Use of US BIRADS for the Differentiation of Male Breast Masses and Interobserver Agreement Assessment

Use of US BIRADS for the Differentiation of Male Breast Masses and Interobserver Agreement Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Use of US BIRADS for the Differentiation of Male Breast Masses and Interobserver Agreement Assessment Di Wang, Meizheng Dang, Jing Wang, pintong huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4006699/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To investigate the efficiency of Breast Imaging Reporting and Data System (BIRADS) proposed by the American College of Radiology for male breast lesions and to evaluate assess inter-observer agreement. Methods Six breast radiologists were divided into three groups to retrospectively analyze 90 male breast nodules by BIRADS classifications. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated for comparative analysis. Interobserver agreement was assessed. Results The group II showed the higher specificity (75.9%) than other two groups while the group III showed the best AUC, sensitivity and accuracy, which were 0.904, 97.2%, 62.9% and 76.7%, respectively. Besides, the whole of three groups expressed excellent ICC on 2(0.934) category and good on BIRADS 3 (0.677) category. The ICC of 4a (0.379) was expressed as poor level. The categories of 4b (0.483) ,4c (0.521) and5(0.491) was expressed as fair level. Only group I had significant difference with the group III in diagnostic effectiveness of the same systems among three groups. Conclusions Male sex did not have a large impact on the diagnosis of BIRADS and use of US BIRADS lexicon shows excellent or good levels of concordance for mass characterizations. But a lower concordance respect to BIRADS classifications in BIRADS 4 categories. ultrasound male breast Breast Imaging Reporting and Data System BIRADS Interobserver agreement was assessed Figures Figure 1 Figure 2 Figure 3 Introduction Male breast cancer (MBC) is a rare type of malignant tumor which accounting for less than 1% of all breast cancers ( 1 ). However, there is a certain increasing trend in MBC incidence all over the world ( 2 – 3 ). The 5-year mortality MBC is higher than that of female breast cancer (FBC). In 2024, the American Cancer Society estimated that mortality of MBC was up to 19% ( 4 – 5 ). Currently, and the principles of diagnosis and treatment are mainly inferred from FBC. But it should be noted that male breasts differ from the biological characteristics and pathological classification of female breasts ( 6 – 7 ). Over time, an increasing number of experts have called for the establishment of a comprehensive diagnostic and treatment system specific for male breast diseases ( 8 ). Hassett et al ( 9 ) has prepared the American Society of Clinical Oncology (ASCO) guidelines to effectively manage male breast disease in 2020. Breast ultrasound, which is mainly applied to female patients, may have lower attendance for male patients ( 10 ). The American College of Radiology (ACR) published the Breast Imaging Reporting and Data System (BIRADS) lexicon for the United States in 2013 which has been widely used to evaluate benign and malignant nodules in the female breast ( 11 ). As our known, few related studies on MBC have been performed. The question remains whether the sax gap is a successful integration for BIRADS diagnosing male breast nodules. Therefore, our objective was to investigate the diagnostic performance of the BIRADS lexicon for the diagnosis MBC. And we also assess interobserver agreement for BIRADS and compared them. Materials and methods Patients All participants gave written, informed consent to have their medical records reviewed. This retrospective study was approved by the Hospital Medical Ethics Committee. This study involved all male patients with breast nodules who underwent surgery at the Second Affiliated Hospital of Zhejiang University from January 2017 to July 2023. The inclusion criteria were as follows: ( 1 ) patients requiring surgical treatment when clinicians suspected a malignant tendency based on clinical information or on strong request by the patient; ( 2 ) patients with a clear ultrasound in the B mode of the breast. ( 3 ) patients with complete clinical information, such as clinical characteristics (age, sex, tumor dimension, and tumor location), ultrasound characteristics (such as echogenicity, aspect ratio, boundary, margin). The following patients were excluded: ( 1 ) patients without complete preoperative ultrasound or postoperative pathological data and ( 2 ) patients with multiple nodules. Finally, a total of 90 patients were included. Breast ultrasound examination and image analysis Ultrasound examination All ultrasound examinations were performed by experienced (≥ 8 years) radiologists. Ultrasound instruments included Resona 7 (Mindray, Shenzhen, China), ESAOTE (My LAb 90 X-vision, Italy), Aplio 500 (Toshiba Medical Systems, Tokyo, Japan), and Logic E9 (GE Healthcare, Wauwatosa, USA). Image analysis All ultrasound images were retrospectively and independently reviewed by six breast cancer specialists to assess the characteristics and presence of signs, according to the fifth edition of the BIRADS lexicon. Readers were divided into three groups (two with 10 years of experience comprised the III group). Any disagreement was resolved by consensus in each group. The readers were required to select the most appropriate single descriptor for each category. All readers were asked to assign a BIRADS category and (including the operator) were blind to the pathological results of the patients and the clinic message. Ultrasonic diagnostic criteria The following ultrasonographic characteristics were recorded for each mass: shape (irregular, regular), orientation (parallel or not parallel), boundary (circumscribed or not circumscribed), margin (smooth, indistinct or angular), echo pattern (hypoechoic, hyperechoic, complex, or echoless), posterior acoustic features (enhancement, none, shadowing), and calcifications (microcalcifications or not). Histopathology Two pathologists blinded to the clinical information evaluated the histopathological findings for the breast specimen. Sections were initially read by a physician with six years of experience and then reviewed by a physician with 20 years of experience. The final result was decided by the later physician. Statistical analysis Quantitative data are reported as mean ± standard deviation (SD). Qualitative data are presented as frequencies. The independent samples t-test was performed for normally distributed data, while the Mann Whitney U test was for non-normally distributed data. Qualitative variables, such as sex and ultrasound characteristics, were analyzed using χ 2 test or Fisher exact test. The frequency and risk of malignancy according to each category were calculated as a percentage. To assess reader reproducibility, the intraclass correlation efficiency (ICC) was calculated. The reliability of the measurement was classified according to common criteria as excellent (ICC > 0.75), good (ICC = 0.60–0.75), fair (ICC = 0.40–0.59), and poor (ICC ≤ 0.40). An analysis of the receiver operating characteristic (ROC) curve was also performed to calculate the optimal cutoff value, diagnostic sensitivity, specificity, and precision based on histopathological results for the entire cohort. The area under the curves (AUCs) of Groups I, II, and III were compared using the Z test. All statistical analyzes were performed using SPSS 29.0 (IBM Corporation, Armonk, NY, USA) and MedCalc 20.4 software (MedCalc Software, Mariakerke, Belgium). A P-value < 0.05 was considered statistically significant. Results Clinical information and pathological findings A total of 90 patients with 54 benign and 36 malignant breast lesions were included. Of these, there were 19 gynecomastia, 13 fibroadenoma, 11 inflammation, 6 epithelial cysts, 3 intraductal papilloma, 1 lipoma and 1 hemangioma in benign nodules, and there were 30 IDC, 2 encapsulated papillary carcinoma (EPC), 2 intraductal papillary carcinoma (IPC), 1 lymphoma and 1EPC with IDC in malignant nodules. The ages and size of the onset of male breast cancer were markedly higher than those of benign breast cancer (P < 0.05). The general information of the patients was summarized in Table 1 . Table 1 Basic characteristics of patients in all breast nodules Basic characteristics Benign (n = 54) Malignant (n = 36) P Age(y) 46.240 ± 17.186 56.580 ± 13.179 0.003 Size(cm) 2.271 ± 1.122 1.489 ± 0.898 0.000 Age(y) and Size(cm)were represented as median ± SD. P < 0.05 was statistically significant. Table 2 Interobserver Agreement for Ultrasonographic features US Features Group I P value Group II P value Group III P value ICC Benign (n=54) Malignant (n=36) Benign (n=54) Malignant (n=36) Benign (n=54) Malignant(n=36) Mass shape 0.040 0.011 0.007 0.677(0.580,0.762) Irregular 17(31.5) 20(55.6) 20(37.0) 24(66.7) 15(27.8) 21(58.3) 0.677(0.580,0.762) regular 37(68.5) 16(44.4) 34(63.0) 12(33.3) 39(72.2) 15(41.7) 0.677(0.580,0.762) Echo pattern 0.177 0.152 0.106 0.923(0.893,0.946) Hypoechoic 44(81.6) 31(86.1) 44(81.6) 32(88.9) 45(81.6) 33(91.7) 0.829(0.769,0.878) Complex 5(9.2) 4(11.1) 5(9.2) 3(8.3) 4(9.2) 2(5.6) 0.718(0.629,0.794) Hyperechoic 0(0) 1(2.8) 0(0) 1(2.8) 0(0) 1(2.7) 1.000(1.000,1.000) echoless 5(9.2) 0(0) 5(9.2) 0(0) 5(9.2) 0(0) 1.000(1.000,1.000) Mass orientation 0.350 0.087 0.173 0.651(0.548,0.741) Parallel 46(85.2) 27(75) 51(94.4) 29(80.6) 51(94.4) 30 (83.3) 0.651(0.548,0.741) Not parallel 8(14.8) 9(25) 3(5.6) 7(19.4) 3(5.6) 6(16.7) 0.651(0.548,0.741) Boundary 0.007 0.025 0.025 0.674(0.578,0.759) Circumscribed 39(72.2) 15(41.7) 38 (70.4) 16(44.4) 38(70.4) 16(44.4) 0.674(0.578,0.759) No- circumscribed 15(27.8) 21(58.3) 16(29.6) 20(55.6) 16(29.6) 20(55.6) 0.674(0.578,0.759) Margin 0.000 0.000 0.000 0.826(0.764,0.875) Smooth 32(59.3) 6(16.7) 34(63.0) 9(25.0) 38(70.4) 11(30.6) 0.793(0.722,0.851) Angular 6(11.1) 18(50) 7(13.0) 18(50.0) 5(9.2) 14(38.8) 0.646(0.543,0.737) Indistinct 16(29.6) 12(33.3) 13(24.0) 9(25.0) 11(20.4) 11(30.6) 0.790(0.718,0.849) Posterior features 0.157 0.237 0.217 0.805(0.738,0.860) None 30(55.6) 22(61.2) 24 (44.4) 14(38.9) 33(61.2) 23(64.5) 0.706(0.664,0.816) Enhancement 19(35.2) 7(19.4) 23 (42.6) 12(33.3) 16(29.6) 6(12.9) 0.707(0.615,0.785) Shadowing 5(9.2) 7(19.4) 7(13) 10(27.8) 5(9.2) 7(22.6) 0.835(0.775,0.882) Microcalcifications 1.00 0.821 1.00 0.773(0.697,0.836) Yes 7(12.9) 5(13.9) 4(7.4) 4(11.1) 6(11.1) 4(11.1) 0.773(0.697,0.836) None 47(87.1) 31 (86.1) 50(92.6) 32(88.9) 48 (88.9) 32(88.9) 0.773(0.697,0.836) Numbers in parentheses are percentages except ICC. Data in parentheses in ICC are 95% CIs. P < 0.05 was statistically significant. Interobserver agreement for ultrasonographic features In seven kinds of ultrasound features with all nodules, the mass shape, boundary and margin characteristics showed an obvious difference ( P < 0.05) between benign and malignant nodules other than echo, orientation, posterior features and microcalcifications in the judgment of the reader for each group. For reader performance, the descriptions of echo (0.923), margin (0.826), posterior (0.805) and microcalcifications (0.773) showed excellent ICCs across the three groups. By contrast, the ICC of the boundary, mass shape and orientation had good levels, and the ICC of the boundary was lower than else (0.651 vs 0.674, 0.677). Furthermore, the echo descriptions as hyperechoic and echoless had the best ICC of 1.00 contrary to the margin described as angular had the lowest ICC at 0.646 among all features ( Table 2 ). Interobserver agreement for BI-RADS classifications The three groups expressed excellent concordance in BIRADS 2 category and good concordance in BIRADS 3 category. Three groups showed fair concordance in the BIRADS 4b,4c and 5 categories and poor concordance in the BIRADS 4a category. In all BIRADS classifications, three groups had good performance (Table 3 ). Table 3 Interobserver Agreement for BI-RADS classifications BI-RADS Group I Group II Group III ICC 2 6 5 5 0.934(0.908,0.954) 3 30 23 30 0.677(0.579,0.761) 4a 20 19 21 0.379(0.249,0.509) 4b 13 14 14 0.483(0.358,0.601) 4c 14 19 11 0.521(0.401,0.634) 5 7 10 9 0.491(0.368,0.608) All 0.709(0.617,0.786) Note—Data in parentheses are 95% CIs. Comparison of diagnostic efficiency for the prediction of malignant nodules based on the three groups In group I, the ROC curve demonstrated that the best cutoff was 4a with an AUC for the ROC was 0.779 (95% CI: 0.681, 0.876), for which the sensitivity, specificity, and accuracy were 88.3%, 55.6%, and 66.7%. In group II, the ROC curve demonstrated that the best cut-off was 4a with the AUC was 0.838 (95% CI: 0.756, 0.920), for which the sensitivity, specificity, and accuracy were 83.3%, 75.9%, and 71.1%. In group III, the ROC curve showed that the best cut-off was 4a with an AUC of 0.904 (95% CI: 0.842, 0.966), which the sensitivity, specificity, and precision were 97.2%, 62.9%, and 76.7% (Fig. 1 ). Moreover, the comparison of the AUCs between two groups, group I had significant differences from group III (z = 3.226, P = 0.0013); others were not (for group III vs. group II: z = 1.548, P = 0.121; for group II vs. group I: z = 0.136, P = 0.256). Discussion We have studied 90 male patients and found out that the AUC of BIRADS showed the excellent performance for male.With respect the BIRADS terminology to evaluate nodules, three groups showed good to excellent levels. Malignant nodules tended to have solid, hypoechoic, irregular margins, but more parallel and fewer microcalcifications. Unlike female breast, not only malignant tumors, but also benign inflammatory, non-neoplastic lesions had irregular margins. The discovery was similar to that reported by Huang et al ( 12 ). this may due that most male breast lumps are gynecomastia and it is clinically classified into three subtypes: florid gynecomastia, dendritic gynecomastia, and diffuse glandular gynecomastia ( 13 ). As Peggy ( 14 ) described the dendritic sign is seen in more than one year gynecomastia patients. Notably, some researchers ( 15 – 17 ) have supposed that microcalcifications had higher specific in MBC than FBC. This hypothesis was consistent with us. Several influencing factors can be considered. First, the pathology of the male breast is a highly heterogeneous; imaging of the lesions can be affected in many respects ( 18 ). Second, males and females differ in their developmental processes and histo-anatomical structures. The male breast has a rudimentary structure, consisting of mainly subcutaneous adipose tissue, remnant ductal tissue, and a small nipple-areolar complex. The main anatomical difference between the male and female breasts was the absence of Cooper ligaments and the engendering of maturation of the ductal lobular unit. Involution and ductal atrophy occur in the male breast due to a significant increase in testosterone levels ( 19 ). Finally, microcalcifications and mass orientation may indicate ductal and stromal involvement ( 20 ). In our cohort, the AUC (0.904), sensitivity (96.2%), and the accuracy (76.7%) of group III were the highest, while the specificity (75.9%) of group I was the highest in the three groups. Our findings were similar to those of Huang et al. ( 12 ), but were somewhat lower AUCs than those reported by Yoon et al. ( 21 ). whose sensitivity was 100%, specificity was 92.2%, and accuracy was 94.0%. This divergence could be due to the number of MBC cases evaluated in our study: 29 MBCs were included in Yoon et al. compared to the 36 cases in our study. As in previous reports evaluating women's breasts ( 22 – 23 ), the sensitivity of BIRADS also higher than the specificity for male. We speculated further that the sex-based differences do not have a large impact on the diagnosis of BIRADS. This hypothesis was agreed by concordant previous studies; their study showed that MBC shares several clinical and histological characteristics with FBC ( 24 – 25 ) Regarding the BIRADS classifications for reader performance, the ICCs of BIRADS 4 categories were poor to fair in three groups and the diagnostic efficacy of group I was significantly lower than group III. Both of two EPCs were misdiagnosed as category 3 in group I and II, one of in group III. Papillary carcinoma comprises the second largest proportion of male breast carcinomas ( 26 ); Most male papillary carcinomas are intracystic and noninvasive ( 27 ), thus their mainly clinical manifestations are painless expansion growth. On ultrasound, they presented as a complex cystic (Fig. 2 ) or solid smooth mass (Fig. 3 ), which is easy to misdiagnose as category 3. Therefore, this also presents a greater challenge for the diagnosis of sonographers. As Jeremy et al. declared that ultrasound diagnosis is highly dependent on the skills and experience of sonographers and radiologists, deep learning has the potential to dramatically save time for experienced medical professionals and potentially reduce interobserver variability ( 28 ). Our study has several limitations. First, some selection bias could not be avoided because this was a retrospective study. Second, we only selected patients who underwent surgery, and thus, the number of patients was small. A multicenter study with a larger number of patients is required. Third, the images used for our analysis were static, which may have caused some error. Finally, we had fewer groups and fewer physicians, which may have caused some biases. In future, a larger sample size and studies from multiple centers are necessary to validate our study. Conclusions Male sex did not have a large impact on the diagnosis of BIRADS compared with female sex, and use of US BIRADS lexicon shows excellent or good levels of concordance for mass characterizations. But a lower concordance respect to BIRADS classifications in malignant nodules. Ultimately, we should enhance the clinical experience of the operator and the reader to facilitate a timely diagnosis and prevent unnecessary biopsies. Declarations Author Contribution Di Wang and Jing Wang wrote the main manuscript text and Meizheng Dang prepared figures and tables.Pintong Huang was supervision. All authors reviewed the manuscript." References Miao H, Verkooijen HM, Chia KS, Bouchardy C, Pukkala E, Larønningen S, et al. (2011) Incidence and outcome of male breast cancer: an international population-based study. ClinOncol 29:4381-4386. https://doi.org/10.1200/JCO.2011.36.8902 . Siegel RL, Miller KD, Jemal A. Cancer statistics, (2019) CA Cancer J Clin 69:7-34. https://doi.org/10.3322/caac.21551 Yadav S, Karam D, Riaz IB, Xie H, Durani U, Duma N, et al.(2020) Male breast cancer in the United States: Treatment patterns and prognostic factors in the 21st century. Cancer 126:26-36. https://doi.org/ 10.1002/cncr.324724 Wang F, Shu X, Meszoely I, Pal T, Mayer IA, Yu Z, et al. (2019) overall mortality after diagnosis of breast cancer in men vs women. JAMA Oncology 5:1589-1596. https://doi.org/10.1001/jamaoncol.2019.2803 Rebecca L Siegel , Angela N Giaquinto, Ahmedin Jemal.(2024) Cancer statistics, 2024. CA Cancer J Clin 74:12-49. https://doi.org/10.3322/caac.21820 Iraj Khalkhali, MD, John Cho. Male Breast Cancer Imaging. (2015) J the Breast 3: 217–218. https://doi.org/10.1111/tbj.12399 Lattin GE Jr, Jesinger RA, Mattu R, Glassman LM. (2013) From the radiologic pathology archives: Diseases of the male breast: Radiologic-pathologic correlation. Radiographics 33:461-89. https://doi.org/10.1148/rg.332125208 Nguyen C, Kettler MD, Swirsky ME, Miller VI, Scott C, Rhett Krause,et al. (2013) Male breast disease: pictorial review with radiologic-pathologic correlation. Radiographics 33:763–79. https://doi.org/10.1148/rg.333125137 Hassett MJ, Somerfield MR, Baker ER, Cardoso F, Kansal KJ, Kwait DC et al.(2020) Management of Male Breast Cancer: ASCO Guideline. Journal of Clinical Oncology 38:1849-1863. https://doi.org/10.1200/JCO.19.03120 Webb JM, Adusei SA, Wang Y, Samreen N, Adler K, Meixner DD, et al. (2021) Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging. Comput Biol Med 139:104966. https://doi.org/10.1016/j.compbiomed.2021.104966 D’Orsi C, Sickles E, Mendelson E, Morris E et al. (2013) ACR BIRADS® Atlas, Breast Imaging Reporting and Data System. 5th ed. American College of Radiology, Reston, VA. Huang Y, Xiao Q, Sun Y, Li Q, Wang S, Gu Y. (2020) Differential diagnosis of benign and malignant male breast lesions in mammography. Eur Radiol 132:109339. https://doi.org/10.1016/j.ejrad.2020.109339 Heather A. Vandeven, Jay M. Pensler. (2024) Gynecomastia In: StatPearls. Treasure Island (FL): StatPearls Peggy P W Yen, Namita Sinha, Penny J Barnes, Robinette Butt, Sian Iles. (2015) Benign and Malignant Male Breast Diseases: Radiologic and Pathologic Correlation Can Assoc Radiol J 66:198-207. https://doi.org/10.1016/j.carj.2015.01.002. M. Madhukar, A. Chetlen (2013) Multimodality imaging of benign and malignant male breast disease. Clin Radiol 68:e698-706. https://doi.org/ 10.1016/j.crad.2013.07.007 Rong X, Zhu Q, Jia W, Ma T, Wang X, Guo N, et al. (2018) Ultrasonographic assessment of male breast diseases. Breast J 24:599-605. https://doi.org/10.1111/tbj.12982 Tangerud Å, Potapenko I, Skjerven HK, Stensrud MJ.(2016) Radiologic evaluation of lumps in the male breast. Acta Radiol 57:809-14. https://doi.org/10.1177/0284185115609802 In review Kim H, Wisniewska K, Regner MJ, Thennavan A, Spanheimer PM, Franco HL. (2023) Single-Cell Transcriptional and Epigenetic Profiles of Male Breast Cancer Nominate Salient Cancer-Specific Enhancers. Int J Mol Sci 24:13053. https://doi.org/10.3390/ijms241713053 Gucal A, Traina TA, Eisner JR, Parker JS, Selitsky SR, Park BH, et al.(2019) Male breast cancer: a disease distinct from female breast cancer. Breast Cancer Res Treat 173:37-48. https://doi.org/10.1007/s10549-018-4921-9 Viana MP, Tucunduva TCdM, Torres US, Aguillar VLN, Bresciani BH, Shimizu C, et al. (2020) Imaging of male breast disease: the good, the bad and the ugly - A pictorial review. Clin Imaging 68:45-56. https://doi.org/10.1016/j.clinimag.2020.06.025 Yoon B, Chae EY, Cha JH, Shin JH, Choi WJ, Kim HH. (2020) Male patients with unilateral breast symptoms: an optimal imaging approach. Eur Radiol 30:4242-4250. https://doi.org/10.1007/s00330-020-06828-3 Jiao Bai, Guilin Wang. (2023) Encapsulated Papillary Carcinoma of the Breast. Radiology 308:e231038. https://doi.org/10.1148/radiol.231038 Menezes GLG, Pijnappel RM, Meeuwis C, Bisschops R, Veltman J, Lavin PT,et al.(2018) Downgrading of breast masses suspicious for cancer by using optoacoustic breast imaging. Radiology 288:355-365. https://doi.org/ 10.1148/radiol.2018170500. Noman Ahmed Jang Khan, Maria Tirona. (2021) An updated review of epidemiology, risk factors, and management of male breast cancer. Medical Oncology 38:39. https://doi.org/10.1007/s12032-021-01486-x Wang B, Chen YY, Yang S, Chen ZW, Luo J, Cui XW, et al.(2022) Combined Use of Shear Wave Elastography, Microvascular Doppler Ultrasound Technique, and BI-RADS for the Differentiation of Benign and Malignant Breast Masses. Front Oncol 12:906501. https://doi.org/10.3389/fonc.2022.906501 Burga AM, Fadare O, Lininger RA, Tavassoli FA.(2006) Invasive carcinomas of the male breast: a morphologic study of the distribution of histologic subtypes and metastatic patterns in 778 cases. Virchows Arch 449:507-512. https://doi.org/10.1007/s00428- 006-0305-3 Jiao Bai, Guilin Wang. (2023) Encapsulated Papillary Carcinoma of the Breast. Radiology 308:e231038. https://doi.org/10.1148/radiol.231038 Sexauer R, Hejduk P, Borkowski K, Ruppert C, Weikert T, Dellas S, et al.(2023) In review Diagnostic accuracy of automated ACR BI‑RADS breast density classification using deep convolutional neural networks. Eur Radiol 33:4589-4596.https://doi.org/ 10.1007/s00330-023-09474-7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies 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-4006699","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276222527,"identity":"45f73b45-fdda-4cf8-9aa8-0a5575237c07","order_by":0,"name":"Di Wang","email":"","orcid":"","institution":"Department of Ultrasound, the Second Affiliated Hospital of Zhejiang University,","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wang","suffix":""},{"id":276222528,"identity":"54240445-2807-4c83-8ecb-1894b2caf7f5","order_by":1,"name":"Meizheng Dang","email":"","orcid":"","institution":"Department of Ultrasound, the Second Affiliated Hospital of Zhejiang University,","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meizheng","middleName":"","lastName":"Dang","suffix":""},{"id":276222529,"identity":"f3005fe6-5b37-410c-81bf-20858c1417e4","order_by":2,"name":"Jing Wang","email":"","orcid":"","institution":"Department of Ultrasound, the Second Affiliated Hospital of Zhejiang University,","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":276222530,"identity":"446e5616-d3db-4f0c-a4c0-ed2295129b33","order_by":3,"name":"pintong huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACAwglAaE+MLAhCRLSwgMkGWeQoIUBrIWZB00QKzCXSH728OsOizx79t7Dr23+8CU2sDdvk2CouYNTi+WMNHNj2TMSxTw859Ksc3jYEht4jpVJMBx7htthNxLMpCXbJBJ7JHLMjHMkgFqADAnGhsN4tKR/Q2ixMABqkX9DSEuOmeRHiBbjxwwJIFt4CGg586ZMmvEMUMuZM2aMPQfYjNt40ootEo7h0XI8fZvkzx11ie3tPcYffvw5JtvPfnjjjQ81uLWAADNvA5hmA6aBY5DITMCrARjpPyFamD8wMNQQUDsKRsEoGAUjEQAAkZ5RJlaiYnIAAAAASUVORK5CYII=","orcid":"","institution":"Department of Ultrasound, the Second Affiliated Hospital of Zhejiang University,","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"pintong","middleName":"","lastName":"huang","suffix":""}],"badges":[],"createdAt":"2024-03-02 14:15:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4006699/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4006699/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52105072,"identity":"54ca4b11-f8ff-439b-80f4-7504fc7da781","added_by":"auto","created_at":"2024-03-06 19:26:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curves of Breast Imaging Reporting and Data System in three groups. AUC for Group I was 0.904 (95% CI, 0.842–0.966). AUC for Group II was 0.884 (95% CI, 0.756–0.920). AUC for Group I was 0.779 (95% CI, 0.681–0.876). AUC = area under curve, CI = confidence interval\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4006699/v1/0276994e6b11db08e54abd5b.png"},{"id":52105075,"identity":"91601861-1a4f-426e-bd05-3e98eec08d7f","added_by":"auto","created_at":"2024-03-06 19:26:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1259218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA 78-year-old man with intraductal papillary carcinoma. Conventional B-mode ultrasound revealed a 30 × 20-mm regular, complex echoic lesion with a clear margin in the right breast, which was categorized as Breast Imaging Reporting and Data System (BIRADS) 3 in group I whereas 4a in group II and III.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4006699/v1/b8930f3bb651c8cf14416096.png"},{"id":52105925,"identity":"a0c352e0-01f9-41ea-a813-c2cd85201257","added_by":"auto","created_at":"2024-03-06 19:34:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":866774,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA 34-year-old man with encapsulated papillary carcinoma. Conventional B-mode ultrasound revealed a 5 × 4 mm regular, hyperechoic solid lesion with a clear margin in the right breast, which was categorized as Breast Imaging Reporting and Data System (BIRADS) 3 in three groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4006699/v1/9622dea24263af8e1c6d4163.png"},{"id":52198023,"identity":"586cd55b-616c-4553-a88e-9d0b68d6c6d3","added_by":"auto","created_at":"2024-03-07 20:21:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2719646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4006699/v1/b7b219ba-c5dc-4d2b-bbb2-fdb0e133b7eb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Use of US BIRADS for the Differentiation of Male Breast Masses and Interobserver Agreement Assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMale breast cancer (MBC) is a rare type of malignant tumor which accounting for less than 1% of all breast cancers (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, there is a certain increasing trend in MBC incidence all over the world (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The 5-year mortality MBC is higher than that of female breast cancer (FBC). In 2024, the American Cancer Society estimated that mortality of MBC was up to 19% (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, and the principles of diagnosis and treatment are mainly inferred from FBC. But it should be noted that male breasts differ from the biological characteristics and pathological classification of female breasts (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Over time, an increasing number of experts have called for the establishment of a comprehensive diagnostic and treatment system specific for male breast diseases (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Hassett et al (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) has prepared the American Society of Clinical Oncology (ASCO) guidelines to effectively manage male breast disease in 2020.\u003c/p\u003e \u003cp\u003eBreast ultrasound, which is mainly applied to female patients, may have lower attendance for male patients (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The American College of Radiology (ACR) published the Breast Imaging Reporting and Data System (BIRADS) lexicon for the United States in 2013 which has been widely used to evaluate benign and malignant nodules in the female breast (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). As our known, few related studies on MBC have been performed. The question remains whether the sax gap is a successful integration for BIRADS diagnosing male breast nodules.\u003c/p\u003e \u003cp\u003eTherefore, our objective was to investigate the diagnostic performance of the BIRADS lexicon for the diagnosis MBC. And we also assess interobserver agreement for BIRADS and compared them.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003ePatients\u003c/b\u003e \u003c/p\u003e \u003cp\u003e All participants gave written, informed consent to have their medical records reviewed. This retrospective study was approved by the Hospital Medical Ethics Committee.\u003c/p\u003e \u003cp\u003eThis study involved all male patients with breast nodules who underwent surgery at the Second Affiliated Hospital of Zhejiang University from January 2017 to July 2023. The inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients requiring surgical treatment when clinicians suspected a malignant tendency based on clinical information or on strong request by the patient; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) patients with a clear ultrasound in the B mode of the breast. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) patients with complete clinical information, such as clinical characteristics (age, sex, tumor dimension, and tumor location), ultrasound characteristics (such as echogenicity, aspect ratio, boundary, margin).\u003c/p\u003e \u003cp\u003eThe following patients were excluded: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients without complete preoperative ultrasound or postoperative pathological data and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) patients with multiple nodules. Finally, a total of 90 patients were included.\u003c/p\u003e\n\u003ch3\u003eBreast ultrasound examination and image analysis\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eUltrasound examination\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll ultrasound examinations were performed by experienced (\u0026ge;\u0026thinsp;8 years) radiologists. Ultrasound instruments included Resona 7 (Mindray, Shenzhen, China), ESAOTE (My LAb 90 X-vision, Italy), Aplio 500 (Toshiba Medical Systems, Tokyo, Japan), and Logic E9 (GE Healthcare, Wauwatosa, USA).\u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003eAll ultrasound images were retrospectively and independently reviewed by six breast cancer specialists to assess the characteristics and presence of signs, according to the fifth edition of the BIRADS lexicon. Readers were divided into three groups (two with \u0026lt;\u0026thinsp;3 years of experience comprised the I group, two with 3 to 10 years of experience comprised the II group, and two readers with \u0026gt;\u0026thinsp;10 years of experience comprised the III group). Any disagreement was resolved by consensus in each group. The readers were required to select the most appropriate single descriptor for each category. All readers were asked to assign a BIRADS category and (including the operator) were blind to the pathological results of the patients and the clinic message.\u003c/p\u003e\n\u003ch3\u003eUltrasonic diagnostic criteria\u003c/h3\u003e\n\u003cp\u003eThe following ultrasonographic characteristics were recorded for each mass: shape (irregular, regular), orientation (parallel or not parallel), boundary (circumscribed or not circumscribed), margin (smooth, indistinct or angular), echo pattern (hypoechoic, hyperechoic, complex, or echoless), posterior acoustic features (enhancement, none, shadowing), and calcifications (microcalcifications or not).\u003c/p\u003e\n\u003ch3\u003eHistopathology\u003c/h3\u003e\n\u003cp\u003eTwo pathologists blinded to the clinical information evaluated the histopathological findings for the breast specimen. Sections were initially read by a physician with six years of experience and then reviewed by a physician with 20 years of experience. The final result was decided by the later physician.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eQuantitative data are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Qualitative data are presented as frequencies. The independent samples t-test was performed for normally distributed data, while the Mann Whitney U test was for non-normally distributed data. Qualitative variables, such as sex and ultrasound characteristics, were analyzed using χ\u003csup\u003e2\u003c/sup\u003e test or Fisher exact test. The frequency and risk of malignancy according to each category were calculated as a percentage.\u003c/p\u003e \u003cp\u003eTo assess reader reproducibility, the intraclass correlation efficiency (ICC) was calculated. The reliability of the measurement was classified according to common criteria as excellent (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.75), good (ICC\u0026thinsp;=\u0026thinsp;0.60\u0026ndash;0.75), fair (ICC\u0026thinsp;=\u0026thinsp;0.40\u0026ndash;0.59), and poor (ICC\u0026thinsp;\u0026le;\u0026thinsp;0.40). An analysis of the receiver operating characteristic (ROC) curve was also performed to calculate the optimal cutoff value, diagnostic sensitivity, specificity, and precision based on histopathological results for the entire cohort. The area under the curves (AUCs) of Groups I, II, and III were compared using the \u003cem\u003eZ\u003c/em\u003e test.\u003c/p\u003e \u003cp\u003eAll statistical analyzes were performed using SPSS 29.0 (IBM Corporation, Armonk, NY, USA) and MedCalc 20.4 software (MedCalc Software, Mariakerke, Belgium). A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical information and pathological findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 90 patients with 54 benign and 36 malignant breast lesions were included. Of these, there were 19 gynecomastia, 13 fibroadenoma, 11 inflammation, 6 epithelial cysts, 3 intraductal papilloma, 1 lipoma and 1 hemangioma in benign nodules, and there were 30 IDC, 2 encapsulated papillary carcinoma (EPC), 2 intraductal papillary carcinoma (IPC), 1 lymphoma and 1EPC with IDC in malignant nodules.\u003c/p\u003e\n\u003cp\u003eThe ages and size of the onset of male breast cancer were markedly higher than those of benign breast cancer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The general information of the patients was summarized in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eBasic characteristics of patients in all breast nodules\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBasic characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.240\u0026thinsp;\u0026plusmn;\u0026thinsp;17.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.580\u0026thinsp;\u0026plusmn;\u0026thinsp;13.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.271\u0026thinsp;\u0026plusmn;\u0026thinsp;1.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.489\u0026thinsp;\u0026plusmn;\u0026thinsp;0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eAge(y) and Size(cm)were represented as median\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was statistically significant.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026nbsp; Interobserver Agreement for Ultrasonographic features\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"107%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS Features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGroup I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.071428571428573%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant(n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMass shape\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.677(0.580,0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e17(31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e20(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e20(37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e24(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e15(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e21(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.677(0.580,0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e37(68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e16(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e34(63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e12(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e39(72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e15(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.677(0.580,0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcho pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.923(0.893,0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eHypoechoic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e44(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e31(86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e44(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e32(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e45(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e33(91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.829(0.769,0.878)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eComplex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e4(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e3(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e4(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e2(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.718(0.629,0.794)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eHyperechoic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.000(1.000,1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eecholess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.000(1.000,1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMass orientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.651(0.548,0.741)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eParallel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e46(85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e27(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e51(94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e29(80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e51(94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e30 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.651(0.548,0.741)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eNot parallel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e8(14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e9(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e3(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e7(19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e3(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.651(0.548,0.741)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoundary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.674(0.578,0.759)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eCircumscribed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e39(72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e15(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e38 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e16(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e38(70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e16(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.674(0.578,0.759)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eNo-\u0026nbsp;circumscribed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e15(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e21(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e16(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e20(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e16(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e20(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.674(0.578,0.759)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMargin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.826(0.764,0.875)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eSmooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e32(59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e6(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e34(63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e9(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e38(70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e11(30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.793(0.722,0.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eAngular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e6(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e18(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e7(13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e18(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e14(38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.646(0.543,0.737)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eIndistinct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e16(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e12(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e13(24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e9(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e11(20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e11(30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.790(0.718,0.849)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosterior features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.805(0.738,0.860)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e30(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e22(61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e24 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e14(38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e33(61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e23(64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.706(0.664,0.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eEnhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e19(35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e7(19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e23 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e12(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e16(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.707(0.615,0.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eShadowing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e7(19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e7(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e10(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7(22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.835(0.775,0.882)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrocalcifications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.773(0.697,0.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e7(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e5(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e4(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e4(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e6(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e4(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.773(0.697,0.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e47(87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e31 (86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e50(92.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e32(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e48 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e32(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.773(0.697,0.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNumbers in parentheses are percentages except ICC.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData in parentheses in ICC are 95% CIs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e0.05 was statistically significant.\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eInterobserver agreement for ultrasonographic features\u003c/h3\u003e\n\u003cp\u003eIn seven kinds of ultrasound features with all nodules, the mass shape, boundary and margin characteristics showed an obvious difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between benign and malignant nodules other than echo, orientation, posterior features and microcalcifications in the judgment of the reader for each group.\u003c/p\u003e\n\u003cp\u003eFor reader performance, the descriptions of echo (0.923), margin (0.826), posterior (0.805) and microcalcifications (0.773) showed excellent ICCs across the three groups. By contrast, the ICC of the boundary, mass shape and orientation had good levels, and the ICC of the boundary was lower than else (0.651 vs 0.674, 0.677). Furthermore, the echo descriptions as hyperechoic and echoless had the best ICC of 1.00 contrary to the margin described as angular had the lowest ICC at 0.646 among all features (\u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003eInterobserver agreement for BI-RADS classifications\u003c/h3\u003e\n\u003cp\u003eThe three groups expressed excellent concordance in BIRADS 2 category and good concordance in BIRADS 3 category. Three groups showed fair concordance in the BIRADS 4b,4c and 5 categories and poor concordance in the BIRADS 4a category. In all BIRADS classifications, three groups had good performance (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eInterobserver Agreement for BI-RADS classifications\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBI-RADS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup I\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup II\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup III\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.934(0.908,0.954)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.677(0.579,0.761)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.379(0.249,0.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.483(0.358,0.601)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.521(0.401,0.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491(0.368,0.608)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.709(0.617,0.786)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u0026mdash;Data in parentheses are 95% CIs.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eComparison of diagnostic efficiency for the prediction of malignant nodules based on the three groups\u003c/h3\u003e\n\u003cp\u003eIn group I, the ROC curve demonstrated that the best cutoff was 4a with an AUC for the ROC was 0.779 (95% CI: 0.681, 0.876), for which the sensitivity, specificity, and accuracy were 88.3%, 55.6%, and 66.7%. In group II, the ROC curve demonstrated that the best cut-off was 4a with the AUC was 0.838 (95% CI: 0.756, 0.920), for which the sensitivity, specificity, and accuracy were 83.3%, 75.9%, and 71.1%. In group III, the ROC curve showed that the best cut-off was 4a with an AUC of 0.904 (95% CI: 0.842, 0.966), which the sensitivity, specificity, and precision were 97.2%, 62.9%, and 76.7% (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Moreover, the comparison of the AUCs between two groups, group I had significant differences from group III (z\u0026thinsp;=\u0026thinsp;3.226, P\u0026thinsp;=\u0026thinsp;0.0013); others were not (for group III vs. group II: z\u0026thinsp;=\u0026thinsp;1.548, P\u0026thinsp;=\u0026thinsp;0.121; for group II vs. group I: z\u0026thinsp;=\u0026thinsp;0.136, P\u0026thinsp;=\u0026thinsp;0.256).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have studied 90 male patients and found out that the AUC of BIRADS showed the excellent performance for male.With respect the BIRADS terminology to evaluate nodules, three groups showed good to excellent levels. Malignant nodules tended to have solid, hypoechoic, irregular margins, but more parallel and fewer microcalcifications. Unlike female breast, not only malignant tumors, but also benign inflammatory, non-neoplastic lesions had irregular margins. The discovery was similar to that reported by Huang et al (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). this may due that most male breast lumps are gynecomastia and it is clinically classified into three subtypes: florid gynecomastia, dendritic gynecomastia, and diffuse glandular gynecomastia (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As Peggy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) described the dendritic sign is seen in more than one year gynecomastia patients. Notably, some researchers (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) have supposed that microcalcifications had higher specific in MBC than FBC. This hypothesis was consistent with us. Several influencing factors can be considered. First, the pathology of the male breast is a highly heterogeneous; imaging of the lesions can be affected in many respects (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Second, males and females differ in their developmental processes and histo-anatomical structures. The male breast has a rudimentary structure, consisting of mainly subcutaneous adipose tissue, remnant ductal tissue, and a small nipple-areolar complex. The main anatomical difference between the male and female breasts was the absence of Cooper ligaments and the engendering of maturation of the ductal lobular unit. Involution and ductal atrophy occur in the male breast due to a significant increase in testosterone levels (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Finally, microcalcifications and mass orientation may indicate ductal and stromal involvement (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our cohort, the AUC (0.904), sensitivity (96.2%), and the accuracy (76.7%) of group III were the highest, while the specificity (75.9%) of group I was the highest in the three groups. Our findings were similar to those of Huang et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), but were somewhat lower AUCs than those reported by Yoon et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). whose sensitivity was 100%, specificity was 92.2%, and accuracy was 94.0%. This divergence could be due to the number of MBC cases evaluated in our study: 29 MBCs were included in Yoon et al. compared to the 36 cases in our study. As in previous reports evaluating women's breasts (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), the sensitivity of BIRADS also higher than the specificity for male. We speculated further that the sex-based differences do not have a large impact on the diagnosis of BIRADS. This hypothesis was agreed by concordant previous studies; their study showed that MBC shares several clinical and histological characteristics with FBC (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRegarding the BIRADS classifications for reader performance, the ICCs of BIRADS 4 categories were poor to fair in three groups and the diagnostic efficacy of group I was significantly lower than group III. Both of two EPCs were misdiagnosed as category 3 in group I and II, one of in group III. Papillary carcinoma comprises the second largest proportion of male breast carcinomas (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e); Most male papillary carcinomas are intracystic and noninvasive (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), thus their mainly clinical manifestations are painless expansion growth. On ultrasound, they presented as a complex cystic (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) or solid smooth mass (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which is easy to misdiagnose as category 3. Therefore, this also presents a greater challenge for the diagnosis of sonographers. As Jeremy et al. declared that ultrasound diagnosis is highly dependent on the skills and experience of sonographers and radiologists, deep learning has the potential to dramatically save time for experienced medical professionals and potentially reduce interobserver variability (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur study has several limitations. First, some selection bias could not be avoided because this was a retrospective study. Second, we only selected patients who underwent surgery, and thus, the number of patients was small. A multicenter study with a larger number of patients is required. Third, the images used for our analysis were static, which may have caused some error. Finally, we had fewer groups and fewer physicians, which may have caused some biases. In future, a larger sample size and studies from multiple centers are necessary to validate our study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMale sex did not have a large impact on the diagnosis of BIRADS compared with female sex, and use of US BIRADS lexicon shows excellent or good levels of concordance for mass characterizations. But a lower concordance respect to BIRADS classifications in malignant nodules. Ultimately, we should enhance the clinical experience of the operator and the reader to facilitate a timely diagnosis and prevent unnecessary biopsies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDi Wang and Jing Wang wrote the main manuscript text and Meizheng Dang prepared figures and tables.Pintong Huang was supervision. All authors reviewed the manuscript.\"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMiao H, Verkooijen HM, Chia KS, Bouchardy C, Pukkala E, Lar\u0026oslash;nningen S, et al. (2011) Incidence and outcome of male breast cancer: an international population-based study. ClinOncol 29:4381-4386. https://doi.org/10.1200/JCO.2011.36.8902 \u003c/li\u003e\n\u003cli\u003e. Siegel RL, Miller KD, Jemal A. Cancer statistics, (2019) CA Cancer J Clin 69:7-34. https://doi.org/10.3322/caac.21551\u003c/li\u003e\n\u003cli\u003eYadav S, Karam D, Riaz IB, Xie H, Durani U, Duma N, et al.(2020) Male breast cancer in the United States: Treatment patterns and prognostic factors in the 21st century. Cancer 126:26-36. https://doi.org/ 10.1002/cncr.324724\u003c/li\u003e\n\u003cli\u003eWang F, Shu X, Meszoely I, Pal T, Mayer IA, Yu Z, et al. (2019) overall mortality after diagnosis of breast cancer in men vs women. JAMA Oncology 5:1589-1596. https://doi.org/10.1001/jamaoncol.2019.2803\u003c/li\u003e\n\u003cli\u003eRebecca L Siegel , Angela N Giaquinto, Ahmedin Jemal.(2024) Cancer statistics, 2024. CA Cancer J Clin 74:12-49. https://doi.org/10.3322/caac.21820 \u003c/li\u003e\n\u003cli\u003eIraj Khalkhali, MD, John Cho. Male Breast Cancer Imaging. (2015) J the Breast 3: 217\u0026ndash;218. https://doi.org/10.1111/tbj.12399\u003c/li\u003e\n\u003cli\u003eLattin GE Jr, Jesinger RA, Mattu R, Glassman LM. (2013) From the radiologic pathology archives: Diseases of the male breast: Radiologic-pathologic correlation. Radiographics 33:461-89. https://doi.org/10.1148/rg.332125208 \u003c/li\u003e\n\u003cli\u003eNguyen C, Kettler MD, Swirsky ME, Miller VI, Scott C, Rhett Krause,et al. (2013) Male breast disease: pictorial review with radiologic-pathologic correlation. Radiographics\u003cem\u003e \u003c/em\u003e33:763\u0026ndash;79. https://doi.org/10.1148/rg.333125137\u003c/li\u003e\n\u003cli\u003eHassett MJ, Somerfield MR, Baker ER, Cardoso F, Kansal KJ, Kwait DC et al.(2020) Management of Male Breast Cancer: ASCO Guideline. Journal of Clinical Oncology 38:1849-1863. https://doi.org/10.1200/JCO.19.03120\u003c/li\u003e\n\u003cli\u003eWebb JM, Adusei SA, Wang Y, Samreen N, Adler K, Meixner DD, et al. (2021) Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging. Comput Biol Med 139:104966. https://doi.org/10.1016/j.compbiomed.2021.104966 \u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Orsi C, Sickles E, Mendelson E, Morris E et al. (2013) ACR BIRADS\u0026reg; Atlas, Breast Imaging Reporting and Data System. 5th ed. American College of Radiology, Reston, VA.\u003c/li\u003e\n\u003cli\u003eHuang Y, Xiao Q, Sun Y, Li Q, Wang S, Gu Y. (2020) Differential diagnosis of benign and malignant male breast lesions in mammography. Eur Radiol 132:109339. https://doi.org/10.1016/j.ejrad.2020.109339 \u003c/li\u003e\n\u003cli\u003eHeather A. Vandeven, Jay M. Pensler. (2024) Gynecomastia In: StatPearls. Treasure Island (FL): StatPearls \u003c/li\u003e\n\u003cli\u003ePeggy P W Yen, Namita Sinha, Penny J Barnes, Robinette Butt, Sian Iles. (2015) Benign and Malignant Male Breast Diseases: Radiologic and Pathologic Correlation Can Assoc Radiol J 66:198-207. https://doi.org/10.1016/j.carj.2015.01.002.\u003c/li\u003e\n\u003cli\u003eM. Madhukar, A. Chetlen (2013) Multimodality imaging of benign and malignant male breast disease. Clin Radiol 68:e698-706. https://doi.org/ 10.1016/j.crad.2013.07.007\u003c/li\u003e\n\u003cli\u003eRong X, Zhu Q, Jia W, Ma T, Wang X, Guo N, et al. (2018) Ultrasonographic assessment of male breast diseases. Breast J\u003cem\u003e \u003c/em\u003e24:599-605. https://doi.org/10.1111/tbj.12982\u003c/li\u003e\n\u003cli\u003eTangerud \u0026Aring;, Potapenko I, Skjerven HK, Stensrud MJ.(2016) Radiologic evaluation of lumps in the male breast. Acta Radiol 57:809-14. https://doi.org/10.1177/0284185115609802 In review\u003c/li\u003e\n\u003cli\u003eKim H, Wisniewska K, Regner MJ, Thennavan A, Spanheimer PM, Franco HL. (2023) Single-Cell Transcriptional and Epigenetic Profiles of Male Breast Cancer Nominate Salient Cancer-Specific Enhancers. Int J Mol Sci 24:13053. https://doi.org/10.3390/ijms241713053\u003c/li\u003e\n\u003cli\u003eGucal A, Traina TA, Eisner JR, Parker JS, Selitsky SR, Park BH, et al.(2019) Male breast cancer: a disease distinct from female breast cancer. Breast Cancer Res Treat\u003cem\u003e \u003c/em\u003e173:37-48. https://doi.org/10.1007/s10549-018-4921-9 \u003c/li\u003e\n\u003cli\u003eViana MP, Tucunduva TCdM, Torres US, Aguillar VLN, Bresciani BH, Shimizu C, et al. (2020) Imaging of male breast disease: the good, the bad and the ugly - A pictorial review. Clin Imaging 68:45-56. https://doi.org/10.1016/j.clinimag.2020.06.025\u003c/li\u003e\n\u003cli\u003eYoon B, Chae EY, Cha JH, Shin JH, Choi WJ, Kim HH. (2020) Male patients with unilateral breast symptoms: an optimal imaging approach. Eur Radiol 30:4242-4250. https://doi.org/10.1007/s00330-020-06828-3\u003c/li\u003e\n\u003cli\u003eJiao Bai, Guilin Wang. (2023) Encapsulated Papillary Carcinoma of the Breast. Radiology 308:e231038. https://doi.org/10.1148/radiol.231038 \u003c/li\u003e\n\u003cli\u003eMenezes GLG, Pijnappel RM, Meeuwis C, Bisschops R, Veltman J, Lavin PT,et al.(2018) Downgrading of breast masses suspicious for cancer by using optoacoustic breast imaging. Radiology 288:355-365. https://doi.org/ 10.1148/radiol.2018170500.\u003c/li\u003e\n\u003cli\u003eNoman Ahmed Jang Khan, Maria Tirona. (2021) An updated review of epidemiology, risk factors, and management of male breast cancer. Medical Oncology 38:39. https://doi.org/10.1007/s12032-021-01486-x\u003c/li\u003e\n\u003cli\u003eWang B, Chen YY, Yang S, Chen ZW, Luo J, Cui XW, et al.(2022) Combined Use of Shear Wave Elastography, Microvascular Doppler Ultrasound Technique, and BI-RADS for the Differentiation of Benign and Malignant Breast Masses. Front Oncol 12:906501. https://doi.org/10.3389/fonc.2022.906501\u003c/li\u003e\n\u003cli\u003eBurga AM, Fadare O, Lininger RA, Tavassoli FA.(2006) Invasive carcinomas of the male breast: a morphologic study of the distribution of histologic subtypes and metastatic patterns in 778 cases. Virchows Arch 449:507-512. https://doi.org/10.1007/s00428- 006-0305-3 \u003c/li\u003e\n\u003cli\u003eJiao Bai, Guilin Wang. (2023) Encapsulated Papillary Carcinoma of the Breast. Radiology 308:e231038. https://doi.org/10.1148/radiol.231038 \u003c/li\u003e\n\u003cli\u003eSexauer R, Hejduk P, Borkowski K, Ruppert C, Weikert T, Dellas S, et al.(2023) In review Diagnostic accuracy of automated ACR BI‑RADS breast density classification using deep convolutional neural networks. Eur Radiol 33:4589-4596.https://doi.org/ 10.1007/s00330-023-09474-7. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ultrasound, male breast, Breast Imaging Reporting and Data System, BIRADS, Interobserver agreement was assessed","lastPublishedDoi":"10.21203/rs.3.rs-4006699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4006699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo investigate the efficiency of Breast Imaging Reporting and Data System (BIRADS) proposed by the American College of Radiology for male breast lesions and to evaluate assess inter-observer agreement.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSix breast radiologists were divided into three groups to retrospectively analyze 90 male breast nodules by BIRADS classifications. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated for comparative analysis. Interobserver agreement was assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe group II showed the higher specificity (75.9%) than other two groups while the group III showed the best AUC, sensitivity and accuracy, which were 0.904, 97.2%, 62.9% and 76.7%, respectively. Besides, the whole of three groups expressed excellent ICC on 2(0.934) category and good on BIRADS 3 (0.677) category. The ICC of 4a (0.379) was expressed as poor level. The categories of 4b (0.483) ,4c (0.521) and5(0.491) was expressed as fair level. Only group I had significant difference with the group III in diagnostic effectiveness of the same systems among three groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMale sex did not have a large impact on the diagnosis of BIRADS and use of US BIRADS lexicon shows excellent or good levels of concordance for mass characterizations. But a lower concordance respect to BIRADS classifications in BIRADS 4 categories.\u003c/p\u003e","manuscriptTitle":"Use of US BIRADS for the Differentiation of Male Breast Masses and Interobserver Agreement Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 19:26:19","doi":"10.21203/rs.3.rs-4006699/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b28ee0bb-427b-4557-a53c-cc516f01e3eb","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-07T20:21:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 19:26:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4006699","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4006699","identity":"rs-4006699","version":["v1"]},"buildId":"pf3fE39SIOqb-0xH_OWvX","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}