Integrating Super-resolution Microvascular Imaging and Conventional Ultrasound with Clinicopathologic Variables: A Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer

Integrating Super-resolution Microvascular Imaging and Conventional Ultrasound with Clinicopathologic Variables: A Nomogram 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 Integrating Super-resolution Microvascular Imaging and Conventional Ultrasound with Clinicopathologic Variables: A Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer Jingzhu Xu, Tao Zhang, Bojuan Wang, Yuhan Wang, Lei Hao, Xinghua Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9373978/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective This study aims to develop and validate a predictive model for axillary lymph node metastasis (ALNM) in breast cancer by integrating clinicopathological factors, conventional ultrasound features, and quantitative parameters from Super-Resolution Microvascular Imaging (SRMI) to enhance diagnostic accuracy. Methods A total of 127 breast cancer patients were enrolled in this study. Each patient underwent conventional ultrasound examination of both the breast mass and axillary lymph nodes, as well as SRMI of the breast mass. Based on the pathological results as the gold standard, the patients were stratified into positive and negative ALNM groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors of ALNM, and a predictive nomogram was constructed based on these selected factors. The nomogram's performance was subsequently evaluated in terms of discrimination using the receiver operating characteristic (ROC) curve, calibration via calibration plot analysis, and clinical utility by decision curve analysis (DCA). Internal validation was subsequently performed using the bootstrap method. Results The multivariate logistic regression analysis identified maximum tumor diameter, microcalcification, Ki67 expression, cortical morphology, hilum status, and pulsatility index (PI) levels as independent risk factors for ALNM. The developed nomogram demonstrated an area under the ROC curve (AUC) of 0.904 (95% CI: 0.854–0.954), with a bootstrap-validated AUC of 0.874 (95% CI: 0.826–0.947). The calibration curve indicated good agreement between the predicted probabilities and actual ALNM outcomes. Furthermore, DCA confirmed the clinical utility of the nomogram. Conclusions Integrating the PI obtained from SRMI with clinical data and conventional ultrasound features significantly improves the predictive performance for ALNM in breast cancer. The resultant integrated model provides a comprehensive and reliable tool for preoperative risk assessment of axillary lymph node status, offering valuable clinical decision support. Axillary lymph node Breast cancer SRMI Ultrasound microvessels Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Breast cancer is one of the most common malignancies and the second leading cause of cancer-related mortality among women worldwide[ 1 , 2 ]. Axillary lymph node status (ALNS) is crucial in the management of breast cancer, as it directly influences staging, treatment planning, and overall prognosis[ 3 , 4 ]. Axillary lymph node dissection (ALND), which involves the removal of all lymph nodes in the axilla, can lead to complications such as injury to surrounding structures and lymphedema[ 5 ]. Sentinel lymph node biopsy (SLNB), a less invasive procedure developed to assess nodal metastasis without extensive dissection, has become the standard method for determining ALNS in early-stage breast cancer[ 6 ]. However, the intraoperative evaluation of SLN can limit its utility in guiding preoperative treatment planning. Consequently, developing a noninvasive, preoperative method to accurately identify ALNS is urgently needed. Ultrasound (US), as a non-invasive, convenient, and cost-effective imaging modality, plays an indispensable role in the preoperative evaluation of ALNS[ 7 ]. Studies have shown that the morphological features of axillary lymph nodes on ultrasound can predict ALNS. Lymph nodes exhibiting a round shape, displaced or absent hilum, indistinct margins, and increased cortical blood flow are considered suspicious for metastasis[ 8 ]. However, the reported sensitivity and specificity of axillary ultrasound vary widely, ranging from 26% to 95% and 44% to 98%, respectively, due to the significant overlap in the ultrasound morphological features of benign and malignant lymph nodes[ 9 – 11 ]. Additionally, the status of axillary lymph nodes can be inferred by evaluating the breast tumor and the surrounding tissue, with reported accuracy rates of 0.748 and 0.659, respectively[ 12 , 13 ]. Despite their potential to predict ALNS, their suboptimal diagnostic performance has limited widespread adoption, creating a pressing need for innovative tools to provide additional information and enhance assessment accuracy. Super-resolution microvascular imaging (SRMI) is a novel ultrasound technique that overcomes the acoustic diffraction limit to achieve micron-level spatial resolution by leveraging contrast microbubbles to visualize microvasculature. This technique enables the generation of micrometer-scale maps of microvessel density (MVD) and flow velocity within masses by localizing and tracking individual microbubbles at subwavelength resolution after contrast agent injection[ 14 – 16 ]. Tumor angiogenesis is a critical biological mechanism whereby tumors induce new blood vessel growth to facilitate their expansion, local invasion, and metastatic dissemination. High MVD levels are strongly associated with an increased risk of metastasis in breast cancer[ 17 ]. SRMI has been successfully applied in research to enable the early detection and differential diagnosis of diseases through the analysis of quantitative microvascular morphological and hemodynamic parameters[ 18 – 20 ]. Despite the promising diagnostic utility of SRMI, few studies have investigated the correlation between its quantitative microvascular parameters and the risk of axillary lymph node metastasis (ALNM) in breast cancer. Therefore, this study develops a predictive nomogram that integrates super-resolution microvascular imaging, conventional ultrasound, and clinicopathological indicators to improve the accuracy of axillary lymph node metastasis prediction and provide a more comprehensive basis for clinical decision-making. This integrated approach aims to investigate the correlation between quantitative SRMI parameters and ALNS, thereby enhancing the accuracy of ALNM prediction. Materials and methods Study patients This study retrospectively enrolled a consecutive series of breast cancer patients with postoperative pathological confirmation from the Second Hospital of Shanxi Medical University between March and October 2025. The inclusion criteria were as follows:(1) female patients aged ≥ 18 years; (2) preoperative conventional ultrasound evaluating both the breast mass and axillary lymph nodes; (3) preoperative SRMI assessment of the breast masse; (4) unifocal breast lesion; (5) underwent surgical resection of the breast lesion with axillary lymph node dissection or sentinel lymph node biopsy. The exclusion criteria were as follows:(1) contraindications to contrast agents; (2) underwent chemotherapy or radiotherapy; and (3) incomplete clinicopathological, ultrasound, or SRMI imaging data. Based on the inclusion and exclusion criteria, a total of 127 patients with breast cancer were finally enrolled (Fig. 1 ). This study received ethical approval from the Institutional Review Board of our hospital (Approval No.: [2025] YX268). Conventional US examination and image analysis All conventional US and SRMI examinations of breast lesions and ALNs were performed by two radiologists, each with over 8 years of experience in breast ultrasound, using a Mindray Resona A20 system equipped with L10-3 (3–10 MHz) and L18-5 (5–18 MHz) linear array transducers. During conventional US examinations, patients were positioned supine with upper limbs abducted to fully expose the breast and axillary regions. A comprehensive scan was performed using the L18-5 transducer to acquire and store two-dimensional grayscale and color Doppler flow images of the detected lesions and lymph nodes. The sonographic features of breast lesions (including size, shape, echo pattern, margins, calcification, aspect ratio, hyperechoic halo, and blood flow) and ALNs (including size, shape, cortical thickness, hilum, and blood flow) were independently evaluated by the two radiologists who were blinded to the pathological results. In cases of disagreement, a senior physician would serve as an arbitrator. SRMI image acquisition and quantification protocol SRMI image acquisition and quantification protocol SRMI was performed using the L10-3 linear array transducer after conventional US. The imaging plane was selected to optimally display the tumor's largest cross-section while including adjacent normal glandular tissue for reference, following administration of the ultrasound contrast agent Sonazoid (GE Healthcare AS) at a low mechanical index (MI = 0.06) to preserve microbubble integrity. A 1 mL bolus of the reconstituted contrast suspension was administered via an indwelling upper-limb venous catheter, followed by a 5 mL saline flush. When ultrasound microbubbles were observed within the breast lesion, the probe was held stationary to initiate SRMI acquisition. The imaging frame rate was set to 500fps and the acquisition time was 12s to capture the signals of the contrast agent flowing through the lesions. SRMI images were subsequently reconstructed from the acquired radiofrequency data using dedicated built-in software, producing microvascular density, velocity, direction, and velocity-direction maps. (Figs. 2 and 3 ). For quantitative analysis, regions of interest (ROIs) were manually delineated on SRMI images by tracing the lesion boundaries defined on corresponding two-dimensional grayscale ultrasound. The ROIs were positioned to fully encompass the target lesions while excluding surrounding macroscopically visible large blood vessels and necrotic areas. The quantitative parameters including microvascular density (MVD), flow-weighted vessel density (FWVD), fractal dimension (FD), perfusion index (PI), mean flow velocity ( \(\:\stackrel{-}{V}\) ), and velocity variance (Vel-var) were extracted. To assess measurement reproducibility, intra-observer agreement was evaluated by having the same physician re-delineate the regions of interest (ROIs) on 30 randomly selected images after a two-week interval. Inter-observer agreement was similarly evaluated by having a second physician independently perform ROI delineation on the same set of images. For both analyses, an intraclass correlation coefficient (ICC) > 0.75 was considered to indicate good agreement. The formulations of each parameter were as follows: MVD was defined as the ratio of the number of microvessel pixels to the total number of pixels in the ROI, measuring the abundance of microvessels within the ROI. $$\:MVD=\frac{Microvessel\:Pixels}{Overall\:Pixels}$$ FWVD was defined as the ratio of the sum of the vessel pixel values (or densities) to the total number of pixels in the ROI, measuring the volume of blood flow within the ROI. $$\:FWVD=\frac{\sum\:Microvessel\:Pixels}{Overall\:Pixels}$$ FD was defined as the ratio of graphical detail changes to measurement scale changes, describing the complexity of microvessel morphology within the ROI. $$\:FD=\underset{S\to\:0}{\text{lim}}\frac{\text{log}N\left(s\right)}{\text{log}1/s}$$ PI was defined as the product of mean blood flow velocity and microvascular density in the ROI, describing the perfusion level within the ROI. $$\:PI=\stackrel{-}{V}\times\:MVD$$ Vel var was defined as a metric quantifying the dispersionof flow velocity values within the ROI. $$\:Vel\:var=\frac{1}{N-1}{\sum\:}_{i=1}^{N}\left|{v}_{i}-\stackrel{-}{v}\right|$$ Pathological analyses Pathologic parameters were assessed using core needle biopsy specimens from the breast tumors. Tumor tissues were acquired from formalin-fixed, paraffin-embedded blocks for histopathological evaluation, including hematoxylin and eosin staining, immunohistochemistry, and fluorescence in situ hybridization analysis. IHC was performed to determine the expression status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the proliferation marker Ki-67, following established ASCO/CAP guideline recommendations where applicable [ 21 – 23 ]. Positivity for ER and PR was defined as IHC staining in ≥ 1% of tumor cells. HER-2 expression was categorized as HER2-positive (IHC 3 + or IHC 2 + with positive FISH), HER2-low (IHC 1 + or IHC 2 + with negative FISH), or HER2-zero (IHC 0). A Ki-67 index of ≥ 14% was defined as high expression, and values below this threshold were defined as low expression. Based on these markers, tumors were classified into four molecular subtypes, luminal A (ER/PR+, HER2−, Ki-67 low); luminal B (ER/PR+, Her2−, Ki-67 high, or ER/PR+, HER2+); HER2-positive (ER−, PR−, HER2+); and triple-negative breast cancer (TNBC; ER−, PR−, HER2−). Patients were classified into axillary lymph node negative and positive groups according to the final pathological results from either sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). All patients underwent either SLNB or ALND, with completion ALND performed in cases where SLNB yielded positive findings. Statistical analysis All statistical analyses were performed using R software (version 4.4.2; R Foundation for Statistical Computing). A two-sided p-value < 0.05 was considered statistically significant. Continuous variables were compared with the Mann-Whitney U test, while categorical variables were analyzed using the χ² test or Fisher’s exact test as appropriate. Intra- and inter-observer reliability of quantitative SRMI parameters was assessed using intraclass correlation coefficients (ICC). Univariate logistic regression was performed using the "glm" function to evaluate the association between pathological ALN status and various predictor variables, including clinicopathological, conventional US, and SRMI features. Predictors with p < 0.05 in the univariate analysis were included in the multivariable logistic regression. The final model retained only variables that remained statistically significant ( p < 0.05) in the multivariable analysis. The model was presented as a nomogram constructed with the “rms” package. Model performance was evaluated using receiver operating characteristic (ROC) curves (“proc” package) for discrimination, calibration curves (“rms” package) for calibration accuracy, and decision curve analysis for clinical utility. Internal validation was conducted via bootstrapping with 1,000 resamples[ 24 ]. Results Clinical and sonographic characteristics This study included 127 breast cancer patients with a mean age of 55.66 ± 11.26 years. Pathological assessment confirmed ALN metastasis in 60 patients (47.2%), while the remaining 67 patients (52.8%) were negative. As shown in Table 1 , significant differences (all p < 0.05) were observed between positive and negative groups across multiple parameters, including PR status, Ki-67 index, molecular subtypes, breast lesion descriptors (maximum diameter, calcification) and ALN descriptors (L/S ratio, shape, cortical morphology, and hilum status). Table 1 Clinical and sonographic characteristics of patients with breast cancer Variables Total(n = 127) Negative ALN (n = 67) Positive ALN (n = 60) P Age(y) 55.66 ± 11.26 54.27 ± 10.66 57.22 ± 11.79 0.144 BMI (kg/m 2 ) 24.22(22.66, 26.03) 23.94 (22.48, 25.94) 24.39 (23.19, 26.53) 0.478 Molecular subtypes 0.035 Luminal A 15 (12) 11 (16) 4 (7) Luminal B 86 (68) 47 (70) 39 (65) HER2-positive 17 (13) 4 (6) 13 (22) TNBC 9 (7) 5 (7) 4 (7) Estrogen receptor status 0.063 Positive 101 (80) 58 (87) 43 (72) Negative 26 (20) 9 (13) 17 (28) Progesterone receptor status 0.033 Positive 81 (64) 49 (73) 32 (53) Negative 46 (36) 18 (27) 28 (47) HER2 expression 0.289 HER2-zero 26 15 11 HER2-low 65 37 28 HER2-positive 36 15 21 Ki-67 status 14% 95 (75) 38 (57) 57 (95) ≤ 14% 32 (25) 29 (43) 3 (5) Maximum diameter 20 mm 58 (46) 19 (28) 39 (65) Shape 0.472 Oval or Round 1 (1) 0 (0) 1 (2) Irregular 126 (99) 67 (100) 59 (98) Internal Echo 0.329 Hypoechoic 101 (80) 56 (84) 45 (75) Other 26 (20) 11 (16) 15 (25) Hyperechoic halo 0.624 Absent 59 (46) 33 (49) 26 (43) Present 68 (54) 34 (51) 34 (57) Posterior Echo 0.146 No posterior features 96 (76) 46 (69) 50 (83) Shadowing 26 (20) 18 (27) 8 (13) Enhancement 5 (4) 3 (4) 2 (3) Orientation 0.26 Parallel 88 (69) 43 (64) 45 (75) Not parallel 39 (31) 24 (36) 15 (25) Calcification < 0.001 Absent 70 (55) 48 (72) 22 (37) Present 57 (45) 19 (28) 38 (63) CDFI 0.08 No 54 (43) 35 (52) 19 (32) Little 54 (43) 24 (36) 30 (50) Moderate 18 (14) 8 (12) 10 (17) Obvious 1 (1) 0 (0) 1 (2) Lymph node L/S ratio 2 (1.65, 2.48) 2.12 (1.82, 2.74) 1.82 (1.58, 2.13) 0.004 Shape 0.025 Oval 103 (81) 59 (88) 44 (73) Round 15 (12) 7 (10) 8 (13) Irregular 9 (7) 1 (1) 8 (13) Cortical morphologic features 3mm 90 (71) 42 (63) 48 (80) Hilum status < 0.001 Normal 31 (24) 25 (37) 6 (10) Eccentric 75 (59) 41 (61) 34 (57) Absent 21 (17) 1 (1) 20 (33) CDFI 0.341 No 42 (33) 24 (36) 18 (30) Hilar blood 73 (57) 39 (58) 34 (57) Nonhilar blood 12 (9) 4 (6) 8 (13) Note. Data are presented as mean ± standard deviation (SD) or median (interquartile range, IQR) for continuous variables and as number (percentage) for categorical variables. ALN: Axillary lymph node; BMI: Body mass index; TNBC: Triple-negative breast cancer; HER2: Human epidermal growth factor receptor-2; Ki67: A marker of cell proliferation; L/S: Long-to-short axis ratio; CDFI: Color doppler flow imaging. Quantitative parameters of SRMI ICC analysis demonstrated high consistency for both intra- and inter-observer measurements of SRMI quantitative parameters. The intra-observer ICC was 0.993 (95% CI: 0.991–0.995), and the inter-observer ICC was 0.981 (95% CI: 0.974–0.986). Table 2 shows the quantitative SRMI parameters of the breast lesion. Regarding the density parameters, MVD was significantly higher in the group of positive ALN than the group of negative ALN (42.59 ± 15.30 versus 35.12 ± 11.85 p = 0.003). Regarding the velocity based parameters, the negative ALN group exhibited a lower \(\:\stackrel{-}{V}\:\) than the positive ALN group( p = 0.004). The perfusion-related parameters were derived by combining the density and velocity information, the negative ALN group had a lower PI than positive ALN group ( p < 0.001). In terms of the FD, the positive ALN group had a higher value than the negative ALN group( p = 0.03). Table 2 The quantitative SRMI parameters of the breast lesion Variables Total(n = 127) Negative ALN (n = 67) Positive ALN (n = 60) P MVD 38.65 ± 14.04 35.12 ± 11.85 42.59 ± 15.3 0.003 FWVD 12.7 (9.17, 18.56) 12.59 (9.2, 18.02) 13.46 (9.06, 19.6) 0.614 FD 1.6 (1.53, 1.64) 1.56 (1.51, 1.62) 1.61 (1.54, 1.65) 0.03 PI 4.52 (3.54, 6.7) 4.3 (3.33, 5.68) 5.96 (4.02, 9.29) < 0.001 \(\:\stackrel{-}{V}\) 12.65 (10.15, 18.2) 12.5 (9.89, 14.8) 14.96 (11.26, 22.85) 0.004 Vel Var 52.93 (34.16, 103.21) 44.83 (30.8, 85.31) 58.25(34.87, 113.62) 0.065 Note. Data are presented as mean ± standard deviation (SD) or median (interquartile range, IQR). ALN: Axillary lymph node; MVD: Microvascular density; FWVD: Flow-weighted vessel density; FD: Fractal dimension; PI: Perfusion index; \(\:\stackrel{-}{V}\) : Mean flow velocity; Vel-var: Velocity variance. Univariate and multivariate regression analyses Univariate logistic regression analysis was first performed to screen potential predictors, and variables with a p-value < 0.05 were subsequently entered into the multivariate regression analysis. Multivariate analysis identified six independent factors associated with ALN metastasis (Table 3 ). A high Ki-67 index demonstrated the strongest positive association (OR: 8.261, 95% CI: 1.866–36.567, p = 0.005). Cortical morphology (OR: 4.033, 95% CI: 1.663–9.781, p = 0.002), microcalcification (OR: 3.981, 95% CI: 1.371–11.563, p = 0.011), tumor size (OR: 3.011, 95% CI: 1.029–8.814, p = 0.044), and perfusion index (PI) (OR: 1.392, 95% CI: 1.138–1.702, p = 0.001) were also significant positive predictors. Conversely, the presence of a normal hilum status was a protective factor, exhibiting a negative correlation (OR: 0.330, 95% CI: 0.147–0.739, p = 0.007). Table 3 Multivariate logistic regression of risk factors of breast cancers Variables Multivariable logistic regression analysis OR 95% CI P Ki-67 status 8.261 1.866–36.567 0.005 Maximum diameter 3.011 1.029–8.814 0.044 Calcification 3.981 1.371–11.563 0.011 Cortical morphologic features 4.033 1.663–9.781 0.002 Hilum status 0.33 0.147–0.739 0.007 PI 1.392 1.138–1.702 0.001 Note. Ki67: A marker of cell proliferation; PI: Perfusion index. Establishment and Evaluation of Nomograms We developed three predictive models (Mod1, Mod2, and Mod3) using the variables identified by multivariate analysis and evaluated their performance with ROC. Mod1, incorporating conventional ultrasound features of the breast lesion and ALN, achieved an AUC of 0.826 (95% CI: 0.756–0.897), with a sensitivity of 0.816 and a specificity of 0.671. Building on this, Mod2 additionally integrated the Ki-67 index, which improved the AUC to 0.862 (95% CI: 0.802–0.923; sensitivity: 0.783, specificity: 0.776). Further integrating PI measurements from the novel SRMI technique, Mod3 demonstrated the highest diagnostic performance, with an AUC of 0.904 (95% CI: 0.854–0.954), with sensitivity and specificity of 0.833 and 0.866, respectively. Mod3 exhibited the highest diagnostic efficacy, showing statistically significant superiority to Mod1 ( p = 0.006) and Mod2 ( p = 0.031). In bootstrap validation, the calibrated AUCs of the three models are 0.874, 0.843, and 0.826, respectively. The summary ROC curves of the three models are shown in Fig. 4 . Based on these results, Mod3 was selected to construct a nomogram for predicting ALNM (Fig. 5 ). The calibration curve of the nomogram indicated excellent agreement between predicted and observed metastasis rates (Fig. 6 ). Decision curve analysis further confirmed the high clinical utility of the nomogram, demonstrating a superior net benefit across a wide range of threshold probabilities (Fig. 7 ). Discussion ALNs in breast cancer is a key determinant of both overall staging and prognosis, and it critically informs the choice of treatment modalities. Therefore, accurate preoperative assessment of ALNs is critically important. Imaging examinations, including ultrasound and MRI, are the primary means for preoperative lymph node assessment and are essential for guiding optimal treatment decisions. However, their sensitivity is often suboptimal in early breast cancer, and MRI is not widely adopted for routine breast cancer screening[ 7 , 25 ]. Thus, novel and more sensitive methods are urgently needed to be developed. To date, numerous nomogram models have been established to predict ALNM in breast cancer, thereby offering valuable support for clinical management. Among existing predictive tools, the nomogram developed by Bevilacqua et al. from Memorial Sloan Kettering Cancer Center (MSKCC) in 2007 stands as the most established model in this field, having been extensively validated across multiple institutions for predicting sentinel lymph node metastasis[ 26 – 28 ]. The MSKCC nomogram integrates nine clinicopathological variables (age, tumor size, histological type, lymphovascular invasion, tumor location, multifocality, and estrogen and progesterone receptor status) and achieved an AUC of 0.754 during internal validation for predicting axillary lymph node metastasis. With the growing emphasis on non-invasive diagnostic strategies, research has progressively incorporated preoperative imaging data into predictive models. Zheng et al. developed a nomogram incorporating ultrasound features and clinicopathological characteristics to predict ALNM, which demonstrated strong predictive performance with an AUC of 0.80[ 29 ]. Building upon these developments, recent advances in artificial intelligence (AI) have significantly expanded the frontiers of predictive modeling in this field. For instance, Tang et al. applied a deep learning framework to develop a preoperative assessment model for ALNM based on ultrasound and magnetic resonance imaging (MRI) images, reporting an AUC of 0.809[ 30 ]. In contrast to previous studies, our prediction model uniquely integrates clinicopathological variables, conventional ultrasound features, and novel SRMI characteristics. All included variables are accessible preoperatively. This integrated model demonstrated markedly superior diagnostic performance, achieving an AUC of 0.874 and outperforming established models with statistical significance. Previous studies have demonstrated that ALNM in breast cancer is directly associated with tumor size and adversely impacts patient survival[ 31 , 32 ]. Our comprehensive predictive model confirmed that larger tumor diameter is associated with a higher risk of ALNM, consistent with findings from previous studies. Larger tumor diameter is positively correlated with elevated rates of tumor cell shedding and enhanced angiogenic activity, thereby promoting tumor cell entry into the lymphatic system and increasing the potential for lymph node metastasis[ 31 ]. Notably, the inclusion of microcalcifications as a significant variable introduces a biologically plausible dimension. The formation of microcalcifications is an active process mediated by calcium-regulating proteins and cytokines, such as the transcription factor Runx2, which is independently implicated in promoting breast cancer metastasis [33] . Thus, the predictive value of microcalcifications for ALNM may stem from this shared molecular pathway facilitating both calcification and metastatic spread. Furthermore, our model reinforces the critical role of axillary ultrasound in preoperative assessment. Consistent with prior research [34, 35] , we confirmed that cortical morphology and hilum status are robust sonographic predictors of ALNM. These imaging findings correspond directly to the underlying pathophysiological process. Metastatic cells migrate through lymphatic vessels to the sentinel lymph node, where they initially establish foci in the marginal sinus before proliferating and disseminating throughout the nodal tissue. This progressive infiltration disrupts the normal lymph node architecture, ultimately manifesting as the characteristic sonographic signs of cortical thickening and effacement of the fatty hilum. As a biomarker closely linked to tumor progression and metastasis, Ki-67 is crucial for breast cancer subtyping and prognosis. Elevated Ki-67 expression is correlated with enhanced tumor aggressiveness and metastatic propensity, notably an increased risk of ALNM. Our findings demonstrated that higher Ki-67 levels significantly predict ALNM (OR: 8.261; 95% CI: 1.866–36.567; p = 0.005), consistent with its identification as an independent predictor in prior multivariate analyses[ 31 ]. Mechanistically, Ki-67-driven proliferation promotes tumor cell shedding and necrosis, fostering a microenvironment conducive to lymphatic invasion. While Ki-67 provides strong prognostic insight, the predictive landscape for ALNM is multifactorial. Notably, our analysis did not corroborate the independent predictive value of molecular subtypes or hormone receptor status reported in some studies[ 34 , 36 ]. This discrepancy may be attributable to the inherent limitation of relying solely on clinical parameters, which provide only a partial representation of the tumor’s biological complexity and heterogeneity. To overcome this limitation and achieve more robust prediction, we developed a comprehensive model that integrates quantitative SRMI parameters with both clinicopathological and ultrasound features. A central finding of this study is that the PI, as quantified by SRMI, serves as a significant and independent predictor of ALNM. PI is calculated as the product of mean blood flow velocity and microvascular density within the region of interest (ROI), providing an integrated metric that reflects both the morphological architecture and the functional hemodynamics of the tumor microvasculature. Our analysis demonstrates a strong association between elevated PI levels in primary breast lesions and a higher likelihood of ALNM. The biological plausibility of this observation is supported by the well-characterized role of angiogenesis in breast cancer progression. Angiogenesis, the process of new blood vessel formation, is a fundamental mechanism driving both local tumor expansion and distant metastasis in breast cancer[ 37 ]. Within this process, vascular endothelial growth factor (VEGF) functions as a principal molecular mediator, while MVD provides a corresponding histological measure of angiogenic activity[ 38 ]. In accordance with this established paradigm, our results show a significantly higher MVD in patients with ALNM compared to those without (42.59 ± 15.30 vs. 35.12 ± 11.85, p = 0.003). However, in our multivariate analysis, MVD did not retain independent predictive value. This critical distinction validates our central hypothesis that metastatic progression is actively driven by the functional derangement of tumor vasculature, a pathological state precisely quantified by PI, rather than by the structural foundation established by MVD alone. SRMI is an advanced ultrasound imaging technique that overcomes the acoustic diffraction barrier to achieve micron-scale spatial resolution. By leveraging contrast-enhanced microbubbles, it quantifies key parameters of both microvascular morphology and hemodynamics. Unlike conventional imaging methods which primarily focus on delineating structural features, SRMI uniquely enables functional characterization of tumor microvasculature[ 17 ]. Consequently, our results demonstrate that PI, as a functional biomarker, provides incremental predictive value over MVD alone for preoperative ALN assessment. The predictive superiority of PI stems from its direct quantification of a pathologic functional triad in tumor vasculature, comprising hyper perfusion, increased permeability, and hemodynamic dysregulation. This aberrant functional state acts as a direct facilitator of metastasis, where mechanistically, hallmarks of hyperpermeable vessels such as elevated interstitial fluid pressure and widened endothelial gaps promote tumor cell intravasation and lymphatic invasion. In conclusion, this study establishes that functional assessment of tumor vasculature holds greater clinical relevance than structural quantification alone in predicting metastatic risk. The PI, obtained noninvasively via SRMI, emerges as a promising functional biomarker to enhance preoperative lymph node staging. Its application may ultimately contribute to more individualized surgical planning and adjuvant therapy strategies. In conclusion, our predictive model integrates clinicopathological factors, conventional ultrasound features, and SRMI derived PI to enable a comprehensive and multidimensional preoperative evaluation of axillary lymph node metastasis risk in breast cancer patients. Despite its encouraging performance, this study has several limitations that warrant consideration. First, its single center, retrospective design and relatively limited sample size may affect the generalizability of the nomogram. Although internal validation indicated good model stability, further external validation in larger, prospective, multi center cohorts is essential to confirm its robustness and clinical applicability. Second, the acquisition and interpretation of SRMI data currently lack standardized protocols. The technique remains operator dependent, which could influence reproducibility and limit direct comparability of results across different institutions. Declarations Ethics approval and consent to participate All procedures performed in this study were in accordance with the ethical standards of the Ethics Committee of Second Hospital of Shanxi Medical University (Approval No.: [2025] YX268) and with the 1964 Helsinki declaration and its later amendments. The requirement for obtaining individual informed consent was formally waived by the aforementioned Ethics Committee. This decision was based on the retrospective nature of the study, the use of fully anonymized patient data, and the assessment that the research posed no more than minimal risk to the participants. Consent for publication Written informed consent for publication was obtained from participants. All ultrasound images used in this manuscript have been fully anonymized by removing all personal identifiers. Competing interests The authors declare no competing financial interests or personal relationships that could have influenced the work reported in this article. Funding This study was supported by Fundamental Research Program of Shanxi Province No.202403021221318. Author Contribution H. W. and J. X. designed the study; J. X. and T. Z. wrote the main manuscript text; J. X., T. Z., B. W., Y. W., and L. H. did literature research, data collection and statistical analysis; H. W., J. X., T. Z. and J. W. edited the manuscript. All authors made approval of the final version of the submitted manuscript. Acknowledgments Some of our experiments were carried out on Python technology provided by the LySono Research Platform. We thank LySono Team’s help in this research. Availability of supporting data Data will be made available on request. References Teng L, Du J, Yan S, Xu P, Liu J, Zhao X, Tao W. A novel nomogram and survival analysis for different lymph node status in breast cancer based on the SEER database. Breast cancer (Tokyo Japan). 2024;31(5):769–86. Giaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A, Jemal A, Siegel RL. 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Sentinel Lymph Node Biopsy for Patients With Early-Stage Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline Update. J Clin oncology: official J Am Soc Clin Oncol. 2017;35(5):561–4. Marino MA, Avendano D, Zapata P, Riedl CC, Pinker K. Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools. Oncologist. 2020;25(2):e231–42. Aladag Kurt S, Kayadibi Y, Onur I, Uslu Besli L, Necati Sanli A, Velidedeoglu M. Predicting axillary nodal metastasis based on the side of asymmetrical cortical thickening in breast cancer: Evaluation with grayscale and microvascular imaging findings. Eur J Radiol. 2023;158:110643. Riedel F, Schaefgen B, Sinn HP, Feisst M, Hennigs A, Hug S, Binnig A, Gomez C, Harcos A, Stieber A, et al. Diagnostic accuracy of axillary staging by ultrasound in early breast cancer patients. Eur J Radiol. 2021;135:109468. Lee B, Lim AK, Krell J, Satchithananda K, Coombes RC, Lewis JS, Stebbing J. The efficacy of axillary ultrasound in the detection of nodal metastasis in breast cancer. AJR Am J Roentgenol. 2013;200(3):W314–320. Dobruch-Sobczak K, Szlenk A, Gumowska M, Mączewska J, Fronczewska K, Łukasiewicz E, Roszkowska-Purska K, Jakubczak M. Multiparametric ultrasound assessment of axillary lymph nodes in patients with breast cancer. Sci Rep. 2024;14(1):23072. Luo Y, Zhao C, Gao Y, Xiao M, Li W, Zhang J, Ma L, Qin J, Jiang Y, Zhu Q. Predicting Axillary Lymph Node Status With a Nomogram Based on Breast Lesion Ultrasound Features: Performance in N1 Breast Cancer Patients. Front Oncol. 2020;10:581321. Zhang H, Dong Y, Jia X, Zhang J, Li Z, Chuan Z, Xu Y, Hu B, Huang Y, Chang C, et al. Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer-A Multicenter Study. Front Oncol. 2022;12:830910. Song P, Rubin JM, Lowerison MR. 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Feasibility Analysis of Ultra-Resolution Microscopy Based on Contrast-Enhanced Ultrasound (CEUS) in Benign and Malignant Breast Lesions. J ultrasound medicine: official J Am Inst Ultrasound Med. 2025;44(8):1437–46. Kaiser U, Vehling-Kaiser U, Kück F, Gilanschah M, Jung F, Jung EM. Super-Resolution Contrast-Enhanced Ultrasound Examination Down to the Microvasculature Enables Quantitative Analysis of Liver Lesions: First Results. Life (Basel Switzerland) 2025, 15(7). He J, Yi H, Tang D, Zhang X, Cui X, Zhang W. Differentiating benign and malignant superficial lymph nodes using super-resolution contrast enhanced ultrasound - a pilot study. BMC Med Imaging. 2025;25(1):170. Wolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS, Bilous M, Ellis IO, Fitzgibbons P, Hanna W, et al. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update. J Clin oncology: official J Am Soc Clin Oncol. 2018;36(20):2105–22. Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thürlimann B, Senn HJ. Strategies for subtypes–dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Annals oncology: official J Eur Soc Med Oncol. 2011;22(8):1736–47. Allison KH, Hammond MEH, Dowsett M, McKernin SE, Carey LA, Fitzgibbons PL, Hayes DF, Lakhani SR, Chavez-MacGregor M, Perlmutter J, et al. Estrogen and Progesterone Receptor Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Guideline Update. Arch Pathol Lab Med. 2020;144(5):545–63. Wang S, Tu J. Nomogram to predict multidrug-resistant tuberculosis. Ann Clin Microbiol Antimicrob. 2020;19(1):27. Chang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology. 2020;295(3):500–15. Bevilacqua JL, Kattan MW, Fey JV, Cody HS 3rd, Borgen PI, Van Zee KJ. Doctor, what are my chances of having a positive sentinel node? A validated nomogram for risk estimation. J Clin oncology: official J Am Soc Clin Oncol. 2007;25(24):3670–9. Kuo YL, Chen WC, Yao WJ, Cheng L, Hsu HP, Lai HW, Kuo SJ, Chen DR, Chang TW. Validation of Memorial Sloan-Kettering Cancer Center nomogram for prediction of non-sentinel lymph node metastasis in sentinel lymph node positive breast cancer patients an international comparison. Int J Surg (London England). 2013;11(7):538–43. Qiu PF, Liu JJ, Wang YS, Yang GR, Liu YB, Sun X, Wang CJ, Zhang ZP. Risk factors for sentinel lymph node metastasis and validation study of the MSKCC nomogram in breast cancer patients. Jpn J Clin Oncol. 2012;42(11):1002–7. Zheng Y, Kuang Y, Shao S, Du Y, Chen J, Wang X, Wu R, Diao X. Sentinel Lymph Node Metastasis Prediction Based on Primary Breast Cancer US and CEUS Images of Clinical T1 Stage Breast Cancer Patients. J Clin ultrasound: JCU 2025. Tang J, Tian Y, Ma J, Xi X, Wang L, Sun Z, Liu X, Yu X, Zhang B. Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study. Cancer imaging: official publication Int Cancer Imaging Soc. 2025;25(1):4. Huang Z, Mo S, Li G, Tian H, Wu H, Chen J, Wang M, Tang S, Xu J, Dong F. Prognosticating axillary lymph node metastasis in breast cancer through integrated photoacoustic imaging, ultrasound, and clinical parameters. Breast cancer research: BCR. 2025;27(1):123. Wang SR, Tian F, Zhu T, Cao CL, Wang JL, Li WX, Li J, Hou JX. Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer. Front Endocrinol. 2025;16:1548888. O'Grady S, Morgan MP. Microcalcifications in breast cancer: From pathophysiology to diagnosis and prognosis. Biochim et Biophys acta Reviews cancer. 2018;1869(2):310–20. Zong Q, Deng J, Ge W, Chen J, Xu D. Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer. Cancer Manage Res. 2020;12:2025–35. Yu X, Hao X, Wan J, Wang Y, Yu L, Liu B. Correlation between Ultrasound Appearance of Small Breast Cancer and Axillary Lymph Node Metastasis. Ultrasound Med Biol. 2018;44(2):342–9. Hu X, Xue J, Peng S, Yang P, Yang Z, Yang L, Dong Y, Yuan L, Wang T, Bao G. Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy. Front Oncol. 2021;11:665240. Krüger K, Silwal-Pandit L, Wik E, Straume O, Stefansson IM, Borgen E, Garred Ø, Naume B, Engebraaten O, Akslen LA. Baseline microvessel density predicts response to neoadjuvant bevacizumab treatment of locally advanced breast cancer. Sci Rep. 2021;11(1):3388. Bai S, Wu Q, Song L. The expression and relationship of VEGF and MVD in type I endometrial cancer. Medicine. 2025;104(26):e42945. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 May, 2026 Editor invited by journal 15 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 09 Apr, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9373978","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639656975,"identity":"db28f0a7-9ee8-4aa5-b627-e631af6db53f","order_by":0,"name":"Jingzhu Xu","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingzhu","middleName":"","lastName":"Xu","suffix":""},{"id":639656976,"identity":"06a523e2-0eb6-4b86-bff2-7006bf032711","order_by":1,"name":"Tao Zhang","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""},{"id":639656977,"identity":"5ec6e154-e210-4c65-8176-be78d228f0b4","order_by":2,"name":"Bojuan Wang","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bojuan","middleName":"","lastName":"Wang","suffix":""},{"id":639656979,"identity":"2c9d5aba-f29a-4a37-9a11-e3232f5bf450","order_by":3,"name":"Yuhan Wang","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Wang","suffix":""},{"id":639656981,"identity":"e4ca30d8-fcc5-4a46-ad74-01298ca1149a","order_by":4,"name":"Lei Hao","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Hao","suffix":""},{"id":639656982,"identity":"e2895028-a5b3-4b2b-b94d-66ac641b4c79","order_by":5,"name":"Xinghua Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYLACxgYGBn5m5sMPSNMi2c6WZkCaFoPzPAoSRKk2OH728GveHYftjQ/zMBgw1NhEE9ZyJi/NcuaZw4nbDvMeeMBwLC23gZAWswM5ZgYf2w4nmB3mSzBgbDhMhJbzb8wMEtuADmvmMZAgTsuNHOMHQFsYNzATq8X+xhszxplt6YkzDgMDOYEYv0j25xh/5m2ztufvP3z4wYcaG8JagIANER0JRCgHAeYPRCocBaNgFIyCkQoAdSdBkfxppcEAAAAASUVORK5CYII=","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xinghua","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-10 03:24:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9373978/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9373978/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109434051,"identity":"73210b71-300c-4596-b169-e5cf3b948b0c","added_by":"auto","created_at":"2026-05-18 05:52:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164895,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient inclusion and exclusion criteria.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9373978/v1/334b1fb36818a5a9cf22f666.png"},{"id":109434052,"identity":"6d37d010-c865-4385-883e-c36580576d93","added_by":"auto","created_at":"2026-05-18 05:52:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":622927,"visible":true,"origin":"","legend":"\u003cp\u003eA 67-year-old female patient with breast cancer and positive axillary lymph node (ALN) (A) Grey-scale ultrasound image of the breast lesion, (B) Grey-scale ultrasound image of the ALN, (C-F) Super-resolution Microvascular Imaging (SRMI) of the breast lesion in microvascular density map, velocity map, direction map and velocity-direction map, respectively. SMRI demonstrated\u003cstrong\u003e \u003c/strong\u003ea high perfusion index (PI=17.79).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9373978/v1/be768d219fba580b50d7fb9a.png"},{"id":109759582,"identity":"54035a61-54ec-4db0-aabe-085367ce5673","added_by":"auto","created_at":"2026-05-22 07:27:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":641452,"visible":true,"origin":"","legend":"\u003cp\u003eA 47-year-old female patient with breast cancer and negative axillary lymph node (ALN). (A) Grey-scale ultrasound image of the breast lesion, (B) Grey-scale ultrasound image of the ALN, (C-F) Super-resolution Microvascular Imaging (SRMI) of the breast lesion in microvascular density map, velocity map, direction map and velocity-direction map, respectively. SMRI demonstrated\u003cstrong\u003e \u003c/strong\u003ea low perfusion index (PI=6.20).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9373978/v1/0018f417401d3ee2e1308e7f.png"},{"id":109906644,"identity":"6f431a5d-419b-4000-af0f-b3908cbf610b","added_by":"auto","created_at":"2026-05-25 06:40:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":215933,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves of the prediction models. (A) ROC and corresponding bootstrap validation curve for Model 1; (B) ROC and bootstrap validation curve for Model 2; (C) ROC and bootstrap validation curve for Model 3.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9373978/v1/c89351df14956a92eb1ed320.png"},{"id":109434056,"identity":"b88203d4-f91d-4075-aec1-3989a22fc3ce","added_by":"auto","created_at":"2026-05-18 05:52:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":346173,"visible":true,"origin":"","legend":"\u003cp\u003eAxillary lymph node metastasis prediction nomogram in breast cancer.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9373978/v1/a665378888da9177bc6d8981.png"},{"id":109434055,"identity":"ca19b120-12a3-4db5-b677-a34fd76b725a","added_by":"auto","created_at":"2026-05-18 05:52:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":139495,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the prediction nomogram.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9373978/v1/85b23cfa68376e726977122a.png"},{"id":109434057,"identity":"a12376b8-c371-4410-a264-21e1951cf7e2","added_by":"auto","created_at":"2026-05-18 05:52:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":152448,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curves of the prediction nomogram.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9373978/v1/10263f1b514e6bab13cd9ebd.png"},{"id":109908097,"identity":"41073388-1b13-4db0-90e8-c8d277185222","added_by":"auto","created_at":"2026-05-25 06:47:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2680295,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9373978/v1/3c757474-647f-4a23-b95b-faf334beccba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Super-resolution Microvascular Imaging and Conventional Ultrasound with Clinicopathologic Variables: A Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is one of the most common malignancies and the second leading cause of cancer-related mortality among women worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Axillary lymph node status (ALNS) is crucial in the management of breast cancer, as it directly influences staging, treatment planning, and overall prognosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Axillary lymph node dissection (ALND), which involves the removal of all lymph nodes in the axilla, can lead to complications such as injury to surrounding structures and lymphedema[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Sentinel lymph node biopsy (SLNB), a less invasive procedure developed to assess nodal metastasis without extensive dissection, has become the standard method for determining ALNS in early-stage breast cancer[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the intraoperative evaluation of SLN can limit its utility in guiding preoperative treatment planning. Consequently, developing a noninvasive, preoperative method to accurately identify ALNS is urgently needed.\u003c/p\u003e \u003cp\u003eUltrasound (US), as a non-invasive, convenient, and cost-effective imaging modality, plays an indispensable role in the preoperative evaluation of ALNS[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Studies have shown that the morphological features of axillary lymph nodes on ultrasound can predict ALNS. Lymph nodes exhibiting a round shape, displaced or absent hilum, indistinct margins, and increased cortical blood flow are considered suspicious for metastasis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the reported sensitivity and specificity of axillary ultrasound vary widely, ranging from 26% to 95% and 44% to 98%, respectively, due to the significant overlap in the ultrasound morphological features of benign and malignant lymph nodes[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, the status of axillary lymph nodes can be inferred by evaluating the breast tumor and the surrounding tissue, with reported accuracy rates of 0.748 and 0.659, respectively[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite their potential to predict ALNS, their suboptimal diagnostic performance has limited widespread adoption, creating a pressing need for innovative tools to provide additional information and enhance assessment accuracy.\u003c/p\u003e \u003cp\u003eSuper-resolution microvascular imaging (SRMI) is a novel ultrasound technique that overcomes the acoustic diffraction limit to achieve micron-level spatial resolution by leveraging contrast microbubbles to visualize microvasculature. This technique enables the generation of micrometer-scale maps of microvessel density (MVD) and flow velocity within masses by localizing and tracking individual microbubbles at subwavelength resolution after contrast agent injection[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Tumor angiogenesis is a critical biological mechanism whereby tumors induce new blood vessel growth to facilitate their expansion, local invasion, and metastatic dissemination. High MVD levels are strongly associated with an increased risk of metastasis in breast cancer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. SRMI has been successfully applied in research to enable the early detection and differential diagnosis of diseases through the analysis of quantitative microvascular morphological and hemodynamic parameters[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Despite the promising diagnostic utility of SRMI, few studies have investigated the correlation between its quantitative microvascular parameters and the risk of axillary lymph node metastasis (ALNM) in breast cancer.\u003c/p\u003e \u003cp\u003eTherefore, this study develops a predictive nomogram that integrates super-resolution microvascular imaging, conventional ultrasound, and clinicopathological indicators to improve the accuracy of axillary lymph node metastasis prediction and provide a more comprehensive basis for clinical decision-making. This integrated approach aims to investigate the correlation between quantitative SRMI parameters and ALNS, thereby enhancing the accuracy of ALNM prediction.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy patients\u003c/h2\u003e \u003cp\u003eThis study retrospectively enrolled a consecutive series of breast cancer patients with postoperative pathological confirmation from the Second Hospital of Shanxi Medical University between March and October 2025. The inclusion criteria were as follows:(1) female patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) preoperative conventional ultrasound evaluating both the breast mass and axillary lymph nodes; (3) preoperative SRMI assessment of the breast masse; (4) unifocal breast lesion; (5) underwent surgical resection of the breast lesion with axillary lymph node dissection or sentinel lymph node biopsy. The exclusion criteria were as follows:(1) contraindications to contrast agents; (2) underwent chemotherapy or radiotherapy; and (3) incomplete clinicopathological, ultrasound, or SRMI imaging data. Based on the inclusion and exclusion criteria, a total of 127 patients with breast cancer were finally enrolled (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study received ethical approval from the Institutional Review Board of our hospital (Approval No.: [2025] YX268).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConventional US examination and image analysis\u003c/h3\u003e\n\u003cp\u003eAll conventional US and SRMI examinations of breast lesions and ALNs were performed by two radiologists, each with over 8 years of experience in breast ultrasound, using a Mindray Resona A20 system equipped with L10-3 (3\u0026ndash;10 MHz) and L18-5 (5\u0026ndash;18 MHz) linear array transducers. During conventional US examinations, patients were positioned supine with upper limbs abducted to fully expose the breast and axillary regions. A comprehensive scan was performed using the L18-5 transducer to acquire and store two-dimensional grayscale and color Doppler flow images of the detected lesions and lymph nodes.\u003c/p\u003e \u003cp\u003eThe sonographic features of breast lesions (including size, shape, echo pattern, margins, calcification, aspect ratio, hyperechoic halo, and blood flow) and ALNs (including size, shape, cortical thickness, hilum, and blood flow) were independently evaluated by the two radiologists who were blinded to the pathological results. In cases of disagreement, a senior physician would serve as an arbitrator.\u003c/p\u003e\n\u003ch3\u003eSRMI image acquisition and quantification protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eSRMI image acquisition and quantification protocol\u003c/div\u003e \u003cp\u003eSRMI was performed using the L10-3 linear array transducer after conventional US. The imaging plane was selected to optimally display the tumor's largest cross-section while including adjacent normal glandular tissue for reference, following administration of the ultrasound contrast agent Sonazoid (GE Healthcare AS) at a low mechanical index (MI\u0026thinsp;=\u0026thinsp;0.06) to preserve microbubble integrity. A 1 mL bolus of the reconstituted contrast suspension was administered via an indwelling upper-limb venous catheter, followed by a 5 mL saline flush. When ultrasound microbubbles were observed within the breast lesion, the probe was held stationary to initiate SRMI acquisition. The imaging frame rate was set to 500fps and the acquisition time was 12s to capture the signals of the contrast agent flowing through the lesions. SRMI images were subsequently reconstructed from the acquired radiofrequency data using dedicated built-in software, producing microvascular density, velocity, direction, and velocity-direction maps. (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor quantitative analysis, regions of interest (ROIs) were manually delineated on SRMI images by tracing the lesion boundaries defined on corresponding two-dimensional grayscale ultrasound. The ROIs were positioned to fully encompass the target lesions while excluding surrounding macroscopically visible large blood vessels and necrotic areas. The quantitative parameters including microvascular density (MVD), flow-weighted vessel density (FWVD), fractal dimension (FD), perfusion index (PI), mean flow velocity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{V}\\)\u003c/span\u003e\u003c/span\u003e), and velocity variance (Vel-var) were extracted. To assess measurement reproducibility, intra-observer agreement was evaluated by having the same physician re-delineate the regions of interest (ROIs) on 30 randomly selected images after a two-week interval. Inter-observer agreement was similarly evaluated by having a second physician independently perform ROI delineation on the same set of images. For both analyses, an intraclass correlation coefficient (ICC)\u0026thinsp;\u0026gt;\u0026thinsp;0.75 was considered to indicate good agreement.\u003c/p\u003e \u003cp\u003eThe formulations of each parameter were as follows:\u003c/p\u003e \u003cp\u003eMVD was defined as the ratio of the number of microvessel pixels to the total number of pixels in the ROI, measuring the abundance of microvessels within the ROI.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:MVD=\\frac{Microvessel\\:Pixels}{Overall\\:Pixels}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFWVD was defined as the ratio of the sum of the vessel pixel values (or densities) to the total number of pixels in the ROI, measuring the volume of blood flow within the ROI.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:FWVD=\\frac{\\sum\\:Microvessel\\:Pixels}{Overall\\:Pixels}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFD was defined as the ratio of graphical detail changes to measurement scale changes, describing the complexity of microvessel morphology within the ROI.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:FD=\\underset{S\\to\\:0}{\\text{lim}}\\frac{\\text{log}N\\left(s\\right)}{\\text{log}1/s}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePI was defined as the product of mean blood flow velocity and microvascular density in the ROI, describing the perfusion level within the ROI.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:PI=\\stackrel{-}{V}\\times\\:MVD$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eVel var was defined as a metric quantifying the dispersionof flow velocity values within the ROI.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:Vel\\:var=\\frac{1}{N-1}{\\sum\\:}_{i=1}^{N}\\left|{v}_{i}-\\stackrel{-}{v}\\right|$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003ePathological analyses\u003c/h3\u003e\n\u003cp\u003ePathologic parameters were assessed using core needle biopsy specimens from the breast tumors. Tumor tissues were acquired from formalin-fixed, paraffin-embedded blocks for histopathological evaluation, including hematoxylin and eosin staining, immunohistochemistry, and fluorescence in situ hybridization analysis. IHC was performed to determine the expression status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the proliferation marker Ki-67, following established ASCO/CAP guideline recommendations where applicable [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Positivity for ER and PR was defined as IHC staining in \u0026ge;\u0026thinsp;1% of tumor cells. HER-2 expression was categorized as HER2-positive (IHC 3\u0026thinsp;+\u0026thinsp;or IHC 2\u0026thinsp;+\u0026thinsp;with positive FISH), HER2-low (IHC 1\u0026thinsp;+\u0026thinsp;or IHC 2\u0026thinsp;+\u0026thinsp;with negative FISH), or HER2-zero (IHC 0). A Ki-67 index of \u0026ge;\u0026thinsp;14% was defined as high expression, and values below this threshold were defined as low expression. Based on these markers, tumors were classified into four molecular subtypes, luminal A (ER/PR+, HER2\u0026minus;, Ki-67 low); luminal B (ER/PR+, Her2\u0026minus;, Ki-67 high, or ER/PR+, HER2+); HER2-positive (ER\u0026minus;, PR\u0026minus;, HER2+); and triple-negative breast cancer (TNBC; ER\u0026minus;, PR\u0026minus;, HER2\u0026minus;).\u003c/p\u003e \u003cp\u003ePatients were classified into axillary lymph node negative and positive groups according to the final pathological results from either sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). All patients underwent either SLNB or ALND, with completion ALND performed in cases where SLNB yielded positive findings.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.4.2; R Foundation for Statistical Computing). A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Continuous variables were compared with the Mann-Whitney U test, while categorical variables were analyzed using the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test as appropriate. Intra- and inter-observer reliability of quantitative SRMI parameters was assessed using intraclass correlation coefficients (ICC). Univariate logistic regression was performed using the \"glm\" function to evaluate the association between pathological ALN status and various predictor variables, including clinicopathological, conventional US, and SRMI features. Predictors with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the multivariable logistic regression. The final model retained only variables that remained statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the multivariable analysis. The model was presented as a nomogram constructed with the \u0026ldquo;rms\u0026rdquo; package. Model performance was evaluated using receiver operating characteristic (ROC) curves (\u0026ldquo;proc\u0026rdquo; package) for discrimination, calibration curves (\u0026ldquo;rms\u0026rdquo; package) for calibration accuracy, and decision curve analysis for clinical utility. Internal validation was conducted via bootstrapping with 1,000 resamples[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical and sonographic characteristics\u003c/h2\u003e \u003cp\u003eThis study included 127 breast cancer patients with a mean age of 55.66\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26 years. Pathological assessment confirmed ALN metastasis in 60 patients (47.2%), while the remaining 67 patients (52.8%) were negative. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, significant differences (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed between positive and negative groups across multiple parameters, including PR status, Ki-67 index, molecular subtypes, breast lesion descriptors (maximum diameter, calcification) and ALN descriptors (L/S ratio, shape, cortical morphology, and hilum status).\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\u003eClinical and sonographic characteristics of patients with breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;127)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative ALN\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive ALN\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.66\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.27\u0026thinsp;\u0026plusmn;\u0026thinsp;10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.22(22.66, 26.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.94 (22.48, 25.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.39 (23.19, 26.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular subtypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstrogen receptor status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesterone receptor status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2-zero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2-low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOval or Round\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Echo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoechoic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperechoic halo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosterior Echo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo posterior features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShadowing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParallel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot parallel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLittle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObvious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node L/S ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.65, 2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12 (1.82, 2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (1.58, 2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortical morphologic features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymmetric thickening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse thickening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortical thickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHilum status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEccentric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHilar blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonhilar blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range, IQR) for continuous variables and as number (percentage) for categorical variables. ALN: Axillary\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003elymph node; BMI: Body mass index; TNBC: Triple-negative breast cancer; HER2: Human epidermal growth factor receptor-2; Ki67: A marker of cell proliferation; L/S: Long-to-short axis ratio; CDFI: Color doppler flow imaging.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuantitative parameters of SRMI\u003c/h3\u003e\n\u003cp\u003eICC analysis demonstrated high consistency for both intra- and inter-observer measurements of SRMI quantitative parameters. The intra-observer ICC was 0.993 (95% CI: 0.991\u0026ndash;0.995), and the inter-observer ICC was 0.981 (95% CI: 0.974\u0026ndash;0.986).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the quantitative SRMI parameters of the breast lesion. Regarding the density parameters, MVD was significantly higher in the group of positive ALN than the group of negative ALN (42.59\u0026thinsp;\u0026plusmn;\u0026thinsp;15.30 versus 35.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.85 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Regarding the velocity based parameters, the negative ALN group exhibited a lower \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{V}\\:\\)\u003c/span\u003e\u003c/span\u003ethan the positive ALN group(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). The perfusion-related parameters were derived by combining the density and velocity information, the negative ALN group had a lower PI than positive ALN group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of the FD, the positive ALN group had a higher value than the negative ALN group(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03).\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\u003eThe quantitative SRMI parameters of the breast lesion\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;127)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative ALN\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive ALN\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.59\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFWVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.7 (9.17, 18.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.59 (9.2, 18.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.46 (9.06, 19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 (1.53, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56 (1.51, 1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (1.54, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.52 (3.54, 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3 (3.33, 5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.96 (4.02, 9.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{V}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.65 (10.15, 18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5 (9.89, 14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.96 (11.26, 22.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVel Var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.93 (34.16, 103.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.83 (30.8, 85.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.25(34.87, 113.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range, IQR). ALN: Axillary lymph node; MVD: Microvascular density; FWVD: Flow-weighted vessel density; FD: Fractal dimension; PI: Perfusion index; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{V}\\)\u003c/span\u003e\u003c/span\u003e: Mean flow velocity; Vel-var: Velocity variance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and multivariate regression analyses\u003c/h2\u003e \u003cp\u003eUnivariate logistic regression analysis was first performed to screen potential predictors, and variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were subsequently entered into the multivariate regression analysis. Multivariate analysis identified six independent factors associated with ALN metastasis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A high Ki-67 index demonstrated the strongest positive association (OR: 8.261, 95% CI: 1.866\u0026ndash;36.567, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Cortical morphology (OR: 4.033, 95% CI: 1.663\u0026ndash;9.781, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), microcalcification (OR: 3.981, 95% CI: 1.371\u0026ndash;11.563, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), tumor size (OR: 3.011, 95% CI: 1.029\u0026ndash;8.814, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), and perfusion index (PI) (OR: 1.392, 95% CI: 1.138\u0026ndash;1.702, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were also significant positive predictors. Conversely, the presence of a normal hilum status was a protective factor, exhibiting a negative correlation (OR: 0.330, 95% CI: 0.147\u0026ndash;0.739, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007).\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\u003eMultivariate logistic regression of risk factors of breast cancers\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMultivariable logistic regression analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67 status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.866\u0026ndash;36.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.029\u0026ndash;8.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.371\u0026ndash;11.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortical morphologic features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.663\u0026ndash;9.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHilum status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.147\u0026ndash;0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.138\u0026ndash;1.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote. Ki67: A marker of cell proliferation; PI: Perfusion index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and Evaluation of Nomograms\u003c/h2\u003e \u003cp\u003eWe developed three predictive models (Mod1, Mod2, and Mod3) using the variables identified by multivariate analysis and evaluated their performance with ROC. Mod1, incorporating conventional ultrasound features of the breast lesion and ALN, achieved an AUC of 0.826 (95% CI: 0.756\u0026ndash;0.897), with a sensitivity of 0.816 and a specificity of 0.671. Building on this, Mod2 additionally integrated the Ki-67 index, which improved the AUC to 0.862 (95% CI: 0.802\u0026ndash;0.923; sensitivity: 0.783, specificity: 0.776). Further integrating PI measurements from the novel SRMI technique, Mod3 demonstrated the highest diagnostic performance, with an AUC of 0.904 (95% CI: 0.854\u0026ndash;0.954), with sensitivity and specificity of 0.833 and 0.866, respectively. Mod3 exhibited the highest diagnostic efficacy, showing statistically significant superiority to Mod1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and Mod2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). In bootstrap validation, the calibrated AUCs of the three models are 0.874, 0.843, and 0.826, respectively. The summary ROC curves of the three models are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Based on these results, Mod3 was selected to construct a nomogram for predicting ALNM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The calibration curve of the nomogram indicated excellent agreement between predicted and observed metastasis rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Decision curve analysis further confirmed the high clinical utility of the nomogram, demonstrating a superior net benefit across a wide range of threshold probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eALNs in breast cancer is a key determinant of both overall staging and prognosis, and it critically informs the choice of treatment modalities. Therefore, accurate preoperative assessment of ALNs is critically important. Imaging examinations, including ultrasound and MRI, are the primary means for preoperative lymph node assessment and are essential for guiding optimal treatment decisions. However, their sensitivity is often suboptimal in early breast cancer, and MRI is not widely adopted for routine breast cancer screening[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, novel and more sensitive methods are urgently needed to be developed.\u003c/p\u003e \u003cp\u003eTo date, numerous nomogram models have been established to predict ALNM in breast cancer, thereby offering valuable support for clinical management. Among existing predictive tools, the nomogram developed by Bevilacqua et al. from Memorial Sloan Kettering Cancer Center (MSKCC) in 2007 stands as the most established model in this field, having been extensively validated across multiple institutions for predicting sentinel lymph node metastasis[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The MSKCC nomogram integrates nine clinicopathological variables (age, tumor size, histological type, lymphovascular invasion, tumor location, multifocality, and estrogen and progesterone receptor status) and achieved an AUC of 0.754 during internal validation for predicting axillary lymph node metastasis. With the growing emphasis on non-invasive diagnostic strategies, research has progressively incorporated preoperative imaging data into predictive models. Zheng et al. developed a nomogram incorporating ultrasound features and clinicopathological characteristics to predict ALNM, which demonstrated strong predictive performance with an AUC of 0.80[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Building upon these developments, recent advances in artificial intelligence (AI) have significantly expanded the frontiers of predictive modeling in this field. For instance, Tang et al. applied a deep learning framework to develop a preoperative assessment model for ALNM based on ultrasound and magnetic resonance imaging (MRI) images, reporting an AUC of 0.809[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In contrast to previous studies, our prediction model uniquely integrates clinicopathological variables, conventional ultrasound features, and novel SRMI characteristics. All included variables are accessible preoperatively. This integrated model demonstrated markedly superior diagnostic performance, achieving an AUC of 0.874 and outperforming established models with statistical significance.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that ALNM in breast cancer is directly associated with tumor size and adversely impacts patient survival[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our comprehensive predictive model confirmed that larger tumor diameter is associated with a higher risk of ALNM, consistent with findings from previous studies. Larger tumor diameter is positively correlated with elevated rates of tumor cell shedding and enhanced angiogenic activity, thereby promoting tumor cell entry into the lymphatic system and increasing the potential for lymph node metastasis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Notably, the inclusion of microcalcifications as a significant variable introduces a biologically plausible dimension. The formation of microcalcifications is an active process mediated by calcium-regulating proteins and cytokines, such as the transcription factor Runx2, which is independently implicated in promoting breast cancer metastasis\u003csup\u003e[33]\u003c/sup\u003e. Thus, the predictive value of microcalcifications for ALNM may stem from this shared molecular pathway facilitating both calcification and metastatic spread. Furthermore, our model reinforces the critical role of axillary ultrasound in preoperative assessment. Consistent with prior research\u003csup\u003e[34, 35]\u003c/sup\u003e, we confirmed that cortical morphology and hilum status are robust sonographic predictors of ALNM. These imaging findings correspond directly to the underlying pathophysiological process. Metastatic cells migrate through lymphatic vessels to the sentinel lymph node, where they initially establish foci in the marginal sinus before proliferating and disseminating throughout the nodal tissue. This progressive infiltration disrupts the normal lymph node architecture, ultimately manifesting as the characteristic sonographic signs of cortical thickening and effacement of the fatty hilum.\u003c/p\u003e \u003cp\u003eAs a biomarker closely linked to tumor progression and metastasis, Ki-67 is crucial for breast cancer subtyping and prognosis. Elevated Ki-67 expression is correlated with enhanced tumor aggressiveness and metastatic propensity, notably an increased risk of ALNM. Our findings demonstrated that higher Ki-67 levels significantly predict ALNM (OR: 8.261; 95% CI: 1.866\u0026ndash;36.567; p\u0026thinsp;=\u0026thinsp;0.005), consistent with its identification as an independent predictor in prior multivariate analyses[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Mechanistically, Ki-67-driven proliferation promotes tumor cell shedding and necrosis, fostering a microenvironment conducive to lymphatic invasion. While Ki-67 provides strong prognostic insight, the predictive landscape for ALNM is multifactorial. Notably, our analysis did not corroborate the independent predictive value of molecular subtypes or hormone receptor status reported in some studies[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This discrepancy may be attributable to the inherent limitation of relying solely on clinical parameters, which provide only a partial representation of the tumor\u0026rsquo;s biological complexity and heterogeneity. To overcome this limitation and achieve more robust prediction, we developed a comprehensive model that integrates quantitative SRMI parameters with both clinicopathological and ultrasound features.\u003c/p\u003e \u003cp\u003eA central finding of this study is that the PI, as quantified by SRMI, serves as a significant and independent predictor of ALNM. PI is calculated as the product of mean blood flow velocity and microvascular density within the region of interest (ROI), providing an integrated metric that reflects both the morphological architecture and the functional hemodynamics of the tumor microvasculature. Our analysis demonstrates a strong association between elevated PI levels in primary breast lesions and a higher likelihood of ALNM. The biological plausibility of this observation is supported by the well-characterized role of angiogenesis in breast cancer progression. Angiogenesis, the process of new blood vessel formation, is a fundamental mechanism driving both local tumor expansion and distant metastasis in breast cancer[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Within this process, vascular endothelial growth factor (VEGF) functions as a principal molecular mediator, while MVD provides a corresponding histological measure of angiogenic activity[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In accordance with this established paradigm, our results show a significantly higher MVD in patients with ALNM compared to those without (42.59\u0026thinsp;\u0026plusmn;\u0026thinsp;15.30 vs. 35.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). However, in our multivariate analysis, MVD did not retain independent predictive value. This critical distinction validates our central hypothesis that metastatic progression is actively driven by the functional derangement of tumor vasculature, a pathological state precisely quantified by PI, rather than by the structural foundation established by MVD alone.\u003c/p\u003e \u003cp\u003eSRMI is an advanced ultrasound imaging technique that overcomes the acoustic diffraction barrier to achieve micron-scale spatial resolution. By leveraging contrast-enhanced microbubbles, it quantifies key parameters of both microvascular morphology and hemodynamics. Unlike conventional imaging methods which primarily focus on delineating structural features, SRMI uniquely enables functional characterization of tumor microvasculature[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, our results demonstrate that PI, as a functional biomarker, provides incremental predictive value over MVD alone for preoperative ALN assessment. The predictive superiority of PI stems from its direct quantification of a pathologic functional triad in tumor vasculature, comprising hyper perfusion, increased permeability, and hemodynamic dysregulation. This aberrant functional state acts as a direct facilitator of metastasis, where mechanistically, hallmarks of hyperpermeable vessels such as elevated interstitial fluid pressure and widened endothelial gaps promote tumor cell intravasation and lymphatic invasion. In conclusion, this study establishes that functional assessment of tumor vasculature holds greater clinical relevance than structural quantification alone in predicting metastatic risk. The PI, obtained noninvasively via SRMI, emerges as a promising functional biomarker to enhance preoperative lymph node staging. Its application may ultimately contribute to more individualized surgical planning and adjuvant therapy strategies.\u003c/p\u003e \u003cp\u003eIn conclusion, our predictive model integrates clinicopathological factors, conventional ultrasound features, and SRMI derived PI to enable a comprehensive and multidimensional preoperative evaluation of axillary lymph node metastasis risk in breast cancer patients.\u003c/p\u003e \u003cp\u003eDespite its encouraging performance, this study has several limitations that warrant consideration. First, its single center, retrospective design and relatively limited sample size may affect the generalizability of the nomogram. Although internal validation indicated good model stability, further external validation in larger, prospective, multi center cohorts is essential to confirm its robustness and clinical applicability. Second, the acquisition and interpretation of SRMI data currently lack standardized protocols. The technique remains operator dependent, which could influence reproducibility and limit direct comparability of results across different institutions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e All procedures performed in this study were in accordance with the ethical standards of the Ethics Committee of Second Hospital of Shanxi Medical University (Approval No.: [2025] YX268) and with the 1964 Helsinki declaration and its later amendments. The requirement for obtaining individual informed consent was formally waived by the aforementioned Ethics Committee. This decision was based on the retrospective nature of the study, the use of fully anonymized patient data, and the assessment that the research posed no more than minimal risk to the participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e Written informed consent for publication was obtained from participants. All ultrasound images used in this manuscript have been fully anonymized by removing all personal identifiers.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial interests or personal relationships that could have influenced the work reported in this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Fundamental Research Program of Shanxi Province No.202403021221318.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH. W. and J. X. designed the study; J. X. and T. Z. wrote the main manuscript text; J. X., T. Z., B. W., Y. W., and L. H. did literature research, data collection and statistical analysis; H. W., J. X., T. Z. and J. W. edited the manuscript. All authors made approval of the final version of the submitted manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e Some of our experiments were carried out on Python technology provided by the LySono Research Platform. We thank LySono Team\u0026rsquo;s help in this research.\u003c/p\u003e\u003ch2\u003eAvailability of supporting data\u003c/h2\u003e \u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTeng L, Du J, Yan S, Xu P, Liu J, Zhao X, Tao W. A novel nomogram and survival analysis for different lymph node status in breast cancer based on the SEER database. 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Sci Rep. 2021;11(1):3388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai S, Wu Q, Song L. The expression and relationship of VEGF and MVD in type I endometrial cancer. Medicine. 2025;104(26):e42945.\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":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":"Axillary lymph node, Breast cancer, SRMI, Ultrasound, microvessels","lastPublishedDoi":"10.21203/rs.3.rs-9373978/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9373978/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to develop and validate a predictive model for axillary lymph node metastasis (ALNM) in breast cancer by integrating clinicopathological factors, conventional ultrasound features, and quantitative parameters from Super-Resolution Microvascular Imaging (SRMI) to enhance diagnostic accuracy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 127 breast cancer patients were enrolled in this study. Each patient underwent conventional ultrasound examination of both the breast mass and axillary lymph nodes, as well as SRMI of the breast mass. Based on the pathological results as the gold standard, the patients were stratified into positive and negative ALNM groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors of ALNM, and a predictive nomogram was constructed based on these selected factors. The nomogram's performance was subsequently evaluated in terms of discrimination using the receiver operating characteristic (ROC) curve, calibration via calibration plot analysis, and clinical utility by decision curve analysis (DCA). Internal validation was subsequently performed using the bootstrap method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe multivariate logistic regression analysis identified maximum tumor diameter, microcalcification, Ki67 expression, cortical morphology, hilum status, and pulsatility index (PI) levels as independent risk factors for ALNM. The developed nomogram demonstrated an area under the ROC curve (AUC) of 0.904 (95% CI: 0.854\u0026ndash;0.954), with a bootstrap-validated AUC of 0.874 (95% CI: 0.826\u0026ndash;0.947). The calibration curve indicated good agreement between the predicted probabilities and actual ALNM outcomes. Furthermore, DCA confirmed the clinical utility of the nomogram.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIntegrating the PI obtained from SRMI with clinical data and conventional ultrasound features significantly improves the predictive performance for ALNM in breast cancer. The resultant integrated model provides a comprehensive and reliable tool for preoperative risk assessment of axillary lymph node status, offering valuable clinical decision support.\u003c/p\u003e","manuscriptTitle":"Integrating Super-resolution Microvascular Imaging and Conventional Ultrasound with Clinicopathologic Variables: A Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 05:52:37","doi":"10.21203/rs.3.rs-9373978/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-06T10:47:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T11:42:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T11:22:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-14T11:22:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-04-10T03:13:29+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":"9fe3a8bf-d1e1-469e-9917-8b539ea2a853","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"30","date":"2026-05-06T10:47:24+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T05:52:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 05:52:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9373978","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9373978","identity":"rs-9373978","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}