underwent fluorescence in situ hybridization (FISH), and HER2 amplification was considered positive when gene amplification was detected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel development and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were randomly assigned to a training cohort (n = 230, 70%) and a validation cohort (n = 99, 30%) using computer-generated random numbers.\u003c/p\u003e\n\u003cp\u003eVariable selection\u003c/p\u003e\n\u003cp\u003eIn the training cohort, categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate. Variables with P \u0026lt; 0.05 in univariable analysis were entered into multivariable logistic regression to identify independent predictors of ALNM. Multivariable logistic regression modeling followed established methodological principles for prediction research [13].\u003c/p\u003e\n\u003cp\u003eMulticollinearity among candidate variables was assessed before model construction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multivariable logistic regression model was developed in the training cohort. Based on the regression coefficients, a nomogram was generated using the rms package (version 4.4.0) in R [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel performance evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel discrimination was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Calibration was assessed using calibration plots. Clinical utility was examined using decision curve analysis (DCA) as originally described by Vickers and Elkin [15].\u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-sided, and a P value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 329 patients were included in the analysis, of whom 101 had histopathologically confirmed ALNM. The training cohort comprised 230 patients and the validation cohort 99 patients.\u003c/p\u003e\n\u003cp\u003eBaseline demographic, imaging, and clinicopathological characteristics are presented in Table 1. There were no statistically significant differences between the training and validation cohorts (all P \u0026gt; 0.05), suggesting that the two datasets were comparable (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariable and multivariable analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the training cohort, univariable analysis showed that maximum tumor diameter, calcifications, TNM stage, histological grade, Ki-67 status, HER2 status, stiff rim sign, and multiple 3D-SWE parameters (including axial Emax, sagittal Emax and Eratio, and coronal Emax, Esd, and Eratio) were associated with ALNM (all P \u0026lt; 0.05; Table 3).\u003c/p\u003e\n\u003cp\u003eVariables meeting the predefined significance threshold were entered into multivariable logistic regression analysis. Five factors remained independently associated with ALNM: Calcifications (OR = 7.66, 95% CI: 2.61\u0026ndash;22.52, P \u0026lt; 0.001), HER2 positivity (OR = 2.94, 95% CI: 1.34\u0026ndash;6.48, P = 0.007), Ki-67 positivity (OR = 9.25, 95% CI: 3.90\u0026ndash;21.98, P \u0026lt; 0.001), 3D sagittal SWE Eratio (OR = 1.07, 95% CI: 1.02\u0026ndash;1.12, P = 0.009), 3D coronal SWE Emax (OR = 1.01, 95% CI: 1.01\u0026ndash;1.02, P = 0.039). (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNomogram development and validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA nomogram was constructed based on these five independent predictors to estimate the probability of ALNM (Figure 2).\u003c/p\u003e\n\u003cp\u003eIn the training cohort, the model achieved an AUC of 0.89 (95% CI, 0.85\u0026ndash;0.93). In the validation cohort, the AUC was 0.83 (95% CI, 0.75\u0026ndash;0.91), indicating stable performance across datasets (Figure 3).\u003c/p\u003e\n\u003cp\u003eCalibration plots demonstrated satisfactory agreement between predicted and observed probabilities in both cohorts (Figure 4).\u003c/p\u003e\n\u003cp\u003eDecision curve analysis showed that the nomogram provided a net clinical benefit across a range of clinically relevant threshold probabilities in both the training and validation cohorts (Figure 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and internally validated a multivariable nomogram integrating quantitative three-dimensional shear wave elastography (3D-SWE), conventional ultrasound findings, and clinicopathological markers for preoperative prediction of axillary lymph node metastasis (ALNM) in breast cancer. The model demonstrated good discrimination and stable performance in both the training and validation cohorts, supporting its potential utility in individualized axillary risk stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological predictors and nodal metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the retained predictors, HER2 positivity and elevated Ki-67 expression were independently associated with ALNM. These findings are consistent with established evidence linking high proliferative activity and HER2-driven tumor biology to increased metastatic potential [16-18]. Ki-67 reflects tumor cell proliferation, and higher indices have been correlated with nodal involvement and adverse prognosis. Similarly, HER2 overexpression has long been associated with aggressive tumor behavior and lymphatic dissemination.\u003c/p\u003e\n\u003cp\u003eCalcifications within the primary lesion also emerged as an independent predictor. Previous imaging\u0026ndash;pathology correlation studies have suggested that microcalcifications may be associated with higher tumor grade and increased likelihood of nodal metastasis [19]. The presence of calcifications may reflect underlying biological heterogeneity and necrotic or proliferative changes within the tumor microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of quantitative elastography\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImportantly, sagittal SWE Eratio and coronal SWE Emax remained independently associated with ALNM after adjustment for clinicopathological factors. Tumor stiffness assessed by shear-wave elastography has been increasingly recognized as a surrogate marker of stromal remodeling, extracellular matrix deposition, and tumor\u0026ndash;stroma interaction [20,21]. These biomechanical alterations are closely linked to invasive potential and metastatic spread.\u003c/p\u003e\n\u003cp\u003eRecent studies have expanded the application of SWE beyond lesion characterization. A 2024 multimodal ultrasonographic study demonstrated that integrating B-mode features and SWE parameters improved ALNM prediction compared with single-modality models (AUC 0.803) [22]. Similarly, a predictive nomogram incorporating SWE for axillary assessment reported promising diagnostic performance in patients with suspicious lymph nodes [23]. More recently, artificial intelligence\u0026ndash;based dual-modality fusion models combining B-mode ultrasound and SWE achieved improved performance for ALNM prediction, with AUC values exceeding 0.83 [24,25]. These findings support the growing role of elastography-derived stiffness metrics in axillary risk assessment.\u003c/p\u003e\n\u003cp\u003eCompared with two-dimensional elastography, 3D-SWE enables evaluation of lesion stiffness across orthogonal planes and may better reflect intratumoral heterogeneity. In our analysis, Eratio\u0026mdash;by normalizing lesion stiffness relative to adjacent tissue\u0026mdash;may mitigate variability caused by probe pressure, while Emax captures focal regions of maximal rigidity that may correspond to invasive fronts. The persistence of these parameters in multivariable analysis underscores their incremental value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with existing nomograms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral nomograms for predicting ALNM have been proposed using conventional ultrasound, radiomics, or multimodal ultrasound features [22,26,27]. While many models demonstrate high AUC values in development cohorts, quantitative elastography is not uniformly incorporated, and external validation remains limited.\u003c/p\u003e\n\u003cp\u003eOur model differs by integrating quantitative 3D-SWE parameters with molecular markers and conventional morphology in a logistic regression framework. Although deep learning\u0026ndash;based approaches show promise [24,25], they often require substantial computational infrastructure and large annotated datasets. In contrast, our nomogram relies on routinely available variables, potentially facilitating clinical implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAxillary management in breast cancer has evolved toward de-escalation strategies in selected patient populations. Accurate preoperative risk stratification is therefore increasingly important. A noninvasive model that integrates tumor biology and biomechanical characteristics may assist in refining surgical planning and multidisciplinary decision-making [12,16]. While such a model cannot replace pathological staging, it may complement existing imaging and biopsy strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, it was a retrospective single-center study, which may introduce selection bias. Second, external validation was not performed, and the generalizability of the model to other populations remains to be confirmed. Third, although internal validation demonstrated stable performance, prospective multicenter studies are warranted to further evaluate clinical utility.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we developed and validated a nomogram integrating 3D-SWE parameters, conventional ultrasound features, and clinicopathological factors for predicting axillary lymph node metastasis in breast cancer. The model demonstrated good discrimination, calibration, and clinical utility, and may serve as a practical tool for individualized preoperative risk assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted in accordance with the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board (IRB) of the Central Hospital of Dalian University of Technology. Due to the retrospective and anonymized nature of the study, the requirement for written informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Dalian Municipal Life and Health Field Guidance Program (Grant No. 2022ZXYG07).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.Y.S. and X.F.P. designed the study.\u003c/p\u003e\n\u003cp\u003eL.L. and T.Z. collected the clinical and imaging data.\u003c/p\u003e\n\u003cp\u003eX.Q.C. and W.P.L. performed the pathological evaluation and data verification.\u003c/p\u003e\n\u003cp\u003eF.Y.S. conducted the statistical analysis and drafted the manuscript.\u003c/p\u003e\n\u003cp\u003eX.F.P. and W.P.L. supervised the study and revised the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the staff of the Central Hospital of Dalian University of Technology for their assistance in data collection and patient management.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\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. CA Cancer J Clin. 2021;71(3):209\u0026ndash;249. doi:10.3322/caac.21660.\u003c/li\u003e\n \u003cli\u003eKrag DN, Anderson SJ, Julian TB, Brown AM, Harlow SP, Costantino JP, et al. Technical outcomes of sentinel-lymph-node resection and conventional axillary-lymph-node dissection in patients with clinically node-negative breast cancer. Lancet Oncol. 2007;8(10):881\u0026ndash;888. doi:10.1016/S1470-2045(07)70278-4.\u003c/li\u003e\n \u003cli\u003eAlvarez S, A\u0026ntilde;orbe E, Alcorta P, L\u0026oacute;pez F, Alonso I, Cort\u0026eacute;s J. Role of sonography in the diagnosis of axillary lymph node metastases in breast cancer: a systematic review. AJR Am J Roentgenol. 2006;186(5):1342\u0026ndash;1348.\u003c/li\u003e\n \u003cli\u003eGillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. 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Radiology. 2019;290(3):715\u0026ndash;723.\u003c/li\u003e\n \u003cli\u003eZhou J, Zhang Y, Chang KT, Wang O, Li J, Huang X, et al. Diagnosis of axillary lymph node metastasis in breast cancer using radiomics of primary tumor and axillary lymph node on ultrasound. Eur Radiol. 2020;30(6):3495\u0026ndash;3503.\u003c/li\u003e\n \u003cli\u003eBercoff J, Tanter M, Fink M. Supersonic shear imaging: a new technique for soft tissue elasticity mapping. IEEE Trans Ultrason Ferroelectr Freq Control. 2004;51(4):396\u0026ndash;409.\u003c/li\u003e\n \u003cli\u003eCosgrove DO, Berg WA, Dor\u0026eacute; CJ, Skyba DM, Henry JP, Gay J, et al. EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Ultraschall Med. 2013;34(3):238\u0026ndash;253.\u003c/li\u003e\n \u003cli\u003eGradishar WJ, Moran MS, Abraham J, et al. Breast cancer, version 3.2024, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2024;22(5):331\u0026ndash;357. doi:10.6004/jnccn.2024.0035.\u003c/li\u003e\n \u003cli\u003eHarrell FE Jr. Regression Modeling Strategies. 2nd ed. Springer; 2015.\u003c/li\u003e\n \u003cli\u003eHarrell FE Jr. rms: Regression Modeling Strategies. R package version 4.4-0. https://CRAN.R-project.org/package=rms\u003c/li\u003e\n \u003cli\u003eVickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565\u0026ndash;574. doi:10.1177/0272989X06295361.\u003c/li\u003e\n \u003cli\u003eCaudle AS, Hunt KK, Tucker SL, Hoffman KE, Gainer SM, Lucci A, et al. American College of Surgeons Oncology Group (ACOSOG) Z0011: implications for axillary management in breast cancer. Ann Surg. 2011;254(3):426\u0026ndash;432.\u003c/li\u003e\n \u003cli\u003eInwald EC, Klinkhammer-Schalke M, Hofst\u0026auml;dter F, Zeman F, Koller M, Gerstenhauer M, et al. 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Comput Med Imaging Graph. 2025;119:102472. doi:10.1016/j.compmedimag.2024.102472.\u003c/li\u003e\n \u003cli\u003eDong L, Cai X, Ge H, Sun L, Pan X, Sun F, et al. Breast cancer diagnosis using a dual-modality complementary deep learning network with integrated attention mechanism fusion of B-mode ultrasound and shear wave elastography. Ultrasound Med Biol. 2025;51(11):2135\u0026ndash;2143. doi:10.1016/j.ultrasmedbio.2025.08.007.\u003c/li\u003e\n \u003cli\u003eChoi JS, Han BK, Ko EY, Ko ES, Hahn SY, Shin JH. Development and validation of a nomogram for predicting axillary lymph node metastasis in breast cancer. Eur Radiol. 2019;29(8):4030\u0026ndash;4039.\u003c/li\u003e\n \u003cli\u003eYao J, Zhou W, Zhu Y, Zhou J, Chen X, Zhan W. Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early-stage invasive breast cancer. Oncol Lett. 2024;27(3):95. doi:10.3892/ol.2024.14228.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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, Axillary lymph node metastasis, Three-dimensional shear wave elastography, Ultrasound, Nomogram, Prediction model","lastPublishedDoi":"10.21203/rs.3.rs-9022552/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9022552/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePreoperative assessment of axillary lymph node metastasis (ALNM) is critical for tailoring surgical and systemic treatment in patients with breast cancer. We aimed to develop and validate a prediction model incorporating three-dimensional shear wave elastography (3D-SWE), conventional ultrasound features, and clinicopathological factors for individualized estimation of ALNM risk.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis retrospective single-center study included 329 consecutive women with pathologically confirmed breast cancer. All patients underwent conventional ultrasound and 3D-SWE before surgery. Clinical and pathological data were retrieved from medical records. Patients were randomly assigned to a training cohort (70%) and a validation cohort (30%). Independent predictors were identified using multivariable logistic regression, and a nomogram was constructed. Model performance was assessed using ROC analysis, calibration curves, and decision curve analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFive variables were independently associated with ALNM: calcifications, HER2 positivity, Ki-67 positivity, sagittal SWE Eratio, and coronal SWE Emax. The nomogram showed good discrimination, with an AUC of 0.89 in the training cohort and 0.83 in the validation cohort. Calibration analysis demonstrated satisfactory agreement between predicted and observed outcomes. Decision curve analysis suggested a meaningful net clinical benefit across a range of threshold probabilities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis nomogram, integrating quantitative elastography parameters with conventional ultrasound and clinicopathological factors, provides a practical tool for estimating the probability of ALNM before surgery in patients with breast cancer.\u003c/p\u003e","manuscriptTitle":"Development and validation of a nomogram incorporating three-dimensional shear wave elastography and clinicopathological factors for predicting axillary lymph node metastasis in breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 12:36:58","doi":"10.21203/rs.3.rs-9022552/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-29T13:17:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44644015109927790502953418762166116644","date":"2026-04-25T14:27:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29204619092093972498041417479722468659","date":"2026-04-22T12:36:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T14:29:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T09:28:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T11:09:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T08:09:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-03-11T14:38:08+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":"11aedce2-161c-48e9-8a4f-ed8c1ab1cdb4","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T12:36:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 12:36:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9022552","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9022552","identity":"rs-9022552","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}