Assessing the Impact of Improved Pre-analytic Tissue Handling on Immunohistochemistry and Breast Cancer Molecular Subtypes in Ghana: A Cross-Sectional Study

Assessing the Impact of Improved Pre-analytic Tissue Handling on Immunohistochemistry and Breast Cancer Molecular Subtypes in Ghana: A Cross-Sectional Study | 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 Assessing the Impact of Improved Pre-analytic Tissue Handling on Immunohistochemistry and Breast Cancer Molecular Subtypes in Ghana: A Cross-Sectional Study Patrick Kafui Akakpo, Emmanuel Gustav Imbeah, Lawrence Edusei, and 27 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6672326/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Breast cancer is a major cause of morbidity and mortality among Ghanaian women, and the higher reported rates of triple-negative breast cancer (TNBC) are associated with worse outcomes. It is established that quality preanalytics has a positive effect on the outcome of immunohistochemistry. Enhanced preanalytic tissue handling may reduce the reported prevalence of TNBC and result in the correct management of breast cancer patients. This study assessed the impact of improved preanalytic processes on diagnostic outcomes in 4,000 breast biopsies. Methods Pathologists Without Borders partnered with Precision Medicine for Aggressive Breast Cancers to provide 10% neutral buffered formalin, offer training in tissue handling, and instruct providers in core needle biopsy. We retrospectively analyzed all core biopsies diagnosed as breast cancer over four years. Results A total of 4,000 cases were included (mean age, 52.7 ± 13.4 years). Male breast cancer accounted for 1.6% of cases (n = 65). In all tumors, 49.1% were estrogen receptor-negative (n = 1,965), 61.8% were progesterone receptor–negative (n = 2,470), and 63.3% were human epidermal growth factor receptor 2–negative (n = 2,532), yielding a TNBC rate of 29.2%. TNBC was most prevalent in patients aged 40–59 years and in those younger than 30 years. Conclusion We identified a 29.2% TNBC rate, which is lower than previously reported. These findings support established reports of the importance of optimized preanalytic procedures to improve diagnostic accuracy. The variation in TNBC rates with age suggest a need for age-specific breast cancer screening and treatment strategies in Ghana. breast cancer diagnosis histopathology characteristics part biopsy preanalytics Ghana Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer is the most frequently diagnosed cancer among women in Ghana, and it is a leading cause of cancer-related morbidity and mortality in this population. Although Ghanaian women (like other African women) have a lower risk of breast cancer diagnosis compared with European women, when they are diagnosed, their tumors are usually of higher grade, more likely to be hormone receptor-negative or human epidermal growth factor receptor 2 (HER2)–amplified, and detected at a late stage. These factors confer a worse prognosis for patients in Ghana. Additionally, a significant number of breast cancers occur in younger Ghanaian women, presenting with the same or worse prognostic factors as older patients 1 , 2 . Accurate histopathologic diagnosis, including molecular subtyping, is crucial to improving survival rates among breast cancer patients. In Ghana, in accordance with the World Health Organization recommendations for reporting breast cancer in 2024, it is advised that suspected breast cancers be confirmed histopathologically before initiating definitive therapy 3 . Consequently, core biopsy or partial biopsy has become the preferred initial diagnostic approach in recent years. These smaller tissue samples are more likely to be well-fixed, which optimizes diagnostic accuracy. Core or partial biopsy allows assessment of critical parameters such as histologic type, grade, hormone receptor status, and HER2 status, as well as other factors, including lymphovascular invasion and perineural invasion, following guidelines such as those of the College of American Pathologists (CAP) 4 . The presence or absence of carcinoma in situ is also evaluated. These core data elements inform patient management decisions regarding neoadjuvant and adjuvant treatments and, to a lesser extent, the surgical approach. Surgery planning depends largely on tumor size and other clinical and imaging parameters related to disease stage. 1 , 2 , 4 Molecular classification has become pivotal in breast cancer management. Determination of estrogen receptor (ER), progesterone receptor (PR), HER2 status, and, more recently, the Ki67 proliferation index has become standard in many centers. These markers guide the use of targeted therapies that can improve prognosis 5 . However, immunohistochemistry (IHC) results depend heavily on optimal preanalytics, particularly tissue fixation in 10% neutral buffered formalin (NBF). Poor preanalytics can lead to false-negative tests, causing many patients to be incorrectly classified as having triple-negative breast cancer (TNBC) 6 , 7 . TNBC is more common in people of African descent and has the poorest prognosis of all breast cancer subtypes, especially in settings where targeted therapies are not available. Although immunotherapy using programmed death-ligand 1 inhibitors is promising, these treatments are not widely accessible in many regions 2 , 4 , 8 – 15 . Previous reports from Ghana have indicated that TNBC is the most common molecular subtype, with reported rates ranging from 21–81% 2,4,8–15 . This wide range may reflect suboptimal preanalytics related to tissue handling and reliance on larger excision specimens (e.g., wide local excision or mastectomy) for IHC. In addition, many centers lack the equipment needed for IHC staining and must use complex manual techniques or send samples abroad. We hypothesized that by using core or partial biopsies, which are typically smaller and therefore better fixed, and by ensuring the availability of 10% NBF for all centers that diagnose breast cancer, the proportion of TNBC cases would be lower than previously reported. This study aims to determine the molecular profile of a large series of breast cancer cases reported at key diagnostic and treatment centers across Ghana, all assessed by a single pathology group. We also characterized the demographic and histopathologic features of these patients’ tumors as part of a comparative assessment of current versus past breast cancer demographics histopathological, and molecular characteristics. Methods Study Location This study was conducted using records from Pathologists Without Borders (PWB), the largest pathology service in Ghana, in partnership with Precision Medicine for Aggressive Breast Cancer (PMABC). PWB is privately owned and was established in 2018 16 to provide crucial services such as IHC and to improve the quality of histopathology reporting by ensuring accuracy, completeness, timeliness, and usability. PWB operates a hub-and-spoke model through several collection centers nationwide and A.C.T. Pathology Consult (a spoke) located in Cape Coast. A.C.T. Pathology Consult receives an average of 1,900 surgical pathology specimens per year, whereas PWB receives an average of 14,500 surgical pathology specimens per year 1 . PWB, located in Accra, runs IHC in-house year-round and serves as the central hub for A.C.T. Pathology Consult. The combined catchment area of these centers includes the Western, Central, Greater Accra, Eastern, Ashanti, Northern, Upper West, Upper East, and Volta regions of Ghana. Specimens are also received from other countries in the West African subregion, including The Gambia, Liberia, and Sierra Leone. PWB reports the largest proportion of breast cancer cases in Ghana and has reported more than 7,500 breast tissue samples to date, some of which are included in this review 21 . PWB conducts IHC using the VENTANA BenchMark GX automated staining system (Ventana Medical Systems, a member of the Roche Group, Tucson, AZ, USA), following the manufacturer’s protocols and reporting results based on CAP guidelines (Appendix 1). PMABC is a research partnership between the Jiagge Laboratory at Henry Ford Health in Detroit, Michigan, USA, and key breast cancer management centers across sub-Saharan Africa. PMABC sites in Ghana include Ho Teaching Hospital (HTH), Cape Coast Teaching Hospital (CCTH), Komfo Anokye Teaching Hospital (KATH), Korle Bu Teaching Hospital (KBTH), Tamale Teaching Hospital (TTH), and the Eastern Regional Hospital. These centers are equipped to handle breast cancer diagnosis and treatment until a referral is required for radiation oncology services at the two national centers in KATH and KBTH. Breast core biopsies from these hospitals are sent to PWB and A.C.T. Pathology Consult; in addition, PWB and A.C.T. receive samples from other hospitals in their catchment areas nationwide. Study Design In 2020, 10% neutral buffered formalin (10% NBF) was provided on a pilot basis to selected sites (teaching hospitals and major hospitals that diagnose and treat breast cancer) to ensure adequate tissue fixation. Personnel at these sites were trained to prepare 10% NBF. Training workshops and one-on-one sessions were also conducted to teach proper tissue handling and fixation procedures, including immediately fixing biopsy samples in adequate volumes of buffered formalin and sending the specimens to the pathology laboratory on the same day. PMABC further trained providers on the use of core needle biopsy for breast cancer diagnosis and supplied free biopsy needles to the pilot sites. This descriptive, cross-sectional, and purposive study was performed on all cases biopsied and confirmed as breast cancer via core or incision biopsies from 2020 to 2023. Inclusion and exclusion criteria We included all complete core or incision biopsy histopathology reports released between January 2020 and December 2023, with corresponding IHC reports available in the electronic medical records of the two private pathology centers (PWB and A.C.T.). Excision specimens were excluded, as were any reports with incomplete histopathological or IHC data. Data source and sampling Retrospective data of histopathology reports of all breast cancer cases reported at PWB and A.C.T. Pathology Consult were retrieved from Nubia Electronic Medical Record (NubiaEMR v2.0). NubiaEMR is an electronic medical record with synoptic reporting for anatomic pathology that was built in-house. The data were exported to Microsoft Excel (Microsoft Corp., Redmond, WA, USA) and de-identified. Patient demographics, sample type (core or incision biopsy), and histopathological core data elements for breast cancer, based on CAP reporting templates, were collected and matched with their IHC reports. The combined data were then exported to SPSS Statistics for Windows, Version 27.0 (Armonk, NY: IBM Corp.), for analysis. These reports were matched with their corresponding IHC records in Microsoft Excel and de-identified. The combined dataset was exported to IBM SPSS Statistics for Windows, Version 27.0 for statistical analysis. Breast Cancer Molecular Subtyping Data extracted from immunohistochemistry reports included estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, and tumor proliferative index (Ki67) score. Breast cancer molecular subtypes were categorized following the criteria established at the 12th St. Gallen International Breast Cancer Conference in 2011. According to the St. Gallen Consensus 2011, breast cancer subtypes are classified as follows: Luminal A (ER+/PR+/HER2-/low Ki67), Luminal B (ER+/PR+/HER2-/+ or high Ki67), HER2-enriched (ER-/PR-/HER2+), and triple-negative breast cancer (TNBC) (ER-/PR-/HER2-) 17 . Statistical analysis In SPSS v27.0, Descriptive statistics with frequencies and percentages were used to describe categorical variables. Cross-tabulations were performed to compare variables, and chi-square tests were used to identify statistical relationships, with a 95% confidence interval and significance defined as p < 0.05. Data visualization was conducted using QGIS (version 3.36; QGIS Development Team under the Open-Source Geospatial Foundation, Beaverton, OR, USA) and GraphPad Prism (version 9, GraphPad Software, Inc., Boston, MA, USA). Results A total of 4,000 cases were identified and included from January 1, 2020, to December 31, 2023, with the majority recorded in 2023. Participant ages ranged from 14 to 101 years, with a mean age of 52.7 ± 13.4 years ( Table 1 ). The number of cases increased each year over the study period. The peak age group for breast cancer was 40–49 years (29.2%), and 53.5% of all cases occurred in individuals aged 40–59 years. By sex, 98.4% of breast cancers were in females (n=3,935), and 1.6% were in males (n=65). Needle core biopsy accounted for 85.7% of all samples (n=3,426). Breast cancer occurred more frequently in the left breast (50.7%; n=2,027) than the right breast, and bilateral breast cancers represented 1.5% of all cases (n=58). The most common histologic type was invasive carcinoma of no special type (NST), representing 89% of cases (n=3,558). Diffuse non-Hodgkin lymphoma and tall cell carcinoma with reversed polarity were the least common, each with a frequency of one (n=1). Most tumors (54.1%; n=2,163) were Grade III, and 24·4% of cases (n=976) showed the presence of an in-situ component. Among cases with in-situ components, 94·7% (n=924) were ductal carcinoma in-situ (DCIS), and 60% were of intermediate grade (n=543). Lymphovascular invasion was present in 18.0% of cases (n=721), and perineural invasion was present in 1.2% (n=46). Table 1: Histopathological Characteristics of Breast Cancer in Ghana Characteristics Variable Frequency Percent Year 2020 256 6.4 2021 1,036 25.9 2022 1,347 33.7 2023 1,361 34.0 Total 4,000 100.0 Age Group (years) ≤19 3 0.1 20–29 51 1.3 30–39 586 14.7 40–49 1,167 29.2 50–59 971 24.3 60–69 717 17.9 ≥70 505 12.6 Total 4,000 100.0 Sex Male 65 1.6 Female 3,935 98.4 Total 4,000 100.0 Biopsy Type Incision/Wedge Biopsy 574 14.4 Needle Core Biopsy 3,426 85.7 Total 4000 100.0 Laterality Left 2027 50.7 Right 1915 47.9 Bilateral 58 1.5 Total 4,000 100.0 Histological Type Group Invasive Carcinoma with Apocrine Features 4 0.1 Cribriform Carcinoma 24 0.6 Diffuse Non-Hodgkin Lymphoma 1 0.0 Ductal Carcinoma In-situ Only 117 2.9 Invasive Carcinoma NST 3,558 89.0 Invasive Papillary Carcinoma 28 0.7 Invasive lobular Carcinoma 68 1.7 Malignant Phyllodes Tumor 8 0.2 Metaplastic Carcinoma 64 1.6 Mixed Type Carcinoma 29 0.7 Mucinous Carcinoma 81 2.0 Tall Cell Carcinoma with Reversed Polarity 1 0.0 Tubular Carcinoma 17 0.4 Total 4,000 100.0 Histological Grade I 416 10.4 II 1,393 34.8 III 2,163 54.1 Not Assessable 28 0.7 Total 4,000 100.0 In-situ Component Present 976 24.4 Not Present 3024 75.6 Total 4000 100.0 In-situ Component Type Ductal 924 94.7 Lobular 12 1.2 Paget’s 40 4.1 Total 976 100.0 DCIS Grade Low 180 19.9 Intermediate 543 60.0 High 182 20.1 Total 905 100.0 Lymphovascular Invasion Present 721 18.0 Not Identified 3,279 82.0 Total 4,000 100.0 Perineural Invasion Present 46 1.2 Not Identified 3,954 98.9 Total 4,000 100.0 [ INSERT TABLE 1 HERE ] Distribution of patients included in the study Cases were received from every region of Ghana ( Figure 1 ). Most breast cancer cases (n=2,395; 59.9%) came from the Greater Accra region, where KBTH, the largest referral center for breast cancer in Ghana, is located. The Ashanti region contributed 10.8% (n=431), the Volta region 8.6% (n=345), and the Central region 6.9% (n=274). For geographic categorization, the Greater Accra, Western, and Central regions were classified as the Coastal Belt; the Middle Belt comprised most of the Volta, Eastern, and Ashanti regions; and the Northern Belt comprised the Northern, Upper West, and Upper East regions. The Coastal Belt accounted for 69.1% of cases (n=2,760), the Middle Belt for 24.1% (n=963), and the Northern Belt for 7.0% (n=277). Immunohistochemistry of breast cancer cases in Ghana Table 2 shows that 47·7% of cases (n=1,907) were ER-positive, 35.1% were PR–positive (n=1,404), and 19.6% had HER2 expression (n=784). Most (85.9%) had a Ki-67 index of more than 20% (n=3,435), whereas 9.8% had a Ki-67 index of less than 10% (n=393). In total, 49.1% of the cases were ER-negative (n=1,965), 61.8% were PR-negative (n=2,470), and 63.3% were HER2-negative (n=2,532). Table 2: Immunohistochemistry pattern of breast cancer cases in Ghana Marker Variable Frequency Percent ER Positive 1,907 47.7 Negative 1,965 49.1 Not Assessable 126 3.2 Total 4,000 100.0 PR Positive 1,404 35.1 Negative 2,470 61.8 Not Assessable 126 3.2 Total 4,000 100.0 HER2 Positive 784 19.6 Negative 2,532 63.3 Equivocal 558 14.0 Not Assessable 126 3.1 Total 4,000 100.0 Ki67 Group 20% 3,435 85.9 Total 4,000 100.0 Abbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2. [ INSERT TABLE 2 HERE ] According to molecular subtype distribution, the most common subtype was Luminal B (39.9%; n=1,627), followed by TNBC (29.2%; n=1,186). Luminal A was the least common (11.4%; n=484). A total of 196 cases (4.9%) were categorized as “other,” defined as hormone receptor–negative and HER2-equivocal with no in-situ hybridization performed. An additional 126 cases (3.2%) were non-classifiable, including non-Hodgkin lymphoma, DCIS-only lesions, and malignant phyllodes tumors, which are not included in molecular subtyping per CAP guidelines. These findings are depicted in Figure 2 . Table 3 and Figure 3 present a comparative analysis of age groups and molecular subtypes. Among individuals older than 40 years, Luminal B was the most frequent subtype (42,3%), followed by TNBC (27,5%). In persons younger than 40 years, TNBC was the most common subtype (40,8%), followed by Luminal B (32,2%). The prevalence of Luminal A increased with age and peaked at 60–69 years (26,3%), then decreased to 22,8% among individuals older than 70 years. Luminal B peaked at 40–49 years (32,1%) and was least common in the extreme age groups. HER2-enriched subtypes peaked at 40–49 years (31,1%) and were least common in extreme age groups. TNBC followed a similar pattern, with its lowest prevalence at the extremes of age and the highest prevalence at 40–49 years (29,4%). Overall, age group was significantly associated with the molecular subtype (X 2 = 183,473; p < 0.001). Table 3: Comparative analysis of age group and molecular subtype of breast cancer in Ghana Molecular Subtype Total Age Group Luminal A Luminal B HER2-enriched TNBC Others (HER2 Equivocal) Non-Classifiable (ER, PR, HER2 –Not Applicable) ≤19 0 (0%) 0 (0%) 0 (0%) 2 (0·2%) 1 (0·5%) 0 (0%) 3 (0·1%) 20–29 4 (0·9%) 13 (0·8%) 1 (0·2%) 28 (2·4%) 5 (2·6%) 0 (0%) 51 (1·3%) 30–39 37 (8·1%) 182 (11·4%) 76 (16·5%) 230 (19·7%) 39 (19·9%) 22 (17·5%) 586 (14·7%) 40–49 84 (18·4%) 511 (32·1%) 143 (31·1%) 343 (29·4%) 47 (24·0%) 39 (31·0%) 1167 (29·2%) 50–59 108 (23·6%) 398 (25·0%) 124 (27·0%) 269 (23·1%) 42 (21·4%) 30 (23·8%) 971 (24·3%) 60–69 120 (26·3%) 298 (18·7%) 73 (15·9%) 166 (14·2%) 37 (18·9%) 23 (18·3%) 717 (17·9%) ≥70 104 (22·8%) 192 (12·0%) 43 (9·3%) 129 (11·1%) 25 (12·8%) 12 (9·5%) 505 (12·6%) Total 457 (100·0%) 1594 (100·0%) 460 (100·0%) 1167 (100·0%) 196 (100·0%) 126 (100%) 4000 (100·0%) Abbreviations: HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; ER, estrogen receptor; PR, progesterone receptor. [ INSERT TABLE 3 HERE ] Because the distribution of cases across Ghana’s regions was uneven, 2,395 cases (59·9%) originated from the Greater Accra region, whereas only six cases (0·2%) came from the Upper East region. Luminal B was the most frequent subtype in the Greater Accra, Upper West, and Ashanti regions, with frequencies compared to TNBC of 1,087 vs. 636, 13 vs. eight, and 154 vs. 142, respectively. In the Western, Volta, Eastern, Northern, and Upper East regions, TNBC was more common than Luminal B. Collectively, molecular subtypes of breast cancer were significantly associated with regional distribution (X 2 = 105·879; p < 0·001), as shown in Table 4 and Figure 4 . Table 4 Comparative analysis of regional distribution of the molecular subtype of breast cancer in Ghana Regional Distribution Molecular subtype Total Luminal A Luminal B HER2-enriched TNBC Others (HER2 Equivocal) Non- Classifiable Greater Accra Region 255 (55·8%) 1061 (66·6%) 250 (54·3%) 627 (53·7%) 122 (62·2%) 80 (63·5%) 2,395 (59·9%) Western Region 17 (3·7%) 27 (1·7%) 12 (2·6%) 31 (2·7%) 2 (1·0%) 2 (1·6%) 91 (2·3%) Volta Region 61 (13·3%) 108 (6·8%) 41 (8·9%) 112 (9·6%) 17 (8·7%) 6 (4·8%) 345 (8·6%) Central Region 37 (8·1%) 93 (5·8%) 35 (7·6%) 94 (8·1%) 11 (5·6%) 4 (3·2%) 274 (6·9%) Upper West Region 0 (0·0%) 12 (0·8%) 1 (0·2%) 7 (0·6%) 0 (0·0%) 3 (2·4%) 23 (0·6%) Eastern Region 13 (2·8%) 66 (4·1%) 24 (5·2%) 68 (5·8%) 9 (4·6%) 7 (5·6%) 187 (4·7%) Ashanti Region 50 (10·9%) 151 (9·5%) 50 (10·9%) 141 (12·1%) 24 (12·2%) 15 (11·9%) 431 (10·8%) Northern Region 24 (5·3%) 74 (4·6%) 46 (10·0%) 84 (7·2%) 11 (5·6%) 9 (7·1%) 248 (6·2%) Upper East 0 (0·0%) 2 (0·1%) 1 (0·2%) 3 (0·3%) 0 (0·0%) 0 (0·0%) 6 (0·2%) Total 457 (100·0%) 1,594 (100·0%) 460 (100·0%) 1167 (100·0%) 196 (100·0%) 126 (100·0%) 4,000 (100·0%) Abbreviations: HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer. [ INSERT TABLE 4 HERE ] Discussion The proportion of Ghanaian breast cancers that are TNBCs has been reported to range from 21% to over 80 1,10–14 . These findings come primarily from single-institution studies that often do not describe the preanalytic phase of tissue handling or its impact on the quality of breast cancer diagnosis, especially with regard to molecular subtyping. Again, many of these studies report IHC testing on excision specimen (WLE and Mastectomy) which are generally accepted to have poorer preanalytical conditions especially relating to adequate fixation. These have been the basis for ASCO/ CAP recommendations relating to IHC testing in breast cancer 1 , 4 , 11 – 15 , 18 . Here, we present the largest cohort (4,000 cases) of breast part biopsies, in which we specifically examined the effect of ensuring quality preanalytics on reported hormone receptor status and TNBC rates. We highlight the importance of high-quality preanalytics in achieving accurate breast cancer diagnoses and guiding subsequent treatment. Unlike most previous studies—which were regional, with smaller sample sizes, and included all specimen types—this study draws data from the entire country and relies on only part biopsies 1 , 4 , 11 – 15 , 18 , 19 . By relying on a synoptic reporting system (NubiaEMR), we emphasize enhanced data quality, facilitating cancer research and registries in resource-limited settings such as Ghana. Our findings reflect improvements in preanalytics through the provision of 10% NBF and the use of core and incision biopsies. By relying on part biopsies (core needle and incision biopsies), adequate tissue fixation was assured as previously reported and recommended in literature 4 . Histopathology diagnostic services and treatment centers are concentrated in the Coastal Belt, followed by the Middle Belt, with the fewest located in the Northern Belt. Consequently, the upper regions of Ghana reported the fewest cases. This disparity may be attributable to limited diagnostic and treatment services and lower population density in these regions. We identified a geographical variation in the proportion of TNBC cases. This finding is consistent with our work (yet to be published ), that showed that tribal ethnicity is associated with different risk for developing TNBC, with the Northern region experiencing more aggressive disease subtypes 19 . In contrast, all other regions reported Luminal B as the predominant molecular subtype. This finding may also suggest potential tribal or regional differences in breast cancer subtypes that warrant further study. Consistent with earlier studies 1 , 10 – 14 , the mean age at diagnosis in our cohort was 52.7 ± 13.4 years, peaking in the 40–49-year age group. This is younger than the reported mean diagnostic age of 60–65 years in Western populations 1 , 10 – 14 , though it is slightly higher than figures from previous studies in Ghana 1 , 10 – 14 , 20 . This discrepancy may be attributable to our larger sample size, which may better reflect the national situation. An earlier age at diagnosis poses specific challenges: patients in their reproductive years may require fertility-sparing treatment, and they are often economically active, so prolonged treatment may result in financial difficulties that compromise adherence. Also, younger patients may face unique imaging challenges during screening, especially given limited availability of high-resolution imaging modalities in Ghana. It is, therefore, necessary to consider the earlier onset of disease when designing and implementing screening protocols. TNBC was most frequently diagnosed in those younger than 40 years, suggesting a possible genetic or hereditary predisposition that deserves further investigation 1 . Of the reported breast cancer cases, 1.6% occurred in men, aligning with prior estimates of 1.2–3.0% 1,21,22 . Little is known, however, about the specific features of male breast cancer in Ghana, underscoring the need for larger studies to reveal its histopathologic and molecular characteristics and to inform appropriate treatment strategies. Invasive carcinoma of NST was the most common histologic subtype, consistent with previous publications. 1 , 6 – 14 However, this cohort also included tumor types that were not previously reported, such as tall cell carcinoma with reversed polarity and solid papillary carcinoma, in addition to other recognized types like lobular, tubular, cribriform, and metaplastic carcinoma. These findings may reflect enhanced preanalytics, which improve hematoxylin and eosin morphology, thereby enabling more accurate classification. Most tumors in our cohort were high-grade with high Ki-67 indices, corroborating earlier reports that breast cancers in Ghanaians often display more aggressive pathobiology 1 , 4 , 11 – 15 , 18 , 19 . These characteristics highlight the need for research to identify biomarkers that can guide targeted interventions. We observed that 49·1% of tumors were ER-negative, 61·8% were PR-negative, and 63.3% were HER2-negative. The TNBC rate of 29.2% although lower than previous reports showing rates as high as 82% is still higher than rates in European and other populations 1 . Published figures have ranged between 21% 11 and 82%, 15 but our study optimized preanalytics and likely captured a more accurate estimate of TNBC prevalence in Ghana, which may extend to other sub-Saharan African countries 15 . Accurate molecular subtyping is vital, given that targeted therapies (e.g., ER and HER2 blockers) are now covered by the National Health Insurance system, thus allowing all patients who test positive for hormone receptors or HER2 to receive the appropriate therapy at no additional cost. In individuals younger than 40 years, TNBC was the most prevalent subtype, whereas in those older than 40 years, Luminal B was more common. Notably, the prevalence of each molecular subtype declined with increasing age, except for Luminal A, which rose with age, suggesting that older age at diagnosis correlates with a higher likelihood of having Luminal A disease. Our analysis showed a statistically significant relationship between age group and molecular subtype (p < 0·001). These findings confirm that breast cancer biology varies by age, highlighting the necessity for further research on early-onset disease 1 . The preanalytic stage of pathology tissue handling is essential for accurate pathology reporting, patient diagnosis, and treatment stratification. The effect of poor preanalytics on IHC cannot be overemphasized and has been reported in literature with recommendations to improve preanalytics in order to guarantee accuracy in diagnostics and assure optimal management of breast cancer patients 7 , 23 . Education and providing necessary resources are vital for obtaining correct pathology diagnoses. Poorer outcomes for breast cancer in sub-Saharan Africa may be partly attributable to preanalytic challenges that lead to misdiagnoses and inappropriate treatment decisions. Conclusions Based on this pilot study, we recommend training providers to perform core needle biopsies, ensure proper tissue handling, and supply 10% NBF to institutions that perform cancer tissue biopsies. Optimal preanalytics enhance immunohistochemistry outcomes and ensure patients receive the most effective treatments. Achieving this goal requires allocating additional resources to maintain the quality of surgical tissue before it reaches the pathology laboratory. LIMITATIONS This study relied on real world evidence and real-world data generated as part of quality improvement efforts aimed at improving the quality of histopathological diagnosis, pathology data and ultimately the prognosis of breast cancer patients. The study is limited by the lack of a control arm in which patients received standard care (anything other than 10% NBF). All centers that were assessed benefited from the intervention to improve preanalytics and pathology data to optimize breast cancer patient treatment and outcome. Declarations Ethics approval and consent to participate Ethical Approval was sought from the Ethical Review Committees (ERC) of the Cape Coast Teaching Hospital (CCTHERC/EC/2020/047), Komfo Anokye Teaching Hospital (KATH IRB/AP/052/20)), Korle Bu Teaching Hospital (KBTH-IRB/000169/2021), and the Tamale Teaching Hospital (TTH/R&D/SR/20/95) as part of a broader study into the “Genetic and Molecular determinants of Breast Cancer Disparity among Africans - Precision Medicine for Aggressive Breast Cancer (PMABC) in partnership with the Jiagge Laboratory at the Henry Ford Cancer Center, Detroit, Michigan, USA. The ethical clearance obtained from ERC of the Cape Coast Teaching Hospital, Komfo Anokye Teaching Hospital, Korle Bu Teaching Hospital, and the Tamale Teaching Hospital indicated that informed consent was not required for the use of such secondary data (i.e., retrospective data) for as long as personal identifiers were omitted from the data. All methods were carried out in accordance with the Declaration of Helsinki and other relevant ethical guidelines and regulations. Consent for Publication: Not Applicable Clinical trial number: Not Applicable Availability of data and materials All materials and data used in the study are available and can be provided as necessary by contacting the corresponding author. Competing interests: The authors declare that they have no competing interests. Funding: This research received funding from Henry Ford Health and U CAN-CER VIVE research funding to the Jiagge Lab at Henry Ford Health. Authors’ Contributions PKA, EGI, and EMJ conceived the idea. All the authors were part of activities related to teaching preanalytical handling of tissues (preparation of 10% buffered formalin and proper biopsy technique) across the country. JN, FD, VN, SM, BW, NA, MN, MS, MK, MAD, MK, RD, FAM, IA, KA, and JA performed the biopsies at the various sites and ensured adherence to quality preanalytics. EGI and PKA analyzed the data and wrote the initial draft. All the authors reviewed the manuscript and agreed on the final version. Acknowledgments We gratefully acknowledge the management and staff of A.C.T. Pathology Consult (A.C.T.), Pathologists Without Borders Ltd (PWB), ToldoIT, and all members of the Precision Medicine for Aggressive Breast Cancer (PMABC) partnership for their efforts in improving breast cancer diagnosis in Ghana, which resulted in the data used for this study. We thank John E. Essex III, BA, of Peak Medical Editing, Indianapolis, IN, USA, who received payment from the study’s sponsor for professional medical editing assistance. Authors’ information Department of Pathology, University of Cape Coast /Cape Coast Teaching Hospital, Cape Coast, Ghana ACT Pathology Consult, PE 117 Pedu Estate, Cape Coast, Ghana Pathologists Without Borders Ltd, 125 Guggisberg Avenue, Mamprobi, Accra, Ghana Department of Surgery, University of Ghana Medical School / Korle Bu Teaching Hospital Accra, Ghana Department of Surgery, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Peace and Love Hospital, Kumasi, Ghana Department of Surgery, Ho Teaching Hospital, Ho, Ghana Department of Surgery, University of Cape Coast / Cape Coast Teaching Hospital, Cape Coast, Ghana Department of Surgery, Tamale Teaching Hospital, Tamale, Ghana Department of Surgery, Eastern Regional Hospital, Koforidua, Ghana Department of Surgery, Komfo-Anokye Teaching Hospital, Kumasi, Ghana Department of Surgery, Effiah Nkwanta Regional Hospital, Ghana Department of Chemistry, University of Cape Coast, Ghana Henry Ford Cancer Institute/Henry Ford Health System, 2799W Grand Blvd, Detroit, MI, USA Precision Medicine for Aggressive Breast Cancer (PMABC), Accra, Ghana The Ohio State University, Columbus, OH, USA Department of Pathology, Korle Bu Teaching Hospital Accra, Ghana References Akakpo PK, Imbeah EG, Edusei L, et al. Clinicopathologic characteristics of early-onset breast cancer: a comparative analysis of cases from across Ghana. BMC Womens Health . Jan 3 2023;23(1):5. doi:10.1186/s12905-022-02142-w Naku Ghartey Jnr F, Anyanful A, Eliason S, Mohammed Adamu S, Debrah S. Pattern of Breast Cancer Distribution in Ghana: A Survey to Enhance Early Detection, Diagnosis, and Treatment. Int J Breast Cancer . 2016;2016:3645308. doi:10.1155/2016/3645308 International Agency for Research on Cancer. Chapter 32 [Internet]. Lyon: WHO Classification of Tumours; [cited 2024 Dec 27]. Available from: https://tumourclassification.iarc.who.int/chapters/32 College of American Pathologists. Cancer Protocol Templates [Internet]. Northfield (IL): CAP; [cited 2024 Dec 27]. Available from: https://www.cap.org/protocols-and-guidelines/cancer-reporting-tools/cancer-protocol-templates Jiagge E, Jin DX, Newberg JY, et al. Tumor sequencing of African ancestry reveals differences in clinically relevant alterations across common cancers. Cancer Cell . Nov 13 2023;41(11):1963-1971.e3. doi:10.1016/j.ccell.2023.10.003 Khoury T, Sait S, Hwang H, et al. Delay to formalin fixation effect on breast biomarkers. Mod Pathol . Nov 2009;22(11):1457-67. doi:10.1038/modpathol.2009.117 Mann GB, Fahey VD, Feleppa F, Buchanan MR. Reliance on hormone receptor assays of surgical specimens may compromise outcome in patients with breast cancer. J Clin Oncol . Aug 1 2005;23(22):5148-54. doi:10.1200/jco.2005.02.076 Akakpo PK, Imbeah EG, Edusei L, et al. Clinicopathologic characteristics of early-onset breast cancer: a comparative analysis of cases from across Ghana. BMC Women's Health . 2023/01/03 2023;23(1):5. doi:10.1186/s12905-022-02142-w Jiagge EM, Ulintz PJ, Wong S, et al. Multiethnic PDX models predict a possible immune signature associated with TNBC of African ancestry. Breast Cancer Res Treat . Apr 2021;186(2):391-401. doi:10.1007/s10549-021-06097-8 Edmund DM, Naaeder SB, Tettey Y, Gyasi RK. Breast cancer in Ghanaian women: what has changed? Am J Clin Pathol . Jul 2013;140(1):97-102. doi:10.1309/ajcpw7tzls3bffiu Ohene-Yeboah M, Adjei E. Breast cancer in Kumasi, Ghana. Ghana Med J . Mar 2012;46(1):8-13. Jiagge E, Oppong JK, Bensenhaver J, et al. Breast Cancer and African Ancestry: Lessons Learned at the 10-Year Anniversary of the Ghana-Michigan Research Partnership and International Breast Registry. J Glob Oncol . Oct 2016;2(5):302-310. doi:10.1200/jgo.2015.002881 Mensah AC, Yarney J, Nokoe SK, Opoku S, Clegg-Lamptey JN. Survival Outcomes of Breast Cancer in Ghana: An Analysis of Clinicopathological Features. Open Access Library Journal . 2016;3(1):1-11. doi:10.4236/oalib.1102145 Seshie B, Adu-Aryee NA, Dedey F, Calys-Tagoe B, Clegg-Lamptey J-N. A retrospective analysis of breast cancer subtype based on ER/PR and HER2 status in Ghanaian patients at the Korle Bu Teaching Hospital, Ghana. BMC Clinical Pathology . 2015/07/09 2015;15(1):14. doi:10.1186/s12907-015-0014-4 Stark A, Kleer CG, Martin I, et al. African ancestry and higher prevalence of triple-negative breast cancer: findings from an international study. Cancer . Nov 1 2010;116(21):4926-32. doi:10.1002/cncr.25276 Pathologist Without Borders, Information available on the internet at: https://pathologistswithoutborders.info/about-us/. Last accessed on 19th January 2025 Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thürlimann B, Senn HJ. Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol . Aug 2011;22(8):1736-47. doi:10.1093/annonc/mdr304 Dakubo JC, Naaeder SB, Tettey Y, Gyasi RK. Colorectal carcinoma: an update of current trends in Accra. West Afr J Med . May-Jun 2010;29(3):178-83. doi:10.4314/wajm.v29i3.68218 Der EM, Awal S, Sherif M. Breast Malignancies in Northern Ghana: A 7-Year Histopathological Review at The Tamale Teaching Hospital (2013 – 2019). Postgraduate Medical Journal of Ghana . 07/12 2022;10(2):110-118. doi:10.60014/pmjg.v10i2.261 Duduyemi BM, Ayibor WG, Agyemang-Yeboah F. Tissue Microarray Immunohistochemical Staining for Androgen Receptor in Breast Cancer in a Ghanaian Cohort. Ann Afr Med . Jul 1 2024;23(3):452-458. doi:10.4103/aam.aam_83_23 Akosa A, Van Norden S, Tettey Y. Hormone receptor expression in male breast cancers. Ghana Med J . Mar 2005;39(1):14-8. doi:10.4314/gmj.v39i1.35976 Quayson SE, Wiredu EK, Adjei DN, Anim JT. Breast cancer in Accra, Ghana. 2014: Jiagge E, Jin DX, Newberg JY, et al. Tumor sequencing of African ancestry reveals differences in clinically relevant alterations across common cancers. Cancer Cell . Nov 13 2023;41(11):1963-1971 e3. doi:10.1016/j.ccell.2023.10.003 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers invited by journal 09 Jun, 2025 Editor invited by journal 19 May, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 19 May, 2025 First submitted to journal 15 May, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6672326","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469098654,"identity":"fcfbb7f3-3101-4411-9c6b-a6612a9ba32e","order_by":0,"name":"Patrick Kafui 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1","display":"","copyAsset":false,"role":"figure","size":72677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional distribution of breast cancer cases included in the study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6672326/v1/5e76c781f271925dc2c1dddb.jpg"},{"id":84644405,"identity":"f3454389-e597-400c-8c73-ef5359c15e76","added_by":"auto","created_at":"2025-06-15 16:09:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of molecular subtype of breast cancer in Ghana\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6672326/v1/8c787fb9a797b6718656eaf4.jpg"},{"id":84643992,"identity":"f7e1d461-7774-490b-891c-f691189d2368","added_by":"auto","created_at":"2025-06-15 16:01:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analysis of age group and molecular subtype\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6672326/v1/85687d5a0004d3c69d388dcd.jpg"},{"id":84643993,"identity":"1bbc3e15-800b-466c-9ff8-606fabb2b323","added_by":"auto","created_at":"2025-06-15 16:01:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart depicting the regional distribution of molecular subtypes of breast cancer in Ghana\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6672326/v1/fc0153374497052202dd6d7b.jpg"},{"id":84645134,"identity":"3b97c2e0-1611-4ce1-b2a6-652d024a07a8","added_by":"auto","created_at":"2025-06-15 16:33:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1596288,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6672326/v1/83267d6e-b845-40b6-965e-4727fe9f26f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Impact of Improved Pre-analytic Tissue Handling on Immunohistochemistry and Breast Cancer Molecular Subtypes in Ghana: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most frequently diagnosed cancer among women in Ghana, and it is a leading cause of cancer-related morbidity and mortality in this population. Although Ghanaian women (like other African women) have a lower risk of breast cancer diagnosis compared with European women, when they are diagnosed, their tumors are usually of higher grade, more likely to be hormone receptor-negative or human epidermal growth factor receptor 2 (HER2)\u0026ndash;amplified, and detected at a late stage. These factors confer a worse prognosis for patients in Ghana. Additionally, a significant number of breast cancers occur in younger Ghanaian women, presenting with the same or worse prognostic factors as older patients\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccurate histopathologic diagnosis, including molecular subtyping, is crucial to improving survival rates among breast cancer patients. In Ghana, in accordance with the World Health Organization recommendations for reporting breast cancer in 2024, it is advised that suspected breast cancers be confirmed histopathologically before initiating definitive therapy\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Consequently, core biopsy or partial biopsy has become the preferred initial diagnostic approach in recent years. These smaller tissue samples are more likely to be well-fixed, which optimizes diagnostic accuracy. Core or partial biopsy allows assessment of critical parameters such as histologic type, grade, hormone receptor status, and HER2 status, as well as other factors, including lymphovascular invasion and perineural invasion, following guidelines such as those of the College of American Pathologists (CAP) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The presence or absence of carcinoma in situ is also evaluated. These core data elements inform patient management decisions regarding neoadjuvant and adjuvant treatments and, to a lesser extent, the surgical approach. Surgery planning depends largely on tumor size and other clinical and imaging parameters related to disease stage.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMolecular classification has become pivotal in breast cancer management. Determination of estrogen receptor (ER), progesterone receptor (PR), HER2 status, and, more recently, the Ki67 proliferation index has become standard in many centers. These markers guide the use of targeted therapies that can improve prognosis\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, immunohistochemistry (IHC) results depend heavily on optimal preanalytics, particularly tissue fixation in 10% neutral buffered formalin (NBF). Poor preanalytics can lead to false-negative tests, causing many patients to be incorrectly classified as having triple-negative breast cancer (TNBC) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. TNBC is more common in people of African descent and has the poorest prognosis of all breast cancer subtypes, especially in settings where targeted therapies are not available. Although immunotherapy using programmed death-ligand 1 inhibitors is promising, these treatments are not widely accessible in many regions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious reports from Ghana have indicated that TNBC is the most common molecular subtype, with reported rates ranging from 21\u0026ndash;81%\u003csup\u003e2,4,8\u0026ndash;15\u003c/sup\u003e. This wide range may reflect suboptimal preanalytics related to tissue handling and reliance on larger excision specimens (e.g., wide local excision or mastectomy) for IHC. In addition, many centers lack the equipment needed for IHC staining and must use complex manual techniques or send samples abroad. We hypothesized that by using core or partial biopsies, which are typically smaller and therefore better fixed, and by ensuring the availability of 10% NBF for all centers that diagnose breast cancer, the proportion of TNBC cases would be lower than previously reported.\u003c/p\u003e \u003cp\u003eThis study aims to determine the molecular profile of a large series of breast cancer cases reported at key diagnostic and treatment centers across Ghana, all assessed by a single pathology group. We also characterized the demographic and histopathologic features of these patients\u0026rsquo; tumors as part of a comparative assessment of current versus past breast cancer demographics histopathological, and molecular characteristics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Location\u003c/h2\u003e \u003cp\u003eThis study was conducted using records from Pathologists Without Borders (PWB), the largest pathology service in Ghana, in partnership with Precision Medicine for Aggressive Breast Cancer (PMABC). PWB is privately owned and was established in 2018\u003csup\u003e16\u003c/sup\u003e to provide crucial services such as IHC and to improve the quality of histopathology reporting by ensuring accuracy, completeness, timeliness, and usability. PWB operates a hub-and-spoke model through several collection centers nationwide and A.C.T. Pathology Consult (a spoke) located in Cape Coast. A.C.T. Pathology Consult receives an average of 1,900 surgical pathology specimens per year, whereas PWB receives an average of 14,500 surgical pathology specimens per year\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePWB, located in Accra, runs IHC in-house year-round and serves as the central hub for A.C.T. Pathology Consult. The combined catchment area of these centers includes the Western, Central, Greater Accra, Eastern, Ashanti, Northern, Upper West, Upper East, and Volta regions of Ghana. Specimens are also received from other countries in the West African subregion, including The Gambia, Liberia, and Sierra Leone. PWB reports the largest proportion of breast cancer cases in Ghana and has reported more than 7,500 breast tissue samples to date, some of which are included in this review\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. PWB conducts IHC using the VENTANA BenchMark GX automated staining system (Ventana Medical Systems, a member of the Roche Group, Tucson, AZ, USA), following the manufacturer\u0026rsquo;s protocols and reporting results based on CAP guidelines (Appendix 1).\u003c/p\u003e \u003cp\u003ePMABC is a research partnership between the Jiagge Laboratory at Henry Ford Health in Detroit, Michigan, USA, and key breast cancer management centers across sub-Saharan Africa. PMABC sites in Ghana include Ho Teaching Hospital (HTH), Cape Coast Teaching Hospital (CCTH), Komfo Anokye Teaching Hospital (KATH), Korle Bu Teaching Hospital (KBTH), Tamale Teaching Hospital (TTH), and the Eastern Regional Hospital. These centers are equipped to handle breast cancer diagnosis and treatment until a referral is required for radiation oncology services at the two national centers in KATH and KBTH. Breast core biopsies from these hospitals are sent to PWB and A.C.T. Pathology Consult; in addition, PWB and A.C.T. receive samples from other hospitals in their catchment areas nationwide.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eIn 2020, 10% neutral buffered formalin (10% NBF) was provided on a pilot basis to selected sites (teaching hospitals and major hospitals that diagnose and treat breast cancer) to ensure adequate tissue fixation. Personnel at these sites were trained to prepare 10% NBF. Training workshops and one-on-one sessions were also conducted to teach proper tissue handling and fixation procedures, including immediately fixing biopsy samples in adequate volumes of buffered formalin and sending the specimens to the pathology laboratory on the same day. PMABC further trained providers on the use of core needle biopsy for breast cancer diagnosis and supplied free biopsy needles to the pilot sites.\u003c/p\u003e \u003cp\u003eThis descriptive, cross-sectional, and purposive study was performed on all cases biopsied and confirmed as breast cancer via core or incision biopsies from 2020 to 2023.\u003c/p\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eWe included all complete core or incision biopsy histopathology reports released between January 2020 and December 2023, with corresponding IHC reports available in the electronic medical records of the two private pathology centers (PWB and A.C.T.). Excision specimens were excluded, as were any reports with incomplete histopathological or IHC data.\u003c/p\u003e\n\u003ch3\u003eData source and sampling\u003c/h3\u003e\n\u003cp\u003eRetrospective data of histopathology reports of all breast cancer cases reported at PWB and A.C.T. Pathology Consult were retrieved from Nubia Electronic Medical Record (NubiaEMR v2.0). NubiaEMR is an electronic medical record with synoptic reporting for anatomic pathology that was built in-house. The data were exported to Microsoft Excel (Microsoft Corp., Redmond, WA, USA) and de-identified. Patient demographics, sample type (core or incision biopsy), and histopathological core data elements for breast cancer, based on CAP reporting templates, were collected and matched with their IHC reports. The combined data were then exported to SPSS Statistics for Windows, Version 27.0 (Armonk, NY: IBM Corp.), for analysis. These reports were matched with their corresponding IHC records in Microsoft Excel and de-identified. The combined dataset was exported to IBM SPSS Statistics for Windows, Version 27.0 for statistical analysis.\u003c/p\u003e\n\u003ch3\u003eBreast Cancer Molecular Subtyping\u003c/h3\u003e\n\u003cp\u003eData extracted from immunohistochemistry reports included estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, and tumor proliferative index (Ki67) score.\u003c/p\u003e \u003cp\u003eBreast cancer molecular subtypes were categorized following the criteria established at the 12th St. Gallen International Breast Cancer Conference in 2011. According to the St. Gallen Consensus 2011, breast cancer subtypes are classified as follows: Luminal A (ER+/PR+/HER2-/low Ki67), Luminal B (ER+/PR+/HER2-/+ or high Ki67), HER2-enriched (ER-/PR-/HER2+), and triple-negative breast cancer (TNBC) (ER-/PR-/HER2-)\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn SPSS v27.0, Descriptive statistics with frequencies and percentages were used to describe categorical variables. Cross-tabulations were performed to compare variables, and chi-square tests were used to identify statistical relationships, with a 95% confidence interval and significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data visualization was conducted using QGIS (version 3.36; QGIS Development Team under the Open-Source Geospatial Foundation, Beaverton, OR, USA) and GraphPad Prism (version 9, GraphPad Software, Inc., Boston, MA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 4,000 cases were identified and included from January 1, 2020, to December 31, 2023, with the majority recorded in 2023. Participant ages ranged from 14 to 101 years, with a mean age of 52.7 \u0026plusmn; 13.4 years (\u003cstrong\u003eTable 1\u003c/strong\u003e). The number of cases increased each year over the study period. The peak age group for breast cancer was 40\u0026ndash;49 years (29.2%), and 53.5% of all cases occurred in individuals aged 40\u0026ndash;59 years. By sex, 98.4% of breast cancers were in females (n=3,935), and 1.6% were in males (n=65). Needle core biopsy accounted for 85.7% of all samples (n=3,426). Breast cancer occurred more frequently in the left breast (50.7%; n=2,027) than the right breast, and bilateral breast cancers represented 1.5% of all cases (n=58).\u003c/p\u003e\n\u003cp\u003eThe most common histologic type was invasive carcinoma of no special type (NST), representing 89% of cases (n=3,558). Diffuse non-Hodgkin lymphoma and tall cell carcinoma with reversed polarity were the least common, each with a frequency of one (n=1). Most tumors (54.1%; n=2,163) were Grade III, and 24\u0026middot;4% of cases (n=976) showed the presence of an in-situ component. Among cases with in-situ components, 94\u0026middot;7% (n=924) were ductal carcinoma in-situ (DCIS), and 60% were of intermediate grade (n=543). Lymphovascular invasion was present in 18.0% of cases (n=721), and perineural invasion was present in 1.2% (n=46).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eHistopathological Characteristics of Breast Cancer in Ghana\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1,036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1,347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1,361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eAge Group (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026le;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1,167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026ge;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e3,935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eBiopsy Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eIncision/Wedge Biopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNeedle Core Biopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e3,426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eLaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e2027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e50.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e47.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eBilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"14\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eHistological Type Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eInvasive Carcinoma with Apocrine Features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eCribriform Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eDiffuse Non-Hodgkin Lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eDuctal Carcinoma In-situ Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eInvasive Carcinoma NST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e3,558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e89.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eInvasive Papillary Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eInvasive lobular Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eMalignant Phyllodes Tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eMetaplastic Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eMixed Type Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eMucinous Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTall Cell Carcinoma with Reversed Polarity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTubular Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eHistological Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1,393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e2,163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e54.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNot Assessable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eIn-situ Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNot Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e3024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eIn-situ Component Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eDuctal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e94.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eLobular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003ePaget\u0026rsquo;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eDCIS Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eLymphovascular Invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNot Identified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e3,279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e82.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003ePerineural Invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNot Identified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e3,954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e98.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e[ INSERT TABLE 1 HERE ]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDistribution of patients included in the study\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCases were received from every region of Ghana (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Most breast cancer cases (n=2,395; 59.9%) came from the Greater Accra region, where KBTH, the largest referral center for breast cancer in Ghana, is located. The Ashanti region contributed 10.8% (n=431), the Volta region 8.6% (n=345), and the Central region 6.9% (n=274). For geographic categorization, the Greater Accra, Western, and Central regions were classified as the Coastal Belt; the Middle Belt comprised most of the Volta, Eastern, and Ashanti regions; and the Northern Belt comprised the Northern, Upper West, and Upper East regions. The Coastal Belt accounted for 69.1% of cases (n=2,760), the Middle Belt for 24.1% (n=963), and the Northern Belt for 7.0% (n=277).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eImmunohistochemistry of breast cancer cases in Ghana\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e shows that 47\u0026middot;7% of cases (n=1,907) were ER-positive, 35.1% were PR\u0026ndash;positive (n=1,404), and 19.6% had HER2 expression (n=784). Most (85.9%) had a Ki-67 index of more than 20% (n=3,435), whereas 9.8% had a Ki-67 index of less than 10% (n=393). In total, 49.1% of the cases were ER-negative (n=1,965), 61.8% were PR-negative (n=2,470), and 63.3% were HER2-negative (n=2,532).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eImmunohistochemistry pattern of breast cancer cases in Ghana\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e1,907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e47.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e1,965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eNot Assessable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e1,404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e2,470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e61.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eNot Assessable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eHER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e2,532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eEquivocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eNot Assessable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eKi67 Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026lt;10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e10\u0026ndash;20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026gt;20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e3,435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[ INSERT TABLE 2 HERE ]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to molecular subtype distribution, the most common subtype was Luminal B (39.9%; n=1,627), followed by TNBC (29.2%; n=1,186). Luminal A was the least common (11.4%; n=484). A total of 196 cases (4.9%) were categorized as \u0026ldquo;other,\u0026rdquo; defined as hormone receptor\u0026ndash;negative and HER2-equivocal with no in-situ hybridization performed. An additional 126 cases (3.2%) were non-classifiable, including non-Hodgkin lymphoma, DCIS-only lesions, and malignant phyllodes tumors, which are not included in molecular subtyping per CAP guidelines. These findings are depicted in \u003cstrong\u003eFigure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e and \u003cstrong\u003eFigure\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;3\u003c/strong\u003e present a comparative analysis of age groups and molecular subtypes. Among individuals older than 40 years, Luminal B was the most frequent subtype (42,3%), followed by TNBC (27,5%). In persons younger than 40 years, TNBC was the most common subtype (40,8%), followed by Luminal B (32,2%). The prevalence of Luminal A increased with age and peaked at 60\u0026ndash;69 years (26,3%), then decreased to 22,8% among individuals older than 70 years. Luminal B peaked at 40\u0026ndash;49 years (32,1%) and was least common in the extreme age groups. HER2-enriched subtypes peaked at 40\u0026ndash;49 years (31,1%) and were least common in extreme age groups. TNBC followed a similar pattern, with its lowest prevalence at the extremes of age and the highest prevalence at 40\u0026ndash;49 years (29,4%). Overall, age group was significantly associated with the molecular subtype (X\u003csup\u003e2\u003c/sup\u003e = 183,473; p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003eComparative analysis of age group and molecular subtype of breast cancer in Ghana\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 431px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular Subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2-enriched\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOthers (HER2 Equivocal)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Classifiable (ER, PR, HER2 \u0026ndash;Not Applicable)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026le;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2 (0\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1 (0\u0026middot;5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3 (0\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e4 (0\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e13 (0\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1 (0\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e28 (2\u0026middot;4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e5 (2\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e51 (1\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e37 (8\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e182 (11\u0026middot;4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e76 (16\u0026middot;5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e230 (19\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e39 (19\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e22 (17\u0026middot;5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e586 (14\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e84 (18\u0026middot;4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e511 (32\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e143 (31\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e343 (29\u0026middot;4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e47 (24\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e39 (31\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1167 (29\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e108 (23\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e398 (25\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e124 (27\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e269 (23\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e42 (21\u0026middot;4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e30 (23\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e971 (24\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e120 (26\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e298 (18\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e73 (15\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e166 (14\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e37 (18\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e23 (18\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e717 (17\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026ge;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e104 (22\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e192 (12\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e43 (9\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e129 (11\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e25 (12\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12 (9\u0026middot;5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e505 (12\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e457 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1594 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e460 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1167 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e196 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e126 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e4000 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; ER, estrogen receptor; PR, progesterone receptor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[ INSERT TABLE 3 HERE ]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause the distribution of cases across Ghana\u0026rsquo;s regions was uneven, 2,395 cases (59\u0026middot;9%) originated from the Greater Accra region, whereas only six cases (0\u0026middot;2%) came from the Upper East region. Luminal B was the most frequent subtype in the Greater Accra, Upper West, and Ashanti regions, with frequencies compared to TNBC of 1,087 vs. 636, 13 vs. eight, and 154 vs. 142, respectively. In the Western, Volta, Eastern, Northern, and Upper East regions, TNBC was more common than Luminal B. Collectively, molecular subtypes of breast cancer were significantly associated with regional distribution (X\u003csup\u003e2\u003c/sup\u003e = 105\u0026middot;879; p \u0026lt; 0\u0026middot;001), as shown in \u003cstrong\u003eTable 4\u003c/strong\u003e and \u003cstrong\u003eFigure\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;4\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Comparative analysis of regional distribution of the molecular subtype of breast cancer in Ghana\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegional Distribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 390px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2-enriched\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOthers (HER2 Equivocal)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon- Classifiable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGreater Accra Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e255 (55\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1061 (66\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e250 (54\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e627 (53\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e122 (62\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e80 (63\u0026middot;5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2,395 (59\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eWestern Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e17 (3\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e27 (1\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e12 (2\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e31 (2\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2 (1\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2 (1\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e91 (2\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eVolta Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e61 (13\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e108 (6\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e41 (8\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e112 (9\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e17 (8\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e6 (4\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e345 (8\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eCentral Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e37 (8\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e93 (5\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e35 (7\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e94 (8\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11 (5\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4 (3\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e274 (6\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eUpper West Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e12 (0\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1 (0\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (0\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3 (2\u0026middot;4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e23 (0\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eEastern Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e13 (2\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e66 (4\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e24 (5\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e68 (5\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9 (4\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7 (5\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e187 (4\u0026middot;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAshanti Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e50 (10\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e151 (9\u0026middot;5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e50 (10\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e141 (12\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e24 (12\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e15 (11\u0026middot;9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e431 (10\u0026middot;8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eNorthern Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e24 (5\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e74 (4\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e46 (10\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e84 (7\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11 (5\u0026middot;6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9 (7\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e248 (6\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eUpper East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (0\u0026middot;1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1 (0\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (0\u0026middot;3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 (0\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e6 (0\u0026middot;2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e457 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1,594 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e460 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1167 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e196 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e126 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e4,000 (100\u0026middot;0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[ INSERT TABLE 4 HERE ]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe proportion of Ghanaian breast cancers that are TNBCs has been reported to range from 21% to over 80\u003csup\u003e1,10\u0026ndash;14\u003c/sup\u003e. These findings come primarily from single-institution studies that often do not describe the preanalytic phase of tissue handling or its impact on the quality of breast cancer diagnosis, especially with regard to molecular subtyping. Again, many of these studies report IHC testing on excision specimen (WLE and Mastectomy) which are generally accepted to have poorer preanalytical conditions especially relating to adequate fixation. These have been the basis for ASCO/ CAP recommendations relating to IHC testing in breast cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we present the largest cohort (4,000 cases) of breast part biopsies, in which we specifically examined the effect of ensuring quality preanalytics on reported hormone receptor status and TNBC rates. We highlight the importance of high-quality preanalytics in achieving accurate breast cancer diagnoses and guiding subsequent treatment. Unlike most previous studies\u0026mdash;which were regional, with smaller sample sizes, and included all specimen types\u0026mdash;this study draws data from the entire country and relies on only part biopsies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. By relying on a synoptic reporting system (NubiaEMR), we emphasize enhanced data quality, facilitating cancer research and registries in resource-limited settings such as Ghana.\u003c/p\u003e \u003cp\u003eOur findings reflect improvements in preanalytics through the provision of 10% NBF and the use of core and incision biopsies. By relying on part biopsies (core needle and incision biopsies), adequate tissue fixation was assured as previously reported and recommended in literature\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Histopathology diagnostic services and treatment centers are concentrated in the Coastal Belt, followed by the Middle Belt, with the fewest located in the Northern Belt. Consequently, the upper regions of Ghana reported the fewest cases. This disparity may be attributable to limited diagnostic and treatment services and lower population density in these regions.\u003c/p\u003e \u003cp\u003eWe identified a geographical variation in the proportion of TNBC cases. This finding is consistent with our work \u003cem\u003e(yet to be published\u003c/em\u003e), that showed that tribal ethnicity is associated with different risk for developing TNBC, with the Northern region experiencing more aggressive disease subtypes\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In contrast, all other regions reported Luminal B as the predominant molecular subtype. This finding may also suggest potential tribal or regional differences in breast cancer subtypes that warrant further study.\u003c/p\u003e \u003cp\u003eConsistent with earlier studies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, the mean age at diagnosis in our cohort was 52.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 years, peaking in the 40\u0026ndash;49-year age group. This is younger than the reported mean diagnostic age of 60\u0026ndash;65 years in Western populations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, though it is slightly higher than figures from previous studies in Ghana\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This discrepancy may be attributable to our larger sample size, which may better reflect the national situation. An earlier age at diagnosis poses specific challenges: patients in their reproductive years may require fertility-sparing treatment, and they are often economically active, so prolonged treatment may result in financial difficulties that compromise adherence. Also, younger patients may face unique imaging challenges during screening, especially given limited availability of high-resolution imaging modalities in Ghana. It is, therefore, necessary to consider the earlier onset of disease when designing and implementing screening protocols. TNBC was most frequently diagnosed in those younger than 40 years, suggesting a possible genetic or hereditary predisposition that deserves further investigation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOf the reported breast cancer cases, 1.6% occurred in men, aligning with prior estimates of 1.2\u0026ndash;3.0%\u003csup\u003e1,21,22\u003c/sup\u003e. Little is known, however, about the specific features of male breast cancer in Ghana, underscoring the need for larger studies to reveal its histopathologic and molecular characteristics and to inform appropriate treatment strategies.\u003c/p\u003e \u003cp\u003eInvasive carcinoma of NST was the most common histologic subtype, consistent with previous publications.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e However, this cohort also included tumor types that were not previously reported, such as tall cell carcinoma with reversed polarity and solid papillary carcinoma, in addition to other recognized types like lobular, tubular, cribriform, and metaplastic carcinoma. These findings may reflect enhanced preanalytics, which improve hematoxylin and eosin morphology, thereby enabling more accurate classification. Most tumors in our cohort were high-grade with high Ki-67 indices, corroborating earlier reports that breast cancers in Ghanaians often display more aggressive pathobiology\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These characteristics highlight the need for research to identify biomarkers that can guide targeted interventions.\u003c/p\u003e \u003cp\u003eWe observed that 49\u0026middot;1% of tumors were ER-negative, 61\u0026middot;8% were PR-negative, and 63.3% were HER2-negative. The TNBC rate of 29.2% although lower than previous reports showing rates as high as 82% is still higher than rates in European and other populations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Published figures have ranged between 21%\u003csup\u003e11\u003c/sup\u003e and 82%,\u003csup\u003e15\u003c/sup\u003e but our study optimized preanalytics and likely captured a more accurate estimate of TNBC prevalence in Ghana, which may extend to other sub-Saharan African countries\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Accurate molecular subtyping is vital, given that targeted therapies (e.g., ER and HER2 blockers) are now covered by the National Health Insurance system, thus allowing all patients who test positive for hormone receptors or HER2 to receive the appropriate therapy at no additional cost.\u003c/p\u003e \u003cp\u003eIn individuals younger than 40 years, TNBC was the most prevalent subtype, whereas in those older than 40 years, Luminal B was more common. Notably, the prevalence of each molecular subtype declined with increasing age, except for Luminal A, which rose with age, suggesting that older age at diagnosis correlates with a higher likelihood of having Luminal A disease. Our analysis showed a statistically significant relationship between age group and molecular subtype (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). These findings confirm that breast cancer biology varies by age, highlighting the necessity for further research on early-onset disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe preanalytic stage of pathology tissue handling is essential for accurate pathology reporting, patient diagnosis, and treatment stratification. The effect of poor preanalytics on IHC cannot be overemphasized and has been reported in literature with recommendations to improve preanalytics in order to guarantee accuracy in diagnostics and assure optimal management of breast cancer patients\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Education and providing necessary resources are vital for obtaining correct pathology diagnoses. Poorer outcomes for breast cancer in sub-Saharan Africa may be partly attributable to preanalytic challenges that lead to misdiagnoses and inappropriate treatment decisions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on this pilot study, we recommend training providers to perform core needle biopsies, ensure proper tissue handling, and supply 10% NBF to institutions that perform cancer tissue biopsies. Optimal preanalytics enhance immunohistochemistry outcomes and ensure patients receive the most effective treatments. Achieving this goal requires allocating additional resources to maintain the quality of surgical tissue before it reaches the pathology laboratory.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eThis study relied on real world evidence and real-world data generated as part of quality improvement efforts aimed at improving the quality of histopathological diagnosis, pathology data and ultimately the prognosis of breast cancer patients. The study is limited by the lack of a control arm in which patients received standard care (anything other than 10% NBF). All centers that were assessed benefited from the intervention to improve preanalytics and pathology data to optimize breast cancer patient treatment and outcome.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical Approval was sought from the Ethical Review Committees (ERC) of the Cape Coast Teaching Hospital (CCTHERC/EC/2020/047), Komfo Anokye Teaching Hospital (KATH IRB/AP/052/20)), Korle Bu Teaching Hospital (KBTH-IRB/000169/2021), and the Tamale Teaching Hospital (TTH/R\u0026amp;D/SR/20/95) as part of a broader study into the \u0026ldquo;Genetic and Molecular determinants of Breast Cancer Disparity among Africans - Precision Medicine for Aggressive Breast Cancer (PMABC) in partnership with the Jiagge Laboratory at the Henry Ford Cancer Center, Detroit, Michigan, USA. The ethical clearance obtained from ERC of the Cape Coast Teaching Hospital, Komfo Anokye Teaching Hospital, Korle Bu Teaching Hospital, and the Tamale Teaching Hospital indicated that informed consent was not required for the use of such secondary data (i.e., retrospective data) for as long as personal identifiers were omitted from the data. All methods were carried out in accordance with the Declaration of Helsinki and other relevant ethical guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll materials and data used in the study are available and can be provided as necessary by contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received funding from Henry Ford Health and U CAN-CER VIVE research funding to the Jiagge Lab at Henry Ford Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePKA,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eEGI, and EMJ conceived the idea. All the authors were part of activities related to teaching preanalytical handling of tissues (preparation of 10% buffered formalin and proper biopsy technique) across the country. JN, FD, VN, SM, BW, NA, MN, MS, MK,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eMAD, MK, RD, FAM, IA, KA, and JA performed the biopsies at the various sites and ensured adherence to quality preanalytics. EGI and PKA analyzed the data and wrote the initial draft. All the authors reviewed the manuscript and agreed on the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the management and staff of A.C.T. Pathology Consult (A.C.T.), Pathologists Without Borders Ltd (PWB), ToldoIT, and all members of the Precision Medicine for Aggressive Breast Cancer (PMABC) partnership for their efforts in improving breast cancer diagnosis in Ghana, which resulted in the data used for this study. We thank John E. Essex III, BA, of Peak Medical Editing, Indianapolis, IN, USA, who received payment from the study\u0026rsquo;s sponsor for professional medical editing assistance.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eDepartment of Pathology, University of Cape Coast /Cape Coast Teaching Hospital, Cape Coast, Ghana\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eACT Pathology Consult, PE 117 Pedu Estate, Cape Coast, Ghana\u003c/li\u003e\n \u003cli\u003ePathologists Without Borders Ltd, 125 Guggisberg Avenue, Mamprobi, Accra, Ghana\u003c/li\u003e\n \u003cli\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eDepartment of Surgery, University of Ghana Medical School / Korle Bu Teaching Hospital Accra, Ghana\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDepartment of Surgery, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana\u003c/li\u003e\n \u003cli\u003ePeace and Love Hospital, Kumasi, Ghana\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDepartment of Surgery, Ho Teaching Hospital, Ho, Ghana\u003c/li\u003e\n \u003cli\u003eDepartment of Surgery, University of Cape Coast / Cape Coast Teaching Hospital, Cape Coast, Ghana\u003c/li\u003e\n \u003cli\u003eDepartment of Surgery, Tamale Teaching Hospital, Tamale, Ghana\u003c/li\u003e\n \u003cli\u003eDepartment of Surgery, Eastern Regional Hospital, Koforidua, Ghana\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDepartment of Surgery, Komfo-Anokye Teaching Hospital, Kumasi, Ghana\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDepartment of Surgery, Effiah Nkwanta Regional Hospital, Ghana\u003c/li\u003e\n \u003cli\u003eDepartment of Chemistry, University of Cape Coast, Ghana\u003c/li\u003e\n \u003cli\u003eHenry Ford Cancer Institute/Henry Ford Health System, 2799W Grand Blvd, Detroit, MI, USA\u003c/li\u003e\n \u003cli\u003ePrecision Medicine for Aggressive Breast Cancer (PMABC), Accra, Ghana\u003c/li\u003e\n \u003cli\u003eThe Ohio State University, Columbus, OH, USA\u003c/li\u003e\n \u003cli\u003eDepartment of Pathology, Korle Bu Teaching Hospital Accra, Ghana\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkakpo PK, Imbeah EG, Edusei L, et al. Clinicopathologic characteristics of early-onset breast cancer: a comparative analysis of cases from across Ghana. \u003cem\u003eBMC Womens Health\u003c/em\u003e. Jan 3 2023;23(1):5. doi:10.1186/s12905-022-02142-w\u003c/li\u003e\n\u003cli\u003eNaku Ghartey Jnr F, Anyanful A, Eliason S, Mohammed Adamu S, Debrah S. Pattern of Breast Cancer Distribution in Ghana: A Survey to Enhance Early Detection, Diagnosis, and Treatment. \u003cem\u003eInt J Breast Cancer\u003c/em\u003e. 2016;2016:3645308. doi:10.1155/2016/3645308\u003c/li\u003e\n\u003cli\u003eInternational Agency for Research on Cancer. Chapter 32 [Internet]. Lyon: WHO Classification of Tumours; [cited 2024 Dec 27]. Available from: https://tumourclassification.iarc.who.int/chapters/32\u003c/li\u003e\n\u003cli\u003eCollege of American Pathologists. Cancer Protocol Templates [Internet]. Northfield (IL): CAP; [cited 2024 Dec 27]. Available from: https://www.cap.org/protocols-and-guidelines/cancer-reporting-tools/cancer-protocol-templates\u003c/li\u003e\n\u003cli\u003eJiagge E, Jin DX, Newberg JY, et al. Tumor sequencing of African ancestry reveals differences in clinically relevant alterations across common cancers. \u003cem\u003eCancer Cell\u003c/em\u003e. Nov 13 2023;41(11):1963-1971.e3. doi:10.1016/j.ccell.2023.10.003\u003c/li\u003e\n\u003cli\u003eKhoury T, Sait S, Hwang H, et al. Delay to formalin fixation effect on breast biomarkers. \u003cem\u003eMod Pathol\u003c/em\u003e. Nov 2009;22(11):1457-67. doi:10.1038/modpathol.2009.117\u003c/li\u003e\n\u003cli\u003eMann GB, Fahey VD, Feleppa F, Buchanan MR. Reliance on hormone receptor assays of surgical specimens may compromise outcome in patients with breast cancer. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. Aug 1 2005;23(22):5148-54. doi:10.1200/jco.2005.02.076\u003c/li\u003e\n\u003cli\u003eAkakpo PK, Imbeah EG, Edusei L, et al. Clinicopathologic characteristics of early-onset breast cancer: a comparative analysis of cases from across Ghana. \u003cem\u003eBMC Women\u0026apos;s Health\u003c/em\u003e. 2023/01/03 2023;23(1):5. doi:10.1186/s12905-022-02142-w\u003c/li\u003e\n\u003cli\u003eJiagge EM, Ulintz PJ, Wong S, et al. Multiethnic PDX models predict a possible immune signature associated with TNBC of African ancestry. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e. Apr 2021;186(2):391-401. doi:10.1007/s10549-021-06097-8\u003c/li\u003e\n\u003cli\u003eEdmund DM, Naaeder SB, Tettey Y, Gyasi RK. Breast cancer in Ghanaian women: what has changed? \u003cem\u003eAm J Clin Pathol\u003c/em\u003e. Jul 2013;140(1):97-102. doi:10.1309/ajcpw7tzls3bffiu\u003c/li\u003e\n\u003cli\u003eOhene-Yeboah M, Adjei E. Breast cancer in Kumasi, Ghana. \u003cem\u003eGhana Med J\u003c/em\u003e. Mar 2012;46(1):8-13. \u003c/li\u003e\n\u003cli\u003eJiagge E, Oppong JK, Bensenhaver J, et al. Breast Cancer and African Ancestry: Lessons Learned at the 10-Year Anniversary of the Ghana-Michigan Research Partnership and International Breast Registry. \u003cem\u003eJ Glob Oncol\u003c/em\u003e. Oct 2016;2(5):302-310. doi:10.1200/jgo.2015.002881\u003c/li\u003e\n\u003cli\u003eMensah AC, Yarney J, Nokoe SK, Opoku S, Clegg-Lamptey JN. Survival Outcomes of Breast Cancer in Ghana: An Analysis of Clinicopathological Features. \u003cem\u003eOpen Access Library Journal\u003c/em\u003e. 2016;3(1):1-11. doi:10.4236/oalib.1102145\u003c/li\u003e\n\u003cli\u003eSeshie B, Adu-Aryee NA, Dedey F, Calys-Tagoe B, Clegg-Lamptey J-N. A retrospective analysis of breast cancer subtype based on ER/PR and HER2 status in Ghanaian patients at the Korle Bu Teaching Hospital, Ghana. \u003cem\u003eBMC Clinical Pathology\u003c/em\u003e. 2015/07/09 2015;15(1):14. doi:10.1186/s12907-015-0014-4\u003c/li\u003e\n\u003cli\u003eStark A, Kleer CG, Martin I, et al. African ancestry and higher prevalence of triple-negative breast cancer: findings from an international study. \u003cem\u003eCancer\u003c/em\u003e. Nov 1 2010;116(21):4926-32. doi:10.1002/cncr.25276\u003c/li\u003e\n\u003cli\u003ePathologist Without Borders, Information available on the internet at: https://pathologistswithoutborders.info/about-us/. Last accessed on 19th January 2025\u003c/li\u003e\n\u003cli\u003eGoldhirsch A, Wood WC, Coates AS, Gelber RD, Th\u0026uuml;rlimann B, Senn HJ. Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. \u003cem\u003eAnn Oncol\u003c/em\u003e. Aug 2011;22(8):1736-47. doi:10.1093/annonc/mdr304\u003c/li\u003e\n\u003cli\u003eDakubo JC, Naaeder SB, Tettey Y, Gyasi RK. Colorectal carcinoma: an update of current trends in Accra. \u003cem\u003eWest Afr J Med\u003c/em\u003e. May-Jun 2010;29(3):178-83. doi:10.4314/wajm.v29i3.68218\u003c/li\u003e\n\u003cli\u003eDer EM, Awal S, Sherif M. Breast Malignancies in Northern Ghana: A 7-Year Histopathological Review at The Tamale Teaching Hospital (2013 \u0026ndash; 2019). \u003cem\u003ePostgraduate Medical Journal of Ghana\u003c/em\u003e. 07/12 2022;10(2):110-118. doi:10.60014/pmjg.v10i2.261\u003c/li\u003e\n\u003cli\u003eDuduyemi BM, Ayibor WG, Agyemang-Yeboah F. Tissue Microarray Immunohistochemical Staining for Androgen Receptor in Breast Cancer in a Ghanaian Cohort. \u003cem\u003eAnn Afr Med\u003c/em\u003e. Jul 1 2024;23(3):452-458. doi:10.4103/aam.aam_83_23\u003c/li\u003e\n\u003cli\u003eAkosa A, Van Norden S, Tettey Y. Hormone receptor expression in male breast cancers. \u003cem\u003eGhana Med J\u003c/em\u003e. Mar 2005;39(1):14-8. doi:10.4314/gmj.v39i1.35976\u003c/li\u003e\n\u003cli\u003eQuayson SE, Wiredu EK, Adjei DN, Anim JT. Breast cancer in Accra, Ghana. 2014:\u003c/li\u003e\n\u003cli\u003eJiagge E, Jin DX, Newberg JY, et al. Tumor sequencing of African ancestry reveals differences in clinically relevant alterations across common cancers. \u003cem\u003eCancer Cell\u003c/em\u003e. Nov 13 2023;41(11):1963-1971 e3. doi:10.1016/j.ccell.2023.10.003\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, diagnosis, histopathology, characteristics, part biopsy, preanalytics, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-6672326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6672326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer is a major cause of morbidity and mortality among Ghanaian women, and the higher reported rates of triple-negative breast cancer (TNBC) are associated with worse outcomes. It is established that quality preanalytics has a positive effect on the outcome of immunohistochemistry. Enhanced preanalytic tissue handling may reduce the reported prevalence of TNBC and result in the correct management of breast cancer patients. This study assessed the impact of improved preanalytic processes on diagnostic outcomes in 4,000 breast biopsies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePathologists Without Borders partnered with Precision Medicine for Aggressive Breast Cancers to provide 10% neutral buffered formalin, offer training in tissue handling, and instruct providers in core needle biopsy. We retrospectively analyzed all core biopsies diagnosed as breast cancer over four years.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 4,000 cases were included (mean age, 52.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 years). Male breast cancer accounted for 1.6% of cases (n\u0026thinsp;=\u0026thinsp;65). In all tumors, 49.1% were estrogen receptor-negative (n\u0026thinsp;=\u0026thinsp;1,965), 61.8% were progesterone receptor\u0026ndash;negative (n\u0026thinsp;=\u0026thinsp;2,470), and 63.3% were human epidermal growth factor receptor 2\u0026ndash;negative (n\u0026thinsp;=\u0026thinsp;2,532), yielding a TNBC rate of 29.2%. TNBC was most prevalent in patients aged 40\u0026ndash;59 years and in those younger than 30 years.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe identified a 29.2% TNBC rate, which is lower than previously reported. These findings support established reports of the importance of optimized preanalytic procedures to improve diagnostic accuracy. The variation in TNBC rates with age suggest a need for age-specific breast cancer screening and treatment strategies in Ghana.\u003c/p\u003e","manuscriptTitle":"Assessing the Impact of Improved Pre-analytic Tissue Handling on Immunohistochemistry and Breast Cancer Molecular Subtypes in Ghana: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-15 16:01:04","doi":"10.21203/rs.3.rs-6672326/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-06-16T17:05:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26815563634169937215083764330964387027","date":"2025-06-11T10:41:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-09T05:16:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-19T11:57:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T09:54:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-19T09:52:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-05-15T11:41:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40bda846-9005-4c08-8fde-14d55c46ae03","owner":[],"postedDate":"June 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-15T16:01:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-15 16:01:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6672326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6672326","identity":"rs-6672326","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}