A Novel Noninvasive Diagnostic Method for Suspicious Cervical Lymph Nodes— Superb Microvascular Imaging

A Novel Noninvasive Diagnostic Method for Suspicious Cervical Lymph Nodes— Superb Microvascular Imaging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel Noninvasive Diagnostic Method for Suspicious Cervical Lymph Nodes— Superb Microvascular Imaging Lilong Xu, Ling Zhou, Xiaoli Yu, Lin-lin Zheng, Gonglin Fan, Min Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4276503/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To explore the diagnostic value of superb microvascular imaging (SMI) for ultrasonically uncertain lymph nodes (LNs). Methods Our center prospectively collected clinical and imaging data of 74 patients who underwent fine-needle aspiration biopsy and thyroglobulin measurement from January 2022 to June 2022. First, univariate analysis was performed to obtain relevant variables that differed between benign and malignant LN groups. Then spearman correlation was used to analyze the correlation between effective variables and pathological results. Finally, receiver operating characteristic was used to analyze the diagnostic efficiency of effective variables. Results In this study, there were 43 benign LNs and 31 malignant LNs. In univariate analysis, gender, age, CDFI diagnosis results and SMI diagnosis results were significantly different in the differentiation of benign and malignant LNs ( P = 0.026, P = 0.041, P = 0.001, P < 0.001). CDFI and SMI diagnosis results had good correlated with pathological findings (r = 0.403, P < 0.001, r = 0.707, P < 0.001). The diagnostic efficiency of SMI (AUC = 0.856, P < 0.001) was higher than that of CDFI (AUC = 0.704, P = 0.003). And the diagnostic results of SMI were superior to those of CDFI. The number of feeding vessels showed by CDFI and SMI in malignant LNs was higher than that in benign LNs (2.00 vs. 1.00, 3.00 vs. 2.00, all P < 0.001). In all enrolled LNs, SMI showed significantly more vessels than CDFI (2.53 ± 1.47 vs. 1.50 ± 1.13, P < 0.001). Conclusions SMI is better than CDFI in displaying small feeding vessels and has important diagnostic value in judging the nature of LNs with ultrasound uncertainty. Cervical lymph node Doppler Metastasis Superb microvascular imaging Ultrasound Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with high incidence and low mortality [ 1 – 2 ] . Studies have found that more than half of PTC patients have cervical lymph nodes (LNs) metastasis, but only 70%-80% of metastatic LNs can be accurately detected before surgery [ 3 ] . Cervical LNs metastasis is considered to be an important adverse factor affecting the prognosis of PTC, and surgical treatment is required for definite metastatic LNs [ 4 ] . Inadequate preoperative evaluation of LN properties often leads to repeat surgery, which greatly increases the physical and psychological burden on patients. On the contrary, if the cervical LNs dissection is performed blindly, it usually increases the risk of surgical complications [ 4 – 5 ] . Therefore, preoperative precise evaluation of LNs has important clinical significance. At present, imaging is the most commonly used method to evaluation LNs, especially ultrasound, which has unique advantages for the identification of superficial organ lesions [ 6 ] . According to the typical ultrasonic characteristics of benign and malignant LNs, the nature of most LNs can be judged by the gray scale ultrasound and CDFI. However, there are still 20%-30% of LNs in the diagnosis process is confused, and such LNs with uncertain significance need other diagnostic means [ 3 , 7 ] . At present, fine-needle aspiration biopsy (FNAB) combined thyroglobulin (Tg) measurement can be performed for further diagnose suspicious LNs, but this method still has a certain false negative rate, and the proportion of secondary FNAB is up to 30% [ 8 ] . In addition, FNAB has technical requirements for sonographers and pathologists, and it is still difficult to carry out in primary hospitals. As we all know, tumor cells often invade LNs from the surrounding cortex, and the invasion is accompanied by the formation of new blood vessels. Therefore, abnormal vascular hyperplasia of LNs is a typical biological feature of metastatic LNs [ 9 ] . However, the ultrasonic grayscale images of early metastatic LNs are often atypical and difficult to be detected by our naked eyes. CDFI has limited ability to display small and low-speed blood vessels, and decreasing the set scale tends to increase artifacts caused by tissue motion [ 10 ] . The technology of superb microvascular imaging (SMI) is just to fill the shortcomings of CDFI, which can show small blood vessels and low blood flow, while shielding the surrounding clutter and artifacts [ 11 ] . Compared with the detection rate of CDFI for small blood flow signals, SMI has higher sensitivity and shows small blood flow signals more easily [ 12 ] . Therefore, the purpose of this study was to explore the diagnostic value of SMI for ultrasonically uncertain LNs. Methods This study protocol was performed in line with the ethical standards of the Declaration of Helsinki. Approval was approved by the Ethics Committee of our institution (No. 20190520-70). Informed consent was obtained from each of the participants prior to the data collection for scientific research. Patients Our center prospectively collected clinical and imaging data from 283 patients who underwent FNAB and Tg measurement between January 2022 and June 2022. The inclusion criteria were as follows: 1) Grey scale ultrasound images were considered as cervical indeterminate LNs, 2) Thyroid nodules on the same side were confirmed by FNAB as PTC, 3) Image and clinical information was complete. The image features of indeterminate LNs included circular shape, inconspicuous or eccentric lymphatic hilum, and abnormal echoes in LNs (cystic change, hyperechogenicity, or calcification). The exclusion criteria were as follows: 1) Thyroid nodules on the same side were confirmed as benign nodules or other thyroid malignancies that were non-PTC, 2) LNs confirmed as other malignancies of non-thyroid origin. Finally, 182 LNs with benign thyroid nodules, 18 malignant LNs with non-PTC origin, and 9 LNs with unsatisfactory image acquisition were excluded. And only 74 indeterminate LNs were included in this study (Fig. 1 ). There were 25 male patients and 49 female patients. The age ranged from 15 to 69 years, with a mean age of 37.50 (31.75, 60.00) years. The maximum diameter of LNs ranged from 4.3 to 23.5mm, with a mean value of 9.60 (7.18, 12.30) mm. Ultrasound images acquisition and interpretation All enrolled patients underwent cervical ultrasound examination. The CDFI and SMI static images and 5-second dynamic images of suspicious LNs were collected by Samsung ultrasonic diagnostic instrument (SAMSUNG, South Korea) with a 14 MHz high-frequency linear probe. The parameters of the image acquisition were adjusted to the best settings during the inspection, which can fully display the blood flow signal and avoid artifacts as much as possible. All suspected LNs were performed by ultrasound-guided FNAB (with 25-gauge needle) and 3–4 cytological smears were required for each LN. Then, the remaining aspirates in each fine-needle were rinsed separately with 1mL of isotonic saline and Tg was detected. Images acquisition and intervention were performed by 2 sonologists with more than 10 years of experience in intervention operation. According to the results of previous studies, the distribution characteristics of feeding vessels in LNs were divided into four categories [ 13 ] : central portal (vascular signals were radially distributed from a center location), peripheral (vascular signals located in the peripheral cortex), avascular (without flow signals within LNs), and mixed (with both central and peripheral vascular signals). The central portal was considered to be a benign outcome, while the remaining three categories were considered to be malignant outcomes. The number of feeding vessels was counted from LNs visible flow images. All static and dynamic images were interpreted by two sonologists with more than 10 years of experience. If no consensus was reached, another sonologist (with more than 15 years of experience) would made the ruling. Reference Standard The final diagnosis of suspicious LNs was determined by pathologic cytological examination combined with Tg measurement. The diagnostic criteria for positive Tg measure included [ 14 ] : 1) FNAB-Tg was higher than serum Tg, 2) FNAB-Tg > 1.00 ng/mL. Cytological results were judged by pathologists with more than 5 years of experience. If no consensus was reached, arbitration from another pathologist (with more than 15 years of experience in LNs pathological diagnosis) was performed. Statistical analysis All statistical analyses were performed using SPSS v19. The normal distribution data were compared between groups using an independent sample t -test. For non-normal distribution data, differences were analyzed using a Mann-Whitney U test. Chi-square test was used to compare the differences between groups for counting data. Spearman correlation was used to analyze the correlation between effective variables and pathological results, and the correlation coefficient r was used to indicate the level of correlation. 0<|r|≤0.4 means low correlation, 0.4<|r|≤0.7 means moderately correlated, and 0.7<|r|≤1 means highly correlated. The area under curve (AUC) of receiver operating characteristic (ROC) was used to analyze the diagnostic efficiency of effective variables. P values less than 0.05 were considered as statistically significant. Results Clinical Characteristics of enrolled Patients In this study, there were 43 benign LNs and 31 malignant LNs (Table 1 ). In univariate analysis, gender, age, CDFI diagnosis results and SMI diagnosis results were significantly different in the differentiation of benign and malignant LNs ( P = 0.026, P = 0.041, P = 0.001, P < 0.001). Patients with benign LNs were older than those with malignant LNs. There were no significant differences in the largest diameter of LNs, laterality of LNs or location of LNs ( P = 0.157, P = 0.169, P = 0.245). Table 1 Basic clinical data of the patients. Characteristic Benign (n = 43) Malignant (n = 31) P Gender Female 24 (55.8%) 25 (80.6%) 0.026 † Male 19 (44.2%) 6 (19.4%) Age, years 42 (33, 61) 35 (25, 58) 0.041 ‡ Largest diameter of LNs, mm 8.10 (6.70, 12.30) 11.30 (8.10, 12.30) 0.157 ‡ Laterality of LNs Right 18 (41.9%) 18 (58.1%) 0.169 † Left 25 (58.1%) 13 (41.9%) Location of LNs, n (%) 2 3 (7.0%) 0 (0%) 0.245 † 3 19 (44.2%) 11 (35.5%) 4 17 (39.5%) 19 (61.3%) 5 1 (2.3%) 0 (0%) 6 3 (7.0%) 1 (3.2%) CDIF diagnosis results, n (%) Benign 30 (69.8%) 9 (29.0%) 0.001 † Malignant 13 (30.2%) 22 (71.0%) SMI diagnosis results, n (%) Benign 32 (74.4%) 1 (3.2%) < 0.001 † Malignant 11 (25.6%) 30 (96.8%) CDFI: Color Doppler Flow Imaging, LN: lymph node, n: number, SMI: Superb Microvascular Imaging, † Chi-square test, ‡ Mann-Whitney U test. Analysis of correlation between effective variables and pathological results of LNs In this study, gender and age had a low negative correlation with LNs pathological findings (r=-0.259, P = 0.026, r=-0.239, P = 0.041), and CDFI diagnosis results had a moderate positive correlation (r = 0.403, P < 0.001), and SMI diagnosis results was highly positively correlated with pathological findings (r = 0.707, P 0.4, P < 0.05) were selected to evaluate the diagnostic efficacy of LNs (Table 2 ). Table 2 The correlation between effective variables of lymph node properties and pathological findings. Effective variables Correlation coefficient P Gender vs. Pathologic Findings -0.259 0.026 Age vs. Pathologic Findings -0.239 0.041 CDFI vs. Pathologic Findings 0.403 < 0.001 SMI vs. Pathologic Findings 0.707 < 0.001 CDFI: Color Doppler Flow Imaging, SMI: Superb Microvascular Imaging. Comparison of evaluation indexes of CDFI and SMI diagnosis results Among the 43 cases benign LNs, 5 cases were misdiagnosed by CDFI but corrected by SMI, 3 cases were misdiagnosed by SMI and corrected by CDFI, and a total of 8 cases were misdiagnosed by both CDFI and SMI. Among the 31 malignant LNs, 8 cases were misdiagnosed by CDFI and corrected by SMI, no case was misdiagnosed by SMI, and only 1 case was misdiagnosed by both CDFI and SMI (Table 3 ). In this study, the diagnostic efficiency of SMI (AUC = 0.856, P < 0.001) was higher than that of CDFI (AUC = 0.704, P = 0.003). In the comparison of sensitivity, specificity and accuracy, the diagnostic results of SMI were superior to those of CDFI (Table 4 and Fig. 2 ). Table 3 Comparison of baseline data on CDFI and SMI diagnostic results. Pathologic Findings CDFI results, n (%) Benign (n = 43) Benign Malignant Total SMI results, n (%) Benign 27 (84.4%) 5 (15.6%) 32 (100%) Malignant 3 (27.3%) 8 (72.7%) 11 (100%) Total 30 (69.8%) 13 (30.2%) 43 (100%) Malignant (n = 31) SMI results, n (%) Benign 1 (100%) 0 (0%) 1 (100%) Malignant 8 (26.7%) 22 (73.3%) 30 (100%) Total 9 (29.0%) 22 (71.0%) 31 (100%) CDFI: Color Doppler Flow Imaging, n:number, SMI: Superb Microvascular Imaging. Table 4 Comparison of diagnostic efficacy of CDFI and SMI in distinguishing lymph node properties. Diagnostic indexes CDFI SMI AUC 0.704 0.856 P 0.003 < 0.001 95%CI 0.581, 0.826 0.767, 0.945 Sensitivity 71.0% 96.8% Specificity 69.8% 74.4% PPV 62.9% 73.2% NPV 76.9% 97.0% Accuracy 70.3% 83.8% AUC: Area under curve, CDFI: Color Doppler Flow Imaging, NPV: Negative predictive value, PPV: Positive predictive value, SMI: Superb Microvascular Imaging. The feeding vessels distribution of CDFI and SMI in LNs with different properties In the feeding vessels distribution shown by CDFI, benign LNs were mainly central portal, followed by avascular, and malignant LNs were mainly peripheral, followed by central portal. While SMI showed benign LNs were mainly central portal, followed by avascular, and malignant LNs were mainly mixed, followed by peripheral type (Fig. 3 and Fig. 4 ). The number of feeding vessels showed by CDFI and SMI in malignant LNs was higher than that in benign LNs (2.00 vs. 1.00, 3.00 vs. 2.00, all P < 0.001). In all enrolled LNs, SMI showed significantly more vessels than CDFI (2.53 ± 1.47 vs. 1.50 ± 1.13, P < 0.001) (Table 5 ). Table 5 Comparison of vascular characteristics of CDFI and SMI in lymph nodes with different properties. CDFI SMI Characteristic Benign (n = 35) Malignant (n = 22) P value Benign (n = 35) Malignant (n = 22) P value Distribution of feeding vessels, n (%) Central portal 30 (69.8%) 9 (29.0%) < 0.001 34 (79.1%) 1 (3.2%) < 0.001 Peripheral 0 (0%) 14 (45.2%) 0 (0%) 10 (32.3%) Avascular 11 (25.6%) 3 (9.7%) 6 (14.0%) 0 (0%) Mixed 2 (4.7%) 5 (16.1%) 3 (7.0%) 20 (64.5%) Number of Internal vessels (n) 1.00 (0, 2.00) 2.00 (2.00, 3.00) < 0.001 2.00 (1.00, 2.00) 3.00 (3.00, 4.00) < 0.001 1.50 ± 1.13 2.53 ± 1.47 < 0.001 CDFI: Color Doppler Flow Imaging, n:number, SMI: Superb Microvascular Imaging. Discussion In this study, the diagnostic results of CDFI and SMI were significantly different in the identification of suspicious LNs. The correlation between the diagnosis results of SMI and pathological results was better than that of CDFI, and the diagnostic efficiency of SMI was also better than that of CDFI [ 15 – 16 ] . The CDFI was mainly formed by the Doppler signal generated by the movement of red blood cells in the blood vessels, and it was often easy to display for vessels with large diameter and large blood flow [ 17 ] . However, early tumors have small vascular diameter and slow blood flow. The Doppler signal intensity of red blood cells in the neovascularization was low, which was easy to be covered by noise, and it was difficult to show clearly on CDFI [ 10 , 18 ] . As an innovative technology with high sensitivity to tiny blood flow, SMI could effectively collect low-speed blood flow signals, enhance the display rate of peripheral and central small blood vessels in LNs, and improve the diagnostic accuracy of suspicious LNs [ 11 – 12 , 19 ] . In this study, compared with CDFI, the diagnostic accuracy of SMI for suspicious LNs increased by 13.5%. In addition, the gender and age in this study were also different in different pathological results of LNs, and the risk of metastatic LNs in young patients was higher than that in the elderly, which was consistent with previous research results [ 20 ] . However, the correlation between these two factors and the pathological results was not satisfactory. As we all know, most cervical metastatic LNs from PTC will have abnormal distribution of feeding vessels, which is often one of the important indicators to predict metastatic LNs [ 13 , 21 ] . In this study, CDFI and SMI in benign LNs were dominated by central portal type, while peripheral type was rare. However, the blood flow distribution characteristics of metastatic LNs were often different, and there were many tiny blood vessels around the LNs, which was mainly related to the way the tumor invaded the LNs. Tumor invasion of LNs is usually centripetal progression, and tumor growth is often accompanied by the proliferation of new blood vessels, so there are often abnormal feeding vessels in the peripheral cortex of LNs [ 13 , 22 ] . In metastatic LNs, the peripheral type was predominant in CDFI, while the mixed type was predominant in SMI. This indicated that CDFI showed insufficient central portal blood flow in some abnormal LNs, which might be related to abnormal portal structure or decreased portal blood flow caused by tumor invasion [ 23 ] . In addition, CDFI had a limited ability to display peripheral microscopic vessels, and partial mixed type was often considered to be central portal type in CDFI, leading to certain false negative results [ 18 , 24 ] . Similarly, the avascular type shown by CDFI often required vigilance against the possibility of tiny vessels being overlooked [ 10 , 18 , 25 ] . Therefore, the ability of SMI to display small vessels was significantly higher than that of CDFI, which was also confirmed in this study. The number of vessels in LNs displayed by SMI was always higher than that of CDFI in both the LNs groups and the overall sample. There are some potential limitations to this study. First, this study was a single-center study, and there was a certain bias in the selection of enrolled cases. Second, only two sonographers interpreted the ultrasound images, and the consistency of interpretation results was not evaluated in the study. Finally, the included sample size of this study was relatively small, and the research results needed to be further verified by larger research samples. Conclusions In summary, SMI is better than CDFI in displaying small feeding vessels and has important diagnostic value in judging the nature of LNs with ultrasound uncertainty for PTC patients. Declarations Ethics approval statement This study protocol was performed in line with the ethical standards of the Declaration of Helsinki. Approval was approved by the Ethics Committee of Sir Run Run Shaw Hospital (No. 20190520-70). Consent for publication Consent for publication of individual data was obtained from each of the participants. Availability of data and materials: The datasets generated or analyzed during the study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding statement This study was supported by the Natural Science Foundation of Zhejiang Province (No: LY20H180005). Author’s contributions Conceptualization: LLX, LZ Methodology: LZ, MZ, LLZ Investigation: LZ, MZ, XLY, GLF Supervision: LLZ, GLF Writing--original draft: LLX, YSL Writing--review & editing: LLX, LZ, YSL All authors read and approved the final manuscript. Acknowledgement Not applicable. 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Surg Oncol. 2021;36:1-6. doi:10.1016/j.suronc.2020.11.004 Sim JK, Lee JY, Hong HS. Differentiation Between Malignant and Benign Lymph Nodes: Role of Superb Microvascular Imaging in the Evaluation of Cervical Lymph Nodes. J Ultrasound Med. 2019;38:3025-3036. doi:10.1002/jum.15010 Luo H, Yin L. Diagnostic value of superb microvascular imaging and color doppler for thyroid nodules: A meta-analysis. Front Oncol. 2023;13:1029936. doi:10.3389/fonc.2023.1029936 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4276503","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293939978,"identity":"4ce61042-9938-49e4-ad40-b0f2b81255c0","order_by":0,"name":"Lilong Xu","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lilong","middleName":"","lastName":"Xu","suffix":""},{"id":293939980,"identity":"71936470-18af-47c1-8bfd-02e3939f28a3","order_by":1,"name":"Ling Zhou","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Zhou","suffix":""},{"id":293939984,"identity":"96845833-d66d-4eb2-8ff6-2a81a09abd1d","order_by":2,"name":"Xiaoli Yu","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Yu","suffix":""},{"id":293939985,"identity":"c59c9962-a513-4dd9-8f1a-b81fb9e00890","order_by":3,"name":"Lin-lin Zheng","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin-lin","middleName":"","lastName":"Zheng","suffix":""},{"id":293939986,"identity":"554e8fdd-e972-47f6-8639-9bb8418ff630","order_by":4,"name":"Gonglin Fan","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gonglin","middleName":"","lastName":"Fan","suffix":""},{"id":293939987,"identity":"3aaef999-d12d-4233-a94c-4733057100d3","order_by":5,"name":"Min Zhang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhang","suffix":""},{"id":293939988,"identity":"27e8caaa-1dc1-48a5-a8e8-2099aff8735a","order_by":6,"name":"Shiyan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3RMQsCIRTA8RcHuki32tB3OAiuIOiz+AhqiZYggoKEwPXWPkYQNBtBLVetjbXc3NgQkSe0pmOQf8Tn4A8EAUKhH6waAQFhDjHYEbkJ+ZCa9CZ2mRJthw+htLjeVGfYOOuCw6SNkh6142GsmaDqjlKtexzyPko2FC5COKoIN1vZ4xW1Q8lZ4iC0MGSO6wUY8vIikBqywxUpifQiLOXidMBlDt2W2Pcbig2+kzg+FLXHeIpZluPlPmvXM5p/J7aK/Rom7GcS9/2yZ7lR7Xc5FAqF/q43s489bpcnzboAAAAASUVORK5CYII=","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shiyan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-16 13:53:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4276503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4276503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55523204,"identity":"eb5a32db-56b0-4301-9f34-6f7e81e3103a","added_by":"auto","created_at":"2024-04-29 14:29:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe patients’ inclusion criteria flowchart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4276503/v1/cb7c0abcce5ff05ab92842a6.jpeg"},{"id":55522404,"identity":"7f7599db-6e94-481f-ab82-2ef556c578d5","added_by":"auto","created_at":"2024-04-29 14:21:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curves of CDFI and SMI diagnosis results. \u003c/strong\u003eCDFI: Color Doppler Flow Imaging, SMI: Superb Microvascular Imaging.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4276503/v1/28426083d547e6f589129725.jpeg"},{"id":55522407,"identity":"b7a0d939-fe7b-4021-ab5b-403b41f2d00f","added_by":"auto","created_at":"2024-04-29 14:21:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":755379,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImaging results and a pathological result of a benign lymph node.\u003c/strong\u003e CDFI showed avascular type and the diagnosis was malignant (figure 3a), SMI showed central type and the diagnosis was benign (figure 3b), and the final pathological result confirmed that the lymph node was benign (HE staining; magnification ×40) (figure 3c).\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4276503/v1/5920f25b61dfe6d80c7d3fec.jpeg"},{"id":55523206,"identity":"6fdd7074-c945-40f1-b826-73a0c6f22eba","added_by":"auto","created_at":"2024-04-29 14:29:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":720238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImaging results and a pathological result of a malignant lymph node.\u003c/strong\u003e CDFI showed central type and the diagnosis was benign (figure 4a), SMI showed mixed type and the diagnosis was malignant (figure 4b), and the final pathological result confirmed that the lymph node was malignant (HE staining; magnification ×40) (figure 4c).\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4276503/v1/8812cb091c1466c5cfae6169.jpeg"},{"id":57188133,"identity":"422d803c-a808-4e88-88a7-23796c4cf70d","added_by":"auto","created_at":"2024-05-27 06:31:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2269061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4276503/v1/e4f3ad39-900e-479e-9df5-17bbcb3ec2c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Noninvasive Diagnostic Method for Suspicious Cervical Lymph Nodes— Superb Microvascular Imaging","fulltext":[{"header":"Background","content":"\u003cp\u003ePapillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with high incidence and low mortality \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Studies have found that more than half of PTC patients have cervical lymph nodes (LNs) metastasis, but only 70%-80% of metastatic LNs can be accurately detected before surgery \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Cervical LNs metastasis is considered to be an important adverse factor affecting the prognosis of PTC, and surgical treatment is required for definite metastatic LNs \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Inadequate preoperative evaluation of LN properties often leads to repeat surgery, which greatly increases the physical and psychological burden on patients. On the contrary, if the cervical LNs dissection is performed blindly, it usually increases the risk of surgical complications \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Therefore, preoperative precise evaluation of LNs has important clinical significance.\u003c/p\u003e \u003cp\u003eAt present, imaging is the most commonly used method to evaluation LNs, especially ultrasound, which has unique advantages for the identification of superficial organ lesions \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. According to the typical ultrasonic characteristics of benign and malignant LNs, the nature of most LNs can be judged by the gray scale ultrasound and CDFI. However, there are still 20%-30% of LNs in the diagnosis process is confused, and such LNs with uncertain significance need other diagnostic means \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. At present, fine-needle aspiration biopsy (FNAB) combined thyroglobulin (Tg) measurement can be performed for further diagnose suspicious LNs, but this method still has a certain false negative rate, and the proportion of secondary FNAB is up to 30% \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. In addition, FNAB has technical requirements for sonographers and pathologists, and it is still difficult to carry out in primary hospitals.\u003c/p\u003e \u003cp\u003eAs we all know, tumor cells often invade LNs from the surrounding cortex, and the invasion is accompanied by the formation of new blood vessels. Therefore, abnormal vascular hyperplasia of LNs is a typical biological feature of metastatic LNs \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, the ultrasonic grayscale images of early metastatic LNs are often atypical and difficult to be detected by our naked eyes. CDFI has limited ability to display small and low-speed blood vessels, and decreasing the set scale tends to increase artifacts caused by tissue motion \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The technology of superb microvascular imaging (SMI) is just to fill the shortcomings of CDFI, which can show small blood vessels and low blood flow, while shielding the surrounding clutter and artifacts \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Compared with the detection rate of CDFI for small blood flow signals, SMI has higher sensitivity and shows small blood flow signals more easily \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Therefore, the purpose of this study was to explore the diagnostic value of SMI for ultrasonically uncertain LNs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study protocol was performed in line with the ethical standards of the Declaration of Helsinki. Approval was approved by the Ethics Committee of our institution (No. 20190520-70). Informed consent was obtained from each of the participants prior to the data collection for scientific research.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eOur center prospectively collected clinical and imaging data from 283 patients who underwent FNAB and Tg measurement between January 2022 and June 2022. The inclusion criteria were as follows: 1) Grey scale ultrasound images were considered as cervical indeterminate LNs, 2) Thyroid nodules on the same side were confirmed by FNAB as PTC, 3) Image and clinical information was complete. The image features of indeterminate LNs included circular shape, inconspicuous or eccentric lymphatic hilum, and abnormal echoes in LNs (cystic change, hyperechogenicity, or calcification). The exclusion criteria were as follows: 1) Thyroid nodules on the same side were confirmed as benign nodules or other thyroid malignancies that were non-PTC, 2) LNs confirmed as other malignancies of non-thyroid origin. Finally, 182 LNs with benign thyroid nodules, 18 malignant LNs with non-PTC origin, and 9 LNs with unsatisfactory image acquisition were excluded. And only 74 indeterminate LNs were included in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were 25 male patients and 49 female patients. The age ranged from 15 to 69 years, with a mean age of 37.50 (31.75, 60.00) years. The maximum diameter of LNs ranged from 4.3 to 23.5mm, with a mean value of 9.60 (7.18, 12.30) mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eUltrasound images acquisition and interpretation\u003c/h2\u003e \u003cp\u003eAll enrolled patients underwent cervical ultrasound examination. The CDFI and SMI static images and 5-second dynamic images of suspicious LNs were collected by Samsung ultrasonic diagnostic instrument (SAMSUNG, South Korea) with a 14 MHz high-frequency linear probe. The parameters of the image acquisition were adjusted to the best settings during the inspection, which can fully display the blood flow signal and avoid artifacts as much as possible. All suspected LNs were performed by ultrasound-guided FNAB (with 25-gauge needle) and 3\u0026ndash;4 cytological smears were required for each LN. Then, the remaining aspirates in each fine-needle were rinsed separately with 1mL of isotonic saline and Tg was detected. Images acquisition and intervention were performed by 2 sonologists with more than 10 years of experience in intervention operation.\u003c/p\u003e \u003cp\u003eAccording to the results of previous studies, the distribution characteristics of feeding vessels in LNs were divided into four categories \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e: central portal (vascular signals were radially distributed from a center location), peripheral (vascular signals located in the peripheral cortex), avascular (without flow signals within LNs), and mixed (with both central and peripheral vascular signals). The central portal was considered to be a benign outcome, while the remaining three categories were considered to be malignant outcomes. The number of feeding vessels was counted from LNs visible flow images. All static and dynamic images were interpreted by two sonologists with more than 10 years of experience. If no consensus was reached, another sonologist (with more than 15 years of experience) would made the ruling.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReference Standard\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe final diagnosis of suspicious LNs was determined by pathologic cytological examination combined with Tg measurement. The diagnostic criteria for positive Tg measure included \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e : 1) FNAB-Tg was higher than serum Tg, 2) FNAB-Tg\u0026thinsp;\u0026gt;\u0026thinsp;1.00 ng/mL. Cytological results were judged by pathologists with more than 5 years of experience. If no consensus was reached, arbitration from another pathologist (with more than 15 years of experience in LNs pathological diagnosis) was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS v19. The normal distribution data were compared between groups using an independent sample \u003cem\u003et\u003c/em\u003e-test. For non-normal distribution data, differences were analyzed using a Mann-Whitney \u003cem\u003eU\u003c/em\u003e test. Chi-square test was used to compare the differences between groups for counting data. Spearman correlation was used to analyze the correlation between effective variables and pathological results, and the correlation coefficient \u003cem\u003er\u003c/em\u003e was used to indicate the level of correlation. 0\u0026lt;|r|\u0026le;0.4 means low correlation, 0.4\u0026lt;|r|\u0026le;0.7 means moderately correlated, and 0.7\u0026lt;|r|\u0026le;1 means highly correlated. The area under curve (AUC) of receiver operating characteristic (ROC) was used to analyze the diagnostic efficiency of effective variables. \u003cem\u003eP\u003c/em\u003e values less than 0.05 were considered as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics of enrolled Patients\u003c/h2\u003e \u003cp\u003eIn this study, there were 43 benign LNs and 31 malignant LNs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In univariate analysis, gender, age, CDFI diagnosis results and SMI diagnosis results were significantly different in the differentiation of benign and malignant LNs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with benign LNs were older than those with malignant LNs. There were no significant differences in the largest diameter of LNs, laterality of LNs or location of LNs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.157, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.169, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.245).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic clinical data of the patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (80.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.026 \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (33, 61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (25, 58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041 \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLargest diameter of LNs, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.10 (6.70, 12.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.30 (8.10, 12.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.157 \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality of LNs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.169 \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of LNs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.245 \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDIF diagnosis results, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001 \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (71.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI diagnosis results, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCDFI: Color Doppler Flow Imaging, LN: lymph node, n: number, SMI: Superb Microvascular Imaging, \u003csup\u003e\u0026dagger;\u003c/sup\u003e Chi-square test, \u003csup\u003e\u0026Dagger;\u003c/sup\u003e Mann-Whitney U test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of correlation between effective variables and pathological results of LNs\u003c/h2\u003e \u003cp\u003eIn this study, gender and age had a low negative correlation with LNs pathological findings (r=-0.259, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, r=-0.239, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), and CDFI diagnosis results had a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.403, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and SMI diagnosis results was highly positively correlated with pathological findings (r\u0026thinsp;=\u0026thinsp;0.707, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At last, CDFI and SMI diagnosis results (all r\u0026thinsp;\u0026gt;\u0026thinsp;0.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were selected to evaluate the diagnostic efficacy of LNs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation between effective variables of lymph node properties and pathological findings.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffective variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender vs. Pathologic Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge vs. Pathologic Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDFI vs. Pathologic Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI vs. Pathologic Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCDFI: Color Doppler Flow Imaging, SMI: Superb Microvascular Imaging.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComparison of evaluation indexes of CDFI and SMI diagnosis results\u003c/h2\u003e \u003cp\u003eAmong the 43 cases benign LNs, 5 cases were misdiagnosed by CDFI but corrected by SMI, 3 cases were misdiagnosed by SMI and corrected by CDFI, and a total of 8 cases were misdiagnosed by both CDFI and SMI. Among the 31 malignant LNs, 8 cases were misdiagnosed by CDFI and corrected by SMI, no case was misdiagnosed by SMI, and only 1 case was misdiagnosed by both CDFI and SMI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In this study, the diagnostic efficiency of SMI (AUC\u0026thinsp;=\u0026thinsp;0.856, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was higher than that of CDFI (AUC\u0026thinsp;=\u0026thinsp;0.704, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). In the comparison of sensitivity, specificity and accuracy, the diagnostic results of SMI were superior to those of CDFI (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline data on CDFI and SMI diagnostic results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePathologic Findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCDFI results, n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSMI results, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (84.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSMI results, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (71.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCDFI: Color Doppler Flow Imaging, n:number, SMI: Superb Microvascular Imaging.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of diagnostic efficacy of CDFI and SMI in distinguishing lymph node properties.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic indexes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.581, 0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.767, 0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAUC: Area under curve, CDFI: Color Doppler Flow Imaging, NPV: Negative predictive value, PPV: Positive predictive value, SMI: Superb Microvascular Imaging.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe feeding vessels distribution of CDFI and SMI in LNs with different properties\u003c/h2\u003e \u003cp\u003eIn the feeding vessels distribution shown by CDFI, benign LNs were mainly central portal, followed by avascular, and malignant LNs were mainly peripheral, followed by central portal. While SMI showed benign LNs were mainly central portal, followed by avascular, and malignant LNs were mainly mixed, followed by peripheral type (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The number of feeding vessels showed by CDFI and SMI in malignant LNs was higher than that in benign LNs (2.00 vs. 1.00, 3.00 vs. 2.00, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In all enrolled LNs, SMI showed significantly more vessels than CDFI (2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47 vs. 1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of vascular characteristics of CDFI and SMI in lymph nodes with different properties.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCDFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMalignant (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution of feeding vessels, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral portal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (79.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of Internal vessels (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (2.00, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00 (1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00 (3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCDFI: Color Doppler Flow Imaging, n:number, SMI: Superb Microvascular Imaging.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the diagnostic results of CDFI and SMI were significantly different in the identification of suspicious LNs. The correlation between the diagnosis results of SMI and pathological results was better than that of CDFI, and the diagnostic efficiency of SMI was also better than that of CDFI \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The CDFI was mainly formed by the Doppler signal generated by the movement of red blood cells in the blood vessels, and it was often easy to display for vessels with large diameter and large blood flow \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. However, early tumors have small vascular diameter and slow blood flow. The Doppler signal intensity of red blood cells in the neovascularization was low, which was easy to be covered by noise, and it was difficult to show clearly on CDFI \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. As an innovative technology with high sensitivity to tiny blood flow, SMI could effectively collect low-speed blood flow signals, enhance the display rate of peripheral and central small blood vessels in LNs, and improve the diagnostic accuracy of suspicious LNs \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In this study, compared with CDFI, the diagnostic accuracy of SMI for suspicious LNs increased by 13.5%. In addition, the gender and age in this study were also different in different pathological results of LNs, and the risk of metastatic LNs in young patients was higher than that in the elderly, which was consistent with previous research results \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. However, the correlation between these two factors and the pathological results was not satisfactory.\u003c/p\u003e \u003cp\u003eAs we all know, most cervical metastatic LNs from PTC will have abnormal distribution of feeding vessels, which is often one of the important indicators to predict metastatic LNs \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In this study, CDFI and SMI in benign LNs were dominated by central portal type, while peripheral type was rare. However, the blood flow distribution characteristics of metastatic LNs were often different, and there were many tiny blood vessels around the LNs, which was mainly related to the way the tumor invaded the LNs. Tumor invasion of LNs is usually centripetal progression, and tumor growth is often accompanied by the proliferation of new blood vessels, so there are often abnormal feeding vessels in the peripheral cortex of LNs \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In metastatic LNs, the peripheral type was predominant in CDFI, while the mixed type was predominant in SMI. This indicated that CDFI showed insufficient central portal blood flow in some abnormal LNs, which might be related to abnormal portal structure or decreased portal blood flow caused by tumor invasion \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In addition, CDFI had a limited ability to display peripheral microscopic vessels, and partial mixed type was often considered to be central portal type in CDFI, leading to certain false negative results \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Similarly, the avascular type shown by CDFI often required vigilance against the possibility of tiny vessels being overlooked \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Therefore, the ability of SMI to display small vessels was significantly higher than that of CDFI, which was also confirmed in this study. The number of vessels in LNs displayed by SMI was always higher than that of CDFI in both the LNs groups and the overall sample.\u003c/p\u003e \u003cp\u003eThere are some potential limitations to this study. First, this study was a single-center study, and there was a certain bias in the selection of enrolled cases. Second, only two sonographers interpreted the ultrasound images, and the consistency of interpretation results was not evaluated in the study. Finally, the included sample size of this study was relatively small, and the research results needed to be further verified by larger research samples.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, SMI is better than CDFI in displaying small feeding vessels and has important diagnostic value in judging the nature of LNs with ultrasound uncertainty for PTC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was performed in line with the ethical standards of the Declaration of Helsinki. Approval was approved by the Ethics Committee of Sir Run Run Shaw Hospital (No. 20190520-70).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication of individual data was obtained from each of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analyzed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Zhejiang Province (No: LY20H180005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: LLX, LZ\u003c/p\u003e\n\u003cp\u003eMethodology: LZ, MZ, LLZ\u003c/p\u003e\n\u003cp\u003eInvestigation: LZ, MZ, XLY, GLF\u003c/p\u003e\n\u003cp\u003eSupervision: LLZ, GLF\u003c/p\u003e\n\u003cp\u003eWriting--original draft: LLX, YSL\u003c/p\u003e\n\u003cp\u003eWriting--review \u0026amp; editing: LLX, LZ, YSL\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analyzed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi M, Dal Maso L, Vaccarella S. 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Ann Surg Oncol. 2019;26:1737-1743. doi:10.1245/s10434-019-07263-5\u003c/li\u003e\n\u003cli\u003eBible KC, Kebebew E, Brierley J, et al. 2021 American Thyroid Association Guidelines for Management of Patients with Anaplastic Thyroid Cancer [published correction appears in Thyroid. 2021 Oct;31(10):1606-1607]. Thyroid. 2021;31:337-386. doi:10.1089/thy.2020.0944\u003c/li\u003e\n\u003cli\u003eHa EJ, Chung SR, Na DG, et al. 2021 Korean Thyroid Imaging Reporting and Data System and Imaging-Based Management of Thyroid Nodules: Korean Society of Thyroid Radiology Consensus Statement and Recommendations. Korean J Radiol. 2021;22:2094-2123. doi:10.3348/kjr.2021.0713\u003c/li\u003e\n\u003cli\u003eYoo RE, Kim JH, Bae JM, et al. Ultrasonographic Indeterminate Lymph Nodes in Preoperative Thyroid Cancer Patients: Malignancy Risk and Ultrasonographic Findings Predictive of Malignancy. 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World J Surg Oncol. 2022;20:106. doi:10.1186/s12957-022-02573-7\u003c/li\u003e\n\u003cli\u003eWakonig KM, Dommerich S, Fischer T, et al. The Diagnostic Performance of Multiparametric Ultrasound in the Qualitative Assessment of Inconclusive Cervical Lymph Nodes. Cancers (Basel). 2023;15:5035. doi:10.3390/cancers15205035\u003c/li\u003e\n\u003cli\u003eLuo ZY, Hong YR, Yan CX, Wang Y, Ye Q, Huang P. Utility of quantitative contrast-enhanced ultrasound for the prediction of lymph node metastasis in patients with papillary thyroid carcinoma. Clin Hemorheol Microcirc. 2022;80:37-48. doi:10.3233/CH-200909\u003c/li\u003e\n\u003cli\u003eLi T, Li H, Xue J, Miao J, Kang C. Shear wave elastography combined with gray-scale ultrasound for predicting central lymph node metastasis of papillary thyroid carcinoma. Surg Oncol. 2021;36:1-6. doi:10.1016/j.suronc.2020.11.004\u003c/li\u003e\n\u003cli\u003eSim JK, Lee JY, Hong HS. Differentiation Between Malignant and Benign Lymph Nodes: Role of Superb Microvascular Imaging in the Evaluation of Cervical Lymph Nodes. J Ultrasound Med. 2019;38:3025-3036. doi:10.1002/jum.15010\u003c/li\u003e\n\u003cli\u003eLuo H, Yin L. Diagnostic value of superb microvascular imaging and color doppler for thyroid nodules: A meta-analysis. Front Oncol. 2023;13:1029936. doi:10.3389/fonc.2023.1029936\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":"Cervical lymph node, Doppler, Metastasis, Superb microvascular imaging, Ultrasound","lastPublishedDoi":"10.21203/rs.3.rs-4276503/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4276503/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo explore the diagnostic value of superb microvascular imaging (SMI) for ultrasonically uncertain lymph nodes (LNs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOur center prospectively collected clinical and imaging data of 74 patients who underwent fine-needle aspiration biopsy and thyroglobulin measurement from January 2022 to June 2022. First, univariate analysis was performed to obtain relevant variables that differed between benign and malignant LN groups. Then spearman correlation was used to analyze the correlation between effective variables and pathological results. Finally, receiver operating characteristic was used to analyze the diagnostic efficiency of effective variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, there were 43 benign LNs and 31 malignant LNs. In univariate analysis, gender, age, CDFI diagnosis results and SMI diagnosis results were significantly different in the differentiation of benign and malignant LNs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). CDFI and SMI diagnosis results had good correlated with pathological findings (r\u0026thinsp;=\u0026thinsp;0.403, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, r\u0026thinsp;=\u0026thinsp;0.707, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The diagnostic efficiency of SMI (AUC\u0026thinsp;=\u0026thinsp;0.856, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was higher than that of CDFI (AUC\u0026thinsp;=\u0026thinsp;0.704, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). And the diagnostic results of SMI were superior to those of CDFI. The number of feeding vessels showed by CDFI and SMI in malignant LNs was higher than that in benign LNs (2.00 vs. 1.00, 3.00 vs. 2.00, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In all enrolled LNs, SMI showed significantly more vessels than CDFI (2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47 vs. 1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSMI is better than CDFI in displaying small feeding vessels and has important diagnostic value in judging the nature of LNs with ultrasound uncertainty.\u003c/p\u003e","manuscriptTitle":"A Novel Noninvasive Diagnostic Method for Suspicious Cervical Lymph Nodes— Superb Microvascular Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 14:21:47","doi":"10.21203/rs.3.rs-4276503/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":"359bc87f-e6d4-4130-ab45-711f71564296","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-27T06:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-29 14:21:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4276503","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4276503","identity":"rs-4276503","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}