Interobserver Reproducibility Study of Macroscopic Classification of Breast Cancer from Representative Cut surface of Resected Specimens and Hematoxylin–Eosin-Stained Slides

Interobserver Reproducibility Study of Macroscopic Classification of Breast Cancer from Representative Cut surface of Resected Specimens and Hematoxylin–Eosin-Stained Slides | 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 Interobserver Reproducibility Study of Macroscopic Classification of Breast Cancer from Representative Cut surface of Resected Specimens and Hematoxylin–Eosin-Stained Slides Akiyoshi Hoshino, Yasuyo Ohi, Rin Yamaguchi, Hitoshi Tsuda, Ichiro Maeda This is a preprint; it has not been peer reviewed by a journal.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-8615732/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Characteristics in shape and margins detected with diagnostic imaging are key features of breast cancers, but consensus on the criteria for macroscopic classification of breast cancer has yet to be established. The General Rules for Clinical and Pathological Recording of Breast Cancer, 19th edition, of Japan has adopted a macroscopic classification of breast cancer. Methods In this study, four breast pathologists evaluated interobserver agreement level of the macroscopic classification for breast cancer cases using macroscopic photographs of cut surfaces of resected specimens and their hematoxylin–eosin (H&E)-stained slide images, which were provided from pathology database of three facilities. From macroscopic shape and margins, these cases were classified into four types: non-mass, expansive, infiltrative, and mixed. Criteria for each type were established for the training set (n = 60), and then four pathologists independently classified 105 cases as the validation set. Interobserver agreement levels were calculated with kappa statistics. Results In the validation set, consensus types were non-mass, expansive, infiltrative, and mixed in 18, 22, 25, and 35 cases, respectively. The four observers gave unanimous types for 47 cases (45%), and three or more observers gave concordant types for 78 cases (74%). An agreement level between the four observers was moderate (kappa = 0.561) in total, but an agreement levels was substantial in pT1c category (kappa = 0.641). Conclusion Although the agreement level of the classification was still moderate in total, setting of more specific and quantitative criteria and further studies may enhance reproducibility of the macroscopic classification. macroscopic classification breast cancer interobserver reproducibility Figures Figure 1 Figure 2 Introduction The 2019 World Health Organization (WHO) classification of tumors of the breast, which is widely used around the world, does not adopt a histological subtype classification of invasive ductal carcinoma, which are classified as invasive breast carcinoma of no special type (IBC-NST), except for the special types. The WHO classification describes the gross histological patterns seen in IBC-NST, including some special gross morphological patterns such as scirrhous invasive pattern, and expansive growth pattern, but these are not considered clinically distinct subtypes ( 1 ). In contrast, up to the 17th edition of the General Rules for Clinical and Pathological Recording of Breast Cancer of Japan, invasive ductal carcinoma was classified into three histological subtypes: namely papillotubular carcinoma, solid-tubular carcinoma, and scirrhous carcinoma, depending on the tumor size and the shape of the tumor margin and histological features ( 2 ). The classification of invasive ductal carcinoma into three histological subtypes was the most distinctive feature of these General Rules. Imaging diagnosis tools, mammography, ultrasound, and magnetic resonance imaging (MRI) are commonly used for qualitative evaluation of breast lesions. The American College of Radiology developed a Breast Imaging-Reporting and Data System (BI-RADS) to standardize the terms for mammography, ultrasound, and MRI readings and their interpretation and to improve the quality of breast cancer screening ( 3 ). In BI-RADS, MRI images as well as mammography and ultrasound of breast cancer can be divided into mass and non-mass enhancements, where breast masses are subcategorized into oval, round, and irregular in shape, and circumscribed and not circumscribed (i.e., irregular and spiculated) in contour ( 4 ). Classifications of morphology of breast cancer using imaging diagnosis, e.g., ultrasound, mammography, computed tomography, and MRI, were shown to be correlated with biological characteristics ( 5 – 7 ). Tsunoda-Shimizu et al classified 186 invasive breast cancers into 4 morphological types using ultrasound features ( 5 ). These four types comprised carcinoma that tend to grow along the mammary ducts (type A1), expansively growing that is relatively well-defined (type A2), irregularly-shaped that retracts surrounding tissue (type A3), and type that does not fall under any of A1 to A3 (mixed), and the ratio of pathological complete response after neoadjuvant chemotherapy was significantly higher in type A2 than other three types ( 5 ). Tamaki et al. showed that mammographic classification of primary invasive breast cancer based on mass shape and margin was significantly correlated with clinical subtype, grade and Ki-67 status ( 6 ). Irregular mass shape and spiculate margin were correlated with luminal A-like type, oval or round shape and microlobulated margin were correlated with HER2 type, and indistinct margin with triple negative type ( 6 ). Correlations of tumor macroscopic morphology according to cut surface of resected specimen was also shown to be associated with histological features and clinical subtypes. Akashi et al. reported that HER2-positive breast cancer had characteristic tumor morphology ( 8 ). Therefore, similarly with imaging diagnostic classifications, gross classifications of resected breast cancer specimens appear to be significantly associated with biological characteristics and clinical subtype classification ( 9 ). In the revision of the 19th edition of the General Rules for Clinical and Pathological Recording of Breast Cancer of Japan, three subtypes of invasive ductal carcinoma, or invasive carcinoma no specific type, i.e., tubule forming, solid, and scirrhous types, were abandoned and, instead, description of macroscopic types based on tumor shape and margin is recommended: these four types comprise non-mass, expansive, infiltrative, and mixed ( 10 ). Based on the revision of the General Rules, Hara et al. demonstrated that a gross classification based on tumor cut surface morphological classification of breast carcinoma correlates with the molecular biological properties of breast cancer ( 9 ). However, how these types are reproducible between pathologists is unknown. In the present study, to validate this macroscopic classification, we investigated the concordance rates of macroscopic diagnoses among four breast pathologists. Methods Cases The training set consists of 60 breast cancer samples resected from patients with 20 cases each from Kitasato University Kitasato Institute Hospital, Nagasaki University Hospital, and Sagara Hospital, respectively. The validation set 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. Macroscopic Photographs of Resected Specimens and H&E-Stained Slides For each breast carcinoma, a representative macroscopic photograph, which was taken with the digital camera during routine work, was collected from the pathology databases of the three hospitals. In parallel to the gross imaging photograph, a representative H&E-stained slide used for routine diagnosis of the corresponding carcinoma was selected by these pathologists. The overview image of each H&E slide was captured using an Ultra Fast Scanner (Philips, Amsterdam, Netherlands), NanoZoomer S210 virtual slide scanner (Hamamatsu Photonics, Hamamatsu, Japan), and VS-M1-IVD1 (Evident, Tatsuno, Nagano, Japan). The four pathologists were provided with JPEG files of both the macroscopic photograph and the low-magnified H&E overview image of the 105 tumors. Classification Criteria With reference to the reports of Tsunoda-Shimizu et al., and Hara et al., macroscopic shape of the primary breast cancers was classified based on both gross photographs of cut surface and representative H&E slides into four types: non-mass, expansive, infiltrative, and mixed (see the schematic figures in Fig. 1 ). Three basic patterns were defined: Pattern 1 was non-mass formation, that exhibits obvious discoloration or multiple small round nests without forming discrete mass. This pattern includes accumulation of dilated ducts. Pattern 2 was round or oval and circumscribed or lobulated shape with a relatively smooth or a distinct margin, and large cystic lesion was also included. Pattern 3 was an irregular shape, with a speculated, serrated or indistinct margin that often retracts the surrounding tissues. When 70% or more part of margin surrounding the cut surface was occupied with patterns 1, 2, and 3, the tumor was nominated as non-mass, expansive, and infiltrative types, respectively. Mixed types was nominated when two or more patterns were mixed, and the second predominant pattern occupied over 30% of the contour. The lesions that cannot be classified into any of the above four types were categorized as unclassified. Training and Validation Studies As the training set, macroscopic images of 60 cases composed of both gross photographs of cut surfaces of resected specimen and H&E-stained slides were provided to the four pathologists. Each of originally defined non-mass, expansive, infiltrative, and mixed types comprised 15 cases. The four pathologists independently classified these cases as one of four macroscopic types without being informed of the originally defined types. After discussion by the four pathologists based on these results, abovementioned consensus criteria of these four types were established. Data for the validation set comprising macroscopic and H&E images of 105 cases were provided for the four observers. The validation was performed after a 2-week washout period based on the College of American Pathologists’ guidelines for validation in digital pathology ( 11 ). Four breast pathologists independently classified the 105 cases into the four macroscopic types. Based on the summary of the evaluations of the four observers, consensus macroscopic type was given to each case. 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 ( 12 ). Association between tumor size and macroscopic pattern Microscopic invasive tumor sizes of 105 carcinomas were stratified into 4 groups, i.e., pT1a (≤ 0.5 cm), pT1b (> 0.5 and ≤ 1 cm), pT1c (> 1 and ≤ 2 cm), and pT2 or higher (> 2 cm). Concordance rates between pathologists in macroscopic type were evaluated for each group. Characteristic macroscopic type and difference in concordance rates in judgement were compared between the groups. Ethical Approval This study was approved by the Ethics Review Committee of Kitasato University Kitasato Institute Hospital (No. 2023-037). Results In 60 cases from the training set, all four pathologists gave concordant types to 27 cases (45%), and three or more pathologists gave concordant types to 43 cases (72%), while two pathologists gave concordant types to 17 cases (28%). There were no cases in which all four pathologists expressed a different opinion. The interobserver agreement levels between two pathologists were between 61.7% and 70.0%. Kappa values between each pair of pathologists ranged from 0.477 to 0.598 without remarkable variations, and the value among the four pathologists was 0.524, indicating moderate agreement. In the validation study for the 105 cases, the four observers gave unanimous types to 47 cases (45%), three or more observers gave concordant types to 78 cases (74%), and two observers gave concordant types to 27 cases (26%) (Table 1). 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%. Kappa values between two pathologists ranged from 0.500 to 0.613 without remarkable variations, and the value among the four pathologists was 0.561, which indicates moderate agreement. Final consensus types given by the consensus of the four observers were non-mass in 18, expansive in 22, infiltrative in 125, mixed in 35, and unclassified in 5. Table 2 shows the number of cases of each macroscopic types given by four pathologists. The number of cases that showed discordance in types between the consensus and each pathologist’s judgments was 28 (26.6%), 28 (26.6%), 27 (25.7%), and 31 (29.5%) in pathologists 1, 2, 3 and 4, respectively. Of the 105 cases, the four pathologists gave unanimous macroscopic types for 10 (56%) of 18 consensus non-mass, 13 (59%) of 22 consensus expansive, 13 (52%) of 25 consensus infiltrative, and 10 (29%) of 35 consensus mixed cases (Table 3). Concordance among three or more pathologists was seen for 13 (72%) cases of the consensus non-mass, 22 (100%) cases of the consensus expansive, 21 (84%) cases of the consensus infiltrative, and 19 (54%) cases of the consensus mixed types. The rates that three or more pathologists gave the consensus types tended to be higher in expansive and infiltrative types than in non-mass and mixed types (P < 0.005). Two pathologists agreed on 26% (27 out of 105 cases) of cases, while the numbers of cases where the pathologists’ opinions split into 2 vs 1 or 2 vs 1 vs 1 observers were 22, 22, 13, and 39 for non-mass, expansive, infiltrative, and mixed, respectively. Figure 2 presents representative cases in which discordant judgments of macroscopic type occurred among pathologists. pT category is one of the important factors in evaluating macroscopic morphologies. No significant differences in characteristic macroscopic type were shown between four pT groups (Table 4). The rates of agreement in macroscopic type evaluation among the 4 observers were 29%, 38%, 56%, and 44% in pT1a, pT1b, pT1c, and ≧pT2 groups, respectively, and among three or more observers were 71%, 70%, 79%, and 74%, in pT1a, pT1b, pT1c, and ≧pT2 groups, respectively (Table 4) .In each pT group, interobserver agreement levels were moderate to substantial, being kappa values 0.352, 0.470, 0.641, and 0.500 in pT1a, pT1b, pT1c, and ≧pT2 groups, respectively. Therefore, the agreement levels among the observers for judgment of macroscopic types did not differ significantly between pT categories. Discussion In this study, we examined interobserver agreement levels of a macroscopic classification of breast carcinoma based on both macroscopic photographs of the cut surfaces of mastectomy specimens and overview images of representative H&E-stained slides. Consensus criteria for the classification, composed of non-mass, expansive, infiltrative, and mixed types, were fixed by examining the training set and validated by analyzing the validation set. Forty-five percent of concordance was achieved among the four pathologists, and three or more pathologists gave concordant judgment in 74% of cases. The agreement level with kappa statistics was moderate among the four pathologists. Therefore, in the macroscopic typing of breast carcinoma. moderate agreement levels were shown to be achieved relatively constantly among observers. Among four macroscopic types, the present data implied that interobserver agreement levels tended to be higher in solid and infiltrative types than in non-mass and mixed types. It appears that the challenge mainly consists in improvement of classification accuracy of non-mass and mixed types. Tumor size appears to have a large influence on the macroscopic classification. If the tumor size is small, the tumor may be classified as a non-mass type regardless of whether it showed an expansive or infiltrative pattern. However, even when the tumor size was stratified into pT1a, pT1b, pT1c, and pT2 or more, macroscopic characteristics were similar between these four groups. In any pT category, three or more pathologists achieved higher than 70% of concordance rates which suggested that the present macroscopic classification is applicable regardless of pT category. Interobserver agreement levels were substantial in pT1c category (kappa = 0.641), suggesting that tumor size between 10 to 20 mm is optimal tumor size for classification. We had proposed this classification method in the 19th edition of the General Rules for Clinical and Pathological Recording of Breast Cancer of Japan, but, from the present results, this classification was suggested to contain some weakness. At first, pathologists’ judgments obviously split in one-quarter of the cases. Based on the training set, the four pathologists appeared to have achieved consensus in identifying the macroscopic patterns. Nevertheless, in the validation set, acquisition of consensus among the four breast pathologists was sometimes difficult. The majority opinion was adopted as the consensus, but for the case where opinions split into 2 vs. 2 or 2 vs. 1 vs. 1, discussion prolonged until the pathologists’ consensus. Examples of the “prolonged” cases are presented in Figure 2. Each pathologist inclined to assign certain types. For example, pathologist 1 tended to assign non-mass and expansive types, while pathologists 3 and 4 tended to assign mixed types (Table 2). Discordance was frequently seen in the tumors that were a mixture of two or more components. In this study, mixed type was defined as a type that contains ³30% of each characteristic, but the interpretation of the mixture of multiple components was found to differ greatly among the pathologists. Therefore, mixed-type tumors appeared one of the main causes of discordance (Table 4). Further measures are required regarding the mixed type. Secondly, the boundary between the non-mass lesion and the expansive lesion was sometimes unclear, especially cases of accumulation of dilated ducts and those of large cysts (Fig. 2A and 2D). Although these two lesions can easily be distinguished in ultrasound’s three-dimensional images, it appeared difficult to distinguish between dilated ducts and cystic lesions from two-dimensional images of cut surfaces and H&E slides. To overcome this weakness, it might be necessary to use multiple H&E slides to grasp the lesion’s overall characteristics. Third, the boundary between infiltrative and expansive types is also sometimes unclear (Fig. 2B and 2C). In this study, some pathologists emphasized subtle changes in tumor contour from H&E-stained slides rather than from macroscopic photographs. The features of tumor shape and margin may appear differently between the images of macroscopic photographs and H&E-stained slides. A comprehensive evaluation from both images were recommended, but specific and quantitative criteria for discriminate these two types are still lacking. For more accurate differentiation, establishment of specific criteria and preparation of macroscopic photographs of good quality and high resolution may be very important. This study has some limitations. First, the number of cases reviewed in this study was small. Although 35 cases were obtained from each of the three facilities, these 105 cases may be somewhat small to obtain definitive results in interobserver agreement levels for pT categories. In addition, the breast pathologists did not achieve consensus in several cases. To improve the reproducibility of agreement levels among observers, it is necessary to establish specific and/or quantitative criteria to discriminate the four macroscopic types more concordantly and conduct more detailed studies for a larger number of cases. Second, a limited number of images were presented for classification. Therefore, the observers had to evaluate macroscopic types from only one pair of a macroscopic photograph and one H&E-stained overview image per case, which may have provided insufficient information in some cases. The present classification showed moderate agreement levels with kappa statistics, and suggested the possibility that breast cancer can be macroscopically classified relatively reproducibly irrespectively of pT categories provided classification criteria are improved. The molecular and biological properties of breast cancer may be reflected in tissue morphology. In future, it may be possible to link genetic characteristics with morphological findings when the genetic characteristics of tumors are elucidated. Histomorphological analysis may be useful in predicting the biological characteristics of breast cancer; therefore, the examination of more cases with more detailed analyses are desirable. Conclusion Interobserver reproducibility study was conducted on a macroscopic classification of breast cancer. Although the agreement level of the classification was still moderate in total, the levels were substantial especially in pT1c category. Setting of more specific and quantitative criteria and further studies may enhance reproducibility of the macroscopic classification. Abbreviations BI-RADS Breast Imaging-Reporting and Data System H&E hematoxylin–eosin IBC-NST invasive breast carcinoma of no special type WHO World Health Organization Declarations Author Contributions AH: Conceptualization, methodology, formal analysis, investigation, data collection and curation, writing–original draft, and project administration. YO and RY: 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. Ethical Approval All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Conflict of Interest Disclosure All authors have no conflicts of interest to declare. Funding Sources For the creation of H&E overview images in this study, a slide scanner was loaned by Evident Co., Ltd. Informed Consent Informed consent was obtained from all participants included in the study. Supplemental Materials Supplemental material for this article is available online. References W.H.O. Classification of Tumours Editorial Board International Agency for Research on, Cancer. Breast tumours. 5th ed: International Agency for Research on Cancer; 2019. The Japanese Breast Society. Histological Classification. Breast Cancer. 2005;12(Issue 1 supplement):S12 - S4. 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. 10.1016/j.diii.2017.01.001 . Epub 20170125. Morris EA, Comstock CE, Lee CH. ACR BI-RADS® Atlas 5th Edition ed2013. 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. 10.1007/s12282-008-0030-7 . PubMed PMID: 18288570. 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. 10.1111/j. Epub 20111103. 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. Epub 20111006. 10.1111/j.1349-7006.2011.02088.x . PubMed PMID: 21895869. 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. Epub 20200203. doi: 10.1111/his.14003. PubMed PMID: 31554015. Hara Y, Yamaguchi R, Otsubo R, Urakawa S, Tanaka A, Akashi M et al. Macroscopic morphology of breast carcinoma: associations with biological subtypes and pathological features. Breast Cancer. 2025;32(6):1423-33. Epub 20250903. 10.1007/s12282-025-01770-y . PubMed PMID: 40900380. The Japanese Breast Society. The 19th Edition of the General Rules for Clinical and Pathological Record of Breast Cancer. Tokyo: Kanehara & Co.,Ltd; 2025. (Japanese). Evans AJ, Brown RW, Bui MM, Chlipala EA, Lacchetti C, Milner DA et al. Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology. Arch Pathol Lab Med. 2022;146(4):440 – 50. 10.5858/arpa.2020-0723-CP . PubMed PMID: 34003251. Kanda Y. 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Tables TABLE 1 | Concordance rates of judgments of macroscopic types among pathologists Judgment No of cases Concordant/Total Concordance rate (%) Kappa value Pathologists 1 – vs 2 73/105 69.5 0.613 Pathologists 1 – vs 3 71/105 67.6 0.554 Pathologists 1 – vs 4 65/105 61.9 0.500 Pathologists 2 – vs 3 70/105 66.7 0.575 Pathologists 2 – vs 4 68/105 64.7 0.557 Pathologists 3 – vs 4 70/105 66.7 0.552 All pathologists 47/105 44.8 0.561 TABLE 2 | Consensus macroscopic types of 105 breast carcinomas and discordance rates between the consensus types and individually given judgments by each pathologist Judgment No of cases (%) Macroscopic type Discordance between consensus and each pathologist Non-mass Expansive Infiltrative Mixed Unclassified Consensus among pathologists 18 (17) 22 (21) 25 (24) 35 (33) 5 (5) Pathologist 1 21 (20) 34 (32) 19 (18) 24 (23) 7 (7) 28 (26.6) Pathologist 2 24 (23) 29 (28) 26 (25) 24 (32) 2 (2) 28 (26.6) Pathologist 3 16 (15) 19 (18) 25 (24) 37 (35) 8 (8) 27 (25.7) Pathologist 4 14 (13) 23 (22) 31 (30) 34 (32) 3 (3) 31 (29.5) Discordance rates between consensus type and the type given by each pathologist did not significantly different between the pathologists. TABLE 3 | Consensus macroscopic types and the rate of cases to which 3 or more observers gave concordant judgment . Judgment Number of cases (%) Macroscopic type Non-mass Expansive Infiltrative Mixed Unclassified Consensus among pathologists 18 22 25 35 5 Concordance of four pathologists 10 (56) 13 (69) 13 (52) 10 (29) 1 (20) Concordance of ³3 pathologists 13 (72) 22 (100) 21 (84) 19 (54 ) 3 (60) The rates that three or more pathologists gave consensus types tended to be higher in expansive and infiltrative types than in non-mass and mixed types (P < 0.005). TABL E 4 | Consensus macroscopic types stratified by tumor size and concordance between pathologists’ judgment Judgment Number of cases (%) Invasive size of tumor (cm) pT1a (n = 7) pT1b (n = 37) pT1c (n = 34) ≧pT2 (n = 27) Consensus among pathologists Non-mass 1 (14) 6 (16) 7 (21) 4 (15) Expansive 0 (0) 5 (14) 5 (15) 12 (45) Infiltrative 1 (14) 13 (35) 9 (26) 2 (7) Mixed 0 (0) 13 (35) 13 (38) 9 (33) Unclassified 5 (72) 0 (0) 0 (0) 0 (0) Concordance by four pathologists 2 (29) 14 (38) 19 (56) 12 (44) Concordance by three or more pathologists 5 (71) 26 (70) 27 (79) 20 (74) Concordance by two pathologists 2 (29) 11 (30) 7 (21) 7 (26) Kappa value among four pathologists 0.352 0.470 0.641 0.500 Supplementary Files 260115BreastCancerSupplementalFigure.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 01 Mar, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 16 Jan, 2026 First submitted to journal 15 Jan, 2026 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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Byoin","correspondingAuthor":false,"prefix":"","firstName":"Ichiro","middleName":"","lastName":"Maeda","suffix":""}],"badges":[],"createdAt":"2026-01-16 07:58:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8615732/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8615732/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100928899,"identity":"0067b404-825d-4a6c-bd99-03f9aeb7aee1","added_by":"auto","created_at":"2026-01-23 00:32:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33933,"visible":true,"origin":"","legend":"","description":"","filename":"260115TableBreastCancer2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8615732/v1/8a9a5a6a1a7539ec9648229d.docx"},{"id":100928893,"identity":"cdd68caf-7d4d-482d-b0c9-f0b1f05d3432","added_by":"auto","created_at":"2026-01-23 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07:11:43","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":671996,"visible":true,"origin":"","legend":"","description":"","filename":"260115FiguresBreastCancer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8615732/v1/4c321da2dd0916aa48f02379.pdf"},{"id":100928900,"identity":"7f1e7eeb-1a9d-4a81-8a8e-71106bade431","added_by":"auto","created_at":"2026-01-23 00:32:14","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":81298,"visible":true,"origin":"","legend":"","description":"","filename":"BRCAD26000540structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8615732/v1/96f9e8e770c616537f94cb99.xml"},{"id":100928901,"identity":"cf02da1d-5698-4bc5-86df-3e924a01198f","added_by":"auto","created_at":"2026-01-23 00:32:14","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89572,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8615732/v1/5e40d0effd73ec00e78a8129.html"},{"id":100951338,"identity":"ff7f455e-4b1d-44d7-b5d6-f3d98a5e6fd7","added_by":"auto","created_at":"2026-01-23 07:10:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":401113,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic illustrations and representative photographs of cut surface of resected specimens and H\u0026amp;E slides of typical macroscopic types of breast carcinoma. \u003cstrong\u003eA\u003c/strong\u003e, A non-mass type. \u003cstrong\u003eB\u003c/strong\u003e, Expansive type, that has round shape with lobulated margins. \u003cstrong\u003eC\u003c/strong\u003e, Infiltrative type, that has irregular shape with spiculated margin.\u003c/p\u003e","description":"","filename":"260115FiguresBreastCancer1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8615732/v1/4d832d22789e9b7a71abaa32.jpg"},{"id":100928894,"identity":"7d6c99e4-7c3e-47a6-90f8-5da65a784c0e","added_by":"auto","created_at":"2026-01-23 00:32:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":514851,"visible":true,"origin":"","legend":"\u003cp\u003eNon-typical cases in which macroscopic types given by four observers were not concordant.\u003cstrong\u003e A\u003c/strong\u003e, Case #11. Two pathologists gave non-mass type, while the other two gave expansive or mixed type. \u003cstrong\u003eB\u003c/strong\u003e, Case #77. Two pathologists gave expansive type, whereas the other two gave infiltrative or mixed type. \u003cstrong\u003eC\u003c/strong\u003e, Case #18. Two pathologists gave infiltrative type, while the other two gave expansive or mixed type. \u003cstrong\u003eD\u003c/strong\u003e, Case #45. Two pathologists gave a mixed type, composed of non-mass and expansive components, while the other two gave non-mass or expansive types.\u003c/p\u003e","description":"","filename":"260115FiguresBreastCancer2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8615732/v1/b2afce44f82eea21beb0c27e.jpg"},{"id":101397957,"identity":"5a29ffa0-09e5-407e-9d4c-744815bafbd5","added_by":"auto","created_at":"2026-01-29 09:38:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1533679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8615732/v1/243606ed-7fb9-4bc9-b64d-99db97911ccf.pdf"},{"id":100928904,"identity":"7e1171ee-f1fa-49c5-be45-7d8881282426","added_by":"auto","created_at":"2026-01-23 00:32:15","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":18177625,"visible":true,"origin":"","legend":"","description":"","filename":"260115BreastCancerSupplementalFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8615732/v1/2d60dbcdc257cc9e063ba6c7.pdf"}],"financialInterests":"","formattedTitle":"Interobserver Reproducibility Study of Macroscopic Classification of Breast Cancer from Representative Cut surface of Resected Specimens and Hematoxylin–Eosin-Stained Slides","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe 2019 World Health Organization (WHO) classification of tumors of the breast, which is widely used around the world, does not adopt a histological subtype classification of invasive ductal carcinoma, which are classified as invasive breast carcinoma of no special type (IBC-NST), except for the special types. The WHO classification describes the gross histological patterns seen in IBC-NST, including some special gross morphological patterns such as scirrhous invasive pattern, and expansive growth pattern, but these are not considered clinically distinct subtypes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In contrast, up to the 17th edition of the General Rules for Clinical and Pathological Recording of Breast Cancer of Japan, invasive ductal carcinoma was classified into three histological subtypes: namely papillotubular carcinoma, solid-tubular carcinoma, and scirrhous carcinoma, depending on the tumor size and the shape of the tumor margin and histological features (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The classification of invasive ductal carcinoma into three histological subtypes was the most distinctive feature of these General Rules.\u003c/p\u003e \u003cp\u003eImaging diagnosis tools, mammography, ultrasound, and magnetic resonance imaging (MRI) are commonly used for qualitative evaluation of breast lesions. The American College of Radiology developed a Breast Imaging-Reporting and Data System (BI-RADS) to standardize the terms for mammography, ultrasound, and MRI readings and their interpretation and to improve the quality of breast cancer screening (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In BI-RADS, MRI images as well as mammography and ultrasound of breast cancer can be divided into mass and non-mass enhancements, where breast masses are subcategorized into oval, round, and irregular in shape, and circumscribed and not circumscribed (i.e., irregular and spiculated) in contour (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClassifications of morphology of breast cancer using imaging diagnosis, e.g., ultrasound, mammography, computed tomography, and MRI, were shown to be correlated with biological characteristics (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Tsunoda-Shimizu et al classified 186 invasive breast cancers into 4 morphological types using ultrasound features (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These four types comprised carcinoma that tend to grow along the mammary ducts (type A1), expansively growing that is relatively well-defined (type A2), irregularly-shaped that retracts surrounding tissue (type A3), and type that does not fall under any of A1 to A3 (mixed), and the ratio of pathological complete response after neoadjuvant chemotherapy was significantly higher in type A2 than other three types (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Tamaki et al. showed that mammographic classification of primary invasive breast cancer based on mass shape and margin was significantly correlated with clinical subtype, grade and Ki-67 status (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Irregular mass shape and spiculate margin were correlated with luminal A-like type, oval or round shape and microlobulated margin were correlated with HER2 type, and indistinct margin with triple negative type (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelations of tumor macroscopic morphology according to cut surface of resected specimen was also shown to be associated with histological features and clinical subtypes. Akashi et al. reported that HER2-positive breast cancer had characteristic tumor morphology (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Therefore, similarly with imaging diagnostic classifications, gross classifications of resected breast cancer specimens appear to be significantly associated with biological characteristics and clinical subtype classification (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the revision of the 19th edition of the General Rules for Clinical and Pathological Recording of Breast Cancer of Japan, three subtypes of invasive ductal carcinoma, or invasive carcinoma no specific type, i.e., tubule forming, solid, and scirrhous types, were abandoned and, instead, description of macroscopic types based on tumor shape and margin is recommended: these four types comprise non-mass, expansive, infiltrative, and mixed (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Based on the revision of the General Rules, Hara et al. demonstrated that a gross classification based on tumor cut surface morphological classification of breast carcinoma correlates with the molecular biological properties of breast cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, how these types are reproducible between pathologists is unknown. In the present study, to validate this macroscopic classification, we investigated the concordance rates of macroscopic diagnoses among four breast pathologists.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCases\u003c/h2\u003e \u003cp\u003eThe training set consists of 60 breast cancer samples resected from patients with 20 cases each from Kitasato University Kitasato Institute Hospital, Nagasaki University Hospital, and Sagara Hospital, respectively. The validation set 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.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMacroscopic Photographs of Resected Specimens and H\u0026E-Stained Slides\u003c/h3\u003e\n\u003cp\u003eFor each breast carcinoma, a representative macroscopic photograph, which was taken with the digital camera during routine work, was collected from the pathology databases of the three hospitals. In parallel to the gross imaging photograph, a representative H\u0026amp;E-stained slide used for routine diagnosis of the corresponding carcinoma was selected by these pathologists. The overview image of each H\u0026amp;E slide was captured using an Ultra Fast Scanner (Philips, Amsterdam, Netherlands), NanoZoomer S210 virtual slide scanner (Hamamatsu Photonics, Hamamatsu, Japan), and VS-M1-IVD1 (Evident, Tatsuno, Nagano, Japan). The four pathologists were provided with JPEG files of both the macroscopic photograph and the low-magnified H\u0026amp;E overview image of the 105 tumors.\u003c/p\u003e\n\u003ch3\u003eClassification Criteria\u003c/h3\u003e\n\u003cp\u003eWith reference to the reports of Tsunoda-Shimizu et al., and Hara et al., macroscopic shape of the primary breast cancers was classified based on both gross photographs of cut surface and representative H\u0026amp;E slides into four types: non-mass, expansive, infiltrative, and mixed (see the schematic figures in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three basic patterns were defined: Pattern 1 was non-mass formation, that exhibits obvious discoloration or multiple small round nests without forming discrete mass. This pattern includes accumulation of dilated ducts. Pattern 2 was round or oval and circumscribed or lobulated shape with a relatively smooth or a distinct margin, and large cystic lesion was also included. Pattern 3 was an irregular shape, with a speculated, serrated or indistinct margin that often retracts the surrounding tissues. When 70% or more part of margin surrounding the cut surface was occupied with patterns 1, 2, and 3, the tumor was nominated as non-mass, expansive, and infiltrative types, respectively. Mixed types was nominated when two or more patterns were mixed, and the second predominant pattern occupied over 30% of the contour. The lesions that cannot be classified into any of the above four types were categorized as unclassified.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTraining and Validation Studies\u003c/h3\u003e\n\u003cp\u003eAs the training set, macroscopic images of 60 cases composed of both gross photographs of cut surfaces of resected specimen and H\u0026amp;E-stained slides were provided to the four pathologists. Each of originally defined non-mass, expansive, infiltrative, and mixed types comprised 15 cases. The four pathologists independently classified these cases as one of four macroscopic types without being informed of the originally defined types. After discussion by the four pathologists based on these results, abovementioned consensus criteria of these four types were established. Data for the validation set comprising macroscopic and H\u0026amp;E images of 105 cases were provided for the four observers. The validation was performed after a 2-week washout period based on the College of American Pathologists\u0026rsquo; guidelines for validation in digital pathology (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Four breast pathologists independently classified the 105 cases into the four macroscopic types. Based on the summary of the evaluations of the four observers, consensus macroscopic type was given to each case.\u003c/p\u003e \u003cp\u003e 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 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociation between tumor size and macroscopic pattern\u003c/h3\u003e\n\u003cp\u003eMicroscopic invasive tumor sizes of 105 carcinomas were stratified into 4 groups, i.e., pT1a (\u0026le;\u0026thinsp;0.5 cm), pT1b (\u0026gt;\u0026thinsp;0.5 and \u0026le;\u0026thinsp;1 cm), pT1c (\u0026gt;\u0026thinsp;1 and \u0026le;\u0026thinsp;2 cm), and pT2 or higher (\u0026gt;\u0026thinsp;2 cm). Concordance rates between pathologists in macroscopic type were evaluated for each group. Characteristic macroscopic type and difference in concordance rates in judgement were compared between the groups.\u003c/p\u003e\u003ch2\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of Kitasato University Kitasato Institute Hospital (No. 2023-037).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn 60 cases from the training set, all four pathologists gave concordant types to 27 cases (45%), and three or more pathologists gave concordant types to 43 cases (72%), while two pathologists gave concordant types to 17 cases (28%). There were no cases in which all four pathologists expressed a different opinion. The interobserver agreement levels between two pathologists were between 61.7% and 70.0%. Kappa values between each pair of pathologists ranged from 0.477 to 0.598 without remarkable variations, and the value among the four pathologists was 0.524, indicating moderate agreement.\u003c/p\u003e\n\u003cp\u003eIn the validation study for the 105 cases, the four observers gave unanimous types to 47 cases (45%), three or more observers gave concordant types to 78 cases (74%), and two observers gave concordant types to 27 cases (26%) (Table 1). 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%. Kappa values between two pathologists ranged from 0.500 to 0.613 without remarkable variations, and the value among the four pathologists was 0.561, which indicates moderate agreement.\u003c/p\u003e\n\u003cp\u003eFinal consensus types given by the consensus of the four observers were non-mass in 18, expansive in 22, infiltrative in 125, mixed in 35, and unclassified in 5. Table 2 shows the number of cases of each macroscopic types given by four pathologists. The number of cases that showed discordance in types between the consensus and each pathologist’s judgments was 28 (26.6%), 28 (26.6%), 27 (25.7%), and 31 (29.5%) in pathologists 1, 2, 3 and 4, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf the 105 cases, the four pathologists gave unanimous macroscopic types for 10 (56%) of 18 consensus non-mass, 13 (59%) of 22 consensus expansive, 13 (52%) of 25 consensus infiltrative, and 10 (29%) of 35 consensus mixed cases (Table 3). Concordance among three or more pathologists was seen for 13 (72%) cases of the consensus non-mass, 22 (100%) cases of the consensus expansive, 21 (84%) cases of the consensus infiltrative, and 19 (54%) cases of the consensus mixed types. The rates that three or more pathologists gave the consensus types tended to be higher in expansive and infiltrative types than in non-mass and mixed types (P \u0026lt; 0.005).\u003c/p\u003e\n\u003cp\u003eTwo pathologists agreed on 26% (27 out of 105 cases) of cases, while the numbers of cases where the pathologists’ opinions split into 2 vs 1 or 2 vs 1 vs 1 observers were 22, 22, 13, and 39 for non-mass, expansive, infiltrative, and mixed, respectively. Figure 2 presents representative cases in which discordant judgments of macroscopic type occurred among pathologists.\u003c/p\u003e\n\u003cp\u003epT category is one of the important factors in evaluating macroscopic morphologies. No significant differences in characteristic macroscopic type were shown between four pT groups (Table 4). The rates of agreement in macroscopic type evaluation among the 4 observers were 29%, 38%, 56%, and 44% in pT1a, pT1b, pT1c, and\u0026nbsp;≧pT2 groups, respectively, and among three or more observers were 71%, 70%, 79%, and 74%, in pT1a, pT1b, pT1c, and\u0026nbsp;≧pT2 groups, respectively (Table 4) .In each pT group, interobserver agreement levels were moderate to substantial, being kappa values 0.352, 0.470, 0.641, and 0.500 in pT1a, pT1b, pT1c, and\u0026nbsp;≧pT2 groups, respectively. Therefore, the agreement levels among the observers for judgment of macroscopic types did not differ significantly between pT categories.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we examined interobserver agreement levels of a macroscopic classification of breast carcinoma based on both macroscopic photographs of the cut surfaces of mastectomy specimens and overview images of representative H\u0026amp;E-stained slides. Consensus criteria for the classification, composed of non-mass, expansive, infiltrative, and mixed types, were fixed by examining the training set and validated by analyzing the validation set. Forty-five percent of concordance was achieved among the four pathologists, and three or more pathologists gave concordant judgment in 74% of cases. The agreement level with kappa statistics was moderate among the four pathologists. Therefore, in the macroscopic typing of breast carcinoma. moderate agreement levels were shown to be achieved relatively constantly among observers. Among four macroscopic types, the present data implied that interobserver agreement levels tended to be higher in solid and infiltrative types than in non-mass and mixed types. It appears that the challenge mainly consists in improvement of classification accuracy of non-mass and mixed types.\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 a non-mass type regardless of whether it showed an expansive or infiltrative pattern. However, even when the tumor size was stratified into pT1a, pT1b, pT1c, and pT2 or more, macroscopic characteristics were similar between these four groups. In any pT category, three or more pathologists achieved higher than 70% of concordance rates which suggested that the present macroscopic classification is applicable regardless of pT category. Interobserver agreement levels were substantial in pT1c category (kappa = 0.641), suggesting that tumor size between 10 to 20 mm is optimal tumor size for classification.\u003c/p\u003e\n\u003cp\u003eWe had proposed this classification method in the 19th edition of the General Rules for Clinical and Pathological Recording of Breast Cancer of Japan, but, from the present results, this classification was suggested to contain some weakness. At first, pathologists’ judgments obviously split in one-quarter of the cases. Based on the training set, the four pathologists appeared to have achieved consensus in identifying the macroscopic patterns. Nevertheless, in the validation set, acquisition of consensus among the four breast pathologists was sometimes difficult. The majority opinion was adopted as the consensus, but for the case where opinions split into 2 vs. 2 or 2 vs. 1 vs. 1, discussion prolonged until the pathologists’ consensus. Examples of the “prolonged” cases are presented in Figure 2.\u003c/p\u003e\n\u003cp\u003eEach pathologist inclined to assign certain types. For example, pathologist 1 tended to assign non-mass and expansive types, while pathologists 3 and 4 tended to assign mixed types (Table 2). Discordance was frequently seen in the tumors that were a mixture of two or more components. In this study, mixed type was defined as a type that contains ³30% of each characteristic, but the interpretation of the mixture of multiple components was found to differ greatly among the pathologists. Therefore, mixed-type tumors appeared one of the main causes of discordance (Table 4). Further measures are required regarding the mixed type.\u003c/p\u003e\n\u003cp\u003eSecondly, the boundary between the non-mass lesion and the expansive lesion was sometimes unclear, especially cases of accumulation of dilated ducts and those of large cysts (Fig. 2A and 2D). Although these two lesions can easily be distinguished in ultrasound’s three-dimensional images, it appeared difficult to distinguish between dilated ducts and cystic lesions from two-dimensional images of cut surfaces and H\u0026amp;E slides. To overcome this weakness, it might be necessary to use multiple H\u0026amp;E slides to grasp the lesion’s overall characteristics.\u003c/p\u003e\n\u003cp\u003eThird, the boundary between infiltrative and expansive types is also sometimes unclear (Fig. 2B and 2C). In this study, some pathologists emphasized subtle changes in tumor contour from H\u0026amp;E-stained slides rather than from macroscopic photographs. The features of tumor shape and margin may appear differently between the images of macroscopic photographs and H\u0026amp;E-stained slides. A comprehensive evaluation from both images were recommended, but specific and quantitative criteria for discriminate these two types are still lacking. For more accurate differentiation, establishment of specific criteria and preparation of macroscopic photographs of good quality and high resolution may be very important.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. First, the number of cases reviewed in this study was small. Although 35 cases were obtained from each of the three facilities, these 105 cases may be somewhat small to obtain definitive results in interobserver agreement levels for pT categories. In addition, the breast pathologists did not achieve consensus in several cases. To improve the reproducibility of agreement levels among observers, it is necessary to establish specific and/or quantitative criteria to discriminate the four macroscopic types more concordantly and conduct more detailed studies for a larger number of cases. Second, a limited number of images were presented for classification. Therefore, the observers had to evaluate macroscopic types from only one pair of a macroscopic photograph and one H\u0026amp;E-stained overview image per case, which may have provided insufficient information in some cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe present classification showed moderate agreement levels with kappa statistics, and suggested the possibility that breast cancer can be macroscopically classified relatively reproducibly irrespectively of pT categories provided classification criteria are improved.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe molecular and biological properties of breast cancer may be reflected in tissue morphology. In future, it may be possible to link genetic characteristics with morphological findings when the genetic characteristics of tumors are elucidated. Histomorphological analysis may be useful in predicting the biological characteristics of breast cancer; therefore, the examination of more cases with more detailed analyses are desirable.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eInterobserver reproducibility study was conducted on a macroscopic classification of breast cancer. Although the agreement level of the classification was still moderate in total, the levels were substantial especially in pT1c category. Setting of more specific and quantitative criteria and further studies may enhance reproducibility of the macroscopic classification.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBI-RADS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBreast Imaging-Reporting and Data System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eH\u0026amp;E\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehematoxylin\u0026ndash;eosin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBC-NST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einvasive breast carcinoma of no special type\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eAH: Conceptualization, methodology, formal analysis, investigation, data collection and curation, writing–original draft, and project administration. YO and RY: 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.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest Disclosure\u003c/h2\u003e\n\u003cp\u003eAll authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003ch2\u003eFunding Sources\u003c/h2\u003e\n\u003cp\u003eFor the creation of H\u0026amp;E overview images in this study, a slide scanner was loaned by Evident Co., Ltd.\u003c/p\u003e\n\u003ch2\u003eInformed Consent\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all participants included in the study.\u003c/p\u003e\n\u003ch2\u003eSupplemental Materials\u003c/h2\u003e\n\u003cp\u003eSupplemental material for this article is available online.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eW.H.O. Classification of Tumours Editorial Board International Agency for Research on, Cancer. Breast tumours. 5th ed: International Agency for Research on Cancer; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Japanese Breast Society. Histological Classification. Breast Cancer. 2005;12(Issue 1 supplement):S12 - S4.\u003c/span\u003e\u003c/li\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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.diii.2017.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.diii.2017.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20170125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris EA, Comstock CE, Lee CH. ACR BI-RADS\u0026reg; Atlas 5th Edition ed2013.\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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12282-008-0030-7\u003c/span\u003e\u003cspan address=\"10.1007/s12282-008-0030-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 18288570.\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. 10.1111/j. Epub 20111103.\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):2179-85. Epub 20111006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1349-7006.2011.02088.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1349-7006.2011.02088.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 21895869.\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. Epub 20200203. doi: 10.1111/his.14003. PubMed PMID: 31554015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHara Y, Yamaguchi R, Otsubo R, Urakawa S, Tanaka A, Akashi M et al. Macroscopic morphology of breast carcinoma: associations with biological subtypes and pathological features. Breast Cancer. 2025;32(6):1423-33. Epub 20250903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12282-025-01770-y\u003c/span\u003e\u003cspan address=\"10.1007/s12282-025-01770-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 40900380.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Japanese Breast Society. The 19th Edition of the General Rules for Clinical and Pathological Record of Breast Cancer. Tokyo: Kanehara \u0026amp; Co.,Ltd; 2025. (Japanese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans AJ, Brown RW, Bui MM, Chlipala EA, Lacchetti C, Milner DA et al. Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology. Arch Pathol Lab Med. 2022;146(4):440\u0026thinsp;\u0026ndash;\u0026thinsp;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5858/arpa.2020-0723-CP\u003c/span\u003e\u003cspan address=\"10.5858/arpa.2020-0723-CP\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 34003251.\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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/bmt.2012.244\u003c/span\u003e\u003cspan address=\"10.1038/bmt.2012.244\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20121203.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1\u003c/strong\u003e | Concordance rates of judgments of macroscopic types among pathologists\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.3846%;\"\u003e\n \u003cp\u003eJudgment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003eNo of cases\u003c/p\u003e\n \u003cp\u003eConcordant/Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003eConcordance rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3077%;\"\u003e\n \u003cp\u003eKappa\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.3846%;\"\u003e\n \u003cp\u003ePathologists 1\u003cs\u003e\u0026ndash;\u003c/s\u003e vs 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e73/105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e69.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3077%;\"\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: 25.3846%;\"\u003e\n \u003cp\u003ePathologists 1\u003cs\u003e\u0026ndash;\u003c/s\u003e vs 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e71/105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e67.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3077%;\"\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: 25.3846%;\"\u003e\n \u003cp\u003ePathologists 1\u003cs\u003e\u0026ndash;\u003c/s\u003e vs 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e65/105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3077%;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.3846%;\"\u003e\n \u003cp\u003ePathologists 2\u003cs\u003e\u0026ndash;\u003c/s\u003e vs 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e70/105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3077%;\"\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: 25.3846%;\"\u003e\n \u003cp\u003ePathologists 2\u003cs\u003e\u0026ndash;\u003c/s\u003e vs 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e68/105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3077%;\"\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: 25.3846%;\"\u003e\n \u003cp\u003ePathologists 3\u003cs\u003e\u0026ndash;\u003c/s\u003e vs 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e70/105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3077%;\"\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: 25.3846%;\"\u003e\n \u003cp\u003eAll pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e47/105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.6538%;\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3077%;\"\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\u003cstrong\u003eTABLE 2\u003c/strong\u003e | Consensus macroscopic types of 105 breast carcinomas and discordance rates between the consensus types and individually given judgments by each pathologist\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 123px;\"\u003e\n \u003cp\u003eJudgment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 482px;\"\u003e\n \u003cp\u003eNo of cases (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 369px;\"\u003e\n \u003cp\u003eMacroscopic type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eDiscordance between consensus and each pathologist\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNon-mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eExpansive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eInfiltrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eUnclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eConsensus among pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e22 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e25 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e35 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePathologist 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e34 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e19 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e24 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e28 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePathologist 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e24 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e29 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e24 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e28 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePathologist 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e19 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e25 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e37 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e27 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePathologist 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e14 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e23 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e31 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e31 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDiscordance rates between consensus type and the type given by each pathologist did not significantly different between the pathologists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3\u003c/strong\u003e | Consensus macroscopic types and the rate of cases to which 3 or more observers gave concordant judgment\u003cs\u003e.\u003c/s\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eJudgment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eNumber of cases (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 387px;\"\u003e\n \u003cp\u003eMacroscopic type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNon-mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eExpansive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eInfiltrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUnclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eConsensus among pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eConcordance of four pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e13\u0026nbsp;(69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e13 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eConcordance of\u0026nbsp;\u0026sup3;3 pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e13 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e22 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21 (84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e19 (54\u003cs\u003e)\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe rates that three or more pathologists gave consensus types tended to be higher in expansive and infiltrative types than in non-mass and mixed types (P \u0026lt; 0.005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABL\u003c/strong\u003e\u003cstrong\u003eE 4 |\u003c/strong\u003e Consensus macroscopic types stratified by tumor size and concordance between pathologists\u0026rsquo; judgment\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 207px;\"\u003e\n \u003cp\u003eJudgment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 313px;\"\u003e\n \u003cp\u003eNumber of cases (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 313px;\"\u003e\n \u003cp\u003eInvasive size of tumor (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003epT1a\u0026nbsp;(n = 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003epT1b (n = 37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003epT1c (n = 34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e≧pT2 (n = 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 95px;\"\u003e\n \u003cp\u003eConsensus among pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eNon-mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e6 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eExpansive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e5 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eInfiltrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e13 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e13 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e13 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eUnclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eConcordance by four pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e14 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e19 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eConcordance by three\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eor more pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e26 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e27 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20 (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eConcordance by two pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e11 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eKappa value among four pathologists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.500\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":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brca","sideBox":"Learn more about [Breast Cancer](http://link.springer.com/journal/12282)","snPcode":"12282","submissionUrl":"https://www.editorialmanager.com/brca/default2.aspx","title":"Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"macroscopic classification, breast cancer, interobserver reproducibility","lastPublishedDoi":"10.21203/rs.3.rs-8615732/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8615732/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCharacteristics in shape and margins detected with diagnostic imaging are key features of breast cancers, but consensus on the criteria for macroscopic classification of breast cancer has yet to be established. The General Rules for Clinical and Pathological Recording of Breast Cancer, 19th edition, of Japan has adopted a macroscopic classification of breast cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, four breast pathologists evaluated interobserver agreement level of the macroscopic classification for breast cancer cases using macroscopic photographs of cut surfaces of resected specimens and their hematoxylin\u0026ndash;eosin (H\u0026amp;E)-stained slide images, which were provided from pathology database of three facilities. From macroscopic shape and margins, these cases were classified into four types: non-mass, expansive, infiltrative, and mixed. Criteria for each type were established for the training set (n\u0026thinsp;=\u0026thinsp;60), and then four pathologists independently classified 105 cases as the validation set. Interobserver agreement levels were calculated with kappa statistics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the validation set, consensus types were non-mass, expansive, infiltrative, and mixed in 18, 22, 25, and 35 cases, respectively. The four observers gave unanimous types for 47 cases (45%), and three or more observers gave concordant types for 78 cases (74%). An agreement level between the four observers was moderate (kappa\u0026thinsp;=\u0026thinsp;0.561) in total, but an agreement levels was substantial in pT1c category (kappa\u0026thinsp;=\u0026thinsp;0.641).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlthough the agreement level of the classification was still moderate in total, setting of more specific and quantitative criteria and further studies may enhance reproducibility of the macroscopic classification.\u003c/p\u003e","manuscriptTitle":"Interobserver Reproducibility Study of Macroscopic Classification of Breast Cancer from Representative Cut surface of Resected Specimens and Hematoxylin–Eosin-Stained Slides","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 00:32:09","doi":"10.21203/rs.3.rs-8615732/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2026-03-01T18:27:06+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-22T08:30:32+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T05:24:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T12:53:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer","date":"2026-01-16T00:59:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brca","sideBox":"Learn more about [Breast Cancer](http://link.springer.com/journal/12282)","snPcode":"12282","submissionUrl":"https://www.editorialmanager.com/brca/default2.aspx","title":"Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4eaa24ff-86ad-4467-8928-76e58aecb3e2","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T23:42:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 00:32:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8615732","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8615732","identity":"rs-8615732","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}