Diagnostic Value and Management Strategy of Nomogram for PND Based on Clinical Characteristics and CEUS

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Additionally, the study aimed to compare the diagnostic performances of different methods to stratify and clinically manage patients with PND. Materials and Methods A total of 593 patients were retrospectively collected from January 2021 to February 2025, resulting in the inclusion of 334 female patients (mean age 55.0 years; age range 21 to 90 years) who met the inclusion criteria. The patients were divided into development, internal validation, and external validation groups. Clinical information, routine ultrasound, and ultrasonographic characteristics were analyzed using univariate logistic regression, multivariate logistic regression, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to develop a risk prediction nomogram. The diagnostic performances of the nomogram, conventional ultrasound BI-RADS, and ultrasonography 5 points related to the BI-RADS category (5 points modified BI-RADS) were compared. Results Predictive variables for the nomogram included age, the maximum diameter of the lesion, calcifications, boundary with duct, enhancement area, and enhancement margin. Nomogram showed superior performance compared to other diagnostic methods. In the internal validation group, there was no significant difference in diagnostic accuracy between the nomogram and 5-point modified BI-RADS. However, in all other groups, the nomogram outperformed the conventional ultrasound BI-RADS and 5-point modified BI-RADS. Conclusion Constructing a nomogram that combines ultrasound, CEUS, and clinical information can improve the diagnostic efficiency of PND. The nomogram shows optimal diagnostic performance and helps avoid unnecessary biopsies of most benign lesions. Pathologic nipple discharge breast tumors BI-RADS CEUS nomogram management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. INTRODUCTION According to statistics, breast cancer has surpassed lung cancer as the most common cancer among women, with an estimated 2.3 million new cases [ 1 ]. Nipple discharge is one of the three most common clinical signs of breast disease in women[ 2 ]. Pathological nipple discharge (PND) is usually defined as spontaneous, unilateral, bloody, serous, or watery discharge. In addition to extra-mammary factors such as pituitary tumors, medications, and endocrine diseases, it usually originates from a single ductal lesion, and common causes include intraductal papilloma, ductal dilatation, and ductal carcinoma in situ[ 2 – 4 ], and is a predominant feature of breast cancer in 5–12% of women[ 5 ]. Many cases of PND are not recognized or do not show abnormalities on X-ray due to dense breast tissue[ 6 ]. High-frequency ultrasound scanning allows for visualization of the breast ductal system, revealing abnormalities and associated changes smaller than 1 cm[ 7 ]. However, the differential diagnosis of intraductal masses is broad and includes concentrated secretions, infections, hemorrhage, single or multiple papillomas, and malignant tumors[ 8 ]. Conventional ultrasound images may lack definitive mass features or may struggle to accurately localize tiny intraductal lesions, often leading to overdiagnosis and false positives, unnecessary tissue biopsies. In recent years, contrast-enhanced ultrasound (CEUS) has proved to be an effective method for identifying benign and malignant breast tumors[ 9 ]. CEUS can clearly show the morphology and boundaries of the lesion, in addition, its demonstration of microcirculatory perfusion within the tumor can provide additional diagnostic information for qualitative and quantitative analysis of the lesion[ 10 ]. Several studies have shown that conventional ultrasound combined with CEUS has significant diagnostic value in identifying the benign and malignant nature of breast ductal-related lesions[ 11 ]. Therefore, it is reasonable to assume that the different enhancement features reflect differences in microcirculatory perfusion of the lesion, providing additional details for differentiating the benign and malignant lesions of nipple discharge, aiming to narrow the differential diagnosis of lesions including concentrated secretions, infections, hemorrhage, single or intraductal papilloma, and malignant tumors. In patients with PND, accurately identifying and focusing on the criminal lesion, evaluating the ductal structures associated with, surrounding, or connected to it, and identifying its benign or malignant nature is essential for further treatment or surgical planning of the patient[ 12 ]. As far as we know, there have been no research reports on the diagnostic value of CEUS for PND. In our study, CEUS focused the imaging field on the "culprit" lesions targeted by conventional ultrasound, and comprehensively evaluated the imaging characteristics of the lesions through the combination of secondary ultrasound and CEUS. Recently, nomograms have been widely adopted as a non-invasive tool for predicting cancer prognosis[ 13 ]. The primary aim of this study was to develop and validate a nomogram based on clinical information, routine ultrasound, and CEUS in patients with PND to predict the risk of malignancy of criminal lesions causing PND. The secondary goal was to compare the diagnostic performance of three methods: US BI-RADS, 5-point modified BI-RADS, and the nomogram. Lastly, the study aimed to establish a combined approach utilizing these three methods for patient stratification and clinical management in PND. 2. MATERIALS AND METHODS 2.1 Study Subjects and Clinical Information Collection The study population consisted of 593 non-lactating women who underwent ultrasound examinations at Shanghai General Hospital between January 2021 to February 2025 and who exhibited PND (spontaneous, unilateral, bloody, serous, or watery discharge). Inclusion criteria included (1) having clear pathological findings, (2) undergoing breast ultrasound and CEUS before biopsy or excision, and (3) undergoing surgery or biopsy within one week of ultrasound detection of the mass. Exclusion criteria included (1) unclear pathological findings, (2) preoperative anticancer treatment (radiotherapy or chemotherapy), (3) participants who did not undergo CEUS, (4) inconsistency of lesions between routine ultrasound and CEUS recordings, (5) substandard quality of image recordings, and (6) lack of complete clinical information in the medical records. Clinical data for all patients, including age(≤ 55years, > 55years), maximum diameter of the lesion (≤ 1 cm, > 1 cm), distance from the nipple (≤ 3 cm, > 3 cm; lesions ≤ 3 cm from the nipple were categorized as central lesions, while lesions > 3 cm from the nipple were classified as peripheral), menopausal status, the nature of the discharge (The following text is abbreviated as discharge, bloody/non-bloody), presence or absence of accompanying symptoms such as breast pain and nipple itching, and presence of surgical pathology or fine-needle aspiration findings, all obtained from the comprehensive medical record system. For patients with multiple lesions in the same breast, only the most suspicious lesion or the lesion with the highest BI-RADS grade was selected for analysis. Ultimately, 334 lesions in 334 patients were included (Fig. 1 A). The data from the North Campus with the most enrolled patients was used as the primary group to reduce overfitting or bias in the analysis. North Campus patients were randomly divided into a development group (n = 189, mean age 55.9 ± 14.8 years; age range 21 to 90 years) and an internal validation group (n = 63, mean age 54.2 ± 15.2 years; age range 21 to 82 years), with a ratio of 3:1. In addition, patients from South Campus were included in an external validation group (n = 82, mean age 52.5 ± 13.9 years; age range 24 to 81 years). This retrospective study was approved by the institutional ethics committee of Shanghai General Hospital, the patients signed informed consent forms before CEUS was performed, and all participating researchers were blinded. 2.2 Imaging Technique and Interpretation 2.2.1 US and CEUS examination Conventional ultrasound was conducted using an Aplio i900 system (Canon Medical Systems Corporation, Otawara, Tochigi, Japan) with an 18LX5 line array probe. Contrast-enhanced ultrasound (CEUS) was performed with the LOGIQ E9 (GE Healthcare, USA), Aplio500 (CANON, Japan) equipped with a 9L probe. SonoVue (Bracco, Milan, Italy) was used as the contrast agent during the CEUS examination. Patients were typically positioned supine during the scan, with additional lateral positioning as necessary, and their arms raised to fully expose the breasts and axillae. The day before the examination, patients should be reminded to avoid nipple compression, and the examination itself should be conducted gently. If ductal abnormalities were detected, the probe should be maneuvered along the ducts to clarify the relationship between the lesion and the ducts as much as possible, particularly to identify the lesion causing any PDN, with special attention given to the nipple-areola area. After identifying the suspected lesion, the probe can be applied with appropriate pressure to observe any mobility within the dilated duct, in addition to examining the lesion itself. The vascularity of the lesion is assessed using color and spectral Doppler ultrasound[ 14 , 15 ]. In conventional ultrasound, the plane that best displays the lesion is selected, and the probe is fixed in place before switching to ultrasonography mode. The focus is then directed toward the suspected lesion for CEUS. Utilizing a dual image mode during CEUS allows for precise localization of lesions, which is particularly beneficial for identifying small intraductal lesions that are challenging to detect with conventional ultrasound. For CEUS, the dual-image mode was employed to enhance the accuracy of the results, and the mechanical index was set to 0.06. Sulfur hexafluoride microbubbles (4.8 mL; SonoVue®, Bracco Imaging S.p.A., Milan, Italy) were injected through the antecubital vein, followed by an injection of 5–10 mL of saline. Videos and images were recorded for 180 seconds, starting immediately after the injection. 2.2.2 Imaging Interpretation A single-blind approach was used to minimize the influence of subjective factors. First, the images were analyzed by two sonographers with 5 years of experience in breast ultrasound from both the North and South hospital districts. Following this, a senior sonographer with more than 20 years of experience in breast ultrasound, B.M. and L.Y. assessed the image quality and the results of the analyses and screened out cases with poor image quality before analysis. Therefore, we hypothesized that inter- and intra-observer variability had no significant effect on the results. 2.2.3 US BI-RADS Interpretation In a conventional ultrasound examination, the following features were observed and recorded according to the BI-RADS® fifth edition[ 16 ]: lesion sharpness (regular or irregular), lesion margin (circumscribed or not circumscribed, not circumscribed including not circumscribed, angular or burr-like), intralesional echo (homogeneous or heterogeneous), calcifications (absent or present) and color doppler (absent or present), boundary with the mammary duct (clear or unclear). Next, the CEUS features were recorded based on the enhancement pattern[ 17 , 18 ]. Key lesion features included (1) Wash-in time: the timing of enhancement in the lesion compared to the surrounding breast tissue—earlier, simultaneous, or later; (2) Enhancement degree: iso-hemorrhagic, hemorrhagic-rich, or hemorrhagic-lacking during the peak enhancement phase; (3) Enhancement area: Amplification or non-amplification; (4) Perfusion defects: present or absent; (5) Enhancement distribution: homogeneous or non-homogeneous; (6) Enhancement margin: bounded or unbounded; unbounded includes unclear, crabapple enhancement and linear conduit enhancement around the lesion; (7) Enhancement sharpness: regular or irregular. Finally, three sonographers rated the imaging features on the CEUS according to the 5-point scoring system proposed by Xiao et al. [ 19 ] and re-rated the CEUS BI-RADS in conjunction with the BI-RADS categories of the US. 2.3 Development of the nomogram A nomogram was developed to predict the risk of malignancy in lesions detected through PND, using data from the development group. Significant factors associated with breast malignancy were identified through both univariate and multivariate logistic regression analyses. The candidate factors included clinical information about the patient as well as features from routine ultrasound and CEUS. Univariate logistic regression was employed to screen for significant factors related to the clinical information. To address potential multicollinearity among ultrasound features, the Least Absolute Shrinkage and Selection Operator (LASSO) regression was utilized. This approach selected the value of λ that resulted in the smallest mean error to narrow down the ultrasound features. In the final model, we combined the selected ultrasound features with the patient’s clinical information and integrated these independent predictors through multivariate logistic regression analysis. This process led to the creation of a nomogram that predicts the risk of malignancy in lesions ( Fig. 1 B). 2.4 Validation of the nomogram First, Relative Operating Characteristic (ROC) curves were created to evaluate the nomogram's ability to differentiate between malignant and benign lesions in both the development and validation groups. The area under the curve (AUC) was used to measure the model's performance. Next, calibration curves were plotted to show the agreement between observed outcome frequencies and predicted probabilities, using data from both groups. The Hosmer-Lemeshow test was conducted to assess the calibration performance of the nomogram. Finally, the clinical utility of the nomogram was evaluated through a decision curve analysis (DCA) in the validation group. This analysis quantified the net benefit at various threshold probabilities and assessed whether the nomogram improved individual outcomes. 2.5 Statistical Tests Statistical analyses were conducted using R software (version 4.2.3). A p-value of less than 0.05 was considered statistically significant for all two-sided tests. Nomograms were developed and assessed with R software, utilizing calibration curves and decision curve analysis (DCA) for their evaluation. The ‘glmnet’ package was used for LASSO regression, while ‘glm’ was employed for both univariate and multivariate logistic regression analyses. Additionally, the ‘rms’ package facilitated the creation of nomogram. For plotting receiver operating characteristic (ROC) curves, calibration curves, and conducting decision curve analyses, the packages ‘pROC’, 'Calibration Curves', and 'Decision Curve' were utilized, respectively. 3. RESULTS 3.1 Patient characteristics Table 1 shows the basic information of the study population. In the development group, the internal validation group, and the external validation group, there was no significant difference in the distribution of clinical characteristics such as age (P = 0.713) and maximum diameter of lesion (P = 0.837) of the patients. In addition, the incidence of breast malignancy in the three groups was 49.2% (93/189), 36.5% (23/63), and 41.5% (34/82), respectively, with no significant difference in the presence of malignancy (P = 0.165). These results indicate that there was no significant difference in the baseline characteristics of the three groups. Table 1 Basic information of study patients Development group (189, %) internal validation group (63, %) external validation group (82, %) p Age (years) ≤ 45 50(27.5%) 23(36.5%) 19(23.2%) 0.086 46–55 40(20.3%) 8(12.7%) 25(30.5%) >55 99(52.4%) 32(50.8%) 38 (46.3%) Diameter (cm) ≤ 1.0 87(46.0%) 27(42.9%) 34(41.5%) 0.759 >1.0 102(54.0%) 36(57.1%) 48(58.5%) Distance from nipple ≥ 3 cm 38 (20.1%) 7(11.1%) 15(18.3%) 0.272 < 3 cm 151 (79.9%) 56(88.9%) 67(81.7%) Discharge bloody 97(51.3%) 37(58.7%) 45(54.9%) 0.573 non-bloody 92(48.7%) 26(41.3%) 37(45.1%) Accompanied symptoms Present 59(31.2%) 18 (28.6%) 26(31.7%) 0.908 Absent 130(68.8%) 45(71.4%) 56(68.3%) Menopausal state menopause 108 (57.1%) 35 (55.6%) 46(56.1%) 0.971 non-menopause 81(42.9%) 28(44.4%) 36(43.9%) US BI-RADS 3-4A 129(68.3%) 47(74.6%) 64(78.0%) 0.223 4B-5 60(31.7%) 16(25.4%) 18(22.0%) 5 point modified BI-RADS 3-4A 91(48.1%) 40(63.5%) 44(53.7%) 0.104 4B-5 98(50.8%) 23(36.5%) 38(46.3%) Lesion pathology Benign 96(49.2%) 40(63.5%) 48(58.5%) 0.165 Malignant 93(48.7%) 23(36.5%) 34(41.5%) Table 2 demonstrates the pathological findings and details of each group. Intraductal papillomas (with or without atypical hyperplasia) were the most common cause of PND in all cases (111/334, 33.2%). Final pathology was suggestive of benignity in 55.1% (184/334) of patients with PND, with ductal dilatation (with or without atypical hyperplasia) and intraductal papillomas (with or without atypical hyperplasia) accounting for the vast majority of benign lesions (174/184, 94.6%). Table 2 Detailed pathological results of three groups Lesion pathology Development group (189, %) internal validation group (63, %) external validation group (82, %) all Benign 96 40 48 184 Duct dilation 29 12 22 63 Intraductal papilloma 62 27 22 111 fibroadenoma 4 - - 4 Sclerosing Adenosis 1 1 2 4 Ductal adenoma - - 1 1 granulomatous mastitis - - 1 1 Malignant 93 23 34 150 Ductal carcinoma in situ 25 3 8 36 invasive breast cancer 23 7 10 50 Paget's Disease 1 - - 1 Papillary carcinoma 43 13 16 62 lobular carcinoma in situ 1 - - 1 3.2 Diagnostic efficacy of US BI-RADS and 5-point modified BI-RADS Table 3 demonstrates the diagnostic results of 5-point modified BI-RADS and US BI-RADS in the three groups. In the development group, the AUC value of 5-point modified BI-RADS showed higher diagnostic performance. In addition, the sensitivity and NPV of 5-point modified BI-RADS in the development group were significantly higher than the sensitivity of US BI-RADS. The diagnostic effects of the internal validation group and external validation group are shown in Table 3 , and the detailed results are shown in Table 3 . Table 4 compares the AUC values of US BI-RADS and 5-point modified BI-RADS in predicting the diagnostic performance of criminal lesions as follows: development group: AUC 0.800 versus AUC 0.690, p < 0.003; internal validation group: AUC 0.851 versus AUC 0.664, p = 0.002; and external validation group: AUC 0.818 versus AUC 0.704, p = 0.044. All three groups showed that the 5-point modified BI-RADS had a higher ability to diagnose breast cancer (p < 0.05). Table 5 summarizes the stratified management of 334 patients based on US BI-RADS. More than half (64.7%, 216/334) of the patients had an initial rating of BI-RADS 4a, with 80.1% (173/216) of the BI-RADS 4a patients correctly diagnosed after 5 points modified BI-RADS reclassification. Of all patients, 81.7% (273/334) were correctly diagnosed after 5 points modified BI-RADS reclassification. Table 3 The AUC(95% CI), Sensitivity, Specificity, NPV, and PPV of the three groups 5 point modified BI-RADS US BI-RADS Nomogram Development group Cutoff value - - 0.54 AUC(95% CI) 0.833(0.770–0.895) 0.691 (0.619–0.763) 0.906 (0.858–0.954) Sensitivity (%) 85.7 51.4 81.4 Specificity (%) 80.9 86.8 85.3 NPV 84.6 63.4 81.7 PPV 82.2 80 85.1 internal validation group Cutoff value - - 0.414 AUC(95% CI) 0.838(0.730–0.945) 0.678 (0.555–0.801) 0.884 (0.764-1.000) Sensitivity (%) 80 45 85 Specificity (%) 87.5 90.6 87.5 NPV 87.5 72.5 90.3 PPV 80 75 81 external validation group Cutoff value - - 0.557 AUC(95% CI) 0.821(0.731–0.911) 0.704 (0.611–0.798) 0.926 (0.850-1.000) Sensitivity (%) 86.7 43.3 86.7 Specificity (%) 77.5 97.5 95 NPV 88.6 69.6 90.5 PPV 74.3 92.9 92.9 all AUC(95% CI) 0.832(0.780–0.885) 0.695(0.629–0.761) 0.860(0.810–0.909) Sensitivity (%) 85 48.3 83.3 Specificity (%) 81.4 90.7 88.6 NPV 86.4 67.2 86.1 PPV 79.7 81.7 86.2 Note. AUC, area under the receiver operating characteristic curve; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value. Data are presented as percentages except AUC. Table 4 Comparison of AUC values among three groups Compare AUC Development group internal validation group external validation group Z p Z p Z p 5 point modified BI-RADS category VS US BI-RADS 3.476 < 0.001 2.137 0.033 2.137 0.037 5 point modified BI-RADS category VS Nomogram 2.708 < 0.001 1 0.317 2.508 0.012 Nomogram VS US BI-RADS 5.258 < 0.001 2.771 < 0.001 4.339 < 0.001 Note. AUC, area under the receiver operating characteristic curve. Table 5 Hierarchical management of 260 patients based on US BI-RADS US BI-RADS (n) Method 1*and Method 2*(n) Method 1 # and Method 2 # (n) Method 1* and Method 2 # (n) Method 2* and Method 1 # (n) 3(19) 16 1 - 2 4a(170) 130 14 9 17 4b(49) 36 6 4 3 4c(17) 15 1 1 - 5(5) 5 - - 260 202 22 14 22 Note. Method 1, 5 points modified BI-RADS; Method 2, nomogram; *Indicates correct diagnosis; # indicates incorrect diagnosis. 3.3 Nomogram Model development and performance 3.3.1 development Table 6 demonstrates the results of univariate and multivariate analyses of the risk of malignancy of criminal lesions in the development group. Seven ultrasound features were screened based on LASSO regression (Fig. 2 A, Fig. 2 B). Through multivariate logistic regression, finally age, diameter, calcifications, boundary with the mammary duct, enhancement area, and enhancement margin were identified as independent predictors for predicting the risk of malignancy of criminal lesions (Table 6 ). A risk prediction nomogram was developed based on the above independent risk predictors (Fig. 2 C). Table 6 The results of univariate and multivariate analyses of the risk of malignancy of criminal lesions in the development group Univariate analysis multivariate analysis Benign(68,%) Malignant(70,%) OR(95% CI) p OR(95% CI) p Age (years) ≤ 45 22 (32.4%) 16 (22.9%) 46–55 19 (27.9%) 9 (12.9%) 0.65 (0.23–1.81) 0.411 0.28 (0.04–2.14) 0.222 >55 27 (39.7%) 45 (64.3%) 2.29 (1.03–5.11) 0.043 0.92 (0.13–6.42) 0.929 Diameter (cm) ≤ 1.0 39 (57.4%) 26 (37.1%) >1.0 29 (42.6%) 44(62.9%) 2.28(1.15–4.50) 0.018 0.12 (0.02–0.60) 0.010 Discharge bloody 31 (45.6%) 49 (70.0%) non-bloody 37 (54.4%) 21(30.0%) 0.36 (0.18–0.72) 0.004 3.45 (1.10-10.75) 0.033 Accompanied symptoms present 21 (30.9%) 15 (21.4%) absent 47(69.1%) 55 (78.6%) 1.64 10.76–3.53) 0.208 - - Menopausal state menopause 30 (44.1%) 47 (67.1%) non-menopause 38 (55.9%) 23 (32.9%) 0.39 (0.19–0.77) 0.007 3.62 (0.57–23.09) 0.173 Distance from nipple ≥ 3 cm 5(7.4%) 7(10.0%) < 3 cm 63 (92.6%) 63(90.0%) 0.71 (0.22–2.37) 0.583 - - Intralesional echo homogeneous echo 46(67.6%) 29(41.4%) heterogenous echo 22 (32.4%) 41 (58.6%) - - 3.49 (0.98–12.40) 0.053 Calcifications absent 65 (95.6%) 49 (70.0%) present 3 (4.4%) 21 (30.0%) - - 8.83 (1.51–51.50) 0.015 Boundary with duct clear 43 (63.2%) 18 (25.7%) unclear 25 (36.8%) 52 (74.3%) - - 7.08 (1.92–26.04) 0.003 Enhancement area non-enlarged 54 (79.4%) 15 (21.4%) enlarged 14 (20.6%) 55 (78.6%) - - 20.95 (5.16–85.09) p < .001 Enhancement distribution homogeneous 50 (73.5%) 33 (47.1%) heterogeneous 18 (26.5%) 37 (52.9%) - - 2.86 (0.76–10.78) 0.121 Enhancement margin clear 51 (75.0%) 24 (34.3%) unclear 17 (25.0%) 46 (65.7%) - - 0.76 (0.19–3.03) 0.700 Enhancement sharpness regular 48 (70.6%) 22 (31.4%) irregular 20 (29.4%) 48 (68.6%) - - 4.02 (1.23–13.16) 0.021 3.3.2 Prediction ROC curves were used to assess the ability of the three diagnostic methods to determine the benignity or malignancy of criminal lesions. Figure 3 A-C demonstrates the ROC curves for the US BI-RADS, the 5-point modified BI-RADS, and the nomogram. To better assess the diagnostic efficacy of the nomogram in clinical applications, the two validation groups included only the ultrasound features of nomogram in the development group. By maximizing Youden 's index, the optimal threshold for the development set to distinguish between benign and malignant nomogram scores of criminal lesions was determined to be 0.328. Table 3 summarizes the nomogram performance for predicting criminal lesions using this optimal threshold. The AUC (95% CI) for the development group, internal validation group, and external validation group were 0.906(0.862–0.950), 0.863(0.749–0.977), and 0.902(0.824–0.980), respectively. Nomogram demonstrated the best diagnostic performance for 334 patients. In addition, the PPV of the nomogram was higher than that of the US BI-RADS and the 5-point modified BI-RADS in all groups except for the third group. Table 4 shows that the diagnostic performance of the nomogram in the three groups was better than that of the US BI-RADS and the difference in AUC was statistically significant (p < 0.001). Except for the internal validation group, the diagnostic performance of the nomogram in the other two groups was better than the 5-point modified BI-RADS, and the difference in AUC was statistically significant (p < 0.05). Table 5 shows that 83.3% (180/216) of the patients with BI-RADS 4a were diagnosed correctly after reclassification of nomogram, which was 7 more than 5-point modified BI-RADS. Of all patients, 84.4% (282/334) were correctly diagnosed after nomogram classification. To further illustrate the clinical utility of the nomogram in distinguishing benign and malignant lesions causing PND, two representative cases are presented as follows (Fig. 4 , Fig. 5 ). 3.3.3 Calibration The nomogram showed that in the development group (C-index: 0.906, calibrated C-index: 0.893), the internal validation group (C-index: 0.863, calibrated C-index: 0.814), and the external validation group (C-index: 0.902, calibrated C-index: 0.868) were well differentiated. All three groups showed statistical significance in the Hosmer-Lemeshow test (p > 0.05). The calibration curves are shown in Fig. 6 (A-C). 3.3.4 Clinical utility Decision curve analysis (DCA) was used to assess the clinical utility of the nomogram, BI-RADS categories, and 5-point modified BI-RADS categories in the development and validation groups (Fig. 7 .A-C). The DCA analysis showed that when the threshold probability exceeded 5%, the use of the nomogram to predict the risk of malignancy of the criminal lesions causing PND could provide a greater benefit when compared to treating all scenarios (assuming that all lesions are malignant) or treat no program (assuming all lesions are benign), which can provide more benefit. In addition, using the nomogram to predict the malignant risk of criminal lesions adds more to the overall net benefit than using only the BI-RADS categories or using only the 5-point modified BI-RADS categories. 4. DISCUSSION Nipple discharge is a common symptom of breast disease in women, with intraductal lesions being the main cause of pathological discharge[ 2 , 3 ]。 The conventional wisdom is that despite the low likelihood of malignancy, these women still need to be diagnosed by central ductal resection or single ductal resection surgery. However, with the development of imaging techniques and an increasing number of clinical studies, it has been shown that short-term observation by repeat imaging and clinical examination is a reasonable approach for low-risk patients (without a strong family or personal history of cancer) or for those who do not wish to undergo palliative surgery[ 20 , 21 ]. The importance of a thorough examination and assessment of patients with PND cannot be ignored. Therefore, in this study, we attempted to develop a nomogram score combining clinical examination and imaging features to predict the degree of malignancy of PND and to compare it with existing diagnostic imaging methods for stratification and clinical management of patients. Our study showed that intraductal papilloma (with or without atypical hyperplasia) was the most common cause of PND (111/334, 33.2%), and the second most common benign cause was ductal dilatation (including with atypical hyperplasia) (63/334, 18.9%), which is in line with the literature[ 22 , 23 ]. In this study, 44.9% of the patients had malignant pathological findings, which is higher than previous literature reports. This may be related to the hospital referral mechanism as most of the patients with breast disease first choose primary hospitals for consultation; therefore, the incidence of malignancy in our sample is not representative of the incidence of malignancy in all patients with PND. As the population included in this study was patients with PND, PND is usually defined as a spontaneous, unilateral, hemorrhagic, serous, or watery discharge, usually originating from a single ductal lesion. In the literature, some malignant lesions causing PND have specific characteristics: very small and entirely within the duct[ 24 , 25 ]. Among the clinical factors included in this study, age(≤ 55years, >55years) and the size of the lesion (≤ 1 cm, > 1 cm) were the most important predictors, whereas the other factors (e.g., the nature of the discharge, distance of the criminal lesion from the nipple, accompanying symptoms such as tenderness and pain, mass, and menopausal status) did not show significant differences between benign and malignant tumors. A previous meta-analysis described that when the focal imaging abnormality of BNP patients was>1 cm, the underlying malignancy upgrade rate was 21% [ 26 ]. Similarly, another meta-analysis reported that patient age (< 50 years, ≥ 50 years) and lesion size were predictive factors for the progression of papillary lesions to cancerous lesions [ 27 ]. Our research found that bloody discharge is not a predictive factor for malignant lesions, but previous studies have reported a hazard ratio of 2.5 for both bloody and non-bloody pathological discharge concerning association with underlying malignancy [ 28 ]. This discrepancy may be attributed to our institution’s role as a referral center: during initial evaluations, we prioritize patients with hemorrhagic discharge, who often undergo preliminary screening via imaging follow-ups, potentially reducing the proportion of undiagnosed malignant cases with hemorrhagic discharge in our cohort. In this study, 81.7% (273/334) of the lesions causing PND were central lesions, i.e., < 3 cm from the nipple. Although the distance from the nipple is not a strong indicator of benign and malignant tumors, it can help radiologists focus the examination on the central area after a thorough breast examination and, at the same time, provide clinicians with more information for a careful physical examination of the key areas and planning the extent of surgical resection. Clinically, ductal dilatation can cause both physiological (bilateral yellow or brown) and pathological (unilateral clear or bloody) discharges, and the pathological and physiological causes of nipple discharge overlap with each other, resulting in a wide range of potential intraductal masses, including concentrated discharge, infection, bleeding, single or multiple papillomas, and malignancy. Previous researchers have classified images into various types based on the presentation of lesions on conventional ultrasound[ 29 , 30 ], Examples include simple mass type without definite ductal dilatation, non-mass type, and ductal dilatation type (including dilatation within the duct with or without definite occupancy). To identify intraductal masses as much as possible, in our study, three sonographers interpreted and reviewed the images acquired by conventional ultrasound to determine the relationship of the lesion to the duct as much as possible. Imaging features of non-physiological ductal dilatation that may indicate malignancy included irregular ductal margins, peripheral dilatation of the duct, thickening of the duct wall, and adjacent hypoechoic tissue that may represent a mass[ 8 ]. Ductal boundaries had excellent diagnostic power for the differential diagnosis of benign and malignant lesions, both in terms of multivariate logistic regression analyses and nomogram visualization (OR (95% CI): 3.57 (1.25–10.21), p = 0.017). In our study, suspicious calcifications on US images proved to be an important marker for identifying malignant lesions in PND (OR(95% CI) 3.54 (1.09–11.48), p = 0.035) and may be a sensitive indicator of early breast cancer, which involves microcalcifications in about 90% of cases of ductal carcinoma in situ[ 31 ]. Our findings underline the fact that microcalcifications may be the only lesions with certain detected abnormalities, especially in microscopic intraductal lesions that cannot be palpated. In CEUS images, the difference between the benign and malignant groups was statistically significant in terms of enhancement area (OR (95% CI) 6.11 (2.09–17.86), p < 0.001), and enhancement margin (OR(95% CI) 3.61 (1.33–9.81), p = 0.012). Previous literature reports [ 32 ] [ 33 ] stated that range expansion is a highly specific indicator for the diagnosis of benign and malignant breast lesions, which is in agreement with our findings. Tuan et al. [ 34 ] concluded that it is highly unlikely that lesions with well-defined margins on conventional ultrasound would have large measurements on ultrasonography. The enhancement pattern of breast masses is consistent with histopathological findings. Both benign and malignant breast lesions tend to have a greater blood supply than normal tissue. Many neoplastic microvessels around the tumor continue to infiltrate and grow into the surrounding tissue, pro-angiogenic growth factors influence the morphology of blood vessels in the vicinity of the tumor, and tumor-associated vascular irregularities can extend beyond the limits of the apparent tumour[ 35 ]. Consequently, growth and infiltration of the malignant mass, coupled with traction on the surrounding tissues, results in a greater extent of the malignant breast lesion being demonstrated by CEUS than by conventional ultrasound. The boundaries of the lesion are more sensitive in enhancement than by two-dimensional ultrasound is more sensitive, showing blurring, angularity, and peripheral linear ductal enhancement. Xia et al. [ 36 ] based on morphological and pathological correlations, similarly found that the linear ductal pattern around the lesion determined by CEUS correlates with ductal origin and that the presence of peripheral radiolucent or perforating vessels is a distinctive feature associated with either atypical or malignant papillomas. A nomogram combining clinical, US, and CEUS features demonstrated strong discrimination between malignant and benign breast lesions. The calibration curves indicated good agreement between the predicted and actual probability of breast malignancy. Decision curve analysis (DCA) revealed the clinical utility of the nomogram, which performed well in the development and validation groups, with AUCs exceeding 0.85. The 5-point modified BI-RADS showed higher diagnostic power for breast cancer than the US BI-RADS but required consideration of initial BI-RADS categories and the CEUS 5-point scale before secondary BI-RADS classification. In contrast, the nomogram only needs to consider 4 ultrasound features and incorporate clinical factors simultaneously, resulting in a higher AUC value and reducing the need to consider additional ultrasound features, thereby enabling more efficient and accurate interpretation for imaging physicians. In addition, false-positive imaging diagnoses may lead to unnecessary biopsies, patient anxiety, and increased healthcare costs.BI-RADS 4a is usually recommended as a threshold for biopsy in clinical settings. In this study, the nomogram model performed well, particularly when using BI-RADS 4a as the threshold for detecting positive lesions. 83.3% (180/216) of US BI-RADS 4a via nomogram models were able to accurately distinguish between benign and malignant, effectively reducing unnecessary biopsies of category 4a lesions and offering guidance in avoiding overdiagnosis of potentially benign lesions. Patients have more options for consultation, such as repeat imaging and short-term clinical observation. For patients in categories 4b-5, the diagnostic results of the nomogram, US BI-RADS and 5-point modified BI-RADS were generally consistent. We advocate biopsy as the primary approach in these categories. It is worth noting that 10 patients with US BI-RADS 4a, who were found to have papillary carcinoma on pathology, were missed by either the 5-point modified BI-RADS or the nomogram. There are several limitations to this study. Firstly, the strict inclusion and exclusion criteria used in this study resulted in the exclusion of approximately 43.6% of patients with PND, thus limiting the number of included patients. Further studies with a larger patient group are needed to confirm our findings. Secondly, a limitation of CEUS is its lack of global coverage. The combined secondary ultrasound in this study focused on suspected lesions suggested by conventional ultrasound and did not cover the entire breast parenchyma. Thirdly, the US and CEUS features included in the nomogram rely on the operator's subjective judgment and experience. Further studies are needed to enhance operator experience and improve nomogram application. Fourthly, some cases in this study originated from primary hospital referrals, potentially increasing the malignancy rate in the patient population. The broad inclusion of representative benign masses may have led to higher AUCs due to inclusion bias. Fifthly, the gold standard for all patients was pathological findings, leading to the exclusion of a significant proportion of follow-up cases, potentially introducing selection bias. In summary, accurate diagnosis of patients with PND is a crucial clinical priority, while ensuring accurate image replication and clinical follow-up of patients remains challenging. Problem-solving algorithms for evaluating suspected PND are continuously developing, and nomograms can significantly improve diagnostic accuracy by integrating clinical indicators and ultrasound imaging features to differentiate between malignant and benign breast lesions. Reassessing patients with PND in US BI-RADS 4a has the potential to conserve clinical resources by preventing a significant number of patients from undergoing invasive biopsy. Abbreviations PND Pathologic Nipple Discharge US Ultrasound CEUS Contrast-Enhanced Ultrasound BI-RADS Breast Imaging Reporting and Data System LASSO Least Absolute Shrinkage and Selection Operator AUC Area Under the Receiver Operating Characteristic Curve NPV Negative Predictive Value PPV Positive Predictive Value ROC Receiver Operating Characteristic DCA Decision Curve Analysis DCIS Ductal Carcinoma In Situ US Ultrasound Declarations Ethics approval and consent to participate This study was approved by the Institutional Ethics Committee of Shanghai General Hospital (Approval No. 2025106). For this retrospective analysis, individual informed consent was waived in accordance with the committee’s decision. All experiments and data analyses were conducted in strict compliance with the ethical principles outlined in the Declaration of Helsinki and other relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials All data used in this study are available from the corresponding author upon reasonable request. Competing Interests The authors declare no competing interests. Funding This research was granted by the National Natural Science Foundation of China (Nos. 82202172). Authors' contributions MZ participated in the design of the study and acquisition of data and drafted the manuscript. SZ made contributions to study conception and design. All authors, MZ, SZ, YW, JM, YF, XZ, GY, FL, YL and MB, participated in revising the manuscript critically for important intellectual content and gave final approval of the version to be published. All authors read and approved the final manuscript. Acknowledgements None. 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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-7348481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522289837,"identity":"ba6252a1-4ccd-4793-9eeb-966571e5fb4b","order_by":0,"name":"Miao Zhu","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Zhu","suffix":""},{"id":522289838,"identity":"305ad940-d8ca-4111-b130-7ddd29e8d000","order_by":1,"name":"Siqi Zheng","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of 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01:01:29","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145376,"visible":true,"origin":"","legend":"","description":"","filename":"6c57fa7ce5c94b3caec2cebaeb6b3d361structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/c6f2ac8da8515e59bb1d1422.xml"},{"id":92679880,"identity":"ffa3232b-2d65-4993-b912-088b7eb3991d","added_by":"auto","created_at":"2025-10-03 01:01:29","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152906,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/f113d357396c16fd5d0ecec6.html"},{"id":92679858,"identity":"f26b8634-5d64-474f-ba27-65243ae55eda","added_by":"auto","created_at":"2025-10-03 01:01:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":275027,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Flowchart of patients (included, excluded, and the percentage) ; (B) Nomogram model development.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/94cecc03d620917048de9217.jpeg"},{"id":92679857,"identity":"e52b67c3-fd5f-4249-8f3b-97e2b5bd7856","added_by":"auto","created_at":"2025-10-03 01:01:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149322,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment of nomogram for development group\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/943267ddaeca6ef0497d6f02.jpeg"},{"id":92679859,"identity":"856014a6-21fe-416e-a7b8-383b3bc9e483","added_by":"auto","created_at":"2025-10-03 01:01:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123796,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for three groups\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/2e334f1581a3786f99a90ef2.jpeg"},{"id":92679867,"identity":"2e4d1b9d-589c-4766-b8bf-a8f69c16c91a","added_by":"auto","created_at":"2025-10-03 01:01:29","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":303854,"visible":true,"origin":"","legend":"\u003cp\u003eCase 1: A patient aged less than 55 years presented with a clear yellow discharge from the left nipple for 8 months. Ultrasonography revealed an intraductal hypoechoic nodule with duct dilatation in the central region of the left breast at the 7 o'clock position. The patient's following clinical and imaging features were as follows: Diameter: \u0026gt;1 cm, discharge: non-bloody, calcifications: absent, Boundary with the mammary duct: part of the boundary is unclear; enhancement area: non-amplification, enhancement margin: bounded (A, B). The total score for this nodule was 67.03, which corresponds to a 4.99% probability of malignancy (defined as the Nomo score) (C). The final pathological confirmation is intraductal papilloma (D).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/7b71f1f28b15501aa2cb1e09.jpeg"},{"id":92682503,"identity":"883f28b6-6de4-4fe7-bc2d-7f8391994aaf","added_by":"auto","created_at":"2025-10-03 01:17:29","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":310875,"visible":true,"origin":"","legend":"\u003cp\u003eCase 2: A patient aged over 55 years had a 3-day history of right-sided bloody nipple discharge. Ultrasonography revealed a ductal hypoechoic area in the central region of the right breast at the 3 o'clock position. The patient's clinical and imaging features were as follows: Diameter: \u0026gt;1 cm, discharge: bloody, calcifications: absent, Boundary with the mammary duct: unclear, enhancement area: amplification, enhancement margin: unbounded (A, B). The total score of the nodule was 262.23, which corresponded to a 90.56% probability of malignancy (defined as Nomo score) (C). The final pathological confirmation is papillary carcinoma(D).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/b160bc0f7c73d6b22d3fecb3.jpeg"},{"id":92679873,"identity":"47d02998-329e-4aa2-ac57-d500e901a1d7","added_by":"auto","created_at":"2025-10-03 01:01:29","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115251,"visible":true,"origin":"","legend":"\u003cp\u003ecalibration curves of nomogram in three groups\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/dce760c04fb891ef741cd55e.jpeg"},{"id":92681405,"identity":"3e8b7cca-0eb3-49db-aebd-d1439a66b954","added_by":"auto","created_at":"2025-10-03 01:09:29","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":124212,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) of the nomogram in the three groups\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/a3b3b2bb96bd286ef66ef63d.jpeg"},{"id":92682936,"identity":"cbdc4b03-3697-452f-85e5-a0d877411bcf","added_by":"auto","created_at":"2025-10-03 01:25:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2709954,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7348481/v1/a739e336-b47b-4fd8-983a-7a896ac62f13.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Value and Management Strategy of Nomogram for PND Based on Clinical Characteristics and CEUS","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAccording to statistics, breast cancer has surpassed lung cancer as the most common cancer among women, with an estimated 2.3\u0026nbsp;million new cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nipple discharge is one of the three most common clinical signs of breast disease in women[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Pathological nipple discharge (PND) is usually defined as spontaneous, unilateral, bloody, serous, or watery discharge. In addition to extra-mammary factors such as pituitary tumors, medications, and endocrine diseases, it usually originates from a single ductal lesion, and common causes include intraductal papilloma, ductal dilatation, and ductal carcinoma in situ[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and is a predominant feature of breast cancer in 5\u0026ndash;12% of women[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMany cases of PND are not recognized or do not show abnormalities on X-ray due to dense breast tissue[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. High-frequency ultrasound scanning allows for visualization of the breast ductal system, revealing abnormalities and associated changes smaller than 1 cm[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the differential diagnosis of intraductal masses is broad and includes concentrated secretions, infections, hemorrhage, single or multiple papillomas, and malignant tumors[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Conventional ultrasound images may lack definitive mass features or may struggle to accurately localize tiny intraductal lesions, often leading to overdiagnosis and false positives, unnecessary tissue biopsies.\u003c/p\u003e\u003cp\u003eIn recent years, contrast-enhanced ultrasound (CEUS) has proved to be an effective method for identifying benign and malignant breast tumors[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. CEUS can clearly show the morphology and boundaries of the lesion, in addition, its demonstration of microcirculatory perfusion within the tumor can provide additional diagnostic information for qualitative and quantitative analysis of the lesion[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Several studies have shown that conventional ultrasound combined with CEUS has significant diagnostic value in identifying the benign and malignant nature of breast ductal-related lesions[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, it is reasonable to assume that the different enhancement features reflect differences in microcirculatory perfusion of the lesion, providing additional details for differentiating the benign and malignant lesions of nipple discharge, aiming to narrow the differential diagnosis of lesions including concentrated secretions, infections, hemorrhage, single or intraductal papilloma, and malignant tumors.\u003c/p\u003e\u003cp\u003eIn patients with PND, accurately identifying and focusing on the criminal lesion, evaluating the ductal structures associated with, surrounding, or connected to it, and identifying its benign or malignant nature is essential for further treatment or surgical planning of the patient[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As far as we know, there have been no research reports on the diagnostic value of CEUS for PND. In our study, CEUS focused the imaging field on the \"culprit\" lesions targeted by conventional ultrasound, and comprehensively evaluated the imaging characteristics of the lesions through the combination of secondary ultrasound and CEUS.\u003c/p\u003e\u003cp\u003eRecently, nomograms have been widely adopted as a non-invasive tool for predicting cancer prognosis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The primary aim of this study was to develop and validate a nomogram based on clinical information, routine ultrasound, and CEUS in patients with PND to predict the risk of malignancy of criminal lesions causing PND. The secondary goal was to compare the diagnostic performance of three methods: US BI-RADS, 5-point modified BI-RADS, and the nomogram. Lastly, the study aimed to establish a combined approach utilizing these three methods for patient stratification and clinical management in PND.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Subjects and Clinical Information Collection\u003c/h2\u003e\u003cp\u003eThe study population consisted of 593 non-lactating women who underwent ultrasound examinations at Shanghai General Hospital between January 2021 to February 2025 and who exhibited PND (spontaneous, unilateral, bloody, serous, or watery discharge). Inclusion criteria included (1) having clear pathological findings, (2) undergoing breast ultrasound and CEUS before biopsy or excision, and (3) undergoing surgery or biopsy within one week of ultrasound detection of the mass. Exclusion criteria included (1) unclear pathological findings, (2) preoperative anticancer treatment (radiotherapy or chemotherapy), (3) participants who did not undergo CEUS, (4) inconsistency of lesions between routine ultrasound and CEUS recordings, (5) substandard quality of image recordings, and (6) lack of complete clinical information in the medical records.\u003c/p\u003e\u003cp\u003eClinical data for all patients, including age(\u0026le;\u0026thinsp;55years, \u0026gt;\u0026thinsp;55years), maximum diameter of the lesion (\u0026le;\u0026thinsp;1 cm, \u0026gt;\u0026thinsp;1 cm), distance from the nipple (\u0026le;\u0026thinsp;3 cm, \u0026gt;\u0026thinsp;3 cm; lesions\u0026thinsp;\u0026le;\u0026thinsp;3 cm from the nipple were categorized as central lesions, while lesions\u0026thinsp;\u0026gt;\u0026thinsp;3 cm from the nipple were classified as peripheral), menopausal status, the nature of the discharge (The following text is abbreviated as discharge, bloody/non-bloody), presence or absence of accompanying symptoms such as breast pain and nipple itching, and presence of surgical pathology or fine-needle aspiration findings, all obtained from the comprehensive medical record system. For patients with multiple lesions in the same breast, only the most suspicious lesion or the lesion with the highest BI-RADS grade was selected for analysis.\u003c/p\u003e\u003cp\u003eUltimately, 334 lesions in 334 patients were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The data from the North Campus with the most enrolled patients was used as the primary group to reduce overfitting or bias in the analysis. North Campus patients were randomly divided into a development group (n\u0026thinsp;=\u0026thinsp;189, mean age 55.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8 years; age range 21 to 90 years) and an internal validation group (n\u0026thinsp;=\u0026thinsp;63, mean age 54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2 years; age range 21 to 82 years), with a ratio of 3:1. In addition, patients from South Campus were included in an external validation group (n\u0026thinsp;=\u0026thinsp;82, mean age 52.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 years; age range 24 to 81 years).\u003c/p\u003e\u003cp\u003e This retrospective study was approved by the institutional ethics committee of Shanghai General Hospital, the patients signed informed consent forms before CEUS was performed, and all participating researchers were blinded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Imaging Technique and Interpretation\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 US and CEUS examination\u003c/h2\u003e\u003cp\u003eConventional ultrasound was conducted using an Aplio i900 system (Canon Medical Systems Corporation, Otawara, Tochigi, Japan) with an 18LX5 line array probe. Contrast-enhanced ultrasound (CEUS) was performed with the LOGIQ E9 (GE Healthcare, USA), Aplio500 (CANON, Japan) equipped with a 9L probe. SonoVue (Bracco, Milan, Italy) was used as the contrast agent during the CEUS examination. Patients were typically positioned supine during the scan, with additional lateral positioning as necessary, and their arms raised to fully expose the breasts and axillae. The day before the examination, patients should be reminded to avoid nipple compression, and the examination itself should be conducted gently. If ductal abnormalities were detected, the probe should be maneuvered along the ducts to clarify the relationship between the lesion and the ducts as much as possible, particularly to identify the lesion causing any PDN, with special attention given to the nipple-areola area. After identifying the suspected lesion, the probe can be applied with appropriate pressure to observe any mobility within the dilated duct, in addition to examining the lesion itself. The vascularity of the lesion is assessed using color and spectral Doppler ultrasound[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn conventional ultrasound, the plane that best displays the lesion is selected, and the probe is fixed in place before switching to ultrasonography mode. The focus is then directed toward the suspected lesion for CEUS. Utilizing a dual image mode during CEUS allows for precise localization of lesions, which is particularly beneficial for identifying small intraductal lesions that are challenging to detect with conventional ultrasound. For CEUS, the dual-image mode was employed to enhance the accuracy of the results, and the mechanical index was set to 0.06. Sulfur hexafluoride microbubbles (4.8 mL; SonoVue\u0026reg;, Bracco Imaging S.p.A., Milan, Italy) were injected through the antecubital vein, followed by an injection of 5\u0026ndash;10 mL of saline. Videos and images were recorded for 180 seconds, starting immediately after the injection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Imaging Interpretation\u003c/h2\u003e\u003cp\u003eA single-blind approach was used to minimize the influence of subjective factors. First, the images were analyzed by two sonographers with 5 years of experience in breast ultrasound from both the North and South hospital districts. Following this, a senior sonographer with more than 20 years of experience in breast ultrasound, B.M. and L.Y. assessed the image quality and the results of the analyses and screened out cases with poor image quality before analysis. Therefore, we hypothesized that inter- and intra-observer variability had no significant effect on the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 US BI-RADS Interpretation\u003c/h2\u003e\u003cp\u003eIn a conventional ultrasound examination, the following features were observed and recorded according to the BI-RADS\u0026reg; fifth edition[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]: lesion sharpness (regular or irregular), lesion margin (circumscribed or not circumscribed, not circumscribed including not circumscribed, angular or burr-like), intralesional echo (homogeneous or heterogeneous), calcifications (absent or present) and color doppler (absent or present), boundary with the mammary duct (clear or unclear).\u003c/p\u003e\u003cp\u003eNext, the CEUS features were recorded based on the enhancement pattern[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Key lesion features included (1) Wash-in time: the timing of enhancement in the lesion compared to the surrounding breast tissue\u0026mdash;earlier, simultaneous, or later; (2) Enhancement degree: iso-hemorrhagic, hemorrhagic-rich, or hemorrhagic-lacking during the peak enhancement phase; (3) Enhancement area: Amplification or non-amplification; (4) Perfusion defects: present or absent; (5) Enhancement distribution: homogeneous or non-homogeneous; (6) Enhancement margin: bounded or unbounded; unbounded includes unclear, crabapple enhancement and linear conduit enhancement around the lesion; (7) Enhancement sharpness: regular or irregular.\u003c/p\u003e\u003cp\u003eFinally, three sonographers rated the imaging features on the CEUS according to the 5-point scoring system proposed by Xiao et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and re-rated the CEUS BI-RADS in conjunction with the BI-RADS categories of the US.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Development of the nomogram\u003c/h2\u003e\u003cp\u003eA nomogram was developed to predict the risk of malignancy in lesions detected through PND, using data from the development group. Significant factors associated with breast malignancy were identified through both univariate and multivariate logistic regression analyses. The candidate factors included clinical information about the patient as well as features from routine ultrasound and CEUS. Univariate logistic regression was employed to screen for significant factors related to the clinical information. To address potential multicollinearity among ultrasound features, the Least Absolute Shrinkage and Selection Operator (LASSO) regression was utilized. This approach selected the value of λ that resulted in the smallest mean error to narrow down the ultrasound features. In the final model, we combined the selected ultrasound features with the patient\u0026rsquo;s clinical information and integrated these independent predictors through multivariate logistic regression analysis. This process led to the creation of a nomogram that predicts the risk of malignancy in lesions ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Validation of the nomogram\u003c/h2\u003e\u003cp\u003eFirst, Relative Operating Characteristic (ROC) curves were created to evaluate the nomogram's ability to differentiate between malignant and benign lesions in both the development and validation groups. The area under the curve (AUC) was used to measure the model's performance. Next, calibration curves were plotted to show the agreement between observed outcome frequencies and predicted probabilities, using data from both groups. The Hosmer-Lemeshow test was conducted to assess the calibration performance of the nomogram. Finally, the clinical utility of the nomogram was evaluated through a decision curve analysis (DCA) in the validation group. This analysis quantified the net benefit at various threshold probabilities and assessed whether the nomogram improved individual outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Tests\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using R software (version 4.2.3). A p-value of less than 0.05 was considered statistically significant for all two-sided tests. Nomograms were developed and assessed with R software, utilizing calibration curves and decision curve analysis (DCA) for their evaluation. The \u0026lsquo;glmnet\u0026rsquo; package was used for LASSO regression, while \u0026lsquo;glm\u0026rsquo; was employed for both univariate and multivariate logistic regression analyses. Additionally, the \u0026lsquo;rms\u0026rsquo; package facilitated the creation of nomogram. For plotting receiver operating characteristic (ROC) curves, calibration curves, and conducting decision curve analyses, the packages \u0026lsquo;pROC\u0026rsquo;, 'Calibration Curves', and 'Decision Curve' were utilized, respectively.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Patient characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the basic information of the study population. In the development group, the internal validation group, and the external validation group, there was no significant difference in the distribution of clinical characteristics such as age (P\u0026thinsp;=\u0026thinsp;0.713) and maximum diameter of lesion (P\u0026thinsp;=\u0026thinsp;0.837) of the patients. In addition, the incidence of breast malignancy in the three groups was 49.2% (93/189), 36.5% (23/63), and 41.5% (34/82), respectively, with no significant difference in the presence of malignancy (P\u0026thinsp;=\u0026thinsp;0.165). These results indicate that there was no significant difference in the baseline characteristics of the three groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic information of study patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDevelopment group (189, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003einternal validation group (63, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eexternal validation group (82, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50(27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23(36.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19(23.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u0026ndash;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40(20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8(12.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25(30.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99(52.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32(50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38 (46.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiameter (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87(46.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27(42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34(41.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102(54.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36(57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48(58.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from nipple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7(11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15(18.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e151 (79.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56(88.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67(81.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDischarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebloody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97(51.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37(58.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45(54.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enon-bloody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92(48.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26(41.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37(45.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccompanied symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59(31.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26(31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130(68.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45(71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56(68.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenopausal state\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emenopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (55.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46(56.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enon-menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81(42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28(44.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36(43.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUS BI-RADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3-4A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e129(68.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47(74.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64(78.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4B-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60(31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16(25.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18(22.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 point modified BI-RADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3-4A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91(48.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40(63.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44(53.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4B-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98(50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23(36.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38(46.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLesion pathology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96(49.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40(63.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48(58.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93(48.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23(36.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34(41.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the pathological findings and details of each group. Intraductal papillomas (with or without atypical hyperplasia) were the most common cause of PND in all cases (111/334, 33.2%). Final pathology was suggestive of benignity in 55.1% (184/334) of patients with PND, with ductal dilatation (with or without atypical hyperplasia) and intraductal papillomas (with or without atypical hyperplasia) accounting for the vast majority of benign lesions (174/184, 94.6%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetailed pathological results of three groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLesion pathology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDevelopment group (189, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003einternal validation group (63, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eexternal validation group (82, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eall\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuct dilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraductal papilloma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efibroadenoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSclerosing Adenosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuctal adenoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egranulomatous mastitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuctal carcinoma in situ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einvasive breast cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaget's Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePapillary carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elobular carcinoma in situ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Diagnostic efficacy of US BI-RADS and 5-point modified BI-RADS\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the diagnostic results of 5-point modified BI-RADS and US BI-RADS in the three groups. In the development group, the AUC value of 5-point modified BI-RADS showed higher diagnostic performance. In addition, the sensitivity and NPV of 5-point modified BI-RADS in the development group were significantly higher than the sensitivity of US BI-RADS. The diagnostic effects of the internal validation group and external validation group are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the detailed results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e compares the AUC values of US BI-RADS and 5-point modified BI-RADS in predicting the diagnostic performance of criminal lesions as follows: development group: AUC 0.800 versus AUC 0.690, p\u0026thinsp;\u0026lt;\u0026thinsp;0.003; internal validation group: AUC 0.851 versus AUC 0.664, p\u0026thinsp;=\u0026thinsp;0.002; and external validation group: AUC 0.818 versus AUC 0.704, p\u0026thinsp;=\u0026thinsp;0.044. All three groups showed that the 5-point modified BI-RADS had a higher ability to diagnose breast cancer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the stratified management of 334 patients based on US BI-RADS. More than half (64.7%, 216/334) of the patients had an initial rating of BI-RADS 4a, with 80.1% (173/216) of the BI-RADS 4a patients correctly diagnosed after 5 points modified BI-RADS reclassification. Of all patients, 81.7% (273/334) were correctly diagnosed after 5 points modified BI-RADS reclassification.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe AUC(95% CI), Sensitivity, Specificity, NPV, and PPV of the three groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003e5 point modified BI-RADS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUS BI-RADS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNomogram\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eDevelopment group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCutoff value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.833(0.770\u0026ndash;0.895)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.691 (0.619\u0026ndash;0.763)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.906 (0.858\u0026ndash;0.954)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003einternal validation group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCutoff value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.838(0.730\u0026ndash;0.945)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.678 (0.555\u0026ndash;0.801)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.884 (0.764-1.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eexternal validation group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCutoff value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.821(0.731\u0026ndash;0.911)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.704 (0.611\u0026ndash;0.798)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.926 (0.850-1.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eall\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.832(0.780\u0026ndash;0.885)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.695(0.629\u0026ndash;0.761)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.860(0.810\u0026ndash;0.909)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. AUC, area under the receiver operating characteristic curve; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData are presented as percentages except AUC.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of AUC values among three groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCompare AUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDevelopment group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003einternal validation group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eexternal validation group\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 point modified BI-RADS category VS US BI-RADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 point modified BI-RADS category VS Nomogram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNomogram VS US BI-RADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. AUC, area under the receiver operating characteristic curve.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHierarchical management of 260 patients based on US BI-RADS\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUS BI-RADS (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMethod 1*and Method 2*(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMethod 1\u003csup\u003e#\u003c/sup\u003eand Method 2\u003csup\u003e#\u003c/sup\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethod 1* and Method 2\u003csup\u003e#\u003c/sup\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMethod 2* and Method 1\u003csup\u003e#\u003c/sup\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3(19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4a(170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4b(49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4c(17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5(5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Method 1, 5 points modified BI-RADS; Method 2, nomogram; *Indicates correct diagnosis; \u003csup\u003e#\u003c/sup\u003e indicates incorrect diagnosis.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Nomogram Model development and performance\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 development\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrates the results of univariate and multivariate analyses of the risk of malignancy of criminal lesions in the development group. Seven ultrasound features were screened based on LASSO regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Through multivariate logistic regression, finally age, diameter, calcifications, boundary with the mammary duct, enhancement area, and enhancement margin were identified as independent predictors for predicting the risk of malignancy of criminal lesions (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A risk prediction nomogram was developed based on the above independent risk predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe results of univariate and multivariate analyses of the risk of malignancy of criminal lesions in the development group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003emultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBenign(68,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMalignant(70,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\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\u003e\u0026le;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u0026ndash;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (27.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65 (0.23\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28 (0.04\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (64.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.29 (1.03\u0026ndash;5.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.92 (0.13\u0026ndash;6.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiameter (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (57.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (42.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44(62.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.28(1.15\u0026ndash;4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12 (0.02\u0026ndash;0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDischarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebloody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (45.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (70.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enon-bloody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (54.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21(30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36 (0.18\u0026ndash;0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.45 (1.10-10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccompanied symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eabsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47(69.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (78.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64 10.76\u0026ndash;3.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenopausal state\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emenopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (44.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (67.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enon-menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (55.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (32.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39 (0.19\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.62 (0.57\u0026ndash;23.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from nipple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7(10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (92.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63(90.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71 (0.22\u0026ndash;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntralesional echo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehomogeneous echo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46(67.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29(41.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eheterogenous echo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (58.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.49 (0.98\u0026ndash;12.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcifications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eabsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (95.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (70.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.83 (1.51\u0026ndash;51.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoundary with duct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eclear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (63.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eunclear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (36.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (74.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.08 (1.92\u0026ndash;26.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnhancement area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enon-enlarged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (79.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenlarged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (20.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (78.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.95 (5.16\u0026ndash;85.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnhancement distribution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehomogeneous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (73.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (47.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eheterogeneous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 (52.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.86 (0.76\u0026ndash;10.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnhancement margin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eclear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (34.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eunclear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (65.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.76 (0.19\u0026ndash;3.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnhancement sharpness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eregular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (70.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (31.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eirregular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (68.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.02 (1.23\u0026ndash;13.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.021\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\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Prediction\u003c/h2\u003e\u003cp\u003eROC curves were used to assess the ability of the three diagnostic methods to determine the benignity or malignancy of criminal lesions. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C demonstrates the ROC curves for the US BI-RADS, the 5-point modified BI-RADS, and the nomogram. To better assess the diagnostic efficacy of the nomogram in clinical applications, the two validation groups included only the ultrasound features of nomogram in the development group. By maximizing Youden 's index, the optimal threshold for the development set to distinguish between benign and malignant nomogram scores of criminal lesions was determined to be 0.328. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the nomogram performance for predicting criminal lesions using this optimal threshold. The AUC (95% CI) for the development group, internal validation group, and external validation group were 0.906(0.862\u0026ndash;0.950), 0.863(0.749\u0026ndash;0.977), and 0.902(0.824\u0026ndash;0.980), respectively. Nomogram demonstrated the best diagnostic performance for 334 patients. In addition, the PPV of the nomogram was higher than that of the US BI-RADS and the 5-point modified BI-RADS in all groups except for the third group. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the diagnostic performance of the nomogram in the three groups was better than that of the US BI-RADS and the difference in AUC was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Except for the internal validation group, the diagnostic performance of the nomogram in the other two groups was better than the 5-point modified BI-RADS, and the difference in AUC was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that 83.3% (180/216) of the patients with BI-RADS 4a were diagnosed correctly after reclassification of nomogram, which was 7 more than 5-point modified BI-RADS. Of all patients, 84.4% (282/334) were correctly diagnosed after nomogram classification. To further illustrate the clinical utility of the nomogram in distinguishing benign and malignant lesions causing PND, two representative cases are presented as follows (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3 Calibration\u003c/h2\u003e\u003cp\u003eThe nomogram showed that in the development group (C-index: 0.906, calibrated C-index: 0.893), the internal validation group (C-index: 0.863, calibrated C-index: 0.814), and the external validation group (C-index: 0.902, calibrated C-index: 0.868) were well differentiated. All three groups showed statistical significance in the Hosmer-Lemeshow test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The calibration curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (A-C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.3.4 Clinical utility\u003c/h2\u003e\u003cp\u003eDecision curve analysis (DCA) was used to assess the clinical utility of the nomogram, BI-RADS categories, and 5-point modified BI-RADS categories in the development and validation groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.A-C). The DCA analysis showed that when the threshold probability exceeded 5%, the use of the nomogram to predict the risk of malignancy of the criminal lesions causing PND could provide a greater benefit when compared to treating all scenarios (assuming that all lesions are malignant) or treat no program (assuming all lesions are benign), which can provide more benefit. In addition, using the nomogram to predict the malignant risk of criminal lesions adds more to the overall net benefit than using only the BI-RADS categories or using only the 5-point modified BI-RADS categories.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eNipple discharge is a common symptom of breast disease in women, with intraductal lesions being the main cause of pathological discharge[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]。 The conventional wisdom is that despite the low likelihood of malignancy, these women still need to be diagnosed by central ductal resection or single ductal resection surgery. However, with the development of imaging techniques and an increasing number of clinical studies, it has been shown that short-term observation by repeat imaging and clinical examination is a reasonable approach for low-risk patients (without a strong family or personal history of cancer) or for those who do not wish to undergo palliative surgery[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The importance of a thorough examination and assessment of patients with PND cannot be ignored. Therefore, in this study, we attempted to develop a nomogram score combining clinical examination and imaging features to predict the degree of malignancy of PND and to compare it with existing diagnostic imaging methods for stratification and clinical management of patients.\u003c/p\u003e\u003cp\u003eOur study showed that intraductal papilloma (with or without atypical hyperplasia) was the most common cause of PND (111/334, 33.2%), and the second most common benign cause was ductal dilatation (including with atypical hyperplasia) (63/334, 18.9%), which is in line with the literature[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, 44.9% of the patients had malignant pathological findings, which is higher than previous literature reports. This may be related to the hospital referral mechanism as most of the patients with breast disease first choose primary hospitals for consultation; therefore, the incidence of malignancy in our sample is not representative of the incidence of malignancy in all patients with PND.\u003c/p\u003e\u003cp\u003eAs the population included in this study was patients with PND, PND is usually defined as a spontaneous, unilateral, hemorrhagic, serous, or watery discharge, usually originating from a single ductal lesion. In the literature, some malignant lesions causing PND have specific characteristics: very small and entirely within the duct[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Among the clinical factors included in this study, age(\u0026le;\u0026thinsp;55years, \u0026gt;55years) and the size of the lesion (\u0026le;\u0026thinsp;1 cm, \u0026gt;\u0026thinsp;1 cm) were the most important predictors, whereas the other factors (e.g., the nature of the discharge, distance of the criminal lesion from the nipple, accompanying symptoms such as tenderness and pain, mass, and menopausal status) did not show significant differences between benign and malignant tumors. A previous meta-analysis described that when the focal imaging abnormality of BNP patients was\u0026gt;1 cm, the underlying malignancy upgrade rate was 21% [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, another meta-analysis reported that patient age (\u0026lt;\u0026thinsp;50 years, \u0026ge; 50 years) and lesion size were predictive factors for the progression of papillary lesions to cancerous lesions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our research found that bloody discharge is not a predictive factor for malignant lesions, but previous studies have reported a hazard ratio of 2.5 for both bloody and non-bloody pathological discharge concerning association with underlying malignancy [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This discrepancy may be attributed to our institution\u0026rsquo;s role as a referral center: during initial evaluations, we prioritize patients with hemorrhagic discharge, who often undergo preliminary screening via imaging follow-ups, potentially reducing the proportion of undiagnosed malignant cases with hemorrhagic discharge in our cohort.\u003c/p\u003e\u003cp\u003eIn this study, 81.7% (273/334) of the lesions causing PND were central lesions, i.e., \u0026lt;\u0026thinsp;3 cm from the nipple. Although the distance from the nipple is not a strong indicator of benign and malignant tumors, it can help radiologists focus the examination on the central area after a thorough breast examination and, at the same time, provide clinicians with more information for a careful physical examination of the key areas and planning the extent of surgical resection.\u003c/p\u003e\u003cp\u003eClinically, ductal dilatation can cause both physiological (bilateral yellow or brown) and pathological (unilateral clear or bloody) discharges, and the pathological and physiological causes of nipple discharge overlap with each other, resulting in a wide range of potential intraductal masses, including concentrated discharge, infection, bleeding, single or multiple papillomas, and malignancy. Previous researchers have classified images into various types based on the presentation of lesions on conventional ultrasound[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], Examples include simple mass type without definite ductal dilatation, non-mass type, and ductal dilatation type (including dilatation within the duct with or without definite occupancy). To identify intraductal masses as much as possible, in our study, three sonographers interpreted and reviewed the images acquired by conventional ultrasound to determine the relationship of the lesion to the duct as much as possible. Imaging features of non-physiological ductal dilatation that may indicate malignancy included irregular ductal margins, peripheral dilatation of the duct, thickening of the duct wall, and adjacent hypoechoic tissue that may represent a mass[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ductal boundaries had excellent diagnostic power for the differential diagnosis of benign and malignant lesions, both in terms of multivariate logistic regression analyses and nomogram visualization (OR (95% CI): 3.57 (1.25\u0026ndash;10.21), p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e\u003cp\u003eIn our study, suspicious calcifications on US images proved to be an important marker for identifying malignant lesions in PND (OR(95% CI) 3.54 (1.09\u0026ndash;11.48), p\u0026thinsp;=\u0026thinsp;0.035) and may be a sensitive indicator of early breast cancer, which involves microcalcifications in about 90% of cases of ductal carcinoma in situ[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our findings underline the fact that microcalcifications may be the only lesions with certain detected abnormalities, especially in microscopic intraductal lesions that cannot be palpated. In CEUS images, the difference between the benign and malignant groups was statistically significant in terms of enhancement area (OR (95% CI) 6.11 (2.09\u0026ndash;17.86), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and enhancement margin (OR(95% CI) 3.61 (1.33\u0026ndash;9.81), p\u0026thinsp;=\u0026thinsp;0.012). Previous literature reports [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] stated that range expansion is a highly specific indicator for the diagnosis of benign and malignant breast lesions, which is in agreement with our findings. Tuan et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] concluded that it is highly unlikely that lesions with well-defined margins on conventional ultrasound would have large measurements on ultrasonography. The enhancement pattern of breast masses is consistent with histopathological findings. Both benign and malignant breast lesions tend to have a greater blood supply than normal tissue. Many neoplastic microvessels around the tumor continue to infiltrate and grow into the surrounding tissue, pro-angiogenic growth factors influence the morphology of blood vessels in the vicinity of the tumor, and tumor-associated vascular irregularities can extend beyond the limits of the apparent tumour[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consequently, growth and infiltration of the malignant mass, coupled with traction on the surrounding tissues, results in a greater extent of the malignant breast lesion being demonstrated by CEUS than by conventional ultrasound. The boundaries of the lesion are more sensitive in enhancement than by two-dimensional ultrasound is more sensitive, showing blurring, angularity, and peripheral linear ductal enhancement. Xia et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] based on morphological and pathological correlations, similarly found that the linear ductal pattern around the lesion determined by CEUS correlates with ductal origin and that the presence of peripheral radiolucent or perforating vessels is a distinctive feature associated with either atypical or malignant papillomas.\u003c/p\u003e\u003cp\u003eA nomogram combining clinical, US, and CEUS features demonstrated strong discrimination between malignant and benign breast lesions. The calibration curves indicated good agreement between the predicted and actual probability of breast malignancy. Decision curve analysis (DCA) revealed the clinical utility of the nomogram, which performed well in the development and validation groups, with AUCs exceeding 0.85. The 5-point modified BI-RADS showed higher diagnostic power for breast cancer than the US BI-RADS but required consideration of initial BI-RADS categories and the CEUS 5-point scale before secondary BI-RADS classification. In contrast, the nomogram only needs to consider 4 ultrasound features and incorporate clinical factors simultaneously, resulting in a higher AUC value and reducing the need to consider additional ultrasound features, thereby enabling more efficient and accurate interpretation for imaging physicians.\u003c/p\u003e\u003cp\u003eIn addition, false-positive imaging diagnoses may lead to unnecessary biopsies, patient anxiety, and increased healthcare costs.BI-RADS 4a is usually recommended as a threshold for biopsy in clinical settings. In this study, the nomogram model performed well, particularly when using BI-RADS 4a as the threshold for detecting positive lesions. 83.3% (180/216) of US BI-RADS 4a via nomogram models were able to accurately distinguish between benign and malignant, effectively reducing unnecessary biopsies of category 4a lesions and offering guidance in avoiding overdiagnosis of potentially benign lesions. Patients have more options for consultation, such as repeat imaging and short-term clinical observation. For patients in categories 4b-5, the diagnostic results of the nomogram, US BI-RADS and 5-point modified BI-RADS were generally consistent. We advocate biopsy as the primary approach in these categories. It is worth noting that 10 patients with US BI-RADS 4a, who were found to have papillary carcinoma on pathology, were missed by either the 5-point modified BI-RADS or the nomogram.\u003c/p\u003e\u003cp\u003eThere are several limitations to this study. Firstly, the strict inclusion and exclusion criteria used in this study resulted in the exclusion of approximately 43.6% of patients with PND, thus limiting the number of included patients. Further studies with a larger patient group are needed to confirm our findings. Secondly, a limitation of CEUS is its lack of global coverage. The combined secondary ultrasound in this study focused on suspected lesions suggested by conventional ultrasound and did not cover the entire breast parenchyma. Thirdly, the US and CEUS features included in the nomogram rely on the operator's subjective judgment and experience. Further studies are needed to enhance operator experience and improve nomogram application. Fourthly, some cases in this study originated from primary hospital referrals, potentially increasing the malignancy rate in the patient population. The broad inclusion of representative benign masses may have led to higher AUCs due to inclusion bias. Fifthly, the gold standard for all patients was pathological findings, leading to the exclusion of a significant proportion of follow-up cases, potentially introducing selection bias.\u003c/p\u003e\u003cp\u003eIn summary, accurate diagnosis of patients with PND is a crucial clinical priority, while ensuring accurate image replication and clinical follow-up of patients remains challenging. Problem-solving algorithms for evaluating suspected PND are continuously developing, and nomograms can significantly improve diagnostic accuracy by integrating clinical indicators and ultrasound imaging features to differentiate between malignant and benign breast lesions. Reassessing patients with PND in US BI-RADS 4a has the potential to conserve clinical resources by preventing a significant number of patients from undergoing invasive biopsy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003ePND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003ePathologic Nipple Discharge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eUltrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eCEUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eContrast-Enhanced Ultrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eBI-RADS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eBreast Imaging Reporting and Data System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eArea Under the Receiver Operating Characteristic Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eNegative Predictive Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003ePositive Predictive Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eDCIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eDuctal Carcinoma In Situ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eUltrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee of Shanghai General Hospital (Approval No. 2025106). For this retrospective analysis, individual informed consent was waived in accordance with the committee\u0026rsquo;s decision. All experiments and data analyses were conducted in strict compliance with the ethical principles outlined in the Declaration of Helsinki and other relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was granted by the National Natural Science Foundation of China (Nos. 82202172).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMZ participated in the design of the study and acquisition of data and drafted the manuscript. SZ made contributions to study conception and design. All authors, MZ, SZ, YW, JM, YF, XZ, GY, FL, YL and MB, participated in revising the manuscript critically for important intellectual content and gave final approval of the version to be published. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray FA-O, Laversanne M, Sung HA-O, Ferlay J, Siegel RA-O, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.\u003c/li\u003e\n\u003cli\u003eBahl M, Baker JA, Greenup RA, Ghate SV. Diagnostic Value of Ultrasound in Female Patients With Nipple Discharge.\u003c/li\u003e\n\u003cli\u003eFilipe MD, Patuleia SIS, de Jong VMT, Vriens MR, van Diest PJ, Witkamp AJ. Network Meta-analysis for the Diagnostic Approach to Pathologic Nipple Discharge.\u003c/li\u003e\n\u003cli\u003eVargas HI, Vargas Mp Fau - Eldrageely K, Eldrageely K Fau - Gonzalez KD, Gonzalez Kd Fau - Khalkhali I, Khalkhali I. Outcomes of clinical and surgical assessment of women with pathological nipple discharge.\u003c/li\u003e\n\u003cli\u003eWaaijer L, Witkamp AJ. Management of Nipple Discharge and the Associated Imaging Findings: Comments to the Editor.\u003c/li\u003e\n\u003cli\u003eYoon JA-O, Yoon HA-O, Kim EA-O, Moon HA-O, Park YA-O, Kim MA-O. Ultrasonographic evaluation of women with pathologic nipple discharge.\u003c/li\u003e\n\u003cli\u003eChoi SH, Choi JA-O, Han BA-O, Ko EA-O, Ko ES, Park KA-O. Long-term Surveillance of Ductal Carcinoma in Situ Detected with Screening Mammography versus US: Factors Associated with Second Breast Cancer.\u003c/li\u003e\n\u003cli\u003eFerris-James DM, Iuanow E Fau - Mehta TS, Mehta Ts Fau - Shaheen RM, Shaheen Rm Fau - Slanetz PJ, Slanetz PJ. Imaging approaches to diagnosis and management of common ductal abnormalities.\u003c/li\u003e\n\u003cli\u003eGu LS, Zhang R, Wang Y, Liu XM, Ma F, Wang JY, et al. Characteristics of contrast-enhanced ultrasonography and strain elastography of locally advanced breast cancer.\u003c/li\u003e\n\u003cli\u003eMoon JH, Koh SH, Park SY, Hwang JY, Woo JY. Comparison of the SR(max), SR(ave), and color map of strain-elastography in differentiating malignant from benign breast lesions.\u003c/li\u003e\n\u003cli\u003eWang B, Yang D, Zhang X, Gong X, Xu T, Han J, et al. The diagnostic value of contrast-enhanced ultrasonography in breast ductal abnormalities.\u003c/li\u003e\n\u003cli\u003eMazzarello S, Arnaout A. Nipple discharge.\u003c/li\u003e\n\u003cli\u003eKajiwara Y, Oka S, Tanaka S, Nakamura T, Saito S, Fukunaga Y, et al. Nomogram as a novel predictive tool for lymph node metastasis in T1 colorectal cancer treated with endoscopic resection: a nationwide, multicenter study.\u003c/li\u003e\n\u003cli\u003eLyons D, Wahab RA, Vijapura C, Mahoney MC. The nipple-areolar complex: comprehensive imaging review.\u003c/li\u003e\n\u003cli\u003eGuirguis MA-O, Arribas EA-O, Kapoor MA-O, Patel MA-O, Perez FA-O, Nia EA-O, et al. Multimodality Imaging of Benign and Malignant Diseases of the Nipple-Areolar Complex.\u003c/li\u003e\n\u003cli\u003eSpak DA, Plaxco JS, Santiago L, Dryden MJ, Dogan BE. BI-RADS(\u0026reg;) fifth edition: A summary of changes. Diagn Interv Imaging. 2017; 98: 179-90.\u003c/li\u003e\n\u003cli\u003eWang Y, Fan W, Zhao S, Zhang K, Zhang L, Zhang P, et al. Qualitative, quantitative and combination score systems in differential diagnosis of breast lesions by contrast-enhanced ultrasound.\u003c/li\u003e\n\u003cli\u003eNiu RL, Li SY, Wang B, Jiang Y, Liu G, Wang ZL. Papillary breast lesions detected using conventional ultrasound and contrast-enhanced ultrasound: Imaging characteristics and associations with malignancy.\u003c/li\u003e\n\u003cli\u003eXiao X, Dong L, Jiang Q, Guan X, Wu H, Luo B. Incorporating Contrast-Enhanced Ultrasound into the BI-RADS Scoring System Improves Accuracy in Breast Tumor Diagnosis: A Preliminary Study in China.\u003c/li\u003e\n\u003cli\u003eSabel MS, Helvie Ma Fau - Breslin T, Breslin T Fau - Curry A, Curry A Fau - Diehl KM, Diehl Km Fau - Cimmino VM, Cimmino Vm Fau - Chang AE, et al. Is duct excision still necessary for all cases of suspicious nipple discharge?\u003c/li\u003e\n\u003cli\u003eGupta D, Mendelson EB, Karst I. Nipple Discharge: Current Clinical and Imaging Evaluation.\u003c/li\u003e\n\u003cli\u003eHan Y, Li J, Han S, Jia S, Zhang Y, Zhang W. Diagnostic value of endoscopic appearance during ductoscopy in patients with pathological nipple discharge.\u003c/li\u003e\n\u003cli\u003eOhlinger R, Stomps A Fau - Paepke S, Paepke S Fau - Blohmer J-U, Blohmer Ju Fau - Grunwald S, Grunwald S Fau - Hahndorf W, Hahndorf W Fau - Camara O, et al. Ductoscopic detection of intraductal lesions in cases of pathologic nipple discharge in comparison with standard diagnostics: the German multicenter study.\u003c/li\u003e\n\u003cli\u003eCardenosa G, Eklund GW. Benign papillary neoplasms of the breast: mammographic findings.\u003c/li\u003e\n\u003cli\u003eSickles EA. Galactography and other imaging investigations of nipple discharge.\u003c/li\u003e\n\u003cli\u003eDupont SC, Boughey JC, Jimenez RE, Hoskin TL, Hieken TJ. Frequency of diagnosis of cancer or high-risk lesion at operation for pathologic nipple discharge.\u003c/li\u003e\n\u003cli\u003eZhang X, Liu W, Hai T, Li F. Upgrade Rate and Predictive Factors for Breast Benign Intraductal Papilloma Diagnosed at Biopsy: A Meta-Analysis.\u003c/li\u003e\n\u003cli\u003eChen L, Zhou Wb Fau - Zhao Y, Zhao Y Fau - Liu X-A, Liu Xa Fau - Ding Q, Ding Q Fau - Zha X-M, Zha Xm Fau - Wang S, et al. Bloody nipple discharge is a predictor of breast cancer risk: a meta-analysis.\u003c/li\u003e\n\u003cli\u003eYuan H, Tang X, Mou X, Fan Y, Yan X, Li J, et al. A comparative analysis of diagnostic values of high-frequency ultrasound and fiberoptic ductoscopy for pathologic nipple discharge.\u003c/li\u003e\n\u003cli\u003eWatanabe T, Yamaguchi T, Tsunoda H, Kaoku S, Tohno E, Yasuda H, et al. Ultrasound Image Classification of Ductal Carcinoma In Situ (DCIS) of the Breast: Analysis of 705 DCIS Lesions.\u003c/li\u003e\n\u003cli\u003eHofvind S, Iversen Bf Fau - Eriksen L, Eriksen L Fau - Styr BM, Styr Bm Fau - Kjellevold K, Kjellevold K Fau - Kurz KD, Kurz KD. Mammographic morphology and distribution of calcifications in ductal carcinoma in situ diagnosed in organized screening.\u003c/li\u003e\n\u003cli\u003eJiang YX, Liu H Fau - Liu J-B, Liu Jb Fau - Zhu Q-L, Zhu Ql Fau - Sun Q, Sun Q Fau - Chang X-Y, Chang XY. Breast tumor size assessment: comparison of conventional ultrasound and contrast-enhanced ultrasound.\u003c/li\u003e\n\u003cli\u003eLiu H, Jiang Y, Dai Q, Zhu Q, Wang L, Zhang J, et al. Differentiation of benign and malignant sub-1-cm breast lesions using contrast-enhanced sonography.\u003c/li\u003e\n\u003cli\u003eTuan Linh L, Minh Duc N, Tra My TT, Viet Bang L, Minh Thong P. Correlations between dynamic contrast-enhanced magnetic resonance imaging parameters and histopathologic factors in breast cancer.\u003c/li\u003e\n\u003cli\u003eBullitt E, Lin Nu Fau - Smith JK, Smith Jk Fau - Zeng D, Zeng D Fau - Winer EP, Winer Ep Fau - Carey LA, Carey La Fau - Lin W, et al. Blood vessel morphologic changes depicted with MR angiography during treatment of brain metastases: a feasibility study.\u003c/li\u003e\n\u003cli\u003eXia HS, Wang X Fau - Ding H, Ding H Fau - Wen J-X, Wen Jx Fau - Fan P-L, Fan Pl Fau - Wang W-P, Wang WP. Papillary breast lesions on contrast-enhanced ultrasound: morphological enhancement patterns and diagnostic strategy.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pathologic nipple discharge, breast tumors, BI-RADS, CEUS, nomogram, management","lastPublishedDoi":"10.21203/rs.3.rs-7348481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7348481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThe aims of this study was to develop and validate a nomogram utilizing patient clinical information, conventional ultrasound, and contrast-enhanced ultrasound (CEUS) to predict the risk of malignant lesions causing pathologic nipple discharge (PND). Additionally, the study aimed to compare the diagnostic performances of different methods to stratify and clinically manage patients with PND.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e\u003cp\u003e A total of 593 patients were retrospectively collected from January 2021 to February 2025, resulting in the inclusion of 334 female patients (mean age 55.0 years; age range 21 to 90 years) who met the inclusion criteria. The patients were divided into development, internal validation, and external validation groups. Clinical information, routine ultrasound, and ultrasonographic characteristics were analyzed using univariate logistic regression, multivariate logistic regression, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to develop a risk prediction nomogram. The diagnostic performances of the nomogram, conventional ultrasound BI-RADS, and ultrasonography 5 points related to the BI-RADS category (5 points modified BI-RADS) were compared.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePredictive variables for the nomogram included age, the maximum diameter of the lesion, calcifications, boundary with duct, enhancement area, and enhancement margin. Nomogram showed superior performance compared to other diagnostic methods. In the internal validation group, there was no significant difference in diagnostic accuracy between the nomogram and 5-point modified BI-RADS. However, in all other groups, the nomogram outperformed the conventional ultrasound BI-RADS and 5-point modified BI-RADS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eConstructing a nomogram that combines ultrasound, CEUS, and clinical information can improve the diagnostic efficiency of PND. The nomogram shows optimal diagnostic performance and helps avoid unnecessary biopsies of most benign lesions.\u003c/p\u003e","manuscriptTitle":"Diagnostic Value and Management Strategy of Nomogram for PND Based on Clinical Characteristics and CEUS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 01:01:24","doi":"10.21203/rs.3.rs-7348481/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-19T13:23:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T06:44:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-25T06:46:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-24T16:48:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-08-24T16:45:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc4cec4b-494c-4664-a17d-adf985947d77","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-03T01:01:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 01:01:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7348481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7348481","identity":"rs-7348481","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}