Expectation Maximization Clustering Reveals Ki-67 Heterogeneity Within Immunohistochemical Subtypes of Invasive Ductal Breast Carcinoma: A 18-year cohort study from Central-Eastern Europe

Expectation Maximization Clustering Reveals Ki-67 Heterogeneity Within Immunohistochemical Subtypes of Invasive Ductal Breast Carcinoma: A 18-year cohort study from Central-Eastern Europe | 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 Expectation Maximization Clustering Reveals Ki-67 Heterogeneity Within Immunohistochemical Subtypes of Invasive Ductal Breast Carcinoma: A 18-year cohort study from Central-Eastern Europe Ivana Begic, Branko Dmitrovic, Sven Kurbel, Irena Zagorac This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9662114/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 : Ki-67 proliferation index is a key marker in breast cancer (BC) classification and treatment recommendations. Widely applied assessment is in accordance to conventional breast cancer immunohistochemistry protocols and St. Gallen guidelines (2013), yet its intra-subtype biological heterogeneity remains underexplored in large cohorts, particularly lacking large studies from Central-Eastern Europe. Our main objective was to apply expectation-maximization (EM) clustering method to Ki-67 pool data and to assess clinicopathological correlations distinguishing biologically divergent subpopulations. Methods : Retrospective study of 2651 consecutive invasive breast cancer (IDC) cases (2004–2021) at a single center at Osijek University Hospital, Croatia. Tumors were immunohistochemically classified into 5 subtypes (IHC 1–5: Luminal A/B1/B2, HER2+, TNBC) according to St. Gallen 2013 (Ki-67 cutoff 20%). Expectation-maximization (EM algorithm, STATISTICA v11) was applied to identify clustering across IHC subtypes. Associations with clinicopathological features were assessed via χ² /Fischer exact test, Mann-Whitney U, odds rations (OR, 95% CI). Results : Median age was 62 years (IQR 53-70); 66,6% postmenopausal. Luminal subtypes A/B1 increased over time (49,2% in 2004 to 72,3% in 2021, P<0,001). EM clustering revealed bimodality with two distinct Ki-67 subpopulations: Cluster 1 (mean 22,6%, n=1711) vs. Cluster 2 (mean 74,4%, n=940; P25 mm) and younger age (<55y), all P<0,001. The highest Ki-67 cluster was found in TNBC (83,4%) with dominat aggressive traits (younger age, larger, grade III in 77 %, P<0,001). Conclusions : EM clustering reveals clinically relevant bimodal pattern in Ki-67 distribution challenging the arbitrary Ki-67 cutoff set at 20% (clusters seem to be superior to rigid binary St.Gallen limits). The largest Central-Eastern Europe IDC cohort highlights regional trends and validates St. Gallen criteria. Breast cancer Ki-67 Expectation-maximization clustering Immunohistochemical subtypes Invasive ductal carcinoma Central-Eastern Europe Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Breast cancer remains the most common malignancy in women worldwide and a leading cause of cancer-related mortality. Invasive ductal carcinoma (IDC), not otherwise specified (NOS/NST), accounts for approximately 80% of cases [ 1 , 2 ]. Accurate profiling of immunohistochemical (IHC) markers includes assessment of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and Ki-67 proliferation index. This is essential for tumor classification, to guide treatment decisions and to predict clinical outcomes. The 2013 St. Gallen International Expert Consensus guidelines classifies breast cancers using Ki-67 threshold of 20% to distinguish Luminal A (low proliferation) from luminal B (high proliferative activity), guiding endocrine vs. chemotherapy [ 3 , 4 ]. Luminal A subtype shows homogeneity. Tumors are smaller in size and exhibit favorable traits – hormone receptor positivity, histological grade I and negative lymph node status. On the other hand, HER2 + and TNBC are associated with more pronounced characteristics of biological aggressiveness [ 5 , 6 ]. Ki-67 is a continuous variable and its distribution is heterogeneous, non-Gaussian, suggesting biologically distinct subtypes - particularly pronounced in aggressive subtypes (HER2+, TNBC), where treatment outcome varies significantly despite identical IHC classification [ 7 , 8 ]. Rigid binary Ki-67 classification underestimates biological nature of IDC. Expectation-maximization (EM) clustering is a novelty approach using probabilistic algorithm to identify data-driven latent Ki-67 subgroups as distinct proliferation phenotypes in large IDC cohort [ 9 , 10 ]. Our objectives were to apply EM clustering method to identify latent Ki-67 subpopulation and to assess heterogeneity within each group as well as to establish clinicopathological associations. Further, to assess temporal trends in distribution of IHC subtypes and to compare regional differences in IHC subtypes over time and regions (comparing our results with literature reports from prior epidemiological studies). This comprehensive study analyzes 2651 IDC cases (2004–2021) from a single tertiary Croatian center (Department of Pathology and Forensic Medicine, Osijek University Clinical Hospital, Osijek, Croatia) – to our knowledge, largest regional cohort – highlighting temporal trends, assessing Ki-67 clusters and its clinicopathological correlates and identifying distinct proliferation clusters in IHC subtypes for potential therapy refinement. Central-Eastern Europe (CEE) data are scarce, despite distinct epidemiology, including higher TNBC incidence in comparison to Western Europe [ 11 , 12 ]. Methods Study design : Retrospective, single-center cohort study in tertiary, referral center at the Department of Pathology and Forensic Medicine, Osijek University Clinical Hospital, Osijek, Croatia in s tudy period January 2004 -December 2021 (longitudinal over 18 years). An average inclusion rate was 147 patients/year. Reporting followed STROBE guidelines for observational studies. Inclusion criteria were histologically confirmed invasive ductal carcinoma, no special type (IDC-NST), complete clinicopathological data, adequate archival formalin-fixed paraffin-embedded tissue blocks and complete IHC panel (ER, PR, HER2, Ki-67). All other histological types, metastatic disease at presentation, prior neoadjuvant therapy, incomplete clinicopathological or IHC data were excluded. Final Cohort included 2,651 consecutive IDC-NST cases. Histopathological Assessment, Grading System Histopathological standard analysis was performed on archival FFPE tissue blocks, reviewed independently by two pathologists experienced in BC diagnostics. Histological grading was based on Nottingham/Elston-Ellis modification of the Scarf-Bloom-Richardson grading system (Grade I: 3–5 points - for well differentiated; Grade II: 6–7 points - for moderately differentiated; Grade III: 8–9 points - for poorly differentiated) [ 13 ]. Immunohistochemistry Assessment and Classification into IHC groups IHC was performed on Ventana BenchMark Ultra automated stainer (Roche Diagnostics, Basel, Switzerland). The assessment of biomarkers was performed according to validated international guidelines: ER, PR: ASCO/CAP Recommendations from 2020 (ASCO/CAP, eng. American Society of Clinical Oncology / College of American Pathologists ) [ 14 ]. HER2: ASCO/CAP 2020 guidelines; amplification confirmed by FISH/ISH in IHC 2 + results [ 15 ]. Ki-67 assesment aligned with International Ki-67 Working Group (IKWG) Consensus Recommendation (2011, revised 2021). All cases were consistently retrospectively reclassified per St. Gallen 2013 Consensus into five subtypes as detailed in Table 1 [ 4 , 16 ]. Table 1 Classification of Breast Cancer based on Immunohistochemical Analysis According to St. Gallen 2013 Concensus [ 4 ]. Immunohistochemical Phenotype Molecular Group Immunohistochemical Features IHC 1 Luminal Luminal A ER + and/or PR+, HER2-negative, Ki-67 < 20% IHC 2 Luminal B1 ER + and/or PR+, HER2-negative, Ki-67 ≥ 20% IHC 3 Luminal B2 ER + and/or PR+, HER2- amplification, any Ki-67 IHC 4 HER2/neu amplification ER–, PR–, HER2- amplification IHC 5 Triple-negative breast cancer ER–, PR–, HER2-negative Immunohistochemical groups were further consolidated into three major super-categories for clustering and statistical purposes (descriptive analyses): HR+ (Hormone Receptor-Positive: IHC1 + IHC2 + IHC3, n = 2159), HER2 amplification (IHC4, n = 199), TNBC (Triple-Negative, IHC5, n = 293) and additionally into three super-categories based on Ki-67 value: low proliferative activity (LMA < 25%, n = 1385; 52,2%), intermediate (IMA 25–65%, n = 949; 35,8%)) and high proliferative activity (HMA ≥ 65%, n = 317; 12,0%). Expectation-Maximization (EM) Clustering and Statistical/Comparative Analysis We applied STATISTICA v11 (StatSoft, Tulsa, OK, USA) software, in particular Expectation-Maximization (EM) algorithm to stratify Ki-67 values within each IHC subtype and super-group (HR+, HER2+, TNBC). The algorithm estimates means, variances and mixing proportions until convergence. Bayesian Information Criterion (BIC) determines optimal number of clusters (n = 2) [ 9 , 10 , 17 , 18 ]. Categorical variables were compared using the Pearson χ² test or Fisher's exact test (for frequency < 5). Continuous non-parametric, non-normally distributed variables (confirmed by Kolmogorov-Smirnov test) were analyzed using the Mann-Whitney U test. For testing associations (Ki-67 cluster and clinicopathological variables) we used odds ratios (OR) with 95% confidence intervals (CI). All tests were two-tailed; P < 0.05 was considered statistically significant [ 19 ]. Ethics Approval was received from Ethical Committee of Medical Faculty Osijek: (602-04/23 − 08/03 on 24th April 2023 and Ethical Committee of Clinical Hospital Osijek (R1-4012/2023 on 31st May 2023). All patients have signed written informed consent at hospital admission for the breast surgical procedure that also included consent to breast tissue examination for clinical, educational and research purposes; all data were anonymized. Data Protection was assured by the General Data Protection Regulation (GDPR, EU 2016/679) and Helsinki Declaration – by compliant anonymization and patient numerical identifiers [ 20 , 21 ]. The initial study was a part of a research project financed by the Croatian Ministry of Science (219-2,192,382–2426). Results General Descriptive Cohort Characteristics : final cohort in our 18-year retrospective study included 2651 patients with IDC-NST. Median age was 62 years (IQR: 53–70) with predominance of postmenopausal women (> 55 years; 66,6%). Tumors were mostly small (in 65,5% <25mm). Histological grade II was the most prevalent (n = 1218 cases; 45,9%), followed by grade I (n = 828, 31,2%) and grade III (n = 605, 22,8%) as shown in Fig. 1 . A concurrent ductal carcinoma in situ (DCIS) component was present in 12,2%. Axillary lymph nodes were positive (metastatic) in 1053 (39,7%) patients as shown in Fig. 2 . The results of immunohistochemistry analysis showed ER positivity (> 10%) in 2081 cases (78,5%) and in additional 78 patients (2,9%) weak positive (1–10%) findings. PR were positive in 1982 cases (74,8%) and HER2 + in 553 cases (20,9%). Distribution of immunohistochemical phenotypes (IHC) IHC assessment and classification into five subtypes according to St. Gallen 2013 Consensus showed following frequencies distribution: IHC1 (Luminal A): 815 cases (30,7%) IHC2 (Luminal B1): 990 cases (37,3%) IHC3 (Luminal B2): 354 cases (13,4%) IHC4 (HER2+): 199 cases (7,5%) IHC5 (TNBC): 293 cases (11,1%) Distribution of immunohistochemical phenotypes showed clear dominance of luminal subtypes. Luminal A and B1 subtypes (hormone receptor-positive, HER2-negative) accounting for 68,1% (n = 1805) of cases an IHC1-3 (n = 2,159 cases; 81,4%) as shown in Fig. 3 . Temporal trends (2004–2021) showed significant frequency increase in luminal subtypes over 18-year study period (49,2% in 2004 towards 72,3% in 2021, P < 0,001); TNBC proportion remained stable at ~ 11% (P = 0,42) over study period. Differences in clinicopathological characteristics in IHC super-categories Hormone receptor (HR) - positive tumors (IHC1-3, n = 2159) exhibited favorable features: ER-positive (> 10%) in 96,4%, PR-positive in 90,7%, lower histological grade (grade I in 36,9%, P < 0,001). In HR+ tumors HER2 was negative in 83,6% cases. Histological grade III was significantly less prevalent in HR+ group: 14,2% vs. 52,8% in HER2 + tumors vs. 65,9% in TNBC tumors (P < 0,001). In contrast, HER2+ (IHC4) and TNBC (IHC5) demonstrated aggressive traits: grade III in 52,8% and 65,9%, respectively ( P < 0.001), with PR-negative rates of 98% and 93,5% respectively ( P < 0.001). Differences in tumor histological grade, presence of a DCIS component, status of ER, PR, and HER2 oncogenes between hormone-positive tumors (HR+) tumors, HER2 + and triple-negative tumors (TNBC) – as shown in Table 2 . Table 2 Distribution of clinicopathological parameters across IHC super-categories HR ¶ positive Luminal A, B1 i B2 (IHC 1–3) HER2 ** amplification (IHC 4) Triple negative BC TNBC †† (IHC5) Total (n, %) P ‡‡ Gradus * I 796 18 14 828 < 0,001 (36,9) (9,0) (4,8) (31,2) II 1056 76 86 1218 (48,9) (38,2) (29,3) (45,9) III 307 105 193 605 (14,2) (52,8) (65,9) (22,8) DCIS status † DCIS 265 29 30 324 0,35 (12,3) (14,6) (10,2) (12,2) No DCIS 1894 170 263 2327 (87,7) (85,4) (89,8) (87,8) ER status ‡ Negative - 199 293 492 10%) 2081 - - 2081 (96,4) (78,5) PR status § Negative 200 195 274 669 < 0,001 (9,3) (98,0) (93,5) (25,2) Positive 1959 4 19 1982 (90,7) (2,0) (6,5) (74,8) HER2 status || Negative 1805 - 293 2098 < 0,001 (83,6) (100,0) (79,1) Positive 354 199 - 553 (16,4) (100,0) (20,9) Total 2159 199 293 2651 * Gradus – histological differentiation † DCIS – engl. ductal in situ carcinoma ; ‡ ER status – estrogen receptor status; § PR status – progesteron receptor status; || HER2 status – HER2 oncogene amplification; ¶ HR – hormone receptor; ** HER2 pos. –HER2 oncogene amplification; †† TNBC– triple negative BC; ‡‡ p – significance in χ2 test Distribution of Ki-67 Proliferation Index across IHC Phenotypes Continuous Ki-67 distribution was arbitrary subdivided in three major pre-defined super-categories for more relevant clinical reflection and statistical purposes low mitotic activity LMA (< 25%, n = 1385, 52,2%), intermediate IMA (25–65%, n = 949, 35,8%,) and high HMA (≥ 65%, n = 317, 12%) (Table 3 , P < 0,001). Table 3 Distribution of Ki-67 proliferation activity categories across IHC phenotypes (IHC 1–5) Ki-67 % (category) Immunohistochemical phenotypes Luminal A IHC 1 § Luminal B1 IHC 2 Luminal B2 IHC 3 HER2 amplification IHC 4 Triple negative BC IHC 5 Total (%) P || < 25% LMA * 815 306 156 52 56 1385 < 0,001 (100,0) (30,9) (44,0) (26,1) (19,1) (52,2) 25–65% IMA † 0 577 162 112 98 949 - (58,3) (45,8) (56,3) (33,4) (35,8) ≥ 65% HMA ‡ 0 107 36 35 139 317 - (10,8) (10, 2) (17,6) (47,4) (12,0) Ukupno 815 990 354 199 293 2651 * LMA, eng. low mitotic activity (Ki-67 < 25%); † IMA, eng. intermediate mitotic activity, Ki-67 25–65%); ‡ HMA, eng. high mitotic activity, (Ki-67 ≥ 65%); § IHC (1–5) – immunohistochemical phenotype (1–5); || χ2 test Ki-67 distribution showed ascending values across IHC subtypes reflecting progressive proliferative nature of tumors. By the St. Gallen Consensus (2013) luminal A has per definition Ki-67 < 20% and was directly arbitrary classified in LMA (Ki-67 < 25%). Luminal B1 was predominantly in IMA (58,3%), HER2 + also in IMA (56,3%), while TNBC demonstrated the highest proportion of HMA tumors (47,4%, P < 0,001). Associations were established for HMA super-category and more adverse features: younger age (< 55 years; 15,5%, P < 0,001), larger tumors (≥ 25 mm; 16–23%, P < 0,001), grade III (in 64%, P < 0,001), nodal positivity (43,5%, P < 0,001), negative hormone-receptor status (ER-/PR-) and positive HER2 + status. LMA tumors (n = 1,385) had favorable histological grade I in 48,2% and they were node-negative in 65,6% (P < 0,001, Table 4 ). Table 4 Clinicopathological features by Ki-67 proliferation activity categories Ki-67% categories n (%) Age (years) < 25% LMA * 25–65% IMA † ≥ 65% HMA ‡ Total P § < 55 436 312 137 885 < 0,001 (49,3) (35,2) (15,5) (33,4) ≥ 55 949 637 180 1766 (53,7) (36,1) (10,2) (66,6) Size (mm) < 24 1015 (58,4) 563 (32,4) 159 (9,2) 1737 (65,5) 25–55 316 (41,6) 321 (42,2) 123 (16,2) 760 (28,7) < 0,001 ≥ 55 54 (35,1) 65 (42,2) 35 (22,7) 154 (5,8) Gradus I 667 (80,5) 152 (18,4) 9 (1,1) 828 (31,2) II 611 (50,2) 502 (41,2) 105 (8,6) 1218 (45,9) < 0,001 III 107 (17,7) 295 (48,8) 203 (33,5) 605 (22,8) Axilary lymph nodes Negative 908 (56,8) 511 (32,0) 179 (11,2) 1598 (60,3) < 0,001 Positive 477 (45,3) 438 (41,6) 138 (13,1) 1053 (39,7) Status ER Negative 108 (21,9) 210 (42,7) 174 (35,4) 492 (18,6) Weak positive 16 (20,5) 32 (41,0) 30 (38,5) 78 (2,9) < 0,001 Positive 1261 (60,6) 707 (34,0) 113 (5,4) 2081 (78,5) Status PR Negative 191 (28,6) 288 (43,0) 190 (28,4) 669 (25,2) < 0,001 Positive 1194 (60,2) 661 (33,4) 127 (6,4) 1982 (74,8) HER2 Negative 1177 (56,1) 675 (32,2) 246 (11,7) 2098 (79,1) < 0,001 Positive 208 (37,6) 274 (49,6) 71 (12,8) 553 (20,9) Total 1385 (52,2) 949 (35,8) 317 (12,0) 2651 * LMA, eng. low mitotic activity (Ki-67 < 25%); † IMA, eng. intermediate mitotic activity, Ki-67 25–65%); ‡ HMA, eng. high mitotic activity, (Ki-67 ≥ 65%); § P-significance, χ2 test EM Clustering in Ki-67 Pooled Data (IHC 2–5) and in Hormon Receptor Positive (HR+; IHC 1–3) group identified clear dichotomy (for IHC 2–5 subtypes shown in Fig. 4 A): - Cluster 1 (low proliferative): mean Ki-67 22,6% (n = 1034; 56,3%) - Cluster 2 (high proliferative): mean Ki-67 74,4% (n = 802; 43,7%; P < 0,001) EM clustering findings for HR+ group and clinicopathological differences between clusters are shown in Table 5 . Histological grade III was more prevalent in high-Ki-67 cluster 2 (47,8% vs. 9,7% in cluster 1; P < 0,001) as well as lymph node positivity (44,3% vs 37,2%, P = 0,03). DCIS co-occurrence in cluster 2 was present in 18% cases (vs. 11,5% in lower-proliferative cluster, P = 0,003). There were no significant differences in size or age. Table 5 EM Clustering in hormone receptor positive (HR+) super-category Ki67 clusters in HR+ super-category (IHC 1–3) Cluster 1 Cluster 2 Total P Gradus I 782 (98,2) 14 (1,8) 796 (36,9) 0,001 II 937 (88,7) 119 (11,3) 1056 (48,9) III 185 (60,3) 122 (39,7) 307(14,2) Axilary lymphnodes Negative 1196 (89,4) 142 (10,6) 1338 (62,0) 0,03 Positive 708 (86,2) 113 (13,8) 821 (38,0) DCIS Yes 219 (82,6) 46 (17,4) 265 (12,3) 0,003 No 1685 (89,0) 209 (11,0) 1894 (87,7) Total 1904 255 2159 EM Clustering across distinct IHC phenotypes Findings as represented in Figs. 4 B-E as follows: Specific results of EM clustering regarding IHC subtypes Luminal A (IHC 1; n = 815) subtype comprises a single homogeneous cluster; Ki-67 was by definition < 20%, corresponding with low-proliferative nature. Luminal B1 (ICH 2, n = 990; Fig. 4 B ) : in cluster 1 mean Ki-67 was 30,3% (n = 771; 77,9%) and in cluster 2 mean Ki-67 67,9% (n = 219; 22,1%). Difference in grade III prevalence was demonstrated: 48,4% (Cluster 2) vs. 13,0% (Cluster 1) as well in size (22mm vs. 19mm, P < 0,001). DCIS co-occurrence was also different (20,5% in cluster 2 vs. 12,5% in cluster 1, P = 0,002). Nodal positivity was not significantly different (P = 0,07). Luminal B2 (IHC 3; n = 354; Fig. 4 C ) also showed bimodal distribution with two distinct clusters; cluster 1 with Ki-67 mean 27,4% vs. high-proliferation cluster (mean 74,5%) which was associated with grade III (42,6% in cluster 2 vs. 19,6% in cluster 1; P < 0,001), larger tumor size (25 vs. 20mm; P = 0,05). No differences in size/age/lymph node metastases/DCIS co-occurrence were found. HER2+ (IHC 4; n = 199; Fig. 4 D; Table 6 ) subtype demonstrated dichotomy in two clusters as shown: Cluster 1 with mean Ki-67 34,5% (n = 157; 78,9%) and cluster 2 with mean Ki-67 77% (n = 42; 21,1%). Differences between cluster 1 and 2 in histological grade III were identified: 49% vs. 66,7% (P = 0,03). No differences in size/age/lymph node metastases/DCIS co-occurrence were found. TNBC (IHC 5; n = 293; Fig. 4 E; Table 7 ) : Ki-67 Cluster 1 (lower proliferation) showed mean Ki-67 33,9% (n = 154; 52,6%) and cluster 2 (high proliferation) mean Ki-67 83,4% (n = 139; 47,4%). Grade III prevalence was significantly higher in cluster 2 (77,0% vs. 55,8%; P < 0,001). Nodal status did not differ significantly between clusters (P = 0,34), nor did DCIS co-occurrence (P = 0,99). Cluster 2 was associated with younger patient age (median 54 vs. 59 years; P = 0,04) and larger tumor size (P = 0,03). Table 6 EM Clustering in HER2 + tumor (IHC4 immunophenotype) Ki67 Clusters in HER2+ (n, %) Cluster 1 Cluster 2 Total P* Gradus I 18 (11,5) 0 (0,0) 18 (9,0) 0,03 II 62 (39,5) 14 (33,0) 76 (38,2) III 77 (49,0) 28 (66,7) 105 (52,8) Axilary lymph nodes Negative 73 (46,5) 15 (35,7) 88 (44,2) 0,22 Positive 84 (53,5) 27 (64,3) 111 (55,8) DCIS Yes 21 (13,4) 8 (19,0) 29 (14,6) 0,34 No 136 (86,6) 34 (81,0) 170 (85,4) Total 157 42 199 In TNBC tumors even cluster 1 exhibited relatively high Ki-67 values compared to luminal subtypes, confirming high proliferative nature and aggressive biology of TNBC tumors. Table 7 EM Clustering in TNBC (IHC5 immunophenotype) Ki67 Clusters in TNBC (Median (QR)) P * r † Cluster 1 Cluster 2 Size (mm) 20 (14,8–30) 25 (16–40) 0,03 0,13 Age (years) 59 (50–70) 54 (44–68) 0,04 0,12 Ki67 Clusters in TNBC (n, %) Klaster 1 Klaster 2 Ukupno P* Gradus I 14 (9,1) 0 (0,0) 14 (4,8) 0,001 II 54 (35,1) 32 (23,0) 86 (29,3) III 86 (55,8) 107 (77,0) 193 (65,9) Axilary lymph nodes Negative 86 (55,8) 86 (61,9) 172 (58,7) 0,34 Positive 68 (44,2) 53 (38,1) 121 (41,3) DCIS Yes 16 (10,4) 14 (10,0) 30 (10,2) 0,99 No 138 (89,6) 125 (90,0) 263 (89,8) Total 154 139 293 Discussion Our comprehensive retrospective study on 2651 IDC-NST cases over 18 years demonstrated that EM clustering of Ki-67, as a key biomarker for BC, reveals significant proliferation heterogeneity across all IHC subtypes , with distinct clinicopathological correlations. Beside establishing temporal and comparing regional differences in IHC subtypes-distribution (CEE vs. Western Europe, based on prior epidemiological reports), the focus of the study was in EM analysis. To our knowledge, this Croatian study is the largest CEE cohort study which applied and validated EM clustering model in a real-world cohort. Aligning to prior studies, our investigation also demonstrated validity of IHC phenotyping as surrogate marker of tumor biology. The IHC findings reflect an interplay between tumor nature and morphological features [ 4 , 22 , 23 ]. The IKWG recommendations emphasize strong limitation of Ki-67 assessment as single cutoff value and question the applicability of binary classification in clinical framework, highlighting the need for standardized assessment methods and complementary findings in decision-making process [ 7 , 8 , 16 ]. General considerations and temporal shift Luminal subtypes dominance was documented with temporal shift toward hormone receptor positive phenotypes with significant increase in frequency over 18-years (49,2% in 2004 to 72,3% in 2021; P < 0,001). This trend likely reflects improved diagnostic accuracy and protocol-standardization (esp. in Ki-67 assessment) as well as efficient screening programs for early BC detection in Western Europe and might be linked to increased awareness of luminal subtypes [ 24 – 26 ]. Clinical perspective of epidemiological shift toward HR+ tumors is therapeutically favorable (endocrine therapy). TNBC showed on the other hand stable incidence at 11% over 18-year study period, consistent to prior CEE studies and in contrast to Western Europe trends of slight decline in TNBC (possibly due to BRCA1/2 screening) [ 27 , 28 ]. Beyond Ki-67 rigid threshold and heterogeneity across IHC subtypes Expectation-Maximization (EM) analysis provided more precise insight into tumor biology, revealing two distinct Ki-67 clusters across the entire cohort of 2,651 patients (medians 22,6% vs. 74,4%; P < 0,001). B imodal Ki-67 distribution is proposed to be an inherent characteristic of invasive ductal breast carcinoma. Controversary, St. Gallen Concensus (2013) established a rigid Ki-67 threshold of 20% and redefined Luminal classification. Luminal A subtype (IHC1) exhibits only a single cluster without typical dichotomy – representing "amputated" part of the Luminal B subtype (in Ki-67 continuum) caused by artificial boundary of Ki-67 20% cutoff [ 4 , 16 , 29 ]. EM analysis reinforces the natural heterogeneity of tumors and is potentially superior to St. Gallen classifications in identifying natural subgroups with distinct prognostic implications, esp. enabling personalized risk stratification and tailored therapy. EM clustering and data-driven stratification challenge the adequacy of St. Gallen binary classification with least prognostic certainty and potential to misclassify tumors. Applying EM clustering, our study clearly showed progressive elevation of Ki-67 across phenotypes : from hormone receptor-positive subtypes (Luminal A/B1/B2: <20%/mean 38,6%/mean 34,5%) toward HER2-positive (34,5%-77%) and triple-negative tumors (33,9%-83,4%), reflecting more aggressive tumor biology in these heterogenous groups (grade III, larger tumors, younger age at manifestation) [ 4 – 6 , 9 , 16 ]. Except in homogeneous low proliferative Luminal A tumors (per definition Ki-67 < 20%), EM clustering revealed in all IHC subtypes starting from Luminal B1 two distinct biological subgroups with distinct clinicopathological features and prognostic implications: exp. Luminal B1: a lower-proliferation subgroup (mean Ki-67 30,3%, predominantly grade II) vs. high-proliferation subgroup (mean Ki-67 67,9%, predominantly grade III and significantly worse nodal status) which might represent potential target to intensify therapy regimens (CDK4/6 inhibitors). This EM clustering finding might lead to change in treatment regime for the distinctive subpopulations within luminal B1 group (exp. de-escalation for low proliferative/endocrine monotherapy vs escalation/chemotherapy for high proliferative tumors). As previously shown, hormone receptor positive super-category (IHC 1–3) exhibits also two distinct Ki-67 clusters and progressive increase in Ki-67 values across distinct phenotypes (IHC 1–3). Low proliferative cluster in HR+ group distinctly showed more favorable features (Grade I, node-negative) in comparison to HMA cluster with more aggressive features (Grade III, node-positive). To assess biological tumor nature more accurately and propose adequate treatment, recent BC recommendations support the use of multiparametric prognostic assessment (e.g.,Oncotype DX, Prosigna) in treatment decisions - beyond binary Ki-67 cutoffs. Uncertainties in treatment options and the need for personalized approach are known also for HER2+ (IHC4) phenotype cases. It exhibits strong heterogeneity with 2 clusters identified - with significant differences in tumor size and grade. The high-proliferation cluster (mean Ki-67 77%) showed grade III in 66,7% of cases compared to 49% in the low-proliferation cluster. Lower-proliferation HER2 + cluster may represent a favorable-risk subgroup potentially benefiting from de-escalated therapy (e.g., trastuzumab monotherapy without dual HER2 blockade). The highest Ki-67 values were found in the second cluster (83,4%) of triple-negative tumors as the most aggressive phenotype (consistent with literature). TNBC subtype showed intrinsic heterogeneity and exhibits two clusters with distinct features. Lower-proliferation TNBC cluster had smaller tumors (20mm vs 25mm, P = 0,03), lower incidence of grade III (55,8% vs 77%, P < 0,001), and older patient age (59y vs 54y, P = 0,04). Even in biologically most aggressive tumors (TNBC) there is a subpopulation of lower proliferative cluster 1 which might represent a favorable-risk TNBC subgroup potentially suitable for moderate de-escalated chemotherapy ( e.g., omission of platinum agents) [ 6 , 16 , 30 ]. Better u nderstanding of proliferation mechanism and interplay with a t umor microenvironment (TME) - especially in such a heterogeneous subtype as TNBC - may also enable early detection of treatment failure (chemotherapy resistance) and guide more accurate therapy selectio n [ 31 ]. EM clustering and clinical framework The clinical implication of applied EM clustering on continuous, data-driven Ki-67 is in refining of prognostic stratification beyond current St. Gallen 2013 classification with rigid arbitrary Ki-67 cutoff (with potential hazard of oversimplifying biological heterogeneity). Complementary integration of Ki-67 clustering with existing prognostic tools (Oncotype DX, Prosigna, MammaPrint) may radically improve diagnostic accuracy [ 32 ]. Further stratification for clinical trials and studies to evaluate de-escalated or escalated therapies are warranted. Novelty approach of EM clustering introduces precision oncology methods and might lead to individualized therapeutical decision-making. Strenghts and limitations of our study The largest single CEE/Croatian center cohort included 2,651 consecutive IDC-NST cases over 18 years providing robust statistical power applying EM clustering across all IHC subtypes as a novel study approach. Methodology was standardized (rigorous IHC protocols, comprehensive data collection, independent pathologist review and validated EM clustering algorithm). Temporal trends revealed 18-year follow-up shifts in IHC subtype distribution and posed clinically relevant findings (applicable to current clinical practice). Our study highlights the regional significance for underrepresented Central-Eastern Europe and need for region-specific epidemiology studies [ 11 , 12 , 26 ]. Limitations of our study include retrospective design, observational data collection in a single tertiary center with limited generalizability beyond CEE epidemiology. Prospective validation is recommended and comparison with other cohorts. The absence of follow-up data on disease-free survival (DFS), overall survival (OS), recurrence and metastasis. The prognostic impact of EM clustering remains to be evaluated. The assessment of Ki-67 and standardized protocols of IHC followed IKWG 2021 recommendations, but still remain critical point with improvement potential in aspect of interlaboratory variability [ 16 ]. Gene expression and molecular profiling were not conducted. Clear need for prospective validation and complementary integration of molecular profiling, epigenetic analysis and therapy oriented clinical trials. Conclusions This comprehensive CEE single-center retrospective study over 18-year period demonstrates that EM clustering of Ki-67 reveals significant proliferation heterogeneity across all IHC subtypes of invasive ductal carcinoma, with distinct clinicopathological correlations. Bimodal Ki-67 distributions were consistently identified across IDC subtypes. High-proliferation clusters were independently associated with adverse clinicopathological features implying tumor treatment escalation. On the other hand, lower-proliferation clusters even in aggressive subtypes (HER2+, TNBC) may identify patients suitable for de-escalated therapy. Ki-67 EM clustering offers a simple, cost-effective IHC-based approach to refine prognostic stratification beyond binary classification, potentially guiding personalized therapy selection. The temporal shift toward increasing luminal subtype prevalence over 18 years might reflect improved screening program and advanced diagnostic protocols in Croatia. Unfortunately, over time unchanged TNBC incidence in CEE might be in conjunction with different genetic and epidemiological regional burden and imposes the need for a further diagnostic refinement (BRCA 1 and 2 testing). Prospective multicenter studies incorporating long-term survival outcome data are essential to translate EM clustering in clinical setting as a stratification tool. Abbreviations ER=estrogen receptor; PR=progesterone receptor; HER2=human epidermal growth factor receptor 2; TNBC=triple-negative breast cancer; HR+=hormone receptor-positive; IHC=immunohistochemistry; EM=expectation-maximization; IDC=invasive ductal carcinoma; NST=no special type; LMA=low Ki-67 activity category; IMA=intermediate Ki-67 activity category; HMA=high Ki-67 activity category; FFPE=formalin-fixed paraffin embedded; St. Gallen=St. Gallen International Expert Consensus; ASCO/CAP= eng. American Society of Clinical Oncology / College of American Pathologists; IKWG= International Ki-67 Working Group; FISH/ISH= fluorescence in situ hybridization- chromogenic/silver in situ hybridization; GDPR=General Data Protection Regulation; CEE= Central-Eastern Europe; BIC= Bayasian Information Criterion; CI= confidence interval; OR= odds ratio; QR= quartile range; DFS= Disease-free survival; OS= overall survival; STROBE= Strengthening the Reporting of Obervational Studies in Epidemiology. Declarations Author Contributions: IB: conceptualization, study design, data curation, statistical analysis, writing (original draft, review and editing); BD: methodology, pathological validation, supervision, writing (review); SK: project supervision, conceptualization, statistical analysis, (deceased prior to submission); IZ: pathological assessment, data resources, writing (review). All authors read and approved the final manuscript. Co-Author, Prof. Sven Kurbel, MD, PhD– deceased on 27 th Dec.2024, before manuscript submission AI (Perplexity AI) assisted drafting/structure optimization in accordance with ICMJE guidelines; all content, data analysis, and interpretations remain the authors' responsibility. No AI was used in data generation or statistical analysis [33]. Acknowledgements: We thank gratefully the staff of the Department of Pathology and Forensic Medicine, Osijek University Clinical Hospital for technical assistance with IHC and tissue processing and to all patients whose data contributed to this study. We acknowledge the Croatian Ministry of Science and Education for funding support. The authors humbly dedicate this work to the memory of Professor Sven Kurbel (1956–2024) , whose profound intellectual contributions , unwavering mentorship , and visionary insights were instrumental in shaping this research. His legacy continues to inspire scientific inquiry and collaboration. Funding: This work was partly supported by the Croatian Ministry of Science and Education (Grant/Project-No: 219-2192382-2426). The funding body had no role in study design, data collection, analysis, or manuscript preparation. Competing Interests: The authors declare no competing financial or non-financial interests. AI tools used per ICMJE guidelines; human oversight ensured originality. Availability of Data and Materials: The datasets used and analyzed during this study are available in Zanedo (DOI 10.5281/zenedo.19858005). Ethics Approval and Consent to Participate: This study was approved by the Medical Faculty Osijek Ethics Committee (602-04/23-08/03, 24 April 2023) and KBC Osijek Ethics Committee (R1-4012/2023, 31 May 2023). All patients have signed the informed consent at the hospital admission to the breast surgical procedure that also includes consent to breast tissue examination for clinical, educational and research purposes; all data were anonymized. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. doi: 10.3322/caac.21492 . Weigelt B, Geyer FC, Reis-Filho JS. Histological types of breast cancer: how special are they? Mol Oncol. 2010;4(3):192–208. 10.1016/j.molonc.2010.04.004 . doi :. Hammond ME, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28(16):2784–2795. doi: 10.1200/JCO.2009.25.6529 . Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24(9):2206–2223. doi : 10.1093/ annonc/mdt303 . Inic Z, Zegarac M, Inic M, Markovic I, Kozomara R. Clinicopathological characteristics of molecular subtypes of breast cancer. J BUON. 2014;19(4):951–8. 10.1016/j.jbuon.2014.10.001 . doi :. Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA et al. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res. 2007;13(15 Pt 1):4429–4434. doi : 10 . 1158/1078-0432.CCR-06-3045 Polley M-YC, Leung SCY, Gao D, Mastropasqua MG, Zabaglo LA, Bartlett JMS et al. An international study to evaluate reproducibility of Ki-67 labeling index in breast cancer using a standardized scoring method. Mod Pathol. 2015;28(9):1155–1164. doi: 10.1038/modpathol.2015.73 . Dowsett M, Nielsen TO, A’Hern R et al. Assessment of Ki67 in breast cancer: Recommendations from the International Ki67 in Breast Cancer Working Group. J Natl Cancer Inst. 2011;103(22):1656–1664. doi : 10.1093/ jnci/djr393 . Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B Stat Methodol. 1977;39(1):1–38. doi : 10.1111/j.2517-6161.1977.tb01600. x . Kurbel S, Kurbel B, Marjanović K, Dmitrović B. Epithelial-mesenchymal transition in cancer: a historical perspective with a focus on breast cancer. BMC Cancer. 2017;17(1):782. doi: 10.1186/s 12885-017-3778- 9 . Didkowska J, Wojciechowska U, Olasek P, Tarkowski W, Trojanowski M, Czaderny M. Breast cancer in Central and Eastern Europe: the untold story. Cancer Epidemiol. 2020;69:101831. 10.1016/j.canep.2020.101831 . doi :. Bray F, Znaor A, Cueva P, Korir A, Swaminathan R, Ullrich A et al. Planning and developing population-based cancer registration in low- and middle-income settings: III. The European experience. Cancer Epidemiol. 2015;39(Suppl 1):S22–S31. doi : 10.1016/j.canep.2014.09 . 013 . Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 1991;19(5):403–410. doi : 10.1111/j.1365-2559.1991. tb00229.x . Allison KH, Hammond MEH, Dowsett M, McKernin SE, Carey LA, Fitzgibbons PL et al. Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update. J Clin Oncol. 2020;38(12):1346–1366. doi: 10.1200/JCO.19.02309 . Wolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS et al. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: ASCO/CAP Clinical Practice Guideline Focused Update. J Clin Oncol. 2018;36(20):2105–2122. doi: 10.1200/JCO.2018.77.8738 . Nielsen TO, Leung SCY, Rimm DL, Dodson A, Acs B, Badve S et al. Assessment of Ki67 in Breast Cancer: Updated Recommendations from the International Ki67 in Breast Cancer Working Group. J Natl Cancer Inst. 2021;113(7):808–819. doi: 10.1093/jnci/djaa201 . McLachlan GJ, Peel D. Finite Mixture Models. New York: Wiley; 2000. doi: 10.1002/0471721182 . Fraley C, Raftery AE. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002;97(458):611–631. doi: 10.1198/016214502760047131 . Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied Logistic Regression. 3rd ed. Hoboken, NJ: Wiley; 2013. doi: 10.1002/9781118548387. European Parliament and Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Off J Eur Union. 2016;L 119:1–88. doi: 10.3000/19770677.L_2016.119.eng . World Medical Association. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191–2194. doi: 10.1001/jama.2013.281053 . Prat A, Pineda E, Adamo B, et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast. 2015;24(Suppl 2):S26–35. : 10.1016/j.breast.2015.07.008 . doi . Polyak K. Heterogeneity in breast cancer. J Clin Invest. 2011;121(10):3786–3788. doi: 10.1172/JCI58742 . Howlader N, Altekruse SF, Li CI et al. US trends in hormone receptor-defined breast cancer incidence and survival, 1992–2016. J Natl Cancer Inst. 2020;112(12):1282–1291. doi: 10.1093/jnci/djaa015 . Puliti D, Bucchi L, Mancini S et al. Overdiagnosis in mammographic screening for breast cancer in Europe: A literature review. J Med Screen. 2012;19(Suppl 1):42–56. doi: 10.1258/jms.2012.012082 . EUROCARE-5 Working Group. Changes in breast cancer incidence and mortality in middle-aged and elderly women in 29 European countries. Int J Cancer. 2020;146(1):178–187. doi: 10.1002/ijc.32380 . Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA. 2019;321(3):288–300. 10.1001/jama.2018.19323 . doi :. Mavaddat N, Michailidou K, Dennis J et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21–34. doi : 10.1016/j.ajhg.2018.11 . 002 . Urruticoechea A, Smith IE, Dowsett M. Proliferation marker Ki-67 in early breast cancer. J Clin Oncol. 2005;23(28):7212–20. 10.1200/JCO.2005.07.501 . doi :. Foidart P, Irrthum A, Blacher S et al. Neoadjuvant chemotherapy with or without platinum in patients with triple-negative breast cancer: A propensity score-matched analysis of the prospective Neoadjuvant Breast Registry Symphony Trial (NBRST). J Clin Oncol. 2023;41(16):2923–2933. doi: 10.1200/JCO.22.02391 . Wu S, Zhu Y, Yang Y et al. Spatial transcriptomics reveals tumor microenvironment-driven proliferation heterogeneity and chemoresistance mechanisms in triple-negative breast cancer. Nat Commun. 2023;14(1):4142. doi: 10.1038/s 41467-023-39855-8 Prat A, Brasó-Maristany F, Llombart-Cussac A et al. Combining Ki-67 and gene expression signatures improves prognostic accuracy in early breast cancer: A translational analysis of the PENELOPE-B trial. J Clin Oncol.2023;41(36):5534–5544. doi: 10.1200/JCO.23.00456 . International Committee of Medical Journal Editors (ICMJE). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE. 2023. https://www.icmje.org Additional Declarations No competing interests reported. Supplementary Files STROBEchecklistv4cohort3.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About 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-9662114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638735809,"identity":"20497893-76d6-40c5-b3b1-e500ddc4d43a","order_by":0,"name":"Ivana Begic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACNjBZcYCBgRlIMzYQreUMKVrAgLHtAJRBjBY+ieRnD37OuyMn7878gOHnDmIcJpFmbti77Zmx4WE2A8beM8Ro4TlgJsG77XDixmYeBmbGNqK0HP8m+XfO4XoStLD3mEnzNhxOkGcmQUuZtMyxZ4YbmNkMDvYSo0W+mX2b5JuaO/Ly/YcfPvhJjBY4MDjAwHCAFA1A6xpIUz8KRsEoGAUjCAAApjIysuYFwV8AAAAASUVORK5CYII=","orcid":"","institution":"Faculty of Medicine, Josip Juraj Strossmayer University of Osijek,","correspondingAuthor":true,"prefix":"","firstName":"Ivana","middleName":"","lastName":"Begic","suffix":""},{"id":638735810,"identity":"a1110450-44d7-4c5f-9cc8-a08ba600f543","order_by":1,"name":"Branko Dmitrovic","email":"","orcid":"","institution":"Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek","correspondingAuthor":false,"prefix":"","firstName":"Branko","middleName":"","lastName":"Dmitrovic","suffix":""},{"id":638735811,"identity":"ec5dacd4-0e29-40ec-a4da-c5484acebce8","order_by":2,"name":"Sven Kurbel","email":"","orcid":"","institution":"Polyclinic Aviva","correspondingAuthor":false,"prefix":"","firstName":"Sven","middleName":"","lastName":"Kurbel","suffix":""},{"id":638735812,"identity":"cb9818c5-8230-4f3e-a060-4eb72fc06f42","order_by":3,"name":"Irena Zagorac","email":"","orcid":"","institution":"Department of Pathology and Forensic Medicine, Osijek University Clinical Hospital","correspondingAuthor":false,"prefix":"","firstName":"Irena","middleName":"","lastName":"Zagorac","suffix":""}],"badges":[],"createdAt":"2026-05-09 09:25:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9662114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9662114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109332925,"identity":"314ea6f3-8338-4ac3-8e24-2f2b13b2cc20","added_by":"auto","created_at":"2026-05-15 16:22:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21192,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrade Distribution in Invasive Breast Cancer patients\u003c/strong\u003e.Distribution of histological grades (Nottingham grading system) in the total cohort of 2,651 invasive ductal breast carcinoma patients. Grade II was most prevalent (45,9%), followed by Grade I (31,2%) and Grade III (22,8%).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9662114/v1/5ee03053cb8140b7ac931359.png"},{"id":109405632,"identity":"6c55c2dc-432a-4f95-998d-69f7017021e0","added_by":"auto","created_at":"2026-05-17 13:19:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAxillary lymph node status in 2651 IDC-NST patients.\u003c/strong\u003e Node-negative disease was present in 60,3% of patients; node-positive in 39,7%.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9662114/v1/45b3e83c188f6a2c37c2a2f6.png"},{"id":109332928,"identity":"9958012f-07cc-4616-b30c-ecf76ffdd8ef","added_by":"auto","created_at":"2026-05-15 16:22:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of immunohistochemical phenotypes (IHC 1-5) per St. Gallen classification across the total cohort (n=2651).\u003c/strong\u003e Luminal subtypes (IHC 1-3) predominated, accounting for 81,4% of cases\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9662114/v1/9ff89a1c0324aeaf4055d816.png"},{"id":109332926,"identity":"c41ab19a-b78e-4204-84e8-c66a4534ef71","added_by":"auto","created_at":"2026-05-15 16:22:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpectation-Maximization (EM) Ki-67 clustering results.\u003c/strong\u003e Probability density plots of Ki-67 values with identified cluster boundaries: \u003cstrong\u003e(A)\u003c/strong\u003e Overall cohort (IHC 2–5; n=1,836): Cluster 1 (mean 22,6%) vs. Cluster 2 (mean 74,4%). \u003cstrong\u003e(B)\u003c/strong\u003e Luminal B1 (IHC 2; n=990): Cluster 1 (mean 30,3%) vs. Cluster 2 (mean 77,9%). \u003cstrong\u003e(C)\u003c/strong\u003e Luminal B2 (IHC 3; n=354): Cluster 1 (mean 27,4%)\u003cem\u003e vs.\u003c/em\u003e Cluster 2 (mean 74,5%). \u003cstrong\u003e(D)\u003c/strong\u003e HER2+ (IHC 4; n=199): Cluster 1 (mean 34,5%) vs. Cluster 2 (mean 78,9%). \u003cstrong\u003e(E)\u003c/strong\u003e TNBC (IHC 5; n=293): Cluster 1 (mean 33,9%) vs. Cluster 2 (mean 83,4%). All cluster separations statistically significant (P\u0026lt;0.001). X-axis: Ki-67 (%); Y-axis: probability density.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9662114/v1/e4635a3714e704da683da55c.png"},{"id":109466569,"identity":"b7f45c63-53a6-40d9-925b-d17bb3d93c29","added_by":"auto","created_at":"2026-05-18 12:10:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":663873,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9662114/v1/9d6ddadd-a0a4-4dbd-aed2-04d0814e8815.pdf"},{"id":109405696,"identity":"17a6eceb-05cd-43b9-9e9e-d2aa37ac9bee","added_by":"auto","created_at":"2026-05-17 13:19:46","extension":"doc","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":85504,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistv4cohort3.doc","url":"https://assets-eu.researchsquare.com/files/rs-9662114/v1/8747656934a676669398f0a2.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expectation Maximization Clustering Reveals Ki-67 Heterogeneity Within Immunohistochemical Subtypes of Invasive Ductal Breast Carcinoma: A 18-year cohort study from Central-Eastern Europe","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer remains the most common malignancy in women worldwide and a leading cause of cancer-related mortality. Invasive ductal carcinoma (IDC), not otherwise specified (NOS/NST), accounts for approximately 80% of cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accurate profiling of immunohistochemical (IHC) markers includes assessment of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and Ki-67 proliferation index. This is essential for tumor classification, to guide treatment decisions and to predict clinical outcomes. The 2013 St. Gallen International Expert Consensus guidelines classifies breast cancers using Ki-67 threshold of 20% to distinguish Luminal A (low proliferation) from luminal B (high proliferative activity), guiding endocrine vs. chemotherapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Luminal A subtype shows homogeneity. Tumors are smaller in size and exhibit favorable traits \u0026ndash; hormone receptor positivity, histological grade I and negative lymph node status. On the other hand, HER2\u0026thinsp;+\u0026thinsp;and TNBC are associated with more pronounced characteristics of biological aggressiveness [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKi-67 is a continuous variable and its distribution is heterogeneous, non-Gaussian, suggesting biologically distinct subtypes - particularly pronounced in aggressive subtypes (HER2+, TNBC), where treatment outcome varies significantly despite identical IHC classification [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Rigid binary Ki-67 classification underestimates biological nature of IDC.\u003c/p\u003e \u003cp\u003eExpectation-maximization (EM) clustering is a novelty approach using probabilistic algorithm to identify data-driven latent Ki-67 subgroups as distinct proliferation phenotypes in large IDC cohort [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur objectives were to apply EM clustering method to identify latent Ki-67 subpopulation and to assess heterogeneity within each group as well as to establish clinicopathological associations. Further, to assess temporal trends in distribution of IHC subtypes and to compare regional differences in IHC subtypes over time and regions (comparing our results with literature reports from prior epidemiological studies).\u003c/p\u003e \u003cp\u003eThis comprehensive study analyzes 2651 IDC cases (2004\u0026ndash;2021) from a single tertiary Croatian center (Department of Pathology and Forensic Medicine, Osijek University Clinical Hospital, Osijek, Croatia) \u0026ndash; to our knowledge, largest regional cohort \u0026ndash; highlighting temporal trends, assessing Ki-67 clusters and its clinicopathological correlates and identifying distinct proliferation clusters in IHC subtypes for potential therapy refinement. Central-Eastern Europe (CEE) data are scarce, despite distinct epidemiology, including higher TNBC incidence in comparison to Western Europe [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy design\u003c/b\u003e: Retrospective, single-center cohort study in tertiary, referral center at the Department of Pathology and Forensic Medicine, Osijek University Clinical Hospital, Osijek, Croatia in s\u003cb\u003etudy period\u003c/b\u003e January 2004 -December 2021 (longitudinal over 18 years). An average inclusion rate was 147 patients/year. Reporting followed STROBE guidelines for observational studies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e were histologically confirmed invasive ductal carcinoma, no special type (IDC-NST), complete clinicopathological data, adequate archival formalin-fixed paraffin-embedded tissue blocks and complete IHC panel (ER, PR, HER2, Ki-67). All other histological types, metastatic disease at presentation, prior neoadjuvant therapy, incomplete clinicopathological or IHC data were excluded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFinal Cohort\u003c/b\u003e included 2,651 consecutive IDC-NST cases.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHistopathological Assessment, Grading System\u003c/h2\u003e \u003cp\u003eHistopathological standard analysis was performed on archival FFPE tissue blocks, reviewed independently by two pathologists experienced in BC diagnostics. Histological grading was based on Nottingham/Elston-Ellis modification of the Scarf-Bloom-Richardson grading system (Grade I: 3\u0026ndash;5 points - for well differentiated; Grade II: 6\u0026ndash;7 points - for moderately differentiated; Grade III: 8\u0026ndash;9 points - for poorly differentiated) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmunohistochemistry Assessment and Classification into IHC groups\u003c/h3\u003e\n \u003cp\u003eIHC was performed on Ventana BenchMark Ultra automated stainer (Roche Diagnostics, Basel, Switzerland). The assessment of biomarkers was performed according to validated international guidelines:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eER, PR: ASCO/CAP Recommendations from 2020 (ASCO/CAP, \u003cem\u003eeng. American Society of Clinical Oncology / College of American Pathologists\u003c/em\u003e) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHER2: ASCO/CAP 2020 guidelines; amplification confirmed by FISH/ISH in IHC 2\u0026thinsp;+\u0026thinsp;results [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eKi-67 assesment aligned with International Ki-67 Working Group (IKWG) Consensus Recommendation (2011, revised 2021). All cases were consistently retrospectively reclassified per St. Gallen 2013 Consensus into five subtypes as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of Breast Cancer based on Immunohistochemical Analysis According to St. Gallen 2013 Concensus [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eImmunohistochemical Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMolecular Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImmunohistochemical Features\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHC 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c3\" namest=\"c2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLuminal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLuminal A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;and/or PR+,\u003c/p\u003e \u003cp\u003eHER2-negative,\u003c/p\u003e \u003cp\u003eKi-67\u0026thinsp;\u0026lt;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHC 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLuminal B1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;and/or PR+,\u003c/p\u003e \u003cp\u003eHER2-negative,\u003c/p\u003e \u003cp\u003eKi-67\u0026thinsp;\u0026ge;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHC 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLuminal B2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eER\u0026thinsp;+\u0026thinsp;and/or PR+,\u003c/p\u003e \u003cp\u003eHER2- amplification,\u003c/p\u003e \u003cp\u003eany Ki-67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHC 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHER2/neu amplification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eER\u0026ndash;, PR\u0026ndash;,\u003c/p\u003e \u003cp\u003eHER2- amplification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHC 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTriple-negative breast cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eER\u0026ndash;, PR\u0026ndash;, HER2-negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunohistochemical groups\u003c/b\u003e were further consolidated into three major super-categories for clustering and statistical purposes (descriptive analyses): HR+ (Hormone Receptor-Positive: IHC1\u0026thinsp;+\u0026thinsp;IHC2\u0026thinsp;+\u0026thinsp;IHC3, n\u0026thinsp;=\u0026thinsp;2159), HER2 amplification (IHC4, n\u0026thinsp;=\u0026thinsp;199), TNBC (Triple-Negative, IHC5, n\u0026thinsp;=\u0026thinsp;293) and additionally into three super-categories based on Ki-67 value: low proliferative activity (LMA\u0026thinsp;\u0026lt;\u0026thinsp;25%, n\u0026thinsp;=\u0026thinsp;1385; 52,2%), intermediate (IMA 25\u0026ndash;65%, n\u0026thinsp;=\u0026thinsp;949; 35,8%)) and high proliferative activity (HMA\u0026thinsp;\u0026ge;\u0026thinsp;65%, n\u0026thinsp;=\u0026thinsp;317; 12,0%).\u003c/p\u003e \u003cp\u003e \u003cb\u003eExpectation-Maximization (EM) Clustering and Statistical/Comparative Analysis\u003c/b\u003e We applied STATISTICA v11 (StatSoft, Tulsa, OK, USA) software, in particular Expectation-Maximization (EM) algorithm to stratify Ki-67 values within each IHC subtype and super-group (HR+, HER2+, TNBC). The algorithm estimates means, variances and mixing proportions until convergence. Bayesian Information Criterion (BIC) determines optimal number of clusters (n\u0026thinsp;=\u0026thinsp;2) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCategorical variables were compared using the Pearson χ\u0026sup2; test or Fisher's exact test (for frequency\u0026thinsp;\u0026lt;\u0026thinsp;5). Continuous non-parametric, non-normally distributed variables (confirmed by Kolmogorov-Smirnov test) were analyzed using the Mann-Whitney U test. For testing associations (Ki-67 cluster and clinicopathological variables) we used odds ratios (OR) with 95% confidence intervals (CI). All tests were two-tailed; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003ewas received from Ethical Committee of Medical Faculty Osijek: (602-04/23\u0026thinsp;\u0026minus;\u0026thinsp;08/03 on 24th April 2023 and Ethical Committee of Clinical Hospital Osijek (R1-4012/2023 on 31st May 2023). All patients have signed written informed consent at hospital admission for the breast surgical procedure that also included consent to breast tissue examination for clinical, educational and research purposes; all data were anonymized.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData Protection\u003c/b\u003e was assured by the General Data Protection Regulation (GDPR, EU 2016/679) and Helsinki Declaration \u0026ndash; by compliant anonymization and patient numerical identifiers [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The initial study was a part of a research project financed by the Croatian Ministry of Science (219-2,192,382\u0026ndash;2426).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral Descriptive Cohort Characteristics\u003c/strong\u003e: final cohort in our 18-year retrospective study included 2651 patients with IDC-NST. Median age was 62 years (IQR: 53\u0026ndash;70) with predominance of postmenopausal women (\u0026gt;\u0026thinsp;55 years; 66,6%). Tumors were mostly small (in 65,5% \u0026lt;25mm). Histological grade II was the most prevalent (n\u0026thinsp;=\u0026thinsp;1218 cases; 45,9%), followed by grade I (n\u0026thinsp;=\u0026thinsp;828, 31,2%) and grade III (n\u0026thinsp;=\u0026thinsp;605, 22,8%) as shown in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eA concurrent ductal carcinoma \u003cem\u003ein situ\u003c/em\u003e (DCIS) component was present in 12,2%. Axillary lymph nodes were positive (metastatic) in 1053 (39,7%) patients as shown in Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe results of immunohistochemistry analysis showed ER positivity (\u0026gt;\u0026thinsp;10%) in 2081 cases (78,5%) and in additional 78 patients (2,9%) weak positive (1\u0026ndash;10%) findings. PR were positive in 1982 cases (74,8%) and HER2\u0026thinsp;+\u0026thinsp;in 553 cases (20,9%).\u003c/p\u003e\n\u003ch3\u003eDistribution of immunohistochemical phenotypes (IHC)\u003c/h3\u003e\n\u003cp\u003eIHC assessment and classification into five subtypes according to St. Gallen 2013 Consensus showed following frequencies distribution:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eIHC1 (Luminal A): 815 cases (30,7%)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIHC2 (Luminal B1): 990 cases (37,3%)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIHC3 (Luminal B2): 354 cases (13,4%)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIHC4 (HER2+): 199 cases (7,5%)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIHC5 (TNBC): 293 cases (11,1%)\u003c/p\u003e\u003cbr\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDistribution of immunohistochemical phenotypes showed clear dominance of luminal subtypes. Luminal A and B1 subtypes (hormone receptor-positive, HER2-negative) accounting for 68,1% (n\u0026thinsp;=\u0026thinsp;1805) of cases an IHC1-3 (n\u0026thinsp;=\u0026thinsp;2,159 cases; 81,4%) as shown in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal trends (2004\u0026ndash;2021)\u003c/strong\u003e showed significant frequency increase in luminal subtypes over 18-year study period (49,2% in 2004 towards 72,3% in 2021, P\u0026thinsp;\u0026lt;\u0026thinsp;0,001); TNBC proportion remained stable at ~\u0026thinsp;11% (P\u0026thinsp;=\u0026thinsp;0,42) over study period.\u003c/p\u003e\n\u003ch3\u003eDifferences in clinicopathological characteristics in IHC super-categories\u003c/h3\u003e\n\u003cp\u003eHormone receptor (HR) - positive tumors (IHC1-3, n\u0026thinsp;=\u0026thinsp;2159) exhibited favorable features: ER-positive (\u0026gt;\u0026thinsp;10%) in 96,4%, PR-positive in 90,7%, lower histological grade (grade I in 36,9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001). In HR+ tumors HER2 was negative in 83,6% cases.\u003c/p\u003e\n\u003cp\u003eHistological grade III was significantly less prevalent in HR+ group: 14,2% vs. 52,8% in HER2\u0026thinsp;+\u0026thinsp;tumors vs. 65,9% in TNBC tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0,001).\u003c/p\u003e\n\u003cp\u003eIn contrast, HER2+ (IHC4) and TNBC (IHC5) demonstrated aggressive traits: grade III in 52,8% and 65,9%, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with PR-negative rates of 98% and 93,5% respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eDifferences in tumor histological grade, presence of a DCIS component, status of ER, PR, and HER2 oncogenes between hormone-positive tumors (HR+) tumors, HER2\u0026thinsp;+\u0026thinsp;and triple-negative tumors (TNBC) \u0026ndash; as shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of clinicopathological parameters across IHC super-categories\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003eHR\u003csup\u003e\u0026para;\u003c/sup\u003e positive\u003c/p\u003e\n \u003cp\u003eLuminal\u003c/p\u003e\n \u003cp\u003eA, B1 i B2\u003c/p\u003e\n \u003cp\u003e(IHC 1\u0026ndash;3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003eHER2 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eamplification\u003c/p\u003e\n \u003cp\u003e(IHC 4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eTriple negative BC\u003c/p\u003e\n \u003cp\u003eTNBC\u003csup\u003e\u0026dagger;\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(IHC5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026Dagger;\u0026Dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGradus\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"5\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(36,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e(9,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(4,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(31,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e1056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(48,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e(38,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(29,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(45,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(14,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e(52,8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e(65,9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(22,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCIS status\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eDCIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(12,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e(14,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(10,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(12,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eNo DCIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e1894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(87,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e(85,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(89,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(87,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eER status\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"5\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e(100,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(100,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(18,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eWeak positive\u003c/p\u003e\n \u003cp\u003e(1\u0026ndash;10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(3,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(2,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(\u0026gt;\u0026thinsp;10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e2081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(96,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(78,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePR status\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026sect;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(9,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e(98,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(93,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(25,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e1959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e(90,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e(2,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(6,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(74,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 status\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e||\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e1805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e(83,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(100,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(79,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e(16,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(100,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e(20,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e2159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e* Gradus \u0026ndash; histological differentiation \u0026dagger; DCIS \u0026ndash; \u003cem\u003eengl. ductal in situ carcinoma\u003c/em\u003e; \u0026Dagger; ER status \u0026ndash; estrogen receptor status; \u0026sect; PR status \u0026ndash; progesteron receptor status; || HER2 status \u0026ndash; HER2 oncogene amplification; \u0026para; HR \u0026ndash; hormone receptor; ** HER2 pos. \u0026ndash;HER2 oncogene amplification; \u0026dagger;\u0026dagger; TNBC\u0026ndash; triple negative BC; \u0026Dagger;\u0026Dagger; p \u0026ndash; significance in \u0026chi;2 test\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDistribution of Ki-67 Proliferation Index across IHC Phenotypes\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous Ki-67 distribution was arbitrary subdivided in three major pre-defined super-categories for more relevant clinical reflection and statistical purposes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003elow mitotic activity LMA (\u0026lt;\u0026thinsp;25%, n\u0026thinsp;=\u0026thinsp;1385, 52,2%), intermediate IMA (25\u0026ndash;65%, n\u0026thinsp;=\u0026thinsp;949, 35,8%,) and high HMA (\u0026ge;\u0026thinsp;65%, n\u0026thinsp;=\u0026thinsp;317, 12%) (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0,001).\u0026nbsp;\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of Ki-67 proliferation activity categories across IHC phenotypes (IHC 1\u0026ndash;5)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eKi-67\u003c/p\u003e\n \u003cp\u003e% (category)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\n \u003cp\u003eImmunohistochemical phenotypes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003cp\u003eIHC 1\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eLuminal B1\u003c/p\u003e\n \u003cp\u003eIHC 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLuminal B2\u003c/p\u003e\n \u003cp\u003eIHC 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eHER2 amplification\u003c/p\u003e\n \u003cp\u003eIHC 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eTriple negative BC\u003c/p\u003e\n \u003cp\u003eIHC 5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e||\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;25%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLMA\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e(100,0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(30,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(44,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(26,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(19,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(52,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u0026ndash;65%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIMA\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(58,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(45,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(56,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(33,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(35,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026thinsp;65%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHMA\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(10,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(10, 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e(17,6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e(47,4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e(12,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eUkupno\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003eLMA, eng. low mitotic activity (Ki-67\u0026thinsp;\u0026lt;\u0026thinsp;25%); \u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003eIMA, eng. intermediate mitotic activity, Ki-67 25\u0026ndash;65%); \u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003eHMA, eng. high mitotic activity, (Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;65%); \u003csup\u003e\u003cstrong\u003e\u0026sect;\u003c/strong\u003e\u003c/sup\u003eIHC (1\u0026ndash;5) \u0026ndash; immunohistochemical phenotype (1\u0026ndash;5); \u003csup\u003e\u003cstrong\u003e||\u003c/strong\u003e\u003c/sup\u003e \u0026chi;2 test\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eKi-67 distribution showed ascending values across IHC subtypes reflecting progressive proliferative nature of tumors. By the St. Gallen Consensus (2013) luminal A has per definition Ki-67\u0026thinsp;\u0026lt;\u0026thinsp;20% and was directly arbitrary classified in LMA (Ki-67\u0026thinsp;\u0026lt;\u0026thinsp;25%). Luminal B1 was predominantly in IMA (58,3%), HER2\u0026thinsp;+\u0026thinsp;also in IMA (56,3%), while TNBC demonstrated the highest proportion of HMA tumors (47,4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001). Associations were established for HMA super-category and more adverse features: younger age (\u0026lt;\u0026thinsp;55 years; 15,5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001), larger tumors (\u0026ge;\u0026thinsp;25 mm; 16\u0026ndash;23%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001), grade III (in 64%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001), nodal positivity (43,5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001), negative hormone-receptor status (ER-/PR-) and positive HER2\u0026thinsp;+\u0026thinsp;status. LMA tumors (n\u0026thinsp;=\u0026thinsp;1,385) had favorable histological grade I in 48,2% and they were node-negative in 65,6% (P\u0026thinsp;\u0026lt;\u0026thinsp;0,001, Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinicopathological features by Ki-67 proliferation activity categories\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\n \u003cp\u003eKi-67% categories\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;25%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLMA\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u0026ndash;65%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIMA\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026thinsp;65%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHMA\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026sect;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e(49,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(35,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(15,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(33,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e(53,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(36,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e(10,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e(66,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize\u003c/strong\u003e (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003cp\u003e(58,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e563\u003c/p\u003e\n \u003cp\u003e(32,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003cp\u003e(9,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1737\u003c/p\u003e\n \u003cp\u003e(65,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e25\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003cp\u003e(41,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003cp\u003e(42,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003cp\u003e(16,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e760\u003c/p\u003e\n \u003cp\u003e(28,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003cp\u003e(35,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003cp\u003e(42,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003cp\u003e(22,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003cp\u003e(5,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eGradus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e667\u003c/p\u003e\n \u003cp\u003e(80,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003cp\u003e(18,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(1,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e828\u003c/p\u003e\n \u003cp\u003e(31,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e611\u003c/p\u003e\n \u003cp\u003e(50,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e502\u003c/p\u003e\n \u003cp\u003e(41,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003cp\u003e(8,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1218\u003c/p\u003e\n \u003cp\u003e(45,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003cp\u003e(17,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003cp\u003e(48,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003cp\u003e(33,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003cp\u003e(22,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxilary lymph nodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e908\u003c/p\u003e\n \u003cp\u003e(56,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e511\u003c/p\u003e\n \u003cp\u003e(32,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003cp\u003e(11,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1598\u003c/p\u003e\n \u003cp\u003e(60,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003cp\u003e(45,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003cp\u003e(41,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003cp\u003e(13,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1053\u003c/p\u003e\n \u003cp\u003e(39,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus ER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003cp\u003e(21,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003cp\u003e(42,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003cp\u003e(35,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003cp\u003e(18,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWeak positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e(20,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003cp\u003e(41,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003e(38,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003cp\u003e(2,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1261\u003c/p\u003e\n \u003cp\u003e(60,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e707\u003c/p\u003e\n \u003cp\u003e(34,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003cp\u003e(5,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2081\u003c/p\u003e\n \u003cp\u003e(78,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus PR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003cp\u003e(28,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003cp\u003e(43,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003cp\u003e(28,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e669\u003c/p\u003e\n \u003cp\u003e(25,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1194\u003c/p\u003e\n \u003cp\u003e(60,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003cp\u003e(33,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003cp\u003e(6,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1982\u003c/p\u003e\n \u003cp\u003e(74,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1177\u003c/p\u003e\n \u003cp\u003e(56,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e675\u003c/p\u003e\n \u003cp\u003e(32,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003cp\u003e(11,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2098\u003c/p\u003e\n \u003cp\u003e(79,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003cp\u003e(37,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003cp\u003e(49,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003cp\u003e(12,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003cp\u003e(20,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1385\u003c/p\u003e\n \u003cp\u003e(52,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e949\u003c/p\u003e\n \u003cp\u003e(35,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003cp\u003e(12,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003eLMA, eng. low mitotic activity (Ki-67\u0026thinsp;\u0026lt;\u0026thinsp;25%); \u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003eIMA, eng. intermediate mitotic activity, Ki-67 25\u0026ndash;65%); \u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003eHMA, eng. high mitotic activity, (Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;65%); \u003csup\u003e\u003cstrong\u003e\u0026sect;\u003c/strong\u003e\u003c/sup\u003e P-significance, \u0026chi;2 test\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eEM Clustering in Ki-67 Pooled Data (IHC 2\u0026ndash;5) and in Hormon Receptor Positive (HR+; IHC 1\u0026ndash;3) group\u003c/strong\u003e identified clear dichotomy (for IHC 2\u0026ndash;5 subtypes shown in Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA):\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e- Cluster 1 (low proliferative): mean Ki-67 22,6% (n\u0026thinsp;=\u0026thinsp;1034; 56,3%)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e- Cluster 2 (high proliferative): mean Ki-67 74,4% (n\u0026thinsp;=\u0026thinsp;802; 43,7%; P\u0026thinsp;\u0026lt;\u0026thinsp;0,001)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;EM clustering findings for HR+ group and clinicopathological differences between clusters are shown in Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Histological grade III was more prevalent in high-Ki-67 cluster 2 (47,8% vs. 9,7% in cluster 1; P\u0026thinsp;\u0026lt;\u0026thinsp;0,001) as well as lymph node positivity (44,3% vs 37,2%, P\u0026thinsp;=\u0026thinsp;0,03). DCIS co-occurrence in cluster 2 was present in 18% cases (vs. 11,5% in lower-proliferative cluster, P\u0026thinsp;=\u0026thinsp;0,003). There were no significant differences in size or age.\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEM Clustering in hormone receptor positive (HR+) super-category\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\n \u003cp\u003eKi67 clusters in HR+ super-category (IHC 1\u0026ndash;3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGradus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e782 (98,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14 (1,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e796 (36,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e937 (88,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e119 (11,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1056 (48,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e185 (60,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e122 (39,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e307(14,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxilary lymphnodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1196 (89,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e142 (10,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1338 (62,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e708 (86,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e113 (13,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e821 (38,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e219 (82,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e46 (17,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e265 (12,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1685 (89,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e209 (11,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1894 (87,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1904\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e255\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e2159\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEM Clustering across distinct IHC phenotypes\u003c/h3\u003e\n\u003cp\u003eFindings as represented in Figs. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecific results of EM clustering regarding IHC subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal A (IHC 1; n\u0026thinsp;=\u0026thinsp;815)\u003c/strong\u003e subtype comprises a single homogeneous cluster; Ki-67 was by definition\u0026thinsp;\u0026lt;\u0026thinsp;20%, corresponding with low-proliferative nature.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B1 (ICH 2, n\u0026thinsp;=\u0026thinsp;990;\u003c/strong\u003e Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e: in cluster 1 mean Ki-67 was 30,3% (n\u0026thinsp;=\u0026thinsp;771; 77,9%) and in cluster 2 mean Ki-67 67,9% (n\u0026thinsp;=\u0026thinsp;219; 22,1%). Difference in grade III prevalence was demonstrated: 48,4% (Cluster 2) vs. 13,0% (Cluster 1) as well in size (22mm vs. 19mm, P\u0026thinsp;\u0026lt;\u0026thinsp;0,001). DCIS co-occurrence was also different (20,5% in cluster 2 vs. 12,5% in cluster 1, P\u0026thinsp;=\u0026thinsp;0,002). Nodal positivity was not significantly different (P\u0026thinsp;=\u0026thinsp;0,07).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B2 (IHC 3; n\u0026thinsp;=\u0026thinsp;354;\u003c/strong\u003e Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e also showed bimodal distribution with two distinct clusters; cluster 1 with Ki-67 mean 27,4% vs. high-proliferation cluster (mean 74,5%) which was associated with grade III (42,6% in cluster 2 vs. 19,6% in cluster 1; P\u0026thinsp;\u0026lt;\u0026thinsp;0,001), larger tumor size (25 vs. 20mm; P\u0026thinsp;=\u0026thinsp;0,05). No differences in size/age/lymph node metastases/DCIS co-occurrence were found.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eHER2+ (IHC 4; n\u0026thinsp;=\u0026thinsp;199;\u003c/strong\u003e Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD; Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e subtype demonstrated dichotomy in two clusters as shown: Cluster 1 with mean Ki-67 34,5% (n\u0026thinsp;=\u0026thinsp;157; 78,9%) and cluster 2 with mean Ki-67 77% (n\u0026thinsp;=\u0026thinsp;42; 21,1%). Differences between cluster 1 and 2 in histological grade III were identified: 49% vs. 66,7% (P\u0026thinsp;=\u0026thinsp;0,03). No differences in size/age/lymph node metastases/DCIS co-occurrence were found.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eTNBC (IHC 5; n\u0026thinsp;=\u0026thinsp;293;\u003c/strong\u003e Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE; Table \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e: Ki-67 Cluster 1 (lower proliferation) showed mean Ki-67 33,9% (n\u0026thinsp;=\u0026thinsp;154; 52,6%) and cluster 2 (high proliferation) mean Ki-67 83,4% (n\u0026thinsp;=\u0026thinsp;139; 47,4%). Grade III prevalence was significantly higher in cluster 2 (77,0% vs. 55,8%; P\u0026thinsp;\u0026lt;\u0026thinsp;0,001). Nodal status did not differ significantly between clusters (P\u0026thinsp;=\u0026thinsp;0,34), nor did DCIS co-occurrence (P\u0026thinsp;=\u0026thinsp;0,99). Cluster 2 was associated with younger patient age (median 54 vs. 59 years; P\u0026thinsp;=\u0026thinsp;0,04) and larger tumor size (P\u0026thinsp;=\u0026thinsp;0,03).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEM Clustering in HER2\u0026thinsp;+\u0026thinsp;tumor (IHC4 immunophenotype)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\n \u003cp\u003eKi67 Clusters in HER2+ (n, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003eP*\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGradus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e18 (11,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0 (0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e18 (9,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e62 (39,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14 (33,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e76 (38,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e77 (49,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28 (66,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e105 (52,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxilary lymph nodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e73 (46,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15 (35,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e88 (44,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e84 (53,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e27 (64,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e111 (55,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21 (13,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 (19,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e29 (14,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e0,34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e136 (86,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e34 (81,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e170 (85,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e157\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e199\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eIn TNBC tumors even cluster 1 exhibited relatively high Ki-67 values compared to luminal subtypes, confirming high proliferative nature and aggressive biology of TNBC tumors.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEM Clustering in TNBC (IHC5 immunophenotype)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\n \u003cp\u003eKi67 Clusters in TNBC\u003c/p\u003e\n \u003cp\u003e(Median (QR))\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c8\" namest=\"c7\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003er\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e20 (14,8\u0026ndash;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e25 (16\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003e59 (50\u0026ndash;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003e54 (44\u0026ndash;68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eKi67 Clusters in TNBC (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eKlaster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eKlaster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003eUkupno\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003e\u003cstrong\u003eP*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eGradus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e14 (9,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e0 (0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e14 (4,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c9\" namest=\"c8\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e54 (35,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e32 (23,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e86 (29,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e86 (55,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e107 (77,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e193 (65,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxilary lymph nodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e86 (55,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e86 (61,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e172 (58,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c9\" namest=\"c8\" rowspan=\"2\"\u003e\n \u003cp\u003e0,34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e68 (44,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e53 (38,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e121 (41,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e16 (10,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e14 (10,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e30 (10,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c9\" namest=\"c8\" rowspan=\"2\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e138 (89,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e125 (90,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e263 (89,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e154\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e139\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e293\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur comprehensive retrospective study on \u003cb\u003e2651 IDC-NST cases\u003c/b\u003e over 18 years demonstrated that \u003cb\u003eEM clustering of Ki-67, as a key biomarker for BC, reveals significant proliferation heterogeneity across all IHC subtypes\u003c/b\u003e, with distinct clinicopathological correlations. Beside establishing temporal and comparing regional differences in IHC subtypes-distribution (CEE vs. Western Europe, based on prior epidemiological reports), the focus of the study was in EM analysis. To our knowledge, this Croatian study is the largest CEE cohort study which applied and validated EM clustering model in a real-world cohort. Aligning to prior studies, our investigation also demonstrated validity of IHC phenotyping as surrogate marker of tumor biology. The IHC findings reflect an interplay between tumor nature and morphological features [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The IKWG recommendations emphasize strong limitation of Ki-67 assessment as single cutoff value and question the applicability of binary classification in clinical framework, highlighting the need for standardized assessment methods and complementary findings in decision-making process [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeneral considerations and temporal shift\u003c/h2\u003e \u003cp\u003e \u003cb\u003eLuminal subtypes dominance was documented with temporal shift toward\u003c/b\u003e hormone receptor positive phenotypes with significant increase in frequency over 18-years (49,2% in 2004 to 72,3% in 2021; P\u0026thinsp;\u0026lt;\u0026thinsp;0,001). This trend likely reflects improved diagnostic accuracy and protocol-standardization (esp. in Ki-67 assessment) as well as efficient screening programs for early BC detection in Western Europe and might be linked to increased awareness of luminal subtypes [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Clinical perspective of epidemiological shift toward HR+ tumors is therapeutically favorable (endocrine therapy). TNBC showed on the other hand stable incidence at 11% over 18-year study period, consistent to prior CEE studies and in contrast to Western Europe trends of slight decline in TNBC (possibly due to BRCA1/2 screening) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBeyond Ki-67 rigid threshold and heterogeneity across IHC subtypes\u003c/h2\u003e \u003cp\u003eExpectation-Maximization (EM) analysis provided more precise insight into tumor biology, revealing two distinct Ki-67 clusters across the entire cohort of 2,651 patients (medians 22,6% vs. 74,4%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001). B\u003cb\u003eimodal Ki-67 distribution is proposed to be an\u003c/b\u003e inherent characteristic of invasive ductal breast carcinoma. \u003cb\u003eControversary, St. Gallen Concensus (2013) established a rigid Ki-67 threshold of 20% and redefined Luminal classification.\u003c/b\u003e Luminal A subtype (IHC1) exhibits only a single cluster without typical dichotomy \u0026ndash; representing \"amputated\" part of the Luminal B subtype (in Ki-67 continuum) caused by artificial boundary of Ki-67 20% cutoff [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. EM analysis reinforces the natural heterogeneity of tumors and is potentially superior to St. Gallen classifications in identifying natural subgroups with distinct prognostic implications, esp. enabling personalized risk stratification and tailored therapy. EM clustering and data-driven stratification challenge the adequacy of St. Gallen binary classification with least prognostic certainty and potential to misclassify tumors.\u003c/p\u003e \u003cp\u003eApplying EM clustering, our study clearly showed progressive elevation of \u003cb\u003eKi-67 across phenotypes\u003c/b\u003e: from hormone receptor-positive subtypes (Luminal A/B1/B2: \u0026lt;20%/mean 38,6%/mean 34,5%) toward HER2-positive (34,5%-77%) and triple-negative tumors (33,9%-83,4%), reflecting more aggressive tumor biology in these heterogenous groups (grade III, larger tumors, younger age at manifestation) [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Except in homogeneous low proliferative Luminal A tumors (per definition Ki-67\u0026thinsp;\u0026lt;\u0026thinsp;20%), EM clustering revealed in all IHC subtypes starting from Luminal B1 two distinct biological subgroups with distinct clinicopathological features and prognostic implications: exp. Luminal B1: a lower-proliferation subgroup (mean Ki-67 30,3%, predominantly grade II) vs. high-proliferation subgroup (mean Ki-67 67,9%, predominantly grade III and significantly worse nodal status) which might represent potential target to intensify therapy regimens (CDK4/6 inhibitors). This EM clustering finding might lead to change in treatment regime for the distinctive subpopulations within luminal B1 group (exp. de-escalation for low proliferative/endocrine monotherapy vs escalation/chemotherapy for high proliferative tumors).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAs previously shown, hormone receptor positive super-category (IHC 1\u0026ndash;3) exhibits also two distinct Ki-67 clusters\u003c/b\u003e and progressive increase in Ki-67 values across distinct phenotypes (IHC 1\u0026ndash;3). Low proliferative cluster in HR+ group distinctly showed more favorable features (Grade I, node-negative) in comparison to HMA cluster with more aggressive features (Grade III, node-positive). To assess biological tumor nature more accurately and propose adequate treatment, recent BC recommendations support the use of \u003cb\u003emultiparametric prognostic assessment\u003c/b\u003e (e.g.,Oncotype DX, Prosigna) in treatment decisions - beyond binary Ki-67 cutoffs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUncertainties in treatment options and the need for personalized approach are known also for HER2+ (IHC4) phenotype cases. It exhibits strong heterogeneity with 2 clusters identified\u003c/b\u003e - with significant differences in tumor size and grade. The high-proliferation cluster (mean Ki-67 77%) showed grade III in 66,7% of cases compared to 49% in the low-proliferation cluster. \u003cb\u003eLower-proliferation HER2\u0026thinsp;+\u0026thinsp;cluster\u003c/b\u003e may represent a favorable-risk subgroup potentially benefiting from \u003cb\u003ede-escalated therapy\u003c/b\u003e (e.g., trastuzumab monotherapy without dual HER2 blockade).\u003c/p\u003e \u003cp\u003eThe highest Ki-67 values were found in the second cluster (83,4%) of \u003cb\u003etriple-negative tumors as the most aggressive phenotype (consistent with literature). TNBC subtype showed intrinsic heterogeneity and exhibits two clusters\u003c/b\u003e with distinct features. \u003cb\u003eLower-proliferation TNBC cluster\u003c/b\u003e had smaller tumors (20mm vs 25mm, P\u0026thinsp;=\u0026thinsp;0,03), lower incidence of grade III (55,8% vs 77%, P\u0026thinsp;\u0026lt;\u0026thinsp;0,001), and older patient age (59y vs 54y, P\u0026thinsp;=\u0026thinsp;0,04). Even in biologically most aggressive tumors (TNBC) there is a subpopulation of lower proliferative cluster 1 which might represent a \u003cb\u003efavorable-risk TNBC subgroup\u003c/b\u003e potentially suitable for moderate \u003cb\u003ede-escalated chemotherapy (\u003c/b\u003ee.g., omission of platinum agents) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cb\u003eBetter u\u003c/b\u003enderstanding of proliferation mechanism and interplay with a t\u003cb\u003eumor microenvironment (TME) -\u003c/b\u003e especially in such a heterogeneous subtype as TNBC - may also enable \u003cb\u003eearly detection of treatment failure (chemotherapy resistance)\u003c/b\u003e and guide \u003cb\u003emore accurate therapy selectio\u003c/b\u003en [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEM clustering and clinical framework\u003c/h2\u003e \u003cp\u003eThe clinical implication of applied EM clustering on continuous, data-driven Ki-67 is in refining of prognostic stratification beyond current St. Gallen 2013 classification with rigid arbitrary Ki-67 cutoff (with potential hazard of oversimplifying biological heterogeneity). Complementary integration of Ki-67 clustering with existing prognostic tools (Oncotype DX, Prosigna, MammaPrint) may radically improve diagnostic accuracy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurther stratification for clinical trials and studies to evaluate de-escalated or escalated therapies are warranted.\u003c/p\u003e \u003cp\u003eNovelty approach of EM clustering introduces precision oncology methods and might lead to individualized therapeutical decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrenghts and limitations of our study\u003c/h2\u003e \u003cp\u003eThe largest single CEE/Croatian center cohort included 2,651 consecutive IDC-NST cases over 18 years providing robust statistical power applying EM clustering across all IHC subtypes as a novel study approach. Methodology was standardized (rigorous IHC protocols, comprehensive data collection, independent pathologist review and validated EM clustering algorithm). Temporal trends revealed 18-year follow-up shifts in IHC subtype distribution and posed clinically relevant findings (applicable to current clinical practice). Our study highlights the regional significance for underrepresented Central-Eastern Europe and need for region-specific epidemiology studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLimitations of our study include retrospective design, observational data collection in a single tertiary center with limited generalizability beyond CEE epidemiology. Prospective validation is recommended and comparison with other cohorts. The absence of follow-up data on disease-free survival (DFS), overall survival (OS), recurrence and metastasis. The prognostic impact of EM clustering remains to be evaluated. The assessment of Ki-67 and standardized protocols of IHC followed IKWG 2021 recommendations, but still remain critical point with improvement potential in aspect of interlaboratory variability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Gene expression and molecular profiling were not conducted.\u003c/p\u003e \u003cp\u003eClear need for prospective validation and complementary integration of molecular profiling, epigenetic analysis and therapy oriented clinical trials.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis comprehensive CEE single-center retrospective study over 18-year period demonstrates that EM clustering of Ki-67 reveals significant proliferation heterogeneity across all IHC subtypes of invasive ductal carcinoma, with distinct clinicopathological correlations. Bimodal Ki-67 distributions were consistently identified across IDC subtypes. High-proliferation clusters were independently associated with adverse clinicopathological features implying tumor treatment escalation. On the other hand, lower-proliferation clusters even in aggressive subtypes (HER2+, TNBC) may identify patients suitable for de-escalated therapy.\u003c/p\u003e \u003cp\u003eKi-67 EM clustering offers a simple, cost-effective IHC-based approach to refine prognostic stratification beyond binary classification, potentially guiding personalized therapy selection. The temporal shift toward increasing luminal subtype prevalence over 18 years might reflect improved screening program and advanced diagnostic protocols in Croatia. Unfortunately, over time unchanged TNBC incidence in CEE might be in conjunction with different genetic and epidemiological regional burden and imposes the need for a further diagnostic refinement (BRCA 1 and 2 testing).\u003c/p\u003e \u003cp\u003eProspective multicenter studies incorporating long-term survival outcome data are essential to translate EM clustering in clinical setting as a stratification tool.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eER=estrogen receptor; PR=progesterone receptor; HER2=human epidermal growth factor receptor 2; TNBC=triple-negative breast cancer; HR+=hormone receptor-positive; IHC=immunohistochemistry; EM=expectation-maximization; IDC=invasive ductal carcinoma; NST=no special type; LMA=low Ki-67 activity category; IMA=intermediate Ki-67 activity category; HMA=high Ki-67 activity category; FFPE=formalin-fixed paraffin embedded; St. Gallen=St. Gallen International Expert Consensus; ASCO/CAP= eng. American Society of Clinical Oncology / College of American Pathologists; IKWG= International Ki-67 Working Group; FISH/ISH= fluorescence in situ hybridization- chromogenic/silver in situ hybridization; GDPR=General Data Protection Regulation; CEE= Central-Eastern Europe; BIC= Bayasian Information Criterion; CI= confidence interval; OR= odds ratio; QR= quartile range; DFS= Disease-free survival; OS= overall survival; STROBE= Strengthening the Reporting of Obervational Studies in Epidemiology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIB: conceptualization, study design, data curation, statistical analysis, writing (original draft, review and editing);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBD: methodology, pathological validation, supervision, writing (review);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSK: project supervision, conceptualization, statistical analysis, (deceased prior to submission);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIZ: pathological assessment, data resources, writing (review).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll authors read and approved the final manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCo-Author,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eProf. Sven Kurbel, MD, PhD\u0026ndash;\u003cstrong\u003e\u003cem\u003e\u0026nbsp;deceased\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eon 27\u003csup\u003eth\u003c/sup\u003e Dec.2024,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ebefore manuscript submission\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI (Perplexity AI)\u003c/strong\u003e assisted drafting/structure optimization in accordance with ICMJE guidelines; all content, data analysis, and interpretations remain the authors\u0026apos; responsibility. No AI was used in data generation or statistical analysis [33].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We thank gratefully the staff of the Department of Pathology and Forensic Medicine, Osijek University Clinical Hospital for technical assistance with IHC and tissue processing and to all patients whose data contributed to this study. We acknowledge the Croatian Ministry of Science and Education for funding support.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors humbly dedicate this work to the memory of \u003cstrong\u003eProfessor Sven Kurbel (1956\u0026ndash;2024)\u003c/strong\u003e, whose \u003cstrong\u003eprofound intellectual contributions\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eunwavering mentorship\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003evisionary insights\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewere instrumental in shaping this research. His legacy continues to inspire scientific inquiry and collaboration.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was partly supported by the Croatian Ministry of Science and Education (Grant/Project-No: 219-2192382-2426).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe funding body had no role in study design, data collection, analysis, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no competing financial or non-financial interests. AI tools used per ICMJE guidelines; human oversight ensured originality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e The datasets used and analyzed during this study are available in Zanedo (DOI 10.5281/zenedo.19858005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e This study was approved by the Medical Faculty Osijek Ethics Committee (602-04/23-08/03, 24 April 2023) and KBC Osijek Ethics Committee (R1-4012/2023, 31 May 2023). All patients have signed the informed consent at the hospital admission to the breast surgical procedure that also includes consent to breast tissue examination for clinical, educational and research purposes; all data were anonymized.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394\u0026ndash;424. \u003cem\u003edoi: 10.3322/caac.21492\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeigelt B, Geyer FC, Reis-Filho JS. Histological types of breast cancer: how special are they? Mol Oncol. 2010;4(3):192\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.molonc.2010.04.004\u003c/span\u003e\u003cspan address=\"http://10.1016/j.molonc.2010.04.004\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003edoi\u003c/em\u003e:.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammond ME, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28(16):2784\u0026ndash;2795. \u003cem\u003edoi: 10.1200/JCO.2009.25.6529\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Th\u0026uuml;rlimann B et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24(9):2206\u0026ndash;2223. \u003cem\u003edoi\u003c/em\u003e:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/\u003c/span\u003e\u003cspan address=\"http://10.1093/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003eannonc/mdt303\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInic Z, Zegarac M, Inic M, Markovic I, Kozomara R. Clinicopathological characteristics of molecular subtypes of breast cancer. J BUON. 2014;19(4):951\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jbuon.2014.10.001\u003c/span\u003e\u003cspan address=\"http://10.1016/j.jbuon.2014.10.001\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003edoi\u003c/em\u003e:.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA et al. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res. 2007;13(15 Pt 1):4429\u0026ndash;4434. \u003cem\u003edoi\u003c/em\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10\u003c/span\u003e\u003cspan address=\"http://10\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003cem\u003e1158/1078-0432.CCR-06-3045\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolley M-YC, Leung SCY, Gao D, Mastropasqua MG, Zabaglo LA, Bartlett JMS et al. An international study to evaluate reproducibility of Ki-67 labeling index in breast cancer using a standardized scoring method. Mod Pathol. 2015;28(9):1155\u0026ndash;1164. \u003cem\u003edoi: 10.1038/modpathol.2015.73\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDowsett M, Nielsen TO, A\u0026rsquo;Hern R et al. Assessment of Ki67 in breast cancer: Recommendations from the International Ki67 in Breast Cancer Working Group. J Natl Cancer Inst. 2011;103(22):1656\u0026ndash;1664. \u003cem\u003edoi\u003c/em\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/\u003c/span\u003e\u003cspan address=\"http://10.1093/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003ejnci/djr393\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B Stat Methodol. 1977;39(1):1\u0026ndash;38. \u003cem\u003edoi\u003c/em\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.2517-6161.1977.tb01600.\u003c/span\u003e\u003cspan address=\"http://10.1111/j.2517-6161.1977.tb01600.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003ex\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurbel S, Kurbel B, Marjanović K, Dmitrović B. Epithelial-mesenchymal transition in cancer: a historical perspective with a focus on breast cancer. BMC Cancer. 2017;17(1):782. \u003cem\u003edoi: 10.1186/s\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e12885-017-3778-\u003c/span\u003e\u003cspan address=\"http://12885-017-3778-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e9\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDidkowska J, Wojciechowska U, Olasek P, Tarkowski W, Trojanowski M, Czaderny M. Breast cancer in Central and Eastern Europe: the untold story. Cancer Epidemiol. 2020;69:101831. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.canep.2020.101831\u003c/span\u003e\u003cspan address=\"http://10.1016/j.canep.2020.101831\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003edoi\u003c/em\u003e:.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray F, Znaor A, Cueva P, Korir A, Swaminathan R, Ullrich A et al. Planning and developing population-based cancer registration in low- and middle-income settings: III. The European experience. Cancer Epidemiol. 2015;39(Suppl 1):S22\u0026ndash;S31. \u003cem\u003edoi\u003c/em\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.canep.2014.09\u003c/span\u003e\u003cspan address=\"http://10.1016/j.canep.2014.09\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003cem\u003e013\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 1991;19(5):403\u0026ndash;410. \u003cem\u003edoi\u003c/em\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2559.1991.\u003c/span\u003e\u003cspan address=\"http://10.1111/j.1365-2559.1991.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003etb00229.x\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllison KH, Hammond MEH, Dowsett M, McKernin SE, Carey LA, Fitzgibbons PL et al. Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update. J Clin Oncol. 2020;38(12):1346\u0026ndash;1366. \u003cem\u003edoi: 10.1200/JCO.19.02309\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS et al. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: ASCO/CAP Clinical Practice Guideline Focused Update. J Clin Oncol. 2018;36(20):2105\u0026ndash;2122. \u003cem\u003edoi: 10.1200/JCO.2018.77.8738\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNielsen TO, Leung SCY, Rimm DL, Dodson A, Acs B, Badve S et al. Assessment of Ki67 in Breast Cancer: Updated Recommendations from the International Ki67 in Breast Cancer Working Group. J Natl Cancer Inst. 2021;113(7):808\u0026ndash;819. \u003cem\u003edoi: 10.1093/jnci/djaa201\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLachlan GJ, Peel D. Finite Mixture Models. New York: Wiley; 2000. \u003cem\u003edoi: 10.1002/0471721182\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraley C, Raftery AE. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002;97(458):611\u0026ndash;631. \u003cem\u003edoi: 10.1198/016214502760047131\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosmer DW Jr, Lemeshow S, Sturdivant RX. Applied Logistic Regression. 3rd ed. Hoboken, NJ: Wiley; 2013. \u003cem\u003edoi: 10.1002/9781118548387.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Parliament and Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Off J Eur Union. 2016;L 119:1\u0026ndash;88. \u003cem\u003edoi: 10.3000/19770677.L_2016.119.eng\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Medical Association. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191\u0026ndash;2194. \u003cem\u003edoi: 10.1001/jama.2013.281053\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrat A, Pineda E, Adamo B, et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast. 2015;24(Suppl 2):S26\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e: 10.1016/j.breast.2015.07.008\u003c/span\u003e\u003cspan address=\"http://: 10.1016/j.breast.2015.07.008\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003edoi\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolyak K. Heterogeneity in breast cancer. J Clin Invest. 2011;121(10):3786\u0026ndash;3788. \u003cem\u003edoi: 10.1172/JCI58742\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowlader N, Altekruse SF, Li CI et al. US trends in hormone receptor-defined breast cancer incidence and survival, 1992\u0026ndash;2016. J Natl Cancer Inst. 2020;112(12):1282\u0026ndash;1291. \u003cem\u003edoi: 10.1093/jnci/djaa015\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuliti D, Bucchi L, Mancini S et al. Overdiagnosis in mammographic screening for breast cancer in Europe: A literature review. J Med Screen. 2012;19(Suppl 1):42\u0026ndash;56. \u003cem\u003edoi: 10.1258/jms.2012.012082\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEUROCARE-5 Working Group. Changes in breast cancer incidence and mortality in middle-aged and elderly women in 29 European countries. Int J Cancer. 2020;146(1):178\u0026ndash;187. \u003cem\u003edoi: 10.1002/ijc.32380\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA. 2019;321(3):288\u0026ndash;300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2018.19323\u003c/span\u003e\u003cspan address=\"http://10.1001/jama.2018.19323\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003edoi\u003c/em\u003e:.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMavaddat N, Michailidou K, Dennis J et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21\u0026ndash;34. \u003cem\u003edoi\u003c/em\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajhg.2018.11\u003c/span\u003e\u003cspan address=\"http://10.1016/j.ajhg.2018.11\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003cem\u003e002\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrruticoechea A, Smith IE, Dowsett M. Proliferation marker Ki-67 in early breast cancer. J Clin Oncol. 2005;23(28):7212\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2005.07.501\u003c/span\u003e\u003cspan address=\"http://10.1200/JCO.2005.07.501\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003edoi\u003c/em\u003e:.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoidart P, Irrthum A, Blacher S et al. Neoadjuvant chemotherapy with or without platinum in patients with triple-negative breast cancer: A propensity score-matched analysis of the prospective Neoadjuvant Breast Registry Symphony Trial (NBRST). J Clin Oncol. 2023;41(16):2923\u0026ndash;2933. \u003cem\u003edoi: 10.1200/JCO.22.02391\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu S, Zhu Y, Yang Y et al. Spatial transcriptomics reveals tumor microenvironment-driven proliferation heterogeneity and chemoresistance mechanisms in triple-negative breast cancer. Nat Commun. 2023;14(1):4142. \u003cem\u003edoi: 10.1038/s\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e41467-023-39855-8\u003c/span\u003e\u003cspan address=\"http://41467-023-39855-8\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrat A, Bras\u0026oacute;-Maristany F, Llombart-Cussac A et al. Combining Ki-67 and gene expression signatures improves prognostic accuracy in early breast cancer: A translational analysis of the PENELOPE-B trial. J Clin Oncol.2023;41(36):5534\u0026ndash;5544. \u003cem\u003edoi: 10.1200/JCO.23.00456\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Committee of Medical Journal Editors (ICMJE). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.icmje.org\u003c/span\u003e\u003cspan address=\"https://www.icmje.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Breast cancer, Ki-67, Expectation-maximization clustering, Immunohistochemical subtypes, Invasive ductal carcinoma, Central-Eastern Europe","lastPublishedDoi":"10.21203/rs.3.rs-9662114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9662114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Ki-67 proliferation index is a key marker in breast cancer (BC) classification and treatment recommendations. Widely applied assessment is in accordance to conventional breast cancer immunohistochemistry protocols and St. Gallen guidelines (2013), yet its intra-subtype biological heterogeneity remains underexplored in large cohorts, particularly lacking large studies from Central-Eastern Europe. Our main objective was to apply expectation-maximization (EM) clustering method to Ki-67 pool data and to assess clinicopathological correlations distinguishing biologically divergent subpopulations.\u003cbr\u003e\n \u003cstrong\u003eMethods\u003c/strong\u003e: Retrospective study of 2651 consecutive invasive breast cancer (IDC) cases (2004–2021) at a single center at Osijek University Hospital, Croatia. Tumors were immunohistochemically classified into 5 subtypes (IHC 1–5: Luminal A/B1/B2, HER2+, TNBC) according to St. Gallen 2013 (Ki-67 cutoff 20%). Expectation-maximization (EM algorithm, STATISTICA v11) was applied to identify clustering across IHC subtypes. Associations with clinicopathological features were assessed via χ² /Fischer exact test, Mann-Whitney U, odds rations (OR, 95% CI).\u003cbr\u003e\n \u003cstrong\u003eResults\u003c/strong\u003e: Median age was 62 years (IQR 53-70); 66,6% postmenopausal. Luminal subtypes A/B1 increased over time (49,2% in 2004 to 72,3% in 2021, P\u0026lt;0,001). EM clustering revealed bimodality with two distinct Ki-67 subpopulations: Cluster 1 (mean 22,6%, n=1711) vs. Cluster 2 (mean 74,4%, n=940; P\u0026lt;0,001). High-Ki-67 cluster showed association with adverse features: grade III, nodal positivity, larger size (\u0026gt;25 mm) and younger age (\u0026lt;55y), all P\u0026lt;0,001. The highest Ki-67 cluster was found in TNBC (83,4%) with dominat aggressive traits (younger age, larger, grade III in 77 %, P\u0026lt;0,001).\u003cbr\u003e\n \u003cstrong\u003eConclusions\u003c/strong\u003e: EM clustering reveals clinically relevant bimodal pattern in Ki-67 distribution challenging the arbitrary Ki-67 cutoff set at 20% (clusters seem to be superior to rigid binary St.Gallen limits). The largest Central-Eastern Europe IDC cohort highlights regional trends and validates St. Gallen criteria.\u003c/p\u003e","manuscriptTitle":"Expectation Maximization Clustering Reveals Ki-67 Heterogeneity Within Immunohistochemical Subtypes of Invasive Ductal Breast Carcinoma: A 18-year cohort study from Central-Eastern Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 16:22:14","doi":"10.21203/rs.3.rs-9662114/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":"bf8c42bb-dfad-4e25-8aef-beee88b16fec","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T11:56:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-12T06:05:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-12T00:24:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research","date":"2026-05-09T09:13:40+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T12:09:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 16:22:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9662114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9662114","identity":"rs-9662114","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}