Macroscopic photograph and HE-stained overview classification of the breast cancer

Macroscopic photograph and HE-stained overview classification of the breast cancer | 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 Macroscopic photograph and HE-stained overview classification of the breast cancer Akiyoshi Hoshino, Yasuyo Ohi, Rin Yamaguchi, Hitoshi Tsuda, Ichiro Maeda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6676805/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 Establishing macroscopic features of primary breast cancer appears to be helpful in reflecting imaging diagnostic features and may be helpful in predicting histological type such as magnetic resonance imaging classification of breast cancer. Methods Hematoxylin-eosin (HE)-stained pathology slides representative of the tumor’s cut surface were collected from 105 resected breast cancers provided by three facilities. Their macroscopic contours were classified into four types: non-mass, expansive, infiltrative, and mixed. Criteria for these four types were established by examination of the test cohort (60 cases). The interobserver agreement level of the criteria was tested to establish the criteria of the four macroscopic types. To validate the criteria, all 105 cases were independently classified by four pathologists. Agreement level was tested with kappa statistics. Results In the validation of the cohort of the 105 cases, the four observers gave unanimous types to 47/105 cases, three observers gave concordant types to 31/105 cases, and two observers gave concordant types to 41/105 cases. Concordance rates among all four pathologists and between three or more pathologists were 44.8% (47/105) and 74.3% (78/105), respectively. The kappa value among the four pathologists was 0.561, which is moderate and valid for classification with no size limit. Significant differences were not detected between matches for non-mass type, expansive/solid type, infiltrate type, and mixed type. Conclusion The classification method found that the both macroscopic photograph and overview of HE-stained slide classification of breast cancer have a relatively high concordance rate among pathologists. Macroscopic photograph Hematoxylin-eosin overview Slide Breast cancer Histopathology Figures Figure 1 Figure 2 Introduction The Breast Imaging-Reporting and Data System (BI-RADS) has been developed by the American College of Radiology to improve the quality of breast cancer screening. BI-RADS standardizes the terms for mammography, ultrasound, and magnetic resonance imaging (MRI) reading and their interpretation. Standardizing the methods of reporting facilitates collection of results and accumulation and analysis of data [ 1 ]. In BI-RADS, breast cancer is focused on MRI and cases divided into mass and non-mass enhancements. Breast mass is subcategorized by shape and margin: oval, round, irregular in its shape and circumscribed or not circumscribed (irregular and speculated) in its margin. Five-millimeter resolution is required for dynamic MRI to detect mammary lesions, and voxel size of 2.5 mm or less is considered optimal for the captured image in the x, y, and z directions [ 2 ]. Although it has been reported that there are differences in prognosis depending on the subtype of invasive breast carcinoma, clinically it is more important to keep the histological type in mind when performing both image diagnosis and cytology because the subtype is well correlated with imaging diagnosis such as MRI and ultrasonic imaging of tumors with solid or scirrhous morphology [ 3 ]. Several critically important features of breast cancers are best evaluated by gross examination and sometimes can only be evaluated by gross examination, such as identification of the lesions, size of the lesions, number of lesions and relationship of lesions to each other, relationship of the lesion to margins, identification of lymph nodes and number of lymph nodes, and size of the largest lymph node metastasis [ 4 ]. Most palpable masses are grossly apparent and can be distinguished from the surrounding fibrous parenchyma. Furthermore, subtype specific characteristics may be reflected in tumor images, suggesting a relationship between subtype and tumor morphology [ 5 , 6 ]. Recently, Akashi, M., et al. reported that HER2-positive breast cancer could be divided into two major subgroups, such as luminal B or non-luminal type, based on tumor morphology and immune response [ 7 ]. Japan is one of the highest countries for MRI penetration rates in the world and MRI is commonly used in breast cancer diagnosis. It is important to compare breast cancer diagnosis obtained by MRI interpretation with that derived from a macroscopic overview of hematoxylin-eosin (HE)-stained slides because the first step in the pathological assessment of a surgical specimen is macroscopic evaluation. The histological classification of breast cancer currently used in Japan is based on the “Clinical and Pathological Breast Cancer Handling Rules’’ compiled by the Japanese Breast Cancer Society [ 9 ], but there is some disparity between these criteria and the World Health Organization (WHO) classification, which is widely used around the world. Invasive cancers are classified into common types and special types. “The Breast Cancer Handling Rules, 17th ” classified the common type into three morphological subtypes: papillotubular carcinoma, solid-tubular carcinoma, and scirrhous carcinoma [ 10 ]. However, the WHO classification does not use this subtyping. Rather, all subtypes are classified as invasive breast carcinoma of no special type. Although these histological categories are useful for comparing image findings of breast cancer subtypes, their role in determining prognosis and evaluating risk in an individual patient is more limited. Better methods are required to help assess prognosis and determine the most appropriate treatment for patients on an individual basis. In this study, to link the macroscopic morphologic diagnosis and image diagnosis, we investigated a macroscopic photograph and overview of HE-stained slide classification of breast cancer. Methods Cases The test cohort consisted of 60 breast cancer patients with 20 cases each from Kitasato University Kitasato Institute Hospital, Nagasaki University Hospital, and Sagara Hospital. The validation cohort consisted of 105 breast cancer samples resected from 105 patients who were entered into the study, 35 cases from Kitasato University Kitasato Institute Hospital, 35 cases from Nagasaki University Hospital, and 35 cases from Sagara Hospital. Gross photographs representative of cut surfaces of these 105 primary breast tumors were routinely taken with a digital camera or were collected from the pathology database of these three hospitals. In parallel, representative hematoxylin-eosin (HE)-stained slides used for routine diagnosis were selected from each breast cancer by the pathologists. JPEG files of both a macroscopic photograph and a magnified HE slide image of the 105 tumors were provided to four observers. Macroscopic photograph and HE-stained overview classification Macroscopic/overview contour of the primary breast cancers on both representative HE slides and gross photographs of cut surfaces were classified into four types: non-mass, expansive, infiltrative, and mixed (schematic figures in Figure 1). Three basic contours were defined: Pattern 1 was no mass formation totally or mostly composed of non-invasive component; Pattern 2 was round or oval shape with relatively smooth contour, and a mixed solid and cystic lesion was also included; and Pattern 3 was an irregular shape, often with a spiculated margin. When 70% or more area of the cut surface was occupied with the component of patterns 1, 2, and 3, the tumor was nominated as non-mass, expansive, and infiltrative types, respectively. Mixed type was given when two or more patterns were mixed and the second predominant pattern occupied over 30% of the area. Unclassified was defined as lesions that cannot be classified into any of the above four types. Overview images of HE slides In parallel to gross imaging photographs, representative HE-stained slides used for routine diagnosis were selected from each breast cancer by the pathologists. The overview images of each HE slide were captured by Ultra Fast scanner (Philips, Amsterdam, Netherlands), NanoZoomer S210 virtual10slide scanner (Hamamatsu Photonics, Hamamatsu, Japan), and VS-M1-IVD1 (Evident, Tatsuno, Nagano, Japan). JPEG files of both a macroscopic photograph and a magnified HE-slide image of the 105 tumors were provided to the four pathologists. Interobserver agreement Members of the test cohort were selected as typical non-mass, expansive, infiltrative, or mixed-type by the pathologist for each facility. The four pathologists independently classified these cases as one of four macroscopic types without being informed of the originally defined types. Interobserver agreement levels were calculated, and based on these results, the four pathologists discussed together, and the criteria of these four types were established. The validation cohort was provided from the three facilities, excluding the test cohort cases. A 2-week washout period was implemented after test cohort evaluation, the four pathologists independently classified the 105 cases into one of four macroscopic types. The interobserver agreement level of macroscopic classification was analyzed with kappa statistic using EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria). More precisely, it is a modified version of R commander designed to add statistical functions frequently used in biostatistics [8]. Ethical approval This study was conducted under the approval of the Ethic Issue Committee of Kitasato University Kitasato Institute Hospital (No. 2023-037). Results Macroscopic type was conferred by the four breast pathologists by viewing both macroscopic photographs and overview images of the HE slides. In 60 cases of the test cohort, all four pathologists gave concordant types to 27 cases, three pathologists gave concordant types to 16 cases, and two pathologists gave concordant types to 17 cases. There were no cases to which all four pathologists expressed a different opinion. Interobserver agreement levels between two pathologists were between 61.7% and 70.0%. Concordance rates between all four pathologists and between three or more pathologists were 45.0% (27/60) and 71.7% (43/60), respectively. The Fleiss’ kappa values between each pair of pathologists ranged from 0.477 to 0.598 without remarkable variations. Kappa values between the four pathologists were 0.524, which indicates that interobserver reproducibility was moderate to good. Through discussions among the pathologists based on the data of the test cohort, the criteria for the four macroscopic types were fixed. Applying the criteria, a validation study on the 105 cases was conducted. The four observers gave unanimous types to 47 cases, three observers gave concordant types to 31 cases, and two observers gave concordant types to 27 cases. There were no cases in which all four pathologists had different opinions. Interobserver agreement levels between two pathologists were between 61.9% and 69.5%. Concordance rates among all four pathologists and between three or more pathologists were 44.8% (47/105) and 74.3% (78/105), respectively. The kappa values between two pathologists ranged from 0.500 to 0.613 without remarkable variations in the values. The kappa value between the four pathologists was 0.561, which indicates that interobserver reproducibility was moderate to good (Table 1). Each pathologist had an inclination to give a certain type on macroscopic classification. For example, pathologist 1 tended to give non-mass type and expansive type in comparison with the other three pathologists, and pathologists 3 and 4 tended to give mixed type more frequently than the other pathologists (Table 2). The Concordance of two pathologists was 25.7% (27/105 cases). The reasons for Discordance are as follows. Twelve cases are the recognition of infiltrative regions, 7 cases are the mixture of the small regions as mixed region, 7 cases are minimal or vague lesion, and 1 case was post-chemotherapeutic status. A representative case in which differences of type recognition occurred between observers is shown in Figure 2: The tumor contains both solid tumor and infiltrative components (Figure 2a), and the tumor contains both solid tumor and intraductal components (Figure 2b). Figure 2c shows a case that is hard to classify due to the small size of the lesion (Figure 2c). Tumor size is one of the important factors in recognizing and evaluating the macroscopic/overview morphology. The measurement of tumor size is one of the most important elements of the gross examination. A representative case with a small tumor lesion is shown in Figure 2d. Although it is hard to recognize the macroscopic type, all four pathologists judged this case as non-mass type. No significant difference was observed in the number of matches regarding the tumor size (5 mm or less, >5–10 mm, >10–20 mm, 20 mm or more) (Table 3). In any of the categories, the number of cases to which interobserver concordance by three or more pathologists was achieved was higher than 70%. For each case, consensus macroscopic type was given according to the results of the interobserver agreement study. The majority opinion was adopted as consensus type, but for the case where opinion split into 2 vs 2 pathologists, discussion was prolonged until consensus on the type was reached. Of the 105 cases, consensus type of the four pathologists was non-mass in 10 (9.5%), expansive in 13 (12.4%), infiltrative in 13 (12.4%), and mixed in 10 (9.5%). There were trends in discordance between consensus macroscopic types (Table 4): Concordance between three or more pathologists was seen in 13 (12.4%), 22 (20.9%), 21 (20.0%), and 19 (18.1%) of consensus non-mass, expansive, infiltrative, and mixed types, respectively. By contrast, the number of cases in which type recognition split into 2 vs 2, or 2 vs 1 vs 1 were 10 (9.5%), 7 (6.7%), 8 (7.6%), and 14 (13.3%) in consensus non-mass, expansive, infiltrative, and mixed, respectively. The ratio of the cases that had split in type recognition into 2 vs 2, or 2 vs 1 vs 1 tended to be higher in consensus non-mass and mixed types (24 of 41) than in consensus expansive and infiltrative types (15 of 41), although the difference was not statistically significant (Table 4). The number of cases in which type recognition was split into 2 vs 2 or 2 vs 1 vs 1 was totally 108 (including duplication). Of these, 39 cases (36.1%) were classified as mixed type by consensus, which indicated that pathologists inclined to choose mixed type when a case had a non-typical contour (Table 5). Discussion In this study, we proposed a classification of macroscopic contours of primary breast cancer based on both representative HE slides and gross photographs of cut surfaces of mastectomy specimens. The classification composed of four types, non-mass, expansive, infiltrative, and mixed types, was fixed by the examination of the test cohort and validated with the analysis of another cohort. The interobserver agreement level of the classification was calculated with Fleiss’ kappa statistic. The macroscopic classification had a moderate agreement (k = 0.561) between the four pathologists. Agreement levels of consensus expansive and infiltrating types were higher (21.0% and 20.0%, respectively) than those of non-mass and mixed types (12.4% and 18.1%, respectively). Of the present classification system, expansive type and infiltrating type appear to be evaluated appropriately with relatively high interobserver agreement. These results suggest that this classification method provides stable results among the pathologists. By contrast, the criteria for non-mass type and mixed type appear to have room for improvement to increase interobserver reproducibility. There are 12 cases which evaluated discordance by three pathologist or 2 vs 2 pathologists in non-mass type, and 8 of 12 cases were evaluated as mixed type. This may be due to the recognition of non-mass feature as a mixture of multiple components. The reason for the low concordance rate of mixed type is assumed to be because mixed type is being used as the waste basket of the classification. We conclude that the method is valid for macroscopic classification. A high agreement level of consensus was achieved especially in expansive and infiltrating types. Furthermore, there were no cases in which all four pathologists provided a different opinion. This result suggests that this new classification method may be a potential method for classifying the macroscopic morphology of breast cancer. It is necessary to increase the number of cases and conduct more detailed studies to improve the concordance rate. Tumor size appears to have a large influence on the macroscopic classification. If the tumor size is small, the tumor may be classified as no-mass type regardless of whether it showed the expansive pattern or the infiltrative pattern. However, when the tumor size was stratified into pT1a, pT1b, pT1c, and pT2 or more, interobserver agreement levels did not differ among these pT categories. In any pT category, the number of cases to which interobserver concordance by three or more pathologists was achieved was higher than 70%. This suggests that the present macroscopic classification is applicable regardless of tumor size, even in pT1a. We propose that this classification method can appropriately evaluate the macroscopic morphology photograph and overview HE-images of breast cancer. By contrast, there are cases where opinion is divided among the pathologists. Concordance of two pathologists is 25.7% (27/105 cases). The possible reasons are assumed to be the differences of type recognition by pathologists. Even so, there were some cases that received different assessments even among breast pathologists. In Figure 2a, there was a difference of opinion between the pathologists who classified the entire lesion as infiltrate type and those who classified it as mixed type, which is solid fringe shape. In Figure 2b, there was a difference of opinion between the pathologists who classified the entire lesion as Expansive type and those who classified it as mixed type, which is Expansive type with DCIS lesion. Most of these cases occurred for tumors that could be considered a mixture of several components, such as mixed type (Table 5), thus indicating that mixed type tumors tend to be the main cause of discordance. In this study, mixed type was defined as a type that contains 30% or more of each characteristic, but it was found that the interpretation of the mixture of multiple components differed greatly among pathologists. Further consideration is required regarding the handling of mixed types. In Figure 2c, there is a small lesion of no recognizable tumor type. The recognition of the overview may depend on the quality of the photograph, therefore it is necessary to take a good quality, enlarged photograph. Alternatively, macroscopic/overview of HE slide may be useful. Conclusion In conclusion, the current classification system remains insufficient in terms of interobserver agreement, but it is thought that by clarifying the definitions, it is possible to improve interobserver reproducibility. Although the subject of this macroscopic classification is limited to histological types of breast cancer, by performing macroscopic classification of invasive carcinoma of no special type, it may be possible that the macroscopic classification will be useful in predicting the biological characteristics of these cancers, and examination of a larger number of cases and diverse analyses are desirable. This new classification method is a beneficial method that can appropriately and easily classify the macroscopic type of individual breast cancers. Furthermore, this method has a high possibility of achieving consistency with data obtained from MRI diagnosis. Declarations Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards This article does not contain any studies with animals performed by any of the authors. Informed consent Informed consent was obtained from all individual participants included in the study. Conflict of interest disclosures All authors have no conflicts of interest to declare. Funding sources For the creation of overview images of HE slides in this study, a slide scanner was loaned by Evident Co., Ltd. Author contributions AH: Conceptualization, methodology, formal analysis, investigation, data collection and curation, writing–original draft, and project administration. YO, RYi: Investigation, data collection and curation, and writing–review and editing. HT: Investigation and writing, review and editing. IM: Conceptualization, methodology, data collection and curation, writing, review and editing, supervision, project administration, and funding acquisition. References Spak DA, Plaxco JS, Santiago L, Dryden MJ, Dogan BE. BI-RADS((R)) fifth edition: A summary of changes. Diagn Interv Imaging. 2017;98(3):179–90. Mann RM, Kuhl CK, Kinkel K, Boetes C. Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol. 2008;18(7):1307–18. Tsunoda-Shimizu H, Hayashi N, Hamaoka T, Kawasaki T, Tsugawa K, Yagata H, et al. Determining the morphological features of breast cancer and predicting the effects of neoadjuvant chemotherapy via diagnostic breast imaging. Breast Cancer. 2008;15(2):133–40. Cleary AS, Lester SC. The Critical Role of Breast Specimen Gross Evaluation for Optimal Personalized Cancer Care. Surg Pathol Clin. 2022;15(1):121–32. Tamaki K, Ishida T, Miyashita M, Amari M, Ohuchi N, Tamaki N, et al. Correlation between mammographic findings and corresponding histopathology: potential predictors for biological characteristics of breast diseases. Cancer Sci. 2011;102(12):2179–85. Tamaki K, Ishida T, Miyashita M, Amari M, Mori N, Ohuchi N, et al. Multidetector row helical computed tomography for invasive ductal carcinoma of the breast: correlation between radiological findings and the corresponding biological characteristics of patients. Cancer Sci. 2012;103(1):67–72. Akashi M, Yamaguchi R, Kusano H, Obara H, Yamaguchi M, Toh U, et al. Diverse histomorphology of HER2-positive breast carcinomas based on differential ER expression. Histopathology. 2020;76(4):560–71. Kanda Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transpl. 2013;48(3):452–8. Tsuda H, General Rule Committee of the Japanese Breast Cancer Society. Histological classification of breast tumors in the General Rules for Clinical and Pathological Recording of Breast Cancer (18th edition). Breast Cancer. 2020;27(3):309–321. Japanese Breast Cancer Society. General rules for clinical and pathological recording of breast cancer. 17th ed. Tokyo: Kanehara Shuppan; 2012. pp. 22–34. Tables Table 1 Concordance rate for diagnosis and Fleiss’ kappa statistic of each pathologist Concordance rate Kappa statistic Pathologist 1-2 69.5% 0.613 Pathologist 1-3 67.6% 0.554 Pathologist 1-4 61.9% 0.5 Pathologist 2-3 66.7% 0.575 Pathologist 2-4 64.7% 0.557 Pathologist 3-4 66.7% 0.552 All pathologists 44.8% 0.561 Table 2 Type selection by each pathologist non-mass type Expansive and solid type infiltrate type mixed type unclassified Pathologist 1 21 34 19 24 7 Pathologist 2 24 29 26 24 2 Pathologist 3 16 19 25 37 8 Pathologist 4 14 23 31 34 3 total 75 105 101 119 20 Table 3 Difference of concordances by tumor size £0.5 cm 0.5 £ 1 cm 1 £ 2 cm >2 cm Concordance by４pathologists 2 (28.6%) 14 (37.8%) 19 (55.9%) 12 (44.4%) Concordance by 3 pathologists 3 (42.9%) 12 (32.4%) 8 (23.5%) 8 (29.6%) Concordance by 2 pathologists 2 (28.6%) 11 (29.7%) 7 (20.6%) 7 (25.9%) total 7 (100%) 37 (100%) 34 (100%) 27 (100%) Table 4 Difference between concordance of more than three pathologists and 2 vs 2 evaluation non-mass type expansive and solid type infiltrate type mixed type unclassified Concordance of more than three pathologists 13 (12.4%) 22 (21.0%) 21 (20.0%) 19 (18.1%) 3 (2.9%) Different cases by 2 vs 2, and 2 vs 1 vs 1 evaluation 10 (9.5%) 7 (6.7%) 8 (7.6%) 14 (13.3%) 2 (1.9%) p 0.310589 0.178884 0.370854 0.257058 0.789753 Table 5 Total case numbers of 2 vs 2 and 2 vs 1 vs 1 evaluation (including duplicates) non-mass type expansive and solid type infiltrate type mixed type unclassified Case numbers by 2 vs 2 and 2 vs 1 vs 1 evaluation 22 22 19 39 6 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6676805","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457479218,"identity":"dd4d7e6b-1267-43cf-9173-cb56b8db54f2","order_by":0,"name":"Akiyoshi Hoshino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBAC9gYgwQgkDJiBjA8MDDwQcR7cWngOIGlhnEGaFiDNjEchkhbpw4dfMO6wsTdn5z342LZtm4w5A/PDDwwyd3Br4UtLs2A8k5a4s5kv2Ti37TaPZQObsQQDzzOcWux5eMyM/7YdTjA4zGMmDdJicIDBDGjUYdy2ALUYMLYdtgdrsQRrYf9GSIvxA6AWxg0gLYxgLTyEbGFLY2BsS0sEajE27DkH1HKYp1giAY9feHiYD39gbLOxNzh/xvDBj7Lb9gbH2zd++NiDO8SAgE0ClQ9KBok9B/BpYf6ARfAHXi2jYBSMglEwsgAATs9Lu3tj9D4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6343-2164","institution":"Kitasato Institute Hospital: Kitasato Kenkyujo Byoin","correspondingAuthor":true,"prefix":"","firstName":"Akiyoshi","middleName":"","lastName":"Hoshino","suffix":""},{"id":457479219,"identity":"0f6410a4-66cb-4ca8-a934-e04c37d32559","order_by":1,"name":"Yasuyo Ohi","email":"","orcid":"","institution":"Sagara Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yasuyo","middleName":"","lastName":"Ohi","suffix":""},{"id":457479220,"identity":"f2ec484a-5f08-4db1-a775-c8e4c472068b","order_by":2,"name":"Rin Yamaguchi","email":"","orcid":"","institution":"Nagasaki University: Nagasaki Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Rin","middleName":"","lastName":"Yamaguchi","suffix":""},{"id":457479221,"identity":"9ab153b5-2b15-4ebf-82a7-33a05ece8b45","order_by":3,"name":"Hitoshi Tsuda","email":"","orcid":"","institution":"national defence medial college","correspondingAuthor":false,"prefix":"","firstName":"Hitoshi","middleName":"","lastName":"Tsuda","suffix":""},{"id":457479222,"identity":"0e90a0e3-bb82-4e19-9a8c-6f836d8fcc69","order_by":4,"name":"Ichiro Maeda","email":"","orcid":"https://orcid.org/0000-0003-4127-7287","institution":"Kitasato Institute Hospital: Kitasato Kenkyujo Byoin","correspondingAuthor":false,"prefix":"","firstName":"Ichiro","middleName":"","lastName":"Maeda","suffix":""}],"badges":[],"createdAt":"2025-05-16 03:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6676805/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6676805/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83284237,"identity":"08734a93-e51c-4b86-bf71-037beab59174","added_by":"auto","created_at":"2025-05-22 10:56:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3404612,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic figure and photos of macroscopic overview of the breast cancer. A, Non-mass type. B, Expansive and solid type. A solid mass growing expansively in the center of the tumor. C, Infiltrated type. Mass growing radially or irregularly.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6676805/v1/ca88d58e7836669a986dd841.png"},{"id":83284236,"identity":"43beac72-1e15-48c3-9999-95b972be548b","added_by":"auto","created_at":"2025-05-22 10:56:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2017159,"visible":true,"origin":"","legend":"\u003cp\u003eThe cases in which differences of type recognition occurred with the pathologists.\u003c/p\u003e\n\u003cp\u003ea), Case #96, two pathologists classified it as infiltrative type, whereas the other two pathologists classified it as mixed type, which is infiltrative type mixed with solid components. b), Case #42, two pathologists classified it as expansive type, whereas the other two pathologists classified it as mixed type, which is expansive type mixed with DCIS lesion. c), Case #33, tumor is too small to classify. \u0026nbsp;d) Case #81\u003cstrong\u003e.\u003c/strong\u003e A case with small tumor lesion. The tumor lesion is hard to recognize by both macroscopic photograph and HE-stained overview, but all four pathologists judged this case as non-mass type.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6676805/v1/1fe262ee9e2b8b4bb1b6cfd8.png"},{"id":85246162,"identity":"a2d1da94-2a0a-4644-b206-1c0e6551e4df","added_by":"auto","created_at":"2025-06-23 21:26:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6491025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6676805/v1/ae47a116-a513-4851-80d0-1f128b79c9d0.pdf"}],"financialInterests":"","formattedTitle":"Macroscopic photograph and HE-stained overview classification of the breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Breast Imaging-Reporting and Data System (BI-RADS) has been developed by the American College of Radiology to improve the quality of breast cancer screening. BI-RADS standardizes the terms for mammography, ultrasound, and magnetic resonance imaging (MRI) reading and their interpretation. Standardizing the methods of reporting facilitates collection of results and accumulation and analysis of data [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In BI-RADS, breast cancer is focused on MRI and cases divided into mass and non-mass enhancements. Breast mass is subcategorized by shape and margin: oval, round, irregular in its shape and circumscribed or not circumscribed (irregular and speculated) in its margin. Five-millimeter resolution is required for dynamic MRI to detect mammary lesions, and voxel size of 2.5 mm or less is considered optimal for the captured image in the x, y, and z directions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough it has been reported that there are differences in prognosis depending on the subtype of invasive breast carcinoma, clinically it is more important to keep the histological type in mind when performing both image diagnosis and cytology because the subtype is well correlated with imaging diagnosis such as MRI and ultrasonic imaging of tumors with solid or scirrhous morphology [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Several critically important features of breast cancers are best evaluated by gross examination and sometimes can only be evaluated by gross examination, such as identification of the lesions, size of the lesions, number of lesions and relationship of lesions to each other, relationship of the lesion to margins, identification of lymph nodes and number of lymph nodes, and size of the largest lymph node metastasis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Most palpable masses are grossly apparent and can be distinguished from the surrounding fibrous parenchyma. Furthermore, subtype specific characteristics may be reflected in tumor images, suggesting a relationship between subtype and tumor morphology [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recently, Akashi, M., et al. reported that HER2-positive breast cancer could be divided into two major subgroups, such as luminal B or non-luminal type, based on tumor morphology and immune response [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eJapan is one of the highest countries for MRI penetration rates in the world and MRI is commonly used in breast cancer diagnosis. It is important to compare breast cancer diagnosis obtained by MRI interpretation with that derived from a macroscopic overview of hematoxylin-eosin (HE)-stained slides because the first step in the pathological assessment of a surgical specimen is macroscopic evaluation. The histological classification of breast cancer currently used in Japan is based on the \u0026ldquo;Clinical and Pathological Breast Cancer Handling Rules\u0026rsquo;\u0026rsquo; compiled by the Japanese Breast Cancer Society [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], but there is some disparity between these criteria and the World Health Organization (WHO) classification, which is widely used around the world. Invasive cancers are classified into common types and special types. \u0026ldquo;The Breast Cancer Handling Rules, 17th \u0026rdquo; classified the common type into three morphological subtypes: papillotubular carcinoma, solid-tubular carcinoma, and scirrhous carcinoma [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the WHO classification does not use this subtyping. Rather, all subtypes are classified as invasive breast carcinoma of no special type. Although these histological categories are useful for comparing image findings of breast cancer subtypes, their role in determining prognosis and evaluating risk in an individual patient is more limited. Better methods are required to help assess prognosis and determine the most appropriate treatment for patients on an individual basis. In this study, to link the macroscopic morphologic diagnosis and image diagnosis, we investigated a macroscopic photograph and overview of HE-stained slide classification of breast cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eCases\u003c/h2\u003e\n\u003cp\u003eThe test cohort consisted of 60 breast cancer patients with 20 cases each from Kitasato University Kitasato Institute Hospital, Nagasaki University Hospital, and Sagara Hospital. The validation cohort consisted of 105 breast cancer samples resected from 105 patients who were entered into the study, 35 cases from Kitasato University Kitasato Institute Hospital, 35 cases from Nagasaki University Hospital, and 35 cases from Sagara Hospital. Gross photographs representative of cut surfaces of these 105 primary breast tumors were routinely taken with a digital camera or were collected from the pathology database of these three hospitals. In parallel, representative hematoxylin-eosin (HE)-stained slides used for routine diagnosis were selected from each breast cancer by the pathologists. JPEG files of both a macroscopic photograph and a magnified HE slide image of the 105 tumors were provided to four observers.\u003c/p\u003e\n\u003ch2\u003eMacroscopic photograph and HE-stained overview classification\u003c/h2\u003e\n\u003cp\u003eMacroscopic/overview contour of the primary breast cancers on both representative HE slides and gross photographs of cut surfaces were classified into four types: non-mass, expansive, infiltrative, and mixed (schematic figures in Figure 1). Three basic contours were defined: Pattern 1 was no mass formation totally or mostly composed of non-invasive component; Pattern 2 was round or oval shape with relatively smooth contour, and a mixed solid and cystic lesion was also included; and Pattern 3 was an irregular shape, often with a spiculated margin. When 70% or more area of the cut surface was occupied with the component of patterns 1, 2, and 3, the tumor was nominated as non-mass, expansive, and infiltrative types, respectively. Mixed type was given when two or more patterns were mixed and the second predominant pattern occupied over 30% of the area. Unclassified was defined as lesions that cannot be classified into any of the above four types.\u003c/p\u003e\n\u003ch2\u003eOverview images of HE slides\u003c/h2\u003e\n\u003cp\u003eIn parallel to gross imaging photographs, representative HE-stained slides used for routine diagnosis were selected from each breast cancer by the pathologists. The overview images of each HE slide were captured by Ultra Fast scanner (Philips, Amsterdam, Netherlands), NanoZoomer S210 virtual10slide scanner (Hamamatsu Photonics, Hamamatsu, Japan), and VS-M1-IVD1 (Evident, Tatsuno, Nagano, Japan).\u003c/p\u003e\n\u003cp\u003eJPEG files of both a macroscopic photograph and a magnified HE-slide image of the 105 tumors were provided to the four pathologists.\u003c/p\u003e\n\u003ch2\u003eInterobserver agreement\u003c/h2\u003e\n\u003cp\u003eMembers of the test cohort were selected as typical non-mass, expansive, infiltrative, or mixed-type by the pathologist for each facility. The four pathologists independently classified these cases as one of four macroscopic types without being informed of the originally defined types. Interobserver agreement levels were calculated, and based on these results, the four pathologists discussed together, and the criteria of these four types were established.\u003c/p\u003e\n\u003cp\u003eThe validation cohort was provided from the three facilities, excluding the test cohort cases. A 2-week washout period was implemented after test cohort evaluation, the four pathologists independently classified the 105 cases into one of four macroscopic types.\u003c/p\u003e\n\u003cp\u003eThe interobserver agreement level of macroscopic classification was analyzed with kappa statistic using EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria). More precisely, it is a modified version of R commander designed to add statistical functions frequently used in biostatistics [8].\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThis study was conducted under the approval of the Ethic Issue Committee of Kitasato University Kitasato Institute Hospital (No. 2023-037).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMacroscopic type was conferred by the four breast pathologists by viewing both macroscopic photographs and overview images of the HE slides.\u003c/p\u003e\n\u003cp\u003eIn 60 cases of the test cohort, all four pathologists gave concordant types to 27 cases, three pathologists gave concordant types to 16 cases, and two pathologists gave concordant types to 17 cases. There were no cases to which all four pathologists expressed a different opinion. Interobserver agreement levels between two pathologists were between 61.7% and 70.0%. Concordance rates between all four pathologists and between three or more pathologists were 45.0% (27/60) and 71.7% (43/60), respectively. The Fleiss\u0026rsquo; kappa values between each pair of pathologists ranged from 0.477 to 0.598 without remarkable variations. Kappa values between the four pathologists were 0.524, which indicates that interobserver reproducibility was moderate to good.\u003c/p\u003e\n\u003cp\u003eThrough discussions among the pathologists based on the data of the test cohort, the criteria for the four macroscopic types were fixed. Applying the criteria, a validation study on the 105 cases was conducted. The four observers gave unanimous types to 47 cases, three observers gave concordant types to 31 cases, and two observers gave concordant types to 27 cases. There were no cases in which all four pathologists had different opinions. Interobserver agreement levels between two pathologists were between 61.9% and 69.5%. Concordance rates among all four pathologists and between three or more pathologists were 44.8% (47/105) and 74.3% (78/105), respectively. The kappa values between two pathologists ranged from 0.500 to 0.613 without remarkable variations in the values. The kappa value between the four pathologists was 0.561, which indicates that interobserver reproducibility was moderate to good (Table 1).\u003c/p\u003e\n\u003cp\u003eEach pathologist had an inclination to give a certain type on macroscopic classification. For example, pathologist 1 tended to give non-mass type and expansive type in comparison with the other three pathologists, and pathologists 3 and 4 tended to give mixed type more frequently than the other pathologists (Table 2).\u003c/p\u003e\n\u003cp\u003eThe Concordance of two pathologists was 25.7% (27/105 cases). The reasons for Discordance are as follows. Twelve cases are the recognition of infiltrative regions, 7 cases are the mixture of the small regions as mixed region, 7 cases are minimal or vague lesion, and 1 case was post-chemotherapeutic status. A representative case in which differences of type recognition occurred between observers is shown in Figure 2: The tumor contains both solid tumor and infiltrative components (Figure 2a), and the tumor contains both solid tumor and intraductal components (Figure 2b). Figure 2c shows a case that is hard to classify due to the small size of the lesion (Figure 2c).\u003c/p\u003e\n\u003cp\u003eTumor size is one of the important factors in recognizing and evaluating the macroscopic/overview morphology. The measurement of tumor size is one of the most important elements of the gross examination. A representative case with a small tumor lesion is shown in Figure 2d. Although it is hard to recognize the macroscopic type, all four pathologists judged this case as non-mass type. No significant difference was observed in the number of matches regarding the tumor size (5 mm or less, \u0026gt;5\u0026ndash;10 mm, \u0026gt;10\u0026ndash;20 mm, 20 mm or more) (Table 3). In any of the categories, the number of cases to which interobserver concordance by three or more pathologists was achieved was higher than 70%.\u003c/p\u003e\n\u003cp\u003eFor each case, consensus macroscopic type was given according to the results of the interobserver agreement study. The majority opinion was adopted as consensus type, but for the case where opinion split into 2 vs 2 pathologists, discussion was prolonged until consensus on the type was reached. Of the 105 cases, consensus type of the four pathologists was non-mass in 10 (9.5%), expansive in 13 (12.4%), infiltrative in 13 (12.4%), and mixed in 10 (9.5%). There were trends in discordance between consensus macroscopic types (Table 4): Concordance between three or more pathologists was seen in 13 (12.4%), 22 (20.9%), 21 (20.0%), and 19 (18.1%) of consensus non-mass, expansive, infiltrative, and mixed types, respectively.\u003c/p\u003e\n\u003cp\u003eBy contrast, the number of cases in which type recognition split into 2 vs 2, or 2 vs 1 vs 1 were 10 (9.5%), 7 (6.7%), 8 (7.6%), and 14 (13.3%) in consensus non-mass, expansive, infiltrative, and mixed, respectively. The ratio of the cases that had split in type recognition into 2 vs 2, or 2 vs 1 vs 1 tended to be higher in consensus non-mass and mixed types (24 of 41) than in consensus expansive and infiltrative types (15 of 41), although the difference was not statistically significant (Table 4). The number of cases in which type recognition was split into 2 vs 2 or 2 vs 1 vs 1 was totally 108 (including duplication). Of these, 39 cases (36.1%) were classified as mixed type by consensus, which indicated that pathologists inclined to choose mixed type when a case had a non-typical contour (Table 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we proposed a classification of macroscopic contours of primary breast cancer based on both representative HE slides and gross photographs of cut surfaces of mastectomy specimens. The classification composed of four types, non-mass, expansive, infiltrative, and mixed types, was fixed by the examination of the test cohort and validated with the analysis of another cohort. The interobserver agreement level of the classification was calculated with Fleiss\u0026rsquo; kappa statistic.\u003c/p\u003e\n\u003cp\u003eThe macroscopic classification had a moderate agreement (k = 0.561) between the four pathologists. Agreement levels of consensus expansive and infiltrating types were higher (21.0% and 20.0%, respectively) than those of non-mass and mixed types (12.4% and 18.1%, respectively). Of the present classification system, expansive type and infiltrating type appear to be evaluated appropriately with relatively high interobserver agreement. These results suggest that this classification method provides stable results among the pathologists. By contrast, the criteria for non-mass type and mixed type appear to have room for improvement to increase interobserver reproducibility. There are 12 cases which evaluated discordance by three pathologist or 2 vs 2 pathologists in non-mass type, and 8 of 12 cases were evaluated as mixed type. This may be due to the recognition of non-mass feature as a mixture of multiple components. The reason for the low concordance rate of mixed type is assumed to be because mixed type is being used as the waste basket of the classification. We conclude that the method is valid for macroscopic classification. A high agreement level of consensus was achieved especially in expansive and infiltrating types. Furthermore, there were no cases in which all four pathologists provided a different opinion. This result suggests that this new classification method may be a potential method for classifying the macroscopic morphology of breast cancer. It is necessary to increase the number of cases and conduct more detailed studies to improve the concordance rate.\u003c/p\u003e\n\u003cp\u003eTumor size appears to have a large influence on the macroscopic classification. If the tumor size is small, the tumor may be classified as no-mass type regardless of whether it showed the expansive pattern or the infiltrative pattern. However, when the tumor size was stratified into pT1a, pT1b, pT1c, and pT2 or more, interobserver agreement levels did not differ among these pT categories. In any pT category, the number of cases to which interobserver concordance by three or more pathologists was achieved was higher than 70%. This suggests that the present macroscopic classification is applicable regardless of tumor size, even in pT1a.\u003c/p\u003e\n\u003cp\u003eWe propose that this classification method can appropriately evaluate the macroscopic morphology photograph and overview HE-images of breast cancer. By contrast, there are cases where opinion is divided among the pathologists. Concordance of two pathologists is 25.7% (27/105 cases). The possible reasons are assumed to be the differences of type recognition by pathologists. Even so, there were some cases that received different assessments even among breast pathologists. In Figure 2a, there was a difference of opinion between the pathologists who classified the entire lesion as infiltrate type and those who classified it as mixed type, which is solid fringe shape. In Figure 2b, there was a difference of opinion between the pathologists who classified the entire lesion as Expansive type and those who classified it as mixed type, which is Expansive type with DCIS lesion. Most of these cases occurred for tumors that could be considered a mixture of several components, such as mixed type (Table 5), thus indicating that mixed type tumors tend to be the main cause of discordance. In this study, mixed type was defined as a type that contains 30% or more of each characteristic, but it was found that the interpretation of the mixture of multiple components differed greatly among pathologists. Further consideration is required regarding the handling of mixed types. In Figure 2c, there is a small lesion of no recognizable tumor type. The recognition of the overview may depend on the quality of the photograph, therefore it is necessary to take a good quality, enlarged photograph. Alternatively, macroscopic/overview of HE slide may be useful.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the current classification system remains insufficient in terms of interobserver agreement, but it is thought that by clarifying the definitions, it is possible to improve interobserver reproducibility. Although the subject of this macroscopic classification is limited to histological types of breast cancer, by performing macroscopic classification of invasive carcinoma of no special type, it may be possible that the macroscopic classification will be useful in predicting the biological characteristics of these cancers, and examination of a larger number of cases and diverse analyses are desirable.\u003c/p\u003e\n\u003cp\u003eThis new classification method is a beneficial method that can appropriately and easily classify the macroscopic type of individual breast cancers. Furthermore, this method has a high possibility of achieving consistency with data obtained from MRI diagnosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the creation of overview images of HE slides in this study, a slide scanner was loaned by Evident Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAH: Conceptualization, methodology, formal analysis, investigation, data collection and curation, writing\u0026ndash;original draft, and project administration. YO, RYi: Investigation, data collection and curation, and writing\u0026ndash;review and editing. HT: Investigation and writing, review and editing. IM: Conceptualization, methodology, data collection and curation, writing, review and editing, supervision, project administration, and funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSpak DA, Plaxco JS, Santiago L, Dryden MJ, Dogan BE. BI-RADS((R)) fifth edition: A summary of changes. Diagn Interv Imaging. 2017;98(3):179\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann RM, Kuhl CK, Kinkel K, Boetes C. Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol. 2008;18(7):1307\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsunoda-Shimizu H, Hayashi N, Hamaoka T, Kawasaki T, Tsugawa K, Yagata H, et al. Determining the morphological features of breast cancer and predicting the effects of neoadjuvant chemotherapy via diagnostic breast imaging. Breast Cancer. 2008;15(2):133\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCleary AS, Lester SC. The Critical Role of Breast Specimen Gross Evaluation for Optimal Personalized Cancer Care. Surg Pathol Clin. 2022;15(1):121\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamaki K, Ishida T, Miyashita M, Amari M, Ohuchi N, Tamaki N, et al. Correlation between mammographic findings and corresponding histopathology: potential predictors for biological characteristics of breast diseases. Cancer Sci. 2011;102(12):2179\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamaki K, Ishida T, Miyashita M, Amari M, Mori N, Ohuchi N, et al. Multidetector row helical computed tomography for invasive ductal carcinoma of the breast: correlation between radiological findings and the corresponding biological characteristics of patients. Cancer Sci. 2012;103(1):67\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkashi M, Yamaguchi R, Kusano H, Obara H, Yamaguchi M, Toh U, et al. Diverse histomorphology of HER2-positive breast carcinomas based on differential ER expression. Histopathology. 2020;76(4):560\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanda Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transpl. 2013;48(3):452\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsuda H, General Rule Committee of the Japanese Breast Cancer Society. Histological classification of breast tumors in the General Rules for Clinical and Pathological Recording of Breast Cancer (18th edition). Breast Cancer. 2020;27(3):309\u0026ndash;321.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJapanese Breast Cancer Society. General rules for clinical and pathological recording of breast cancer. 17th ed. Tokyo: Kanehara Shuppan; 2012. pp. 22\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Concordance rate for diagnosis and Fleiss\u0026rsquo; kappa statistic of each pathologist\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"463\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003eConcordance rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKappa\u0026nbsp;statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePathologist 1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e69.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePathologist 1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e67.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePathologist 1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e61.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePathologist 2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePathologist 2-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e64.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePathologist 3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eAll pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e44.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Type selection by each pathologist\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003enon-mass type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eExpansive and solid type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003einfiltrate type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003emixed type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eunclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePathologist 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePathologist 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePathologist 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePathologist 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Difference of concordances by tumor size\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026pound;0.5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.5\u0026nbsp;\u0026pound;\u0026nbsp;1 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u0026pound;\u0026nbsp;2 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026gt;2 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eConcordance\u0026nbsp;by４pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e14 (37.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e19 (55.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eConcordance\u0026nbsp;by 3 pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e12 (32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eConcordance\u0026nbsp;by 2 pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e11 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e37 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e34 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e27 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Difference between concordance of more than three pathologists and 2 vs 2 evaluation\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003enon-mass type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eexpansive and solid type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003einfiltrate type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003emixed type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eunclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eConcordance\u0026nbsp;of more than three pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e13 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e22 (21.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e21 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e19 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eDifferent cases by 2 vs 2, and 2 vs 1 vs 1 evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e10 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e8 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e14 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.310589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.178884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.370854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.257058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.789753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Total case numbers of 2 vs 2 and 2 vs 1 vs 1 evaluation (including duplicates)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003enon-mass type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eexpansive and solid type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003einfiltrate type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003emixed type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eunclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eCase numbers by 2 vs 2 and 2 vs 1 vs 1 evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Macroscopic photograph, Hematoxylin-eosin overview, Slide, Breast cancer, Histopathology","lastPublishedDoi":"10.21203/rs.3.rs-6676805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6676805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e Establishing macroscopic features of primary breast cancer appears to be helpful in reflecting imaging diagnostic features and may be helpful in predicting histological type such as magnetic resonance imaging classification of breast cancer.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e Hematoxylin-eosin (HE)-stained pathology slides representative of the tumor\u0026rsquo;s cut surface were collected from 105 resected breast cancers provided by three facilities. Their macroscopic contours were classified into four types: non-mass, expansive, infiltrative, and mixed. Criteria for these four types were established by examination of the test cohort (60 cases). The interobserver agreement level of the criteria was tested to establish the criteria of the four macroscopic types. To validate the criteria, all 105 cases were independently classified by four pathologists. Agreement level was tested with kappa statistics.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e In the validation of the cohort of the 105 cases, the four observers gave unanimous types to 47/105 cases, three observers gave concordant types to 31/105 cases, and two observers gave concordant types to 41/105 cases. Concordance rates among all four pathologists and between three or more pathologists were 44.8% (47/105) and 74.3% (78/105), respectively. The kappa value among the four pathologists was 0.561, which is moderate and valid for classification with no size limit. Significant differences were not detected between matches for non-mass type, expansive/solid type, infiltrate type, and mixed type.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e The classification method found that the both macroscopic photograph and overview of HE-stained slide classification of breast cancer have a relatively high concordance rate among pathologists.\u003c/p\u003e","manuscriptTitle":"Macroscopic photograph and HE-stained overview classification of the breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 10:48:53","doi":"10.21203/rs.3.rs-6676805/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":"4eaa24ff-86ad-4467-8928-76e58aecb3e2","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T21:18:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-22 10:48:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6676805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6676805","identity":"rs-6676805","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}