Development and Validation of a Nomogram Combining Ultrasound Parameters and Clinical Indicators for Predicting Sarcopenia in Breast Cancer Patients

Development and Validation of a Nomogram Combining Ultrasound Parameters and Clinical Indicators for Predicting Sarcopenia in Breast Cancer Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Nomogram Combining Ultrasound Parameters and Clinical Indicators for Predicting Sarcopenia in Breast Cancer Patients Ying Wang, Yan Hu, Yue Liu, Mingxin Ji, Ruxue sun, Shuang Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8599311/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Sarcopenia, a condition prevalent among breast cancer patients, correlates with adverse treatment outcomes. To address the limited accessibility of gold-standard diagnostic methods, this study aimed to develop a practical nomogram based on gastrocnemius muscle ultrasound for sarcopenia screening in this population. Methods: This retrospective study enrolled 158 patients with primary breast cancer who were scheduled for TAC chemotherapy. We measured ultrasound parameters of the dominant medial gastrocnemius, including muscle thickness, pennation angle, and Young’s modulus in the relaxed state. Clinical data such as handgrip strength and age were also recorded. Sarcopenia was diagnosed on the basis of low handgrip strength combined with a low skeletal muscle index measured by computed tomography at the 12th thoracic vertebra level. Variables with P < 0.05 in the univariate analysis were entered into the multivariate logistic regression analysis to identify independent predictors, which were then used to construct the nomogram. The model underwent internal validation via the bootstrap method. Its discriminative ability, calibration, and clinical utility were assessed by the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. Results: Multivariate analysis revealed that muscle thickness, pennation angle, Young’s modulus in the relaxed state, and age were independent predictors of sarcopenia. The nomogram achieved an AUC of 0.911 (95% CI: 0.868–0.954). After internal bootstrap validation, the AUC remained high at 0.898, with sensitivity and specificity values of 86.1% and 72.6%, respectively. The calibration curve indicated good agreement between the predicted and observed probabilities. Decision curve analysis confirmed that the model yielded a net clinical benefit across a wide range of threshold probabilities. Conclusion: The nomogram integrating gastrocnemius ultrasound parameters and age has favorable predictive performance for sarcopenia in breast cancer patients, suggesting its potential as a practical screening tool in clinical practice. Trial registration: Not applicable. Ultrasonography Sarcopenia Breast cancer Shear wave elastography Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer continues to be the most prevalent malignancy in women globally, with increasing incidence rates imposing a significant health burden( 1 ). Despite significant advances in breast cancer research, persistent challenges in prognostic stratification hinder the precise identification of individuals at greater risk of short-term mortality or increased susceptibility to treatment-related toxicities. In addition to established clinicopathological prognostic factors—such as high histological grade, lymph node involvement, positive margins, and tumor size( 2 )—the identification of novel molecular biomarkers and clinical indicators to optimize therapeutic strategies and monitoring protocols remains a critical research focus. Sarcopenia is an age-related syndrome characterized by reduced skeletal muscle mass, diminished muscle strength, and impaired physical function( 3 ). This condition is closely associated with physical disability, decreased quality of life, and elevated mortality risk( 4 ). Among oncological patients, sarcopenia is relatively common, with reported prevalence ranging from 15% to 50% ( 5 , 6 ), and appears to occur even more frequently in breast cancer patients ( 7 , 8 ). Studies indicate that sarcopenia in breast cancer patients is significantly correlated with reduced tolerance to chemotherapy, increased treatment-related toxicity, and poorer overall survival( 8 ). Thus, accurately assessing and promptly intervening in muscle atrophy are crucial for the clinical management of breast cancer. Currently, the diagnosis of sarcopenia should adhere to the criteria established by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGS), which involve a comprehensive assessment of muscle strength, physical performance, and skeletal muscle mass( 3 , 9 , 10 ). Skeletal muscle mass serves as the foundation for muscular motor capacity and is closely related to muscle strength, contractile properties, and metabolic status( 11 – 13 ). Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are commonly used in clinical practice to evaluate muscle mass, although their accuracy remains somewhat limited( 13 ). Other techniques, such as magnetic resonance imaging (MRI), computed tomography (CT), and the D3-creatine dilution method, are also employed to measure muscle cross-sectional area and mass. Among these methods, CT and MRI are regarded as the gold standards for noninvasive assessment of muscle mass and quantity( 14 , 15 ). However, owing to high equipment costs and limited accessibility, the availability of MRI and CT in primary healthcare settings remains relatively low( 16 , 17 ), which to some extent hampers the early identification of sarcopenia( 18 ). Muscle ultrasound, noninvasive, convenient, and radiation-free imaging technique, has considerable potential for assessing muscle structure and mechanical properties. Studies have shown that morphological parameters measured by ultrasound, such as muscle thickness, correlate well with skeletal muscle mass assessed by CT or MRI( 19 ). Furthermore, Young's modulus values obtained through shear-wave elastography (SWE) have been found to be associated with muscle functional status and structural alterations( 20 ). These findings suggest that ultrasound parameters may concurrently reflect both muscle mass and functional conditions. Therefore, this study aimed to develop and validate a nomogram that combines gastrocnemius ultrasound parameters and clinical indicators for predicting sarcopenia in breast cancer patients undergoing chemotherapy. The objective of this study is to provide an integrated, practical, and noninvasive assessment tool for the early screening of sarcopenia in this population. Materials and methods 1. Study population This retrospective study consecutively enrolled 158 patients with pathologically confirmed primary breast cancer who were scheduled to receive neoadjuvant chemotherapy with the docetaxel, doxorubicin, and cyclophosphamide (TAC) regimen at our institution between June 2024 and August 2025. The inclusion criteria were as follows: ( 1 ) age over 18 years; ( 2 ) diagnosis of primary breast cancer confirmed by core needle biopsy; ( 3 ) planned treatment with TAC neoadjuvant chemotherapy; ( 4 ) completion of chest computed tomography (CT), dominant handgrip strength test, and gastrocnemius muscle ultrasonography within two weeks before chemotherapy initiation, with complete clinical data available; and ( 5 ) body mass index (BMI) between 18.0 and 28.0 kg/m².The exclusion criteria were as follows: ( 1 ) the presence of other primary malignant tumors; ( 2 ) comorbidities severely affecting metabolism or muscle function (e.g., hyperthyroidism, liver cirrhosis, chronic kidney disease stage ≥ 3); ( 3 ) CT or ultrasound images of insufficient quality for analysis, or the presence of metal implants interfering with CT measurements; ( 4 ) long-term bedridden status or limb dysfunction due to neurological or osteoarticular diseases; and ( 5 ) pregnancy or lactation. The study protocol was approved by the Ethics Committee of The Affiliated Zhongshan Hospital of Dalian University (Approval No. : KY2025-175-1), and the requirement for informed consent was waived. 2. Diagnostic criteria for sarcopenia According to the 2019 AWGS consensus criteria( 3 ), sarcopenia was defined as meeting both of the following criteria: 1) low skeletal muscle mass, determined by a skeletal muscle index (SMI) < 20.16 cm²/m²( 21 ) ; and 2) low muscle strength, defined as a dominant handgrip strength < 18 kg for female patients. 3. Instruments and methods 3.1. Collection of general data Baseline characteristics, including age, height, body weight, body mass index (BMI), menopausal status, clinical tumor stage, molecular subtype, Ki67 index, lymph node metastasis status, and comorbidities, were collected through the electronic medical record system. 3.2. Assessment of Skeletal Muscle Mass and Grip Strength 3.2.1. CT-based skeletal muscle mass assessment Two radiologists, blinded to the group assignment, manually outlined all skeletal muscle areas at the mid-level of the 12th thoracic vertebra (T12) via 3D-Slicer software. The skeletal muscle index (SMI) was calculated as follows: SMI (cm²/m²) = cross-sectional area of skeletal muscle (cm²) / height² (m²) ( 21 ). 3.2.2. Grip strength test The maximum grip strength of the dominant hand was measured via an electronic dynamometer. The maximum value was recorded from three consecutive measurements. 3.3. Muscle Ultrasonography A Canon Aplio i900 ultrasound system equipped with an i18LX5 linear array transducer (6–12 MHz) was used for all examinations. Two sonographers, who underwent standardized training and each possessed over five years of experience in musculoskeletal ultrasound, performed the scans and measurements while being blinded to patient group allocation. With the participant in a prone position and the ankle in a neutral posture, the calf was fully exposed and relaxed. The transducer was placed over the most prominent portion of the medial head of the dominant-side gastrocnemius muscle. Two-dimensional ultrasound measurements : With the muscle at rest, the following parameters were measured parallel to the muscle's longitudinal axis: muscle thickness (MT, the vertical distance from the superficial fascia to the deep fascia), pennation angle (PA, the angle between the muscle fascicles and the deep aponeurosis), and fascicle length (FL, the linear distance between the superficial and deep aponeuroses). Each parameter was measured three times, and the average value was calculated (Fig. 1). SWE The system was switched to SWE mode, and the transducer was lightly placed on the skin surface. After the image stabilized, a circular region of interest (ROI) with a 1-mm diameter was positioned at the mid-portion of the muscle belly to measure the Young's modulus values. Measurements were obtained three times at the same location with the muscle at rest and three times during maximal voluntary contraction, with an interval of at least 3 seconds between each measurement. The average value for each state was recorded (Fig. 1). Figure 1 : Measurement of ultrasound parameters for the medial gastrocnemius muscle. A: Muscle thickness; B: Fascicle length; C: Pennation angle; D: Young’s modulus in the relaxed state; E: Young’s modulus in the contracted state. 4. Statistical analysis The data were analyzed via SPSS 25.0 and R software (version 4.2.2). Normally distributed continuous data are expressed as the means ± standard deviations and were compared between groups via the independent samples t test. Nonnormally distributed continuous data are presented as medians (interquartile ranges) and were compared via the Mann‒Whitney U test. Categorical variables are reported as numbers (percentages) and were compared via the chi-square test or Fisher's exact test, as appropriate. The interobserver agreement between the two sonographers was evaluated via the intraclass correlation coefficient (ICC). Spearman's rank correlation analysis was used to assess the relationships between clinical indicators and ultrasound characteristics. Variables showing statistical significance in the univariate analyses were included in a multivariable logistic regression analysis to identify independent predictors of sarcopenia. These independent predictors were subsequently used to construct a nomogram model. Considering the sample size, internal validation of the model was performed via the bootstrap method with 1000 resamples. The diagnostic performance of each independent predictor and the combined model for sarcopenia was evaluated via the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The DeLong test was applied to compare the AUC of the combined model with that of each individual predictor. Calibration was assessed via calibration curves and the Hosmer‒Lemeshow goodness-of-fit test. Clinical utility was evaluated by decision curve analysis. A p value < 0.05 was considered statistically significant. Results 5.1 Comparison of Clinical and Ultrasonographic Characteristics According to the diagnostic criteria for sarcopenia, 57 patients (36.1%) were identified with sarcopenia, whereas 101 patients (63.9%) composed the nonsarcopenia group from the total cohort of 158. The baseline clinical characteristics and ultrasound parameters of the two groups are compared in Table 1 (Located at the end of the document text file, preceding the references.). Patients in the sarcopenia group were significantly older and had a greater BMI than those in the nonsarcopenia group were (P < 0.01). Consistent with the diagnostic definition, the median handgrip strength (17.17 kg) and median SMI (20.01 cm²/m²) were lower in the sarcopenia group than in the nonsarcopenia group (24.22 kg and 24.32 cm²/m², respectively). The ultrasound parameters, MT, PA, and Young’s modulus in the relaxed state were significantly lower in the sarcopenia group (P < 0.01). In contrast, FL, Young’s modulus in the contracted state, and other clinicopathological features—including menopausal status, tumor stage, and molecular subtype—were not significantly different between the two groups (P > 0.05). 5.2 Analysis of Interobserver Agreement Measurements of the medial gastrocnemius ultrasound parameters by the two sonographers revealed good interobserver agreement, with an intraclass correlation coefficient (ICC) of 0.82. 5.3 Correlation analysis between ultrasound parameters and clinical indicators Spearman correlation analysis demonstrated that, in the overall population, both SMI and handgrip strength were positively correlated with FL (r = 0.402, and 0.376, respectively) and Young’s modulus in the contracted state (r = 0.354, and 0.367, respectively). Similarly, the SMI and handgrip strength were also positively correlated with PA (r = 0.769, 0.789), Young’s modulus in the relaxed state (r = 0.699, 0.656), and MT (r = 0.534, 0.508). These correlations were stronger than those observed for FL and Young’s modulus in the contracted state (P < 0.01, Table 2 ). Table 1 Comparison of baseline clinical characteristics, clinical metrics, and ultrasonographic features between sarcopenia and non-sarcopenia groups. Characteristics Nonsarcopenia (n = 101) Sarcopenia (n = 57) P value Age (years) 49.23 ± 8.95 62.49 ± 7.82 < 0.001 * Height (cm) 1.62 ± 0.05 1.62 ± 0.05 0.79 Weight (kg) 62.93 ± 5.83 64.44 ± 5.34 0.11 BMI (kg/m 2 ) 23.91 ± 1.76 24.55 ± 1.37 0.02 * SMI (cm²/m 2 ) 24.32 (22.84, 26.68) 20.01 (19.63, 20.12) < 0.001 * Handgrip strength (kg) 24.22 (22.64, 25.64) 17.17 (16.34, 17.64) < 0.001 * muscle thickness (cm) 1.39 (1.33, 1.46) 1.28 (1.23, 1.34) < 0.001 * pennation angle (°) 20.00 (19.00, 21.00) 18.00 (17.00, 19.00) < 0.001 * fascicle length(cm) 3.52 (3.38, 3.67) 3.50 (3.34, 3.63) 0.125 Young’s modulus in the relaxed state ( Kpa) 18.67 (17.14, 19.81) 15.57 (15.09, 17.16) < 0.001 * Young’s modulus in the contracted state (Kpa) 48.33 (44.61, 52.23) 47.37 (43.67, 52.21) 0.210 Menopausal status 0.068 No 56 (55.45) 23 (40.35) Yes 45 (44.55) 34 (59.65) Comorbidities 0.606 None 76 (75.25) 38 (66.67) Hypertension 17 (16.83) 11 (19.30) Diabetes 4 (3.96) 4 (7.02) Hypertension + Diabetes 4 (3.96) 4 (7.02) Pathological type 0.406 Invasive carcinoma, NOS 92 (91.09) 54 (94.74) Others 9 (8.91) 3 (5.26) Clinical T stage 0.065 T1 13 (12.87) 2 (3.51) T2 54 (53.47) 35 (61.40) T3 19 (18.81) 6 (10.53) T4 15 (14.85) 14 (24.56) AJCC stage 0.951 Ⅰ 3 (2.97) 2 (3.51) Ⅱ 46 (45.54) 27 (47.37) Ⅲ 52 (51.49) 28 (49.12) Lymph node metastasis 0.344 No 32 (31.68) 14 (24.56) Yes 69 (68.32) 43 (75.44) Molecular subtype 0.617 HR+/HER2- 78 (77.23) 42 (73.68) Triple negative 23 (22.77) 15 (26.32) Ki67 0.969 <14% 21 (20.79) 12 (21.05) ≥ 14% 80 (79.21) 45 (78.95) Data are presented as means ± standards deviations, medians (interquartile ranges), and number (%). * , a statistically significant difference. BMI, body mass index; SMI, skeletal muscle index; AJCC, American Joint Committee on Cancer staging; HR, hormone receptor; HER2, human epidermal growth factor receptor 2 Table 2 Correlations between Clinical Metrics and Ultrasonographic Features. MT (cm) PA (°) FL (cm) Young’s modulus in the relaxed state(Kpa) Young’s modulus in the contracted state(Kpa) SMI (cm²/m 2 ) r 0.534 0.769 0.402 0.699 0.354 P value < 0.001 * < 0.001 * < 0.001 * < 0.001 * < 0.001 * Handgrip strength (kg) r 0.508 0.789 0.376 0.656 0.367 P value < 0.001 * < 0.001 * < 0.001 * < 0.001 * < 0.001 * *, a statistically significant difference. MT, muscle thickness; PA, pennation angle; FL, fascicle length. 5.4 Multivariable logistic regression analysis for predictors of sarcopenia To further identify independent predictors of sarcopenia, variables showing statistical significance in the univariate analyses were entered into a multivariable logistic regression analysis. The results revealed that age, MT, PA, and Young’s modulus in the relaxed state were independent predictors of sarcopenia (P < 0.05). BMI did not demonstrate independent predictive value in the multivariable analysis (P = 0.202, Table 3 ). Table 3 Multivariable Logistic Regression Analysis for Predictors of Sarcopenia. Characteristics B SE Wald Odds ratio(95% CI) P value Age (years) 0.078 0.036 4.525 1.081 (1.006, 1.161) 0.033 * Body mass index (kg/m²) 0.204 0.159 1.630 1.226 (0.897, 1.675) 0.202 Muscle thickness (cm) -5.914 2.817 4.408 0.603 (0.000, 0.675) 0.036 * Pennation angle (°) -0.537 0.269 3.988 0.585 (0.345, 0.990) 0.046 * Young’s modulus in the relaxed state(Kpa) -0.364 0.161 5.100 0.695 (0.506, 0.953) 0.024 * *, a statistically significant difference. B, regression coefficient; SE, standard error; OR, odds ratio; CI, confidence interval. 5.5 Comparison of diagnostic performance among individual predictors and the combined model The aforementioned independent predictors were incorporated to construct a nomogram model (Fig. 2). To systematically evaluate the diagnostic performance of this combined model, the diagnostic value of each individual predictor was first analyzed. The AUC values with 95% CIs were as follows: age, 0.863 (0.805–0.922); MT, 0.791 (0.723–0.860); PA, 0.867 (0.810–0.924); and Young’s modulus in the relaxed state, 0.837 (0.776–0.899) (P < 0.01, Table 4 ). The nomogram model combining these factors for predicting sarcopenia achieved an AUC of 0.911 (95% CI: 0.868–0.954). This value was significantly greater than the AUC of any single predictor (DeLong test, all P-values < 0.05). Following internal bootstrap validation, the AUC was 0.898, with a sensitivity of 86.1% and a specificity of 72.6%. The model demonstrated good calibration, with the calibration curve showing close agreement between the predicted and observed probabilities (Fig. 3); the Hosmer–Lemeshow goodness-of-fit test result was not statistically significant (χ² = 11.035, P = 0.1997). Decision curve analysis indicated that the model provided a net clinical benefit across a wide range of threshold probabilities (Fig. 4). Figure 2. Nomogram incorporating clinical characteristics, two-dimensional ultrasound, and SWE parameters for predicting sarcopenia. Note Points: points assigned for each variable; Total Points: sum of points from all variables; Linear Predictor: linear predictor value; Risk: predicted risk of sarcopenia. Table 4 Diagnostic Performance of Ultrasonographic Parameters for Sarcopenia in Breast Cancer Patients. Predictor AUC SE 95% CI P value Age (years) 0.863 0.030 0.805–0.922 <0.01 * Muscle thickness (cm) 0.791 0.035 0.723–0.860 <0.01 * Pennation angle (°) 0.867 0.029 0.810–0.924 <0.01 * Young’s modulus in the relaxed state(Kpa) 0.837 0.031 0.776–0.899 <0.01 * *, a statistically significant difference. Figure 3. Calibration curve of the nomogram. Note Apparent: apparent curve; bias-corrected: bias-corrected curve; ideal: ideal curve. Figure 4. Decision curve analysis for the nomogram. Discussion Sarcopenia is a multifactorial muscle syndrome that is particularly prevalent in cancer patients. Breast cancer patients frequently experience accelerated loss of muscle mass and function due to the metabolic impact of the tumor itself, along with factors such as chemotherapy and endocrine therapy. This decline contributes to reduced tolerance to chemotherapy, increased treatment-related toxicity, and a worse prognosis( 22 , 23 ). Therefore, developing practical tools for early sarcopenia detection in these patients is essential for personalizing treatment and improving supportive care. In this study, multivariable analysis revealed that reduced MT, PA, and Young’s modulus at rest, and increased age were independent predictors of sarcopenia. These specific alterations directly reflect the core pathological triad of sarcopenia: loss of muscle mass (indicated by decreased thickness), disorganization of muscle architecture (indicated by a smaller PA), and deterioration of intrinsic muscle tissue quality (indicated by lower stiffness at rest). The moderate correlations we observed between these ultrasound parameters and both CT-derived SMI and handgrip strength (all P < 0.01, Table 2 ) validate their relevance to the defining features of sarcopenia—low muscle mass and low muscle strength. Our findings are consistent with prior research underscoring the utility of ultrasound in muscle assessment( 24 ). Age was a significant contributor to our model, aligning with its established role as a primary risk factor for sarcopenia. The aging process involves well-documented structural changes, including type II muscle fiber atrophy, increased fibrofatty infiltration, and alterations in the extracellular matrix composition( 25 , 26 ). In the context of our study, these age-related phenomena provide a plausible pathophysiological basis for the observed ultrasound profile: fiber atrophy and loss contribute to decreased MT; disruption of the normal parallel fascicle arrangement reduces the PA; and changes in connective tissue composition and elasticity can lower the Young’s modulus at rest. A reduced PA, as observed in our sarcopenia group, indicates that fewer sarcomeres are arranged in parallel, which mechanically translates to a diminished force-generating capacity for a given muscle volume( 27 ). This architectural inefficiency likely underlies part of the grip strength weakness that defines sarcopenia( 28 ). MT represents another critical morphological parameter. Research has demonstrated a positive correlation between gastrocnemius thickness and whole-body skeletal muscle mass, with calf MT showing a particularly strong association with appendicular skeletal muscle mass( 29 ). Wang et al.( 30 ) further proposed that a gastrocnemius thickness of less than 1.5 cm, as measured by ultrasound, may serve as a reference threshold suggestive of low muscle mass. The present study also confirmed a significant reduction in MT among patients with sarcopenia. SWE enables the noninvasive assessment of muscle stiffness (quantified as Young's modulus), which dynamically reflects the mechanical properties of muscle in both contracted and relaxed states( 31 ). Our analysis revealed a significantly lower Young’s modulus in the relaxed state in patients with sarcopenia, which is consistent with previous reports( 24 ). This phenomenon may be associated with the following mechanisms: a decrease in intrinsic elasticity due to structural deformation and rupture of elastic muscle fibers( 32 ), and hyperplasia of intramuscular connective tissue, which increases the distance between capillaries and muscle fibers, reduces the local blood supply, exacerbates muscle fiber atrophy, and disrupts the normal spatial conformation of collagen fibers, ultimately leading to diminished skeletal muscle elasticity( 33 ). Notably, Young's modulus in the contracted state did not differ significantly, suggesting that active contractile properties might be preserved until later stages or are less sensitively captured by SWE in this context. The superior discriminative performance of our nomogram (AUC: 0.911), which integrates these multifaceted ultrasound features with age, underscores that sarcopenia is a multidimensional syndrome that is best assessed compositely. Importantly, in breast cancer patients, skeletal muscle alterations captured by ultrasound are likely accelerated and modulated by tumor-specific pathophysiology. The tumor microenvironment and systemic effects of cancer can drive muscle wasting through several interconnected pathways: elevated proinflammatory cytokines (e.g., TNF-α, and IL-6) activate proteolytic systems such as the ubiquitin‒proteasome and autophagy‒lysosome pathways, leading to myofibrillar breakdown; metabolic disturbances and oxidative stress can impair mitochondrial function and protein synthesis; and cancer-associated anorexia can exacerbate negative energy balance ( 34 ). Collectively, these processes result in the precise structural and compositional muscle damage that our ultrasound parameters quantify: accelerated loss of muscle protein mass manifests as reduced MT; disruption of myofibrillar integrity and organization leads to a smaller PA; and alterations in the extracellular matrix composition (e.g., fibrosis, fat infiltration) decrease tissue stiffness, reflected as a lower Young's modulus at rest. In breast cancer patients, such mechanisms have been specifically linked to the development of sarcopenia and, consequently, to reduced treatment tolerance and poorer survival outcomes( 8 ). Thus, the nomogram may noninvasively encapsulate the functional impact of these complex, cancer-specific catabolic states on skeletal muscle. On the basis of the independent predictors identified, this study constructed a diagnostic nomogram model integrating clinical indicators and ultrasound features. Following internal validation via the bootstrap method, the model demonstrated favorable discriminative ability (AUC = 0.89) and good calibration (with the calibration curve closely approximating the ideal line and an absolute error of 0.03). These findings indicate high diagnostic consistency and suggest potential clinical utility, positioning the model as a promising tool for screening for sarcopenia in breast cancer patients. The good calibration and net clinical benefit across a range of threshold probabilities, as shown by decision curve analysis, suggest the potential clinical utility of this nomogram as a preoperative or prechemotherapy screening tool. Limitations The findings of this study should be interpreted within its methodological context. First, the single-center, retrospective design, while suitable for initial model development, may limit the generalizability of the results. The sample size, although adequate for the statistical analyses performed, warrants validation in larger cohorts. Second, our study specifically focused on breast cancer patients scheduled for TAC neoadjuvant chemotherapy; therefore, the applicability of the nomogram to patients with other tumor types or treatment regimens remains to be investigated. Finally, in accordance with the diagnostic criteria adopted, our assessment was based on muscle mass and strength; incorporating physical performance metrics (e.g., gait speed) in future studies could provide a more comprehensive evaluation. These considerations highlight the necessity for future multicenter, prospective studies to confirm the robustness and broad applicability of this model. Conclusion In conclusion, we developed and validated a nomogram that integrates gastrocnemius ultrasound parameters with age for predicting sarcopenia in breast cancer patients. This model demonstrates favorable predictive accuracy and calibration. From a clinical perspective, this tool holds particular promise for resource-limited settings such as primary care clinics or outpatient chemotherapy units. The use of widely available ultrasound equipment, offers a relatively low-cost, convenient, and radiation-free screening option that could facilitate the early identification of high-risk patients. This may, in turn, prompt timely nutritional, exercise, or supportive care interventions aimed at preserving muscle mass and function during anticancer therapy. Future multicenter, prospective studies are warranted to further validate its generalizability and refine its implementation pathways in routine clinical practice. Declarations Ethics approval and consent to participate This retrospective study was approved by the Ethics Committee of The Affiliated Zhongshan Hospital of Dalian University (Approval No.: KY2025-175-1). The requirement for written informed consent was waived by the ethics committee due to the retrospective design and the use of fully anonymized data. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This study received no specific funding. Author Contribution (I) Conception and design: Y. W.; (II) Administrative support: J.l. W.; (III) Provision of study materials or patients: S. Y.; (IV) Collection and assembly of data: Y.W., Y.H., M.x. J.; (V) Data analysis and interpretation: Y.W., Y.L., R.x. S.; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Acknowledgement The authors would like to thank all the participants in the study. Availability of data and materials The data analyzed during the current study are not publicly available due to (reason for data restriction), but are available from the corresponding author upon reasonable request. 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Diagnosing sarcopenia and myosteatosis based on chest computed tomography images in healthy Chinese adults. Insights into imaging. 2021:163. Aleixo GFP, Williams GR, Nyrop KA, Muss HB, Shachar SS. Muscle composition and outcomes in patients with breast cancer: meta-analysis and systematic review. Breast Cancer Res Treat. 2019:569–79. Zhang X-M, Dou Q-L, Zeng Y, Yang Y, Cheng ASK, Zhang W-W. Sarcopenia as a predictor of mortality in women with breast cancer: a meta-analysis and systematic review. BMC Cancer. 2020:1–11. Han X, Li Q, Zhang G, Zhang Z. Application value of two-dimensional ultrasound and shear-wave elastography parameters in evaluating sarcopenia with essential hypertension. Quantitative imaging in medicine and surgery. 2025:831 – 42. Lieber RL, Roberts TJ, Blemker SS, Lee SSM, Herzog W. Skeletal muscle mechanics, energetics and plasticity. J Neuroeng Rehabil. 2017:108. Yoshimura M, Taniguchi T, Yoshitomi T, Hashimoto Y. 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Association between morphological and mechanical properties of lower limb muscles and metabolic syndrome in type 2 diabetes. Diabetol Metab Syndr. 2025:270. Gosline JM. Hydrophobic interaction and a model for the elasticity of elastin. Biopolymers. 2010;17. Järvinen TAH, Józsa L, Kannus P, Järvinen TLN, Järvinen M. Organization and distribution of intramuscular connective tissue in normal and immobilized skeletal muscles. J Muscle Res Cell Motil. 2002;23:245–54. Siff T, Parajuli P, Razzaque MS, Azeddine Atfi1 C. Cancer-Mediated Muscle Cachexia: Etiology and Clinical Management. Trends Endocrinol Metab. 2021:382–402. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8599311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578378947,"identity":"42912549-1c2a-4a64-9b63-54d39b2821cd","order_by":0,"name":"Ying Wang","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wang","suffix":""},{"id":578378948,"identity":"41f93314-559a-4f10-9421-700c3110a85a","order_by":1,"name":"Yan Hu","email":"","orcid":"","institution":"Zhongshan Hospital Affiliated to Dalian 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00:39:45","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102385,"visible":true,"origin":"","legend":"","description":"","filename":"6a26833a0a8e4108b0a370494f4aa73c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8599311/v1/695ef812cb5cef886613aa51.xml"},{"id":100930157,"identity":"d50e533b-beec-4a2f-aab4-81165e48a018","added_by":"auto","created_at":"2026-01-23 00:39:45","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115335,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8599311/v1/83bafd7c06435ded8ed27d23.html"},{"id":100951573,"identity":"113afecb-c82c-493e-afb9-fb5c659944b0","added_by":"auto","created_at":"2026-01-23 07:10:53","extension":"tif","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7268057,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of ultrasound parameters for the medial gastrocnemius muscle. A: Muscle thickness; B: Fascicle length; C: Pennation angle; D: Young’s modulus in the relaxed state; E: Young’s modulus in the contracted state.\u003c/p\u003e","description":"","filename":"Figure1Measurementofultrasoundparametersforthemedialgastrocnemiusmuscle..tif","url":"https://assets-eu.researchsquare.com/files/rs-8599311/v1/d44c5471a8824d35598c36b4.tif"},{"id":100930160,"identity":"49899215-aff6-411a-a5b3-b72271ad0461","added_by":"auto","created_at":"2026-01-23 00:39:46","extension":"tif","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":417499,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram incorporating clinical characteristics, two-dimensional ultrasound, and SWE parameters for predicting sarcopenia.\u003c/p\u003e\n\u003cp\u003eNote: Points: points assigned for each variable; Total Points: sum of points from all variables; Linear Predictor: linear predictor value; Risk: predicted risk of sarcopenia.\u003c/p\u003e","description":"","filename":"Figure2.NomogramincorporatingclinicalcharacteristicstwodimensionalultrasoundandSWEparametersforpredictingsarcopenia..tif","url":"https://assets-eu.researchsquare.com/files/rs-8599311/v1/5328317fecff9c1d87e53873.tif"},{"id":100930148,"identity":"c3a45977-7c38-4692-9221-2ae773852b78","added_by":"auto","created_at":"2026-01-23 00:39:45","extension":"tif","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129540,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram.\u003c/p\u003e\n\u003cp\u003eNote: Apparent: apparent curve; bias-corrected: bias-corrected curve; ideal: ideal curve.\u003c/p\u003e","description":"","filename":"Figure3.Calibrationcurveofthenomogram..tif","url":"https://assets-eu.researchsquare.com/files/rs-8599311/v1/47ee81ba6996a6562463126a.tif"},{"id":100930164,"identity":"5da8b546-01d4-4a7f-a776-4f24f0970ea5","added_by":"auto","created_at":"2026-01-23 00:39:48","extension":"tif","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":146536,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the nomogram.\u003c/p\u003e","description":"","filename":"Figure4.Decisioncurveanalysisforthenomogram..tif","url":"https://assets-eu.researchsquare.com/files/rs-8599311/v1/d0a0e67455037036ffbecf57.tif"},{"id":104366756,"identity":"907249a5-0b29-4227-83bb-3edb1cf8b744","added_by":"auto","created_at":"2026-03-11 03:25:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2398641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8599311/v1/2aff99b1-d652-4ddb-b983-34e01e59826c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Nomogram Combining Ultrasound Parameters and Clinical Indicators for Predicting Sarcopenia in Breast Cancer Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer continues to be the most prevalent malignancy in women globally, with increasing incidence rates imposing a significant health burden(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite significant advances in breast cancer research, persistent challenges in prognostic stratification hinder the precise identification of individuals at greater risk of short-term mortality or increased susceptibility to treatment-related toxicities. In addition to established clinicopathological prognostic factors\u0026mdash;such as high histological grade, lymph node involvement, positive margins, and tumor size(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u0026mdash;the identification of novel molecular biomarkers and clinical indicators to optimize therapeutic strategies and monitoring protocols remains a critical research focus.\u003c/p\u003e \u003cp\u003eSarcopenia is an age-related syndrome characterized by reduced skeletal muscle mass, diminished muscle strength, and impaired physical function(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This condition is closely associated with physical disability, decreased quality of life, and elevated mortality risk(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Among oncological patients, sarcopenia is relatively common, with reported prevalence ranging from 15% to 50% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and appears to occur even more frequently in breast cancer patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Studies indicate that sarcopenia in breast cancer patients is significantly correlated with reduced tolerance to chemotherapy, increased treatment-related toxicity, and poorer overall survival(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Thus, accurately assessing and promptly intervening in muscle atrophy are crucial for the clinical management of breast cancer.\u003c/p\u003e \u003cp\u003eCurrently, the diagnosis of sarcopenia should adhere to the criteria established by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGS), which involve a comprehensive assessment of muscle strength, physical performance, and skeletal muscle mass(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Skeletal muscle mass serves as the foundation for muscular motor capacity and is closely related to muscle strength, contractile properties, and metabolic status(\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are commonly used in clinical practice to evaluate muscle mass, although their accuracy remains somewhat limited(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Other techniques, such as magnetic resonance imaging (MRI), computed tomography (CT), and the D3-creatine dilution method, are also employed to measure muscle cross-sectional area and mass. Among these methods, CT and MRI are regarded as the gold standards for noninvasive assessment of muscle mass and quantity(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, owing to high equipment costs and limited accessibility, the availability of MRI and CT in primary healthcare settings remains relatively low(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), which to some extent hampers the early identification of sarcopenia(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMuscle ultrasound, noninvasive, convenient, and radiation-free imaging technique, has considerable potential for assessing muscle structure and mechanical properties. Studies have shown that morphological parameters measured by ultrasound, such as muscle thickness, correlate well with skeletal muscle mass assessed by CT or MRI(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, Young's modulus values obtained through shear-wave elastography (SWE) have been found to be associated with muscle functional status and structural alterations(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These findings suggest that ultrasound parameters may concurrently reflect both muscle mass and functional conditions.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to develop and validate a nomogram that combines gastrocnemius ultrasound parameters and clinical indicators for predicting sarcopenia in breast cancer patients undergoing chemotherapy. The objective of this study is to provide an integrated, practical, and noninvasive assessment tool for the early screening of sarcopenia in this population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\n\u003ch3\u003e1. Study population\u003c/h3\u003e\n\u003cp\u003eThis retrospective study consecutively enrolled 158 patients with pathologically confirmed primary breast cancer who were scheduled to receive neoadjuvant chemotherapy with the docetaxel, doxorubicin, and cyclophosphamide (TAC) regimen at our institution between June 2024 and August 2025. The inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age over 18 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) diagnosis of primary breast cancer confirmed by core needle biopsy; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) planned treatment with TAC neoadjuvant chemotherapy; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) completion of chest computed tomography (CT), dominant handgrip strength test, and gastrocnemius muscle ultrasonography within two weeks before chemotherapy initiation, with complete clinical data available; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) body mass index (BMI) between 18.0 and 28.0 kg/m\u0026sup2;.The exclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the presence of other primary malignant tumors; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) comorbidities severely affecting metabolism or muscle function (e.g., hyperthyroidism, liver cirrhosis, chronic kidney disease stage\u0026thinsp;\u0026ge;\u0026thinsp;3); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) CT or ultrasound images of insufficient quality for analysis, or the presence of metal implants interfering with CT measurements; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) long-term bedridden status or limb dysfunction due to neurological or osteoarticular diseases; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) pregnancy or lactation. The study protocol was approved by the Ethics Committee of The Affiliated Zhongshan Hospital of Dalian University (Approval No. : KY2025-175-1), and the requirement for informed consent was waived.\u003c/p\u003e\n\u003ch3\u003e2. Diagnostic criteria for sarcopenia\u003c/h3\u003e\n\u003cp\u003eAccording to the 2019 AWGS consensus criteria(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), sarcopenia was defined as meeting both of the following criteria: 1) low skeletal muscle mass, determined by a skeletal muscle index (SMI)\u0026thinsp;\u0026lt;\u0026thinsp;20.16 cm\u0026sup2;/m\u0026sup2;(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) ; and 2) low muscle strength, defined as a dominant handgrip strength\u0026thinsp;\u0026lt;\u0026thinsp;18 kg for female patients.\u003c/p\u003e\n\u003ch3\u003e3. Instruments and methods\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Collection of general data\u003c/h2\u003e \u003cp\u003eBaseline characteristics, including age, height, body weight, body mass index (BMI), menopausal status, clinical tumor stage, molecular subtype, Ki67 index, lymph node metastasis status, and comorbidities, were collected through the electronic medical record system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Assessment of Skeletal Muscle Mass and Grip Strength\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. CT-based skeletal muscle mass assessment\u003c/h2\u003e \u003cp\u003eTwo radiologists, blinded to the group assignment, manually outlined all skeletal muscle areas at the mid-level of the 12th thoracic vertebra (T12) via 3D-Slicer software. The skeletal muscle index (SMI) was calculated as follows: SMI (cm\u0026sup2;/m\u0026sup2;)\u0026thinsp;=\u0026thinsp;cross-sectional area of skeletal muscle (cm\u0026sup2;) / height\u0026sup2; (m\u0026sup2;) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Grip strength test\u003c/h2\u003e \u003cp\u003eThe maximum grip strength of the dominant hand was measured via an electronic dynamometer. The maximum value was recorded from three consecutive measurements.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Muscle Ultrasonography\u003c/h2\u003e \u003cp\u003eA Canon Aplio i900 ultrasound system equipped with an i18LX5 linear array transducer (6\u0026ndash;12 MHz) was used for all examinations. Two sonographers, who underwent standardized training and each possessed over five years of experience in musculoskeletal ultrasound, performed the scans and measurements while being blinded to patient group allocation. With the participant in a prone position and the ankle in a neutral posture, the calf was fully exposed and relaxed. The transducer was placed over the most prominent portion of the medial head of the dominant-side gastrocnemius muscle.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTwo-dimensional ultrasound measurements\u003c/b\u003e: With the muscle at rest, the following parameters were measured parallel to the muscle's longitudinal axis: muscle thickness (MT, the vertical distance from the superficial fascia to the deep fascia), pennation angle (PA, the angle between the muscle fascicles and the deep aponeurosis), and fascicle length (FL, the linear distance between the superficial and deep aponeuroses). Each parameter was measured three times, and the average value was calculated (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSWE\u003c/strong\u003e \u003cp\u003eThe system was switched to SWE mode, and the transducer was lightly placed on the skin surface. After the image stabilized, a circular region of interest (ROI) with a 1-mm diameter was positioned at the mid-portion of the muscle belly to measure the Young's modulus values. Measurements were obtained three times at the same location with the muscle at rest and three times during maximal voluntary contraction, with an interval of at least 3 seconds between each measurement. The average value for each state was recorded (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e: Measurement of ultrasound parameters for the medial gastrocnemius muscle. A: Muscle thickness; B: Fascicle length; C: Pennation angle; D: Young\u0026rsquo;s modulus in the relaxed state; E: Young\u0026rsquo;s modulus in the contracted state.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4. Statistical analysis\u003c/h3\u003e\n\u003cp\u003eThe data were analyzed via SPSS 25.0 and R software (version 4.2.2). Normally distributed continuous data are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations and were compared between groups via the independent samples t test. Nonnormally distributed continuous data are presented as medians (interquartile ranges) and were compared via the Mann‒Whitney U test. Categorical variables are reported as numbers (percentages) and were compared via the chi-square test or Fisher's exact test, as appropriate. The interobserver agreement between the two sonographers was evaluated via the intraclass correlation coefficient (ICC). Spearman's rank correlation analysis was used to assess the relationships between clinical indicators and ultrasound characteristics.\u003c/p\u003e \u003cp\u003eVariables showing statistical significance in the univariate analyses were included in a multivariable logistic regression analysis to identify independent predictors of sarcopenia. These independent predictors were subsequently used to construct a nomogram model. Considering the sample size, internal validation of the model was performed via the bootstrap method with 1000 resamples. The diagnostic performance of each independent predictor and the combined model for sarcopenia was evaluated via the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The DeLong test was applied to compare the AUC of the combined model with that of each individual predictor. Calibration was assessed via calibration curves and the Hosmer‒Lemeshow goodness-of-fit test. Clinical utility was evaluated by decision curve analysis. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Comparison of Clinical and Ultrasonographic Characteristics\u003c/h2\u003e \u003cp\u003eAccording to the diagnostic criteria for sarcopenia, 57 patients (36.1%) were identified with sarcopenia, whereas 101 patients (63.9%) composed the nonsarcopenia group from the total cohort of 158. The baseline clinical characteristics and ultrasound parameters of the two groups are compared in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Located at the end of the document text file, preceding the references.). Patients in the sarcopenia group were significantly older and had a greater BMI than those in the nonsarcopenia group were (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Consistent with the diagnostic definition, the median handgrip strength (17.17 kg) and median SMI (20.01 cm\u0026sup2;/m\u0026sup2;) were lower in the sarcopenia group than in the nonsarcopenia group (24.22 kg and 24.32 cm\u0026sup2;/m\u0026sup2;, respectively). The ultrasound parameters, MT, PA, and Young\u0026rsquo;s modulus in the relaxed state were significantly lower in the sarcopenia group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, FL, Young\u0026rsquo;s modulus in the contracted state, and other clinicopathological features\u0026mdash;including menopausal status, tumor stage, and molecular subtype\u0026mdash;were not significantly different between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Analysis of Interobserver Agreement\u003c/h2\u003e \u003cp\u003eMeasurements of the medial gastrocnemius ultrasound parameters by the two sonographers revealed good interobserver agreement, with an intraclass correlation coefficient (ICC) of 0.82.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Correlation analysis between ultrasound parameters and clinical indicators\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis demonstrated that, in the overall population, both SMI and handgrip strength were positively correlated with FL (r\u0026thinsp;=\u0026thinsp;0.402, and 0.376, respectively) and Young\u0026rsquo;s modulus in the contracted state (r\u0026thinsp;=\u0026thinsp;0.354, and 0.367, respectively). Similarly, the SMI and handgrip strength were also positively correlated with PA (r\u0026thinsp;=\u0026thinsp;0.769, 0.789), Young\u0026rsquo;s modulus in the relaxed state (r\u0026thinsp;=\u0026thinsp;0.699, 0.656), and MT (r\u0026thinsp;=\u0026thinsp;0.534, 0.508). These correlations were stronger than those observed for FL and Young\u0026rsquo;s modulus in the contracted state (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline clinical characteristics, clinical metrics, and ultrasonographic features between sarcopenia and non-sarcopenia groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonsarcopenia (n\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSarcopenia (n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.49\u0026thinsp;\u0026plusmn;\u0026thinsp;7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\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\u003e23.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (cm\u0026sup2;/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.32 (22.84, 26.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.01 (19.63, 20.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandgrip strength (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.22 (22.64, 25.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.17 (16.34, 17.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emuscle thickness (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39 (1.33, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (1.23, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epennation angle (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00 (19.00, 21.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00 (17.00, 19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efascicle length(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52 (3.38, 3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50 (3.34, 3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung\u0026rsquo;s modulus in the relaxed state \u003cb\u003e(\u003c/b\u003eKpa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.67 (17.14, 19.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.57 (15.09, 17.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung\u0026rsquo;s modulus in the contracted state (Kpa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.33 (44.61, 52.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.37 (43.67, 52.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopausal 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 \u003cp\u003e0.068\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\u003e56 (55.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (40.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (44.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (59.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\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 \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (75.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (16.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (19.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type\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 \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive carcinoma, NOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (91.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (94.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (8.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClinical T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (12.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (53.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (61.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (18.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (14.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (24.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAJCC stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (45.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (47.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (51.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (49.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.344\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\u003e32 (31.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (24.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (68.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (75.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR+/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (77.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (73.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (22.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (26.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67\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 \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (20.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (21.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (79.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (78.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standards deviations, medians (interquartile ranges), and number (%). \u003csup\u003e*\u003c/sup\u003e, a statistically significant difference.\u003c/p\u003e \u003cp\u003eBMI, body mass index; SMI, skeletal muscle index; AJCC, American Joint Committee on Cancer staging; HR, hormone receptor; HER2, human epidermal growth factor receptor 2\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between Clinical Metrics and Ultrasonographic Features.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003cp\u003e(\u0026deg;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYoung\u0026rsquo;s modulus in the relaxed state(Kpa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYoung\u0026rsquo;s modulus in the contracted state(Kpa)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSMI (cm\u0026sup2;/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHandgrip strength (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*, a statistically significant difference.\u003c/p\u003e \u003cp\u003eMT, muscle thickness; PA, pennation angle; FL, fascicle length.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Multivariable logistic regression analysis for predictors of sarcopenia\u003c/h2\u003e \u003cp\u003eTo further identify independent predictors of sarcopenia, variables showing statistical significance in the univariate analyses were entered into a multivariable logistic regression analysis. The results revealed that age, MT, PA, and Young\u0026rsquo;s modulus in the relaxed state were independent predictors of sarcopenia (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BMI did not demonstrate independent predictive value in the multivariable analysis (P\u0026thinsp;=\u0026thinsp;0.202, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression Analysis for Predictors of Sarcopenia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds ratio(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.081 (1.006, 1.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.226 (0.897, 1.675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle thickness (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.603 (0.000, 0.675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePennation angle (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585 (0.345, 0.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung\u0026rsquo;s modulus in the relaxed state(Kpa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.695 (0.506, 0.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*, a statistically significant difference.\u003c/p\u003e \u003cp\u003eB, regression coefficient; SE, standard error; OR, odds ratio; CI, confidence interval.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Comparison of diagnostic performance among individual predictors and the combined model\u003c/h2\u003e \u003cp\u003eThe aforementioned independent predictors were incorporated to construct a nomogram model (Fig.\u0026nbsp;2). To systematically evaluate the diagnostic performance of this combined model, the diagnostic value of each individual predictor was first analyzed. The AUC values with 95% CIs were as follows: age, 0.863 (0.805\u0026ndash;0.922); MT, 0.791 (0.723\u0026ndash;0.860); PA, 0.867 (0.810\u0026ndash;0.924); and Young\u0026rsquo;s modulus in the relaxed state, 0.837 (0.776\u0026ndash;0.899) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The nomogram model combining these factors for predicting sarcopenia achieved an AUC of 0.911 (95% CI: 0.868\u0026ndash;0.954). This value was significantly greater than the AUC of any single predictor (DeLong test, all P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Following internal bootstrap validation, the AUC was 0.898, with a sensitivity of 86.1% and a specificity of 72.6%. The model demonstrated good calibration, with the calibration curve showing close agreement between the predicted and observed probabilities (Fig.\u0026nbsp;3); the Hosmer\u0026ndash;Lemeshow goodness-of-fit test result was not statistically significant (χ\u0026sup2; = 11.035, P\u0026thinsp;=\u0026thinsp;0.1997). Decision curve analysis indicated that the model provided a net clinical benefit across a wide range of threshold probabilities (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2.\u003c/b\u003e Nomogram incorporating clinical characteristics, two-dimensional ultrasound, and SWE parameters for predicting sarcopenia.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003ePoints: points assigned for each variable; Total Points: sum of points from all variables; Linear Predictor: linear predictor value; Risk: predicted risk of sarcopenia.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic Performance of Ultrasonographic Parameters for Sarcopenia in Breast Cancer Patients.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.805\u0026ndash;0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle thickness (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.723\u0026ndash;0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePennation angle (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.810\u0026ndash;0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung\u0026rsquo;s modulus in the relaxed state(Kpa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.776\u0026ndash;0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*, a statistically significant difference.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3.\u003c/b\u003e Calibration curve of the nomogram.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eApparent: apparent curve; bias-corrected: bias-corrected curve; ideal: ideal curve.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4.\u003c/b\u003e Decision curve analysis for the nomogram.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSarcopenia is a multifactorial muscle syndrome that is particularly prevalent in cancer patients. Breast cancer patients frequently experience accelerated loss of muscle mass and function due to the metabolic impact of the tumor itself, along with factors such as chemotherapy and endocrine therapy. This decline contributes to reduced tolerance to chemotherapy, increased treatment-related toxicity, and a worse prognosis(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Therefore, developing practical tools for early sarcopenia detection in these patients is essential for personalizing treatment and improving supportive care.\u003c/p\u003e \u003cp\u003eIn this study, multivariable analysis revealed that reduced MT, PA, and Young\u0026rsquo;s modulus at rest, and increased age were independent predictors of sarcopenia. These specific alterations directly reflect the core pathological triad of sarcopenia: loss of muscle mass (indicated by decreased thickness), disorganization of muscle architecture (indicated by a smaller PA), and deterioration of intrinsic muscle tissue quality (indicated by lower stiffness at rest). The moderate correlations we observed between these ultrasound parameters and both CT-derived SMI and handgrip strength (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e) validate their relevance to the defining features of sarcopenia\u0026mdash;low muscle mass and low muscle strength. Our findings are consistent with prior research underscoring the utility of ultrasound in muscle assessment(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAge was a significant contributor to our model, aligning with its established role as a primary risk factor for sarcopenia. The aging process involves well-documented structural changes, including type II muscle fiber atrophy, increased fibrofatty infiltration, and alterations in the extracellular matrix composition(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In the context of our study, these age-related phenomena provide a plausible pathophysiological basis for the observed ultrasound profile: fiber atrophy and loss contribute to decreased MT; disruption of the normal parallel fascicle arrangement reduces the PA; and changes in connective tissue composition and elasticity can lower the Young\u0026rsquo;s modulus at rest.\u003c/p\u003e \u003cp\u003eA reduced PA, as observed in our sarcopenia group, indicates that fewer sarcomeres are arranged in parallel, which mechanically translates to a diminished force-generating capacity for a given muscle volume(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This architectural inefficiency likely underlies part of the grip strength weakness that defines sarcopenia(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). MT represents another critical morphological parameter. Research has demonstrated a positive correlation between gastrocnemius thickness and whole-body skeletal muscle mass, with calf MT showing a particularly strong association with appendicular skeletal muscle mass(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Wang et al.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) further proposed that a gastrocnemius thickness of less than 1.5 cm, as measured by ultrasound, may serve as a reference threshold suggestive of low muscle mass. The present study also confirmed a significant reduction in MT among patients with sarcopenia.\u003c/p\u003e \u003cp\u003eSWE enables the noninvasive assessment of muscle stiffness (quantified as Young's modulus), which dynamically reflects the mechanical properties of muscle in both contracted and relaxed states(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our analysis revealed a significantly lower Young\u0026rsquo;s modulus in the relaxed state in patients with sarcopenia, which is consistent with previous reports(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This phenomenon may be associated with the following mechanisms: a decrease in intrinsic elasticity due to structural deformation and rupture of elastic muscle fibers(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and hyperplasia of intramuscular connective tissue, which increases the distance between capillaries and muscle fibers, reduces the local blood supply, exacerbates muscle fiber atrophy, and disrupts the normal spatial conformation of collagen fibers, ultimately leading to diminished skeletal muscle elasticity(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Notably, Young's modulus in the contracted state did not differ significantly, suggesting that active contractile properties might be preserved until later stages or are less sensitively captured by SWE in this context.\u003c/p\u003e \u003cp\u003eThe superior discriminative performance of our nomogram (AUC: 0.911), which integrates these multifaceted ultrasound features with age, underscores that sarcopenia is a multidimensional syndrome that is best assessed compositely. Importantly, in breast cancer patients, skeletal muscle alterations captured by ultrasound are likely accelerated and modulated by tumor-specific pathophysiology. The tumor microenvironment and systemic effects of cancer can drive muscle wasting through several interconnected pathways: elevated proinflammatory cytokines (e.g., TNF-α, and IL-6) activate proteolytic systems such as the ubiquitin‒proteasome and autophagy‒lysosome pathways, leading to myofibrillar breakdown; metabolic disturbances and oxidative stress can impair mitochondrial function and protein synthesis; and cancer-associated anorexia can exacerbate negative energy balance (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Collectively, these processes result in the precise structural and compositional muscle damage that our ultrasound parameters quantify: accelerated loss of muscle protein mass manifests as reduced MT; disruption of myofibrillar integrity and organization leads to a smaller PA; and alterations in the extracellular matrix composition (e.g., fibrosis, fat infiltration) decrease tissue stiffness, reflected as a lower Young's modulus at rest. In breast cancer patients, such mechanisms have been specifically linked to the development of sarcopenia and, consequently, to reduced treatment tolerance and poorer survival outcomes(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Thus, the nomogram may noninvasively encapsulate the functional impact of these complex, cancer-specific catabolic states on skeletal muscle.\u003c/p\u003e \u003cp\u003eOn the basis of the independent predictors identified, this study constructed a diagnostic nomogram model integrating clinical indicators and ultrasound features. Following internal validation via the bootstrap method, the model demonstrated favorable discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.89) and good calibration (with the calibration curve closely approximating the ideal line and an absolute error of 0.03). These findings indicate high diagnostic consistency and suggest potential clinical utility, positioning the model as a promising tool for screening for sarcopenia in breast cancer patients. The good calibration and net clinical benefit across a range of threshold probabilities, as shown by decision curve analysis, suggest the potential clinical utility of this nomogram as a preoperative or prechemotherapy screening tool.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThe findings of this study should be interpreted within its methodological context. First, the single-center, retrospective design, while suitable for initial model development, may limit the generalizability of the results. The sample size, although adequate for the statistical analyses performed, warrants validation in larger cohorts. Second, our study specifically focused on breast cancer patients scheduled for TAC neoadjuvant chemotherapy; therefore, the applicability of the nomogram to patients with other tumor types or treatment regimens remains to be investigated. Finally, in accordance with the diagnostic criteria adopted, our assessment was based on muscle mass and strength; incorporating physical performance metrics (e.g., gait speed) in future studies could provide a more comprehensive evaluation. These considerations highlight the necessity for future multicenter, prospective studies to confirm the robustness and broad applicability of this model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we developed and validated a nomogram that integrates gastrocnemius ultrasound parameters with age for predicting sarcopenia in breast cancer patients. This model demonstrates favorable predictive accuracy and calibration. From a clinical perspective, this tool holds particular promise for resource-limited settings such as primary care clinics or outpatient chemotherapy units. The use of widely available ultrasound equipment, offers a relatively low-cost, convenient, and radiation-free screening option that could facilitate the early identification of high-risk patients. This may, in turn, prompt timely nutritional, exercise, or supportive care interventions aimed at preserving muscle mass and function during anticancer therapy. Future multicenter, prospective studies are warranted to further validate its generalizability and refine its implementation pathways in routine clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This retrospective study was approved by the Ethics Committee of The Affiliated Zhongshan Hospital of Dalian University (Approval No.: KY2025-175-1). The requirement for written informed consent was waived by the ethics committee due to the retrospective design and the use of fully anonymized data.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study received no specific funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e(I) Conception and design: Y. W.; (II) Administrative support: J.l. W.; (III) Provision of study materials or patients: S. Y.; (IV) Collection and assembly of data: Y.W., Y.H., M.x. J.; (V) Data analysis and interpretation: Y.W., Y.L., R.x. S.; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank all the participants in the study.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe data analyzed during the current study are not publicly available due to (reason for data restriction), but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, et al. Breast cancer statistics, 2019. CA Cancer J Clin. 2019:438\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisser LL, Groen EJ, Van Leeuwen FE, Lips EH, Schmidt MK, Wesseling J. Predictors of an Invasive Breast Cancer Recurrence after DCIS: A Systematic Review and Meta-analyses. 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Evaluation of Appendicular Muscle Mass in Sarcopenia in Older Adults Using Ultrasonography: A Systematic Review and Meta-Analysis. Gerontology. 2022:1\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen ZT, Jin FS, Guo LH, Li X-L, Wang Q, 2,3, Zhao H, 2,3, et al. Value of conventional ultrasound and shear wave elastography in the assessment of muscle mass and function in elderly people with type 2 diabetes. Eur Radiol. 2023:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLingling Tan, Guiyi Ji, Ting Bao, Hongbo Fu, LY, Ming Yang. Diagnosing sarcopenia and myosteatosis based on chest computed tomography images in healthy Chinese adults. Insights into imaging. 2021:163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAleixo GFP, Williams GR, Nyrop KA, Muss HB, Shachar SS. Muscle composition and outcomes in patients with breast cancer: meta-analysis and systematic review. Breast Cancer Res Treat. 2019:569\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X-M, Dou Q-L, Zeng Y, Yang Y, Cheng ASK, Zhang W-W. Sarcopenia as a predictor of mortality in women with breast cancer: a meta-analysis and systematic review. BMC Cancer. 2020:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan X, Li Q, Zhang G, Zhang Z. Application value of two-dimensional ultrasound and shear-wave elastography parameters in evaluating sarcopenia with essential hypertension. Quantitative imaging in medicine and surgery. 2025:831\u0026thinsp;\u0026ndash;\u0026thinsp;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLieber RL, Roberts TJ, Blemker SS, Lee SSM, Herzog W. Skeletal muscle mechanics, energetics and plasticity. J Neuroeng Rehabil. 2017:108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshimura M, Taniguchi T, Yoshitomi T, Hashimoto Y. 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Int J Environ Res Public Health. 2022:4042.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Hu Y, Tian G. Ultrasound measurements of gastrocnemius muscle thickness in older people with sarcopenia. Clin Interv Aging. 2018:2193\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan Y, An L, Yuan Y, Shi J, Zhang M, Wang Y. Association between morphological and mechanical properties of lower limb muscles and metabolic syndrome in type 2 diabetes. Diabetol Metab Syndr. 2025:270.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGosline JM. Hydrophobic interaction and a model for the elasticity of elastin. Biopolymers. 2010;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ\u0026auml;rvinen TAH, J\u0026oacute;zsa L, Kannus P, J\u0026auml;rvinen TLN, J\u0026auml;rvinen M. Organization and distribution of intramuscular connective tissue in normal and immobilized skeletal muscles. J Muscle Res Cell Motil. 2002;23:245\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiff T, Parajuli P, Razzaque MS, Azeddine Atfi1 C. Cancer-Mediated Muscle Cachexia: Etiology and Clinical Management. Trends Endocrinol Metab. 2021:382\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ultrasonography, Sarcopenia, Breast cancer, Shear wave elastography, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-8599311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8599311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSarcopenia, a condition prevalent among breast cancer patients, correlates with adverse treatment outcomes. To address the limited accessibility of gold-standard diagnostic methods, this study aimed to develop a practical nomogram based on gastrocnemius muscle ultrasound for sarcopenia screening in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective study enrolled 158 patients with primary breast cancer who were scheduled for TAC chemotherapy. We measured ultrasound parameters of the dominant medial gastrocnemius, including muscle thickness, pennation angle, and Young’s modulus in the relaxed state. Clinical data such as handgrip strength and age were also recorded. Sarcopenia was diagnosed on the basis of low handgrip strength combined with a low skeletal muscle index measured by computed tomography at the 12th thoracic vertebra level. Variables with P \u0026lt; 0.05 in the univariate analysis were entered into the multivariate logistic regression analysis to identify independent predictors, which were then used to construct the nomogram. The model underwent internal validation via the bootstrap method. Its discriminative ability, calibration, and clinical utility were assessed by the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMultivariate analysis revealed that muscle thickness, pennation angle, Young’s modulus in the relaxed state, and age were independent predictors of sarcopenia. The nomogram achieved an AUC of 0.911 (95% CI: 0.868–0.954). After internal bootstrap validation, the AUC remained high at 0.898, with sensitivity and specificity values of 86.1% and 72.6%, respectively. The calibration curve indicated good agreement between the\u003cstrong\u003e \u003c/strong\u003epredicted and observed probabilities. Decision curve analysis confirmed that the model yielded a net clinical benefit across a wide range of threshold probabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The nomogram integrating gastrocnemius ultrasound parameters and age has favorable predictive performance for sarcopenia in breast cancer patients, suggesting its potential as a practical screening tool in clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e Not applicable.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram Combining Ultrasound Parameters and Clinical Indicators for Predicting Sarcopenia in Breast Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 00:39:26","doi":"10.21203/rs.3.rs-8599311/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2805ce70-f09a-4f35-bbc8-be0d625d1a44","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T03:25:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 00:39:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8599311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8599311","identity":"rs-8599311","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}