Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A | 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 Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A Sihua Niu, Jianhua Huang, Jia Li, Xueling Liu, Dan Wang, Ruifang Zhang, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-40074/v3 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2020 Read the published version in BMC Cancer → Version 3 posted 2 You are reading this latest preprint version Show more versions Abstract Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group ( P =0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions. Cancer Biology Oncology Artificial intelligence Breast BI-RADS 4A Differential diagnosis Figures Figure 1 Figure 2 Full Text Cite Share Download PDF Status: Published Journal Publication published 02 Oct, 2020 Read the published version in BMC Cancer → Version 3 posted Submission checks completed at journal 15 Sep, 2020 Editorial decision: Accept 14 Sep, 2020 You are reading this latest preprint version Show more versions 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. 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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-40074","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":2387213,"identity":"b03b064c-76ab-4d68-9fe2-a9e526d42a51","order_by":0,"name":"Sihua Niu","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sihua","middleName":"","lastName":"Niu","suffix":""},{"id":2387214,"identity":"c37d576d-3903-4b64-a0ac-1c9b2f6753a5","order_by":1,"name":"Jianhua Huang","email":"","orcid":"","institution":"Harbin Institute of 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17:05:35","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-40074/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-40074/v3","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-020-07413-z","type":"published","date":"2020-10-02T12:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2440681,"identity":"2a20279a-5534-4123-8608-9687cd01fbb5","added_by":"auto","created_at":"2020-09-16 19:10:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33531,"visible":true,"origin":"","legend":"Intraductal carcinoma in situ classified as BI-RADS 4A. a Ultrasound revealed a solid hypoechoic mass with lobulation. b Image contoured manually. ","description":"","filename":"Fig1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-40074/v3/Fig1.JPG"},{"id":2440682,"identity":"bd1c7f4c-5f8d-4a3c-9f29-8d3912e64165","added_by":"auto","created_at":"2020-09-16 19:10:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":923607,"visible":true,"origin":"","legend":"Adenosis classified as BI-RADS 4A. a Ultrasound revealed a solid irregular hypoechoic mass. b Image contoured manually.","description":"","filename":"2a.png","url":"https://assets-eu.researchsquare.com/files/rs-40074/v3/2a.png"},{"id":13531083,"identity":"58757dc1-fe2b-4168-8f39-957bdca7443a","added_by":"auto","created_at":"2021-09-17 01:11:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":402492,"visible":true,"origin":"","legend":"","description":"","filename":"renamedca066.pdf","url":"https://assets-eu.researchsquare.com/files/rs-40074/v3_covered.pdf"},{"id":2440683,"identity":"2ff30bb3-4028-4a83-99f1-34e69cdd1602","added_by":"auto","created_at":"2020-09-16 19:10:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":380758,"visible":true,"origin":"","legend":"","description":"","filename":"renamedca066.pdf","url":"https://assets-eu.researchsquare.com/files/rs-40074/v3/renamedca066.pdf"},{"id":2440680,"identity":"75aa73e1-9465-4537-8a9b-847eb7d56ba7","added_by":"auto","created_at":"2020-09-16 19:10:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":480355,"visible":true,"origin":"","legend":"","description":"","filename":"renamedca066.pdf","url":"https://assets-eu.researchsquare.com/files/rs-40074/v3_stamped.pdf"}],"financialInterests":"","formattedTitle":"Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A","fulltext":[{"header":"Full Text","content":"\u003cp\u003eThis preprint is available for \u003ca href='/article/rs-40074/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, Breast, BI-RADS 4A, Differential diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-40074/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-40074/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (\u003cem\u003eP\u003c/em\u003e=0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.\u003c/p\u003e","manuscriptTitle":"Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-09-16 18:50:59","doi":"10.21203/rs.3.rs-40074/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2020-09-15T12:00:00+00:00","index":"","fulltext":""},{"type":"decision","content":"Accept","date":"2020-09-14T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2020-08-20 18:31:16","doi":"10.21203/rs.3.rs-40074/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-09-08T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-08-21T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after discretionary revisions\nForm responses:\n---\n\nComments to Author:\n---\nThis study aimed to assess the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. The retrospective study analyzed the morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI and compared the results of the AI analysis to the ultrasonic characteristics of BI-RADS 4A benign and malignant lesions.\nThe paper has certain novelty and clinical value for this field research. It is concluded that AI does help in the differential diagnosis of BI-RADS 4A benign and malignant lesions. In the \"Author's Response to Reviewer Comments\", several flaws have been explained and revised.\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **No**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"editorInvitedReview","content":"","date":"2020-08-21T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept without revision\nForm responses:\n---\n\nComments to Author:\n---\nAll of my concerns have been addressed and the manuscript has been greatly improved. It can be accepted.* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2020-08-18T12:00:00+00:00","index":1,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-08-18T12:00:00+00:00","index":2,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-08-17T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-08-16T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-08-15T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-08-15T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2020-07-06 19:09:57","doi":"10.21203/rs.3.rs-40074/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-08-02T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-07-30T12:00:00+00:00","index":2,"fulltext":"Recommendation: Major revisions required\nForm responses:\n---\n\nComments to Author:\n---\nThe manuscript entitled \"Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A\" by Sihua Niu and coworkers, discusses the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Despite the work may be of interest for doctors working in the fields of ultrasonography or breast cancer, major revisions are needed for publication because the contents are not enough to explain the significance of this study. English editing should be undertaken since few sentences are not clear.\n\nAbstract:\n1. I think you need to emphasize the significance of AI in differentiation of BI-RADS 4A lesions in Conclusion portion.\n\nBackground:\n2. The background part is too simple to explain the significance of this study. Descriptions of current research results on the application of AI in BI-RADS classification should be added, and relevant references should be attached. It is also necessary to explain the advantages and significance of this study compared with previous studies.\n\nMethods：\n3.The inclusion and exclusion criteria should be explained.\n\n4.Although classifications performed by US doctors were stated \"according to the ACR BI-RADS® Atlas Fifth Edition\", specific diagnostic criteria for BI-RADS 4A should be listed.\n\n5. I think you need to explain the detailed definitions of analyzed characteristics, including \"edge roughness, edge fuzziness, margin lobules, energy, entropy, mean of grey level, grey level variance, grey level similarity, ALS.\"\n\n6. Is the data normally distributed? Relative description should be added as Means ± standard deviation and t-test can only be used for normally distributed data.\n\nResult:\n7. I suggest that demographic characteristics and pathological types be summarized in a table.\n\n8. In table 1, characteristics that have units should be labeled with units.\n\nDiscussion:\n9. As \"There were statistically significant differences in margin lobules, entropy, internal calcification and ALS between the benign and malignant groups\", I think all of those four characteristics need to be fully discussed but it seems that only the significance of entropy has been explained.\nConclusion:\n10. Again, you need to point out how AI assist in the differentiation of BI-RADS 4A lesions.* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"editorInvitedReview","content":"","date":"2020-07-30T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nThis study aimed to assess the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. The retrospective study analyzed the morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI and compared the results of the AI analysis to the ultrasonic characteristics of BI-RADS 4A benign and malignant lesions.\nThe results indicated that it is of great clinical value to apply AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. However, there are several issues need improvement.\n1. In the method part Page 4 line 79 \"A total of 206 BI-RADS 4A lesions were examined using ultrasonography\", the author should make it clear that who performed the examination and gave the BI-RADS assessment.\n2. In the method part, it is noted that all cases were confirmed using pathology. How were the pathological specimens obtained (core-needle biopsy of surgery specimen)? Please clarify.\n3. In the method part, it is noted that two doctors performed the classifications of this lesions. Did the two doctors evaluate the 206 lesions separately or did they evaluate part of that? If they evaluate the 206 lesions separately, how about the interobserver agreement?\n4. There is no mentioning of inclusion and exclusion criteria. Please clarify.\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **No**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **No**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2020-07-15T12:00:00+00:00","index":2,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-07-14T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-07-14T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorAssigned","content":"","date":"2020-06-30T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-06-29T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-06-29T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"82e523c5-c810-4c36-b452-211d92a2481b","owner":[],"postedDate":"September 16th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":334982,"name":"Cancer Biology"},{"id":334983,"name":"Oncology"}],"tags":[],"updatedAt":"2020-10-04T15:02:08+00:00","versionOfRecord":{"articleIdentity":"rs-40074","link":"https://doi.org/10.1186/s12885-020-07413-z","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2020-10-02 12:00:00","publishedOnDateReadable":"October 2nd, 2020"},"versionCreatedAt":"2020-09-16 18:50:59","video":"","vorDoi":"10.1186/s12885-020-07413-z","vorDoiUrl":"https://doi.org/10.1186/s12885-020-07413-z","workflowStages":[]},"version":"v3","identity":"rs-40074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-40074","identity":"rs-40074","version":["v3"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}