Acinic Cell Carcinoma of The Breast: A Population-Based Clinicopathologic Study

Acinic Cell Carcinoma of The Breast: A Population-Based Clinicopathologic 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 Acinic Cell Carcinoma of The Breast: A Population-Based Clinicopathologic Study Faruk Skenderi, Giridhara Rathnaiah Babu, Una Glamoclija, Emir Veledar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4450431/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Acinic cell carcinoma (ACC) of the breast is a very rare, primary salivary gland-type breast malignancy with ~ 100 reported cases in the literature. Limited information about the clinical features and outcomes of the patients with ACC is available. Methods We utilized the Surveillance, Epidemiology, and End Results (SEER) database to identify ACC cases diagnosed between 2000 and 2018. For comparison, we also examined a cohort of invasive breast carcinomas of no special type (NST). Results Thirty ACC cases were identified among over 248,000 invasive breast carcinoma NST cases in the SEER database. Most ACC cases affected the White population (87%) and individuals over 50 years old (70%). ACCs were predominantly grade 3 carcinomas (44%), diagnosed at an early stage (AJCC TNM stages I and II, 67%). Hormone receptor (HR) and HER2 status were available for 13 cases, revealing molecular heterogeneity: HR-/HER2- (four cases, 31%), HR-/HER2+ (two cases, 15%), HR+/HER2- (four cases, 31%), and HR+/HER2+ (three cases, 23%). Surgery was the primary treatment modality for 26 out of 30 (86.7%) ACC patients, with chemotherapy and radiotherapy used in 46.7% and 33% of cases, respectively. The median survival time for ACC patients was 19 months compared to 48 months for invasive breast carcinoma NST patients (p < 0.001). Year-wise survival rates for ACC patients showed a dramatic decrease in the number of at-risk patients over time, starting at 30 months and decreasing rapidly, compared to a slower decline in the invasive breast carcinoma NST group (p < 0.01). The log odds of death for ACC patients were significantly higher (by 4.5 times) than for invasive breast carcinoma NST patients (p < 0.01), indicating a substantially worse prognosis. Conclusions Acinic cell carcinoma of the breast is a very rare (0.01%) primary breast malignancy. Despite its early clinical presentation, ACC of the breast appears to have a more aggressive clinical course and poorer clinical outcomes compared with conventional breast cancer. Oncology Breast cancer special types acinic cell carcinoma survival outcome Figures Figure 1 Figure 2 Introduction Breast cancer is the most common malignancy among women worldwide and a leading cause of female cancer-related mortality [ 1 , 4 ]. It is a complex and heterogeneous disease, with various morphologies, molecular genomic, and clinical features [ 1 ]. Invasive breast carcinoma of no special type (NST) is the most common subtype of breast cancer, constituting ~ 80% of all breast cancers. Special types, defined by > 90% of distinct and clinically relevant morphology, are a large and heterogeneous group of neoplasms, constituting the remaining ~ 20% of all breast cancers [ 1 , 12 ]. Among special types, salivary gland-type cancers are a distinct and very rare group, encompassing several peculiar entities, including acinic cell carcinoma (ACC). Most salivary gland-type breast cancers have a triple-negative phenotype [Estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2 negative], but their clinical course may be different from triple-negative breast carcinomas NST [ 1 , 3 , 6 , 8 , 11 ]. ACC is defined by its characteristic morphology (clear and/or granular epithelial cells, some of which may have intracytoplasmic zymogen granules, typically arranged in solid or microglandular arrangements) [ 1 , 8 , 9 ]. The incidence of ACC in the breast is unknown but our comprehensive literature survey completed in 2022, revealed only 68 well-documented cases in the literature [ 3 ]. Since then, ~ 35 additional ACC cases have been reported in the current literature (PubMed/MEDLINE literature survey completed in early May 2024). Similar to other salivary gland-type breast cancers, ACC usually exhibits a triple-negative phenotype, with rare cases with reported ER and PR positivity. Despite this, the clinical course of patients with ACC appears less aggressive compared with that of the NST subtype [ 1 , 3 ]. In the current study, we explored the Surveillance, Epidemiology, and End Results (SEER) Program to analyze the clinicopathologic characteristics of ACC and compare them with those of invasive breast carcinoma NST. Materials and Methods Patients’ selection and cohorts We utilized the SEER database to select our cohorts (ACC and a control cohort consisting of invasive breast carcinoma NST) using SEER*Stat V.8.4.0.1. The SEER database includes demographic data, tumor characteristics (such as tumor type, histologic grade, TNM stage (AJCC), tumor size, lymph node status, and the presence or absence of distant metastases), treatment options (surgery, chemotherapy, radiotherapy), and follow-up for vital status. The SEER database encompasses clinicopathologic data from 18 population-based cancer registries, covering approximately one-third of the American population [ 10 ]. TNM variables were described according to the seventh edition of the AJCC. The status of estrogen receptor (ER) or progesterone receptor (PR) was defined based on the result of immunohistochemical (IHC) analysis. In contrast, HER2 results were based on IHC and/or in situ hybridization tests. Based on the reported immunohistochemical expressions of ER and PR (hormonal receptors, [HR]), and HER2, all cases were classified into four molecular subtypes: HR+/HER2-, HR+/HER2+, HR-/HER2+, and HR-/HER2- [ 10 ]. For our study cohort, we selected the cases with histopathologically confirmed diagnoses of ACC between 2000 and 2018. For the control cohort, we selected the cases with histopathologically confirmed invasive breast carcinoma NST. Only cases with a complete treatment and follow-up were used. The study excluded all in situ carcinomas, mixed carcinomas (e.g., ductal or any other histotype combined with ACC), and other special types of breast cancers, including salivary gland-type breast cancers. Statistical analysis Data from the SEER database were analyzed to compare clinicopathologic parameters between ACC and invasive ductal carcinoma NST using chi-square (χ²) tests for age and racial distributions, with significance set at p < 0.05. Means and standard deviations were computed for continuous variables, while frequencies and percentages were calculated for categorical variables. Mortality differences by type were stratified by stage at diagnosis. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CI), adjusting for stage at diagnosis, age, year of diagnosis, lymph node status, and treatment regimen. Kaplan-Meier methods were used to generate survival curves and compare mortality by subtype, with significance assessed via the log-rank test. Follow-up began on the date of diagnosis (2000) and ended on the date of death, termination of health plan membership, or December 31, 2019. We ran a logistic regression analysis to compare the survival of patients with ACC to those with invasive breast carcinoma NST. The model included age, race, TNM staging, tumor grade, surgery, radiotherapy, chemotherapy, and hormonal status. Coefficients, standard errors, t-values, p-values, and 95% confidence intervals were calculated for each variable, with p-values less than 0.05 considered significant. A Receiver Operating Characteristic (ROC) curve was generated to evaluate the discrimination ability of the logistic regression model and a link test was performed to diagnose model specification in the logistic regression analysis. Kaplan-Meier survival analysis was performed to compare the survival probabilities of patients with ACC to those with invasive ductal carcinoma NST. The survival curves were generated and the differences between the groups were assessed using the log-rank test. The number of patients at risk was recorded at various time points throughout the analysis period. We used a log-log survival plot to compare the survival of patients to visualize the survival probability over time, indicating periods of higher hazard. We used the Stata version 18 [ 2 ] for the analysis. Results Clinicopathologic characteristics of the acinic cell carcinoma cohort Clinicopathologic and demographic characteristics of the ACC cohort are summarized in Table 1 . Comparisons of the relevant clinicopathologic and demographic variables between ACC and the invasive breast carcinoma NST cohorts are shown in Table 2 . Table 1 Clinicopathologic characteristics of acinic cell carcinoma of the breast cohort. Age (levels) Freq. Percent 30–34 years 35–39 years 40–44 years 45–49 years 50–54 years 55–59 years 60–64 years 65–69 years 70–74 years 85 + years 1 3.33 2 6.67 1 3.33 5 16.67 1 3.33 5 16.67 5 16.67 3 10.00 5 16.67 2 6.67 Race Asian or Pacific Islander Black White 2 (6.7%) 2 (6.7%) 26 (86.7%) Histologic grading Grade 1 Grade 2 Grade 3 Unknown 7 (23.3%) 7 (23.3%) 11 (36.7%) 5 (16.7%) pT stage T1a T1b T1c T2 T3 pTx Any T, Mets 2 (6.7%) 3 (10.0%) 6 (20.0%) 9 (30.0%) 1 (3.3%) 7 (23.3%) 2 (6.7%) pN stage N0 N1 N2 N3 NX 16 (53.3%) 4 (13.3%) 2 (6.7%) 2 (6.7%) 6 (20%) pM stage M0 M1 MX 23 (76.7%) 2 (6.7%) 5 (16.6%) AJCC TNM stage I II III IV Unknown 7 (23.3%) 13 (43.3%) 2 (6.6%) 2 (6.7%) 7 (20%) Radiotherapy No radiotherapy Adjuvant radiotherapy The sequence is unknown, but both were given 20 (66.7%) 9 (30.0%) 1 (3.3%) Surgery Not performed Surgery performed 4 (13.3%) 26 (86.7%) Chemotherapy Yes No/Unknown 14 (46.7%) 16 (53.3%) Molecular subtype HR-/HER2- HR-/HER2+ HR+/HER2- HR+/HER2+ Recode not available Unknown 4 (13.3%) 2 (6.7%) 4 (13.3%) 3 (10.0%) 14 (46.7%) 3 (10.0%) Estrogen receptor (ER) Borderline/Unknown Negative Positive 5 (16.7%) 10 (33.3%) 15 (50.0%) Progesterone receptor (PR) Borderline/Unknown Negative Positive 5 (16.7%) 10 (33.3%) 15 (50.0%) HER-2/neu receptor Borderline/Unknown Negative Positive Recode not available 3 (10.0%) 8 (26.7%) 5 (16.7%) 14 (46.7%) Clinical outcome (cause of death) Alive or dead of other cause Dead (attributable to this cancer) Dead (missing/unknown cause of death) 23 (76.7%) 6 (20.0%) 1 (3.3%) Table 2 Comparative analysis of clinicopathologic parameters between acinic cell carcinoma and invasive ductal carcinoma NST cohorts. Variable ACC (n = 30) NST carcinoma (n = 248076) p-value Age 15–19 years 20–24 years 25–29 years 30–34 years 35–39 years 40–44 years 45–49 years 50–54 years 55–59 years 60–64 years 65–69 years 70–74 years 75–79 years 80–84 years 85 + years 0 (0.0%) 14 (< 1%) 0.63 0 (0.0%) 189 (0.1%) 0 (0.0%) 1338 (0.5%) 1 (3.3%) 3861 (1.6%) 2 (6.7%) 7824 (3.2%) 1 (3.3%) 14464 (5.8%) 5 (16.7%) 21875 (8.8%) 1 (3.3%) 26400 (10.6%) 5 (16.7%) 30541 (12.3%) 5 (16.7%) 34193 (13.8%) 3 (10.0%) 35114 (14.2%) 5 (16.7%) 29308 (11.8%) 0 (0.0%) 19392 (7.8%) 0 (0.0%) 12550 (5.1%) 2 (6.7%) 11013 (4.4%) Race American Indian/Alaska Native Asian or Pacific Islander Black White Unknown 0 (0.0%) 1685 (0.7%) 0.76 2 (6.7%) 26005 (10.5%) 2 (6.7%) 27423 (11.1%) 26 (86.7%) 190217 (76.7%) 0 (0.0%) 2746 (1.1%) Histologic grading Grade 1 Grade 2 Grade 3 Grade 4 (anaplastic/undifferentiated) Unknown 7 (23.3%) 29098 (11.7%) 0.006 7 (23.3%) 57578 (23.2%) 11 (36.7%) 46110 (18.6%) 0 (0.0%) 34 (< 1%) 5 (16.7%) 115256 (46.5%) pT stage T0 T1a T1b T1c T1mic T2 T3 T4a T4b T4c T4d Tis TX Adjusted Any T, Mets Blank(s) 0 (0.0%) 43 (< 1%) < 0.001 2 (6.7%) 3273 (1.3%) 3 (10.0%) 7972 (3.2%) 6 (20.0%) 14915 (6.0%) 0 (0.0%) 949 (0.4%) 9 (30.0%) 12482 (5.0%) 1 (3.3%) 1977 (0.8%) 0 (0.0%) 145 (0.1%) 0 (0.0%) 640 (0.3%) 0 (0.0%) 22 (< 1%) 0 (0.0%) 309 (0.1%) 0 (0.0%) 1 (< 1%) 3 (10.0%) 1488 (0.6%) 2 (6.7%) 2015 (0.8%) 4 (13.3%) 201845 (81.4%) pN stage N0 N1 N2 N3 NX Adjusted Blank(s) 16 (53.3%) 30820 (12.4%) < 0.001 4 (13.3%) 10341 (4.2%) 2 (6.7%) 1894 (0.8%) 2 (6.7%) 1585 (0.6%) 2 (6.7%) 1591 (0.6%) 4 (13.3%) 201845 (81.4%) pM stage M0 M1 MX Blank(s) 23 (76.7%) 43593 (17.6%) < 0.001 2 (6.7%) 2015 (0.8%) 1 (3.3%) 623 (0.3%) 4 (13.3%) 201845 (81.4%) AJCC TNM stage 0 I IIA IIB IIIA IIIB IIIC IIINOS IV UNK Stage Blank(s) 0 (0.0%) 1 (< 1%) < 0.001 7 (23.3%) 22746 (9.2%) 13 (43.3%) 10677 (4.3%) 0 (0.0%) 4967 (2.0%) 0 (0.0%) 2429 (1.0%) 0 (0.0%) 884 (0.4%) 1 (3.3%) 1020 (0.4%) 1 (3.3%) 46 (< 1%) 2 (6.7%) 2015 (0.8%) 2 (6.7%) 1446 (0.6%) 4 (13.3%) 201845 (81.4%) Radiotherapy Intraoperative radiotherapy with other radiotherapy before/after surgery Intraoperative radiotherapy No radiotherapy Adjuvant radiotherapy Neoadjuvant and adjuvant radiotherapy Neoadjuvant radiotherapy The sequence is unknown, but both were given 0 (0.0%) 436 (0.2%) < 0.001 0 (0.0%) 2033 (0.8%) 20 (66.7%) 122448 (49.4%) 9 (30.0%) 122125 (49.2%) 0 (0.0%) 388 (0.2%) 0 (0.0%) 488 (0.2%) 1 (3.3%) 60 (< 1%) Surgery Surgery both before and after radiation 0 (0.0%) 98 (< 1%) Not performed, a patient died before the recommended surgery 0 (0.0%) 260 (0.1%) 0.94 Not performed 4 (13.3%) 18309 (7.4%) Not recommended, contraindicated due to other conditions; autopsy only (1973–2002) 0 (0.0%) 1324 (0.5%) Recommended but not performed, patient refused 0 (0.0%) 2232 (0.9%) Recommended but not performed, unknown reason 0 (0.0%) 835 (0.3%) Recommended, unknown if performed 0 (0.0%) 2224 (0.9%) Surgery performed 26 (86.7%) 222388 (89.6%) Unknown; death certificate; or autopsy only (2003+) 0 (0.0%) 504 (0.2%) Chemotherapy Yes No/Unknown 14 (46.7%) 99123 (40.0%) 0.45 16 (53.3%) 148953 (60.0%) Molecular subtype HR-/HER2- HR-/HER2+ HR+/HER2- HR+/HER2+ Recode not available Unknown 4 (13.3%) 27864 (11.2%) < 0.001 2 (6.7%) 11748 (4.7%) 4 (13.3%) 168077 (67.8%) 3 (10.0%) 28179 (11.4%) 14 (46.7%) 0 (0.0%) 3 (10.0%) 12208 (4.9%) Estrogen receptor (ER) Borderline/Unknown Negative Positive 5 (16.7%) 4699 (1.9%) < 0.001 10 (33.3%) 43556 (17.6%) 15 (50.0%) 199821 (80.5%) Progesterone receptor (PR) Borderline/Unknown Negative Positive 5 (16.7%) 5093 (2.1%) < 0.001 10 (33.3%) 68487 (27.6%) 15 (50.0%) 174496 (70.3%) HER-2/neu receptor Borderline/Unknown Negative Positive Recode not available 3 (10.0%) 11884 (4.8%) < 0.001 8 (26.7%) 196159 (79.1%) 5 (16.7%) 40033 (16.1%) 14 (46.7%) 0 (0.0%) Clinical outcome (cause of death) Alive or dead of other cause Dead (attributable to this cancer) Dead (missing/unknown cause of death) 23(76.7%) 231137 (93.2%) 6 (20.0%) 16461 (6.6%) < 0.001 1 (3.3%) 463 (0.2%) From the SEER database, 30 ACC cases and approximately 248,000 invasive breast carcinoma NST cases were retrieved from 2000 to 2018, indicating a frequency of ACC at about 0.01%. All ACC patients were women, with no cases found in men. 63% of ACC patients were between 45 and 69 years old. There was no significant age difference between ACC patients and those with invasive breast carcinoma NST (Table 2 , p = 0.63). Similar to invasive breast carcinoma NST patients, ACC predominantly affected the white population (86.7%). The two cohorts differed in most clinicopathologic variables, including histologic grade, tumor size (pT), axillary lymph node status (pN), and the presence or absence of distant metastases (pM) (Table 2 ). 44% of ACC cases were high-grade (G3) carcinomas, predominantly presenting as early-stage breast cancers (pT1-2 and N0, 66.6%). Surgery was the primary treatment modality for the majority (86.7%) of ACC cases, while adjuvant radiotherapy and chemotherapy were administered to 33% and 46.6% of patients, respectively (Table 1 ). Molecular characteristics of the acinic cell carcinoma cohort The status of HR and HER2 receptors, used as surrogates for molecular classification in the ACC cohort, was available for 13 patients. Four cases were triple-negative (HR-/HER2-), two were of the HER2 subtype (HR-/HER2+), and the remaining seven were luminal cancers. Of the luminal cases, four were likely luminal A (HR+/HER2-) and three were luminal B/HER2 + ACC (HR+/HER2+) (Table 1 ). Survival and outcome of ACC patients The median survival time for ACC patients was only 19 months compared with 48 months for invasive breast carcinoma NST patients (Fig. 1 ). The mean follow-up for 21.4 months for the ACC cohort vs. 52.2 months for the control. The corresponding year-wise survival rates for ACC patients in the risk table below the Kaplan-Meier plot indicated a dramatic decrease in the number of at-risk ACC patients over time, starting with 30 and decreasing rapidly, compared to a slower decline in the invasive breast carcinoma NST cohort (p = 0.0006). The survival plot showed that the log-log survival curves for NST patients (blue line) demonstrated higher survival probability over time than ACC patients (red line) (Fig. 2 ) The steeper decline in the survival curve for ACC patients indicates a significantly lower survival rate than for patients with invasive breast carcinomas NST. The log odds of death in patients with ACC were 4.5 times higher than in patients with invasive breast carcinoma NST (p < 0.01). Age, race, histologic grading, and treatment types (surgery, radiotherapy, chemotherapy) had significant coefficients, indicating that they are important predictors of survival in ACC patients (Table 3 ). The negative coefficients for age, race, and grading indicated that higher values of these variables were associated with decreased survival, while the positive coefficients for the treatment types suggested that receiving these treatments was associated with improved survival outcomes. The overall model was highly significant (chi-square statistic) and had a good model fit (Akaike and Bayesian information criteria), suggesting the regression model was well-suited for predicting survival (Table 3 ). The regression diagnostics revealed an area under the curve of 0.76, suggesting that the model was reasonably accurate at discrimination (Supplemental File 1). The link test showed the predicted values from the model were statistically significant (Supplemental File 2), indicating that the model's predicted values are an important predictor of the outcome. Table 3 Logistic regression comparing survival of the patients with acinic cell carcinoma compared with the patients with invasive ductal carcinoma NST patients. Variable Coef. St. Error t-value p-value [95% CI] Sig. Acinic cell carcinoma 3.649 1.579 2.99 .003 1.563 8.521 *** Age 1.276 .004 84.57 0 1.269 1.283 *** Race .951 .009 -5.15 0 .933 .969 *** TNM staging $ 1.345 .004 94.17 0 1.337 1.353 *** Histologic grading 1.41 .009 55.32 0 1.393 1.427 *** Surgery .647 .016 -17.34 0 .616 .68 *** Radiotherapy 1.073 .004 17.72 0 1.064 1.081 *** Chemotherapy .773 .012 -16.29 0 .749 .797 *** Hormonal status .826 .005 -31.64 0 .817 .836 *** Constant .002 0 -49.68 0 .002 .003 *** Mean dependent variable 0.112 SD dependent variable 0.316 Pseudo r-squared 0.151 The number of obs. 248106 Chi-square 26292.782 Prob > chi2 0.000 Akaike crit. (AIC) 147944.580 Bayesian crit. (BIC) 148048.796 *** p < .01, ** p < .05, * p < .1 $ Derived EOD 2018 Stage Group (2018+) Discussion Salivary gland-type tumors of the breast are rare primary mammary malignancies, consisting of six well-defined entities that typically exhibit triple-negative phenotypes and have clinically low to intermediate aggressive potential [ 1 ]. ACC is one of the rarest, as confirmed in our study (~ 0.01%). Our cohort, based on data from the SEER (2000–2018), is the largest to date with 30 mammary ACC cases identified among over 248,000 invasive breast carcinomas NST. Despite similar treatment options, our data indicate that patients with mammary ACC have worse outcomes compared to those with the more common NST subtype of breast carcinoma. ACC patients had significantly shorter median survival times than IDC NST patients (p < 0.001) and faced a 4.5 times higher risk of death, indicating a substantially worse prognosis. Despite their heterogeneous molecular profile and subtypes, our study revealed that ACC behaves more like ER-negative and is similar to more aggressive subtypes of breast cancers, such as triple-negative breast carcinoma [ 7 ]. The year-by-year survival rates for ACC patients revealed a sharp decline in the number of at-risk patients over time, beginning around three years and dropping quickly. In contrast, the IDC NST group experienced a more gradual decline. This pattern aligns with the recurrence trend seen in patients with triple-negative breast cancer, where recurrence risk peaks around three years post-diagnosis and then swiftly decreases [ 7 ]. Notably, one-third of ACC patients in our cohort presented with axillary lymph node metastases, partially explaining the more aggressive clinical course and poor outcomes in ACC patients. These findings contrast with previous data showing a low frequency of axillary lymph node metastases in patients with mammary ACC [ 1 , 3 ]. For instance, Zhong et al. (2014) reported no axillary metastases among the 11 ACC patients in their cohort [ 13 ]. Another significant observation from the SEER database is the molecular heterogeneity among ACC cases. Although HR and HER2 status was available for only 13 cases, the study found that approximately 50% were ER-negative, and only four out of 13 had a triple-negative phenotype. This contrasts with most previous studies, which indicated a predominantly triple-negative phenotype, with around 10% of ACC cases being positive for ER and PR, and no HER2 positivity reported [ 1 , 3 ]. The only study reporting HER2 expression in mammary ACC is a recent publication by Ch’ng ES [ 5 ], which also examined the SEER database for a different salivary gland-type breast cohort, including a subset of ACC cases. Given the nature of the SEER database, re-reviewing previously diagnosed ACC cases is not possible. However, due to the inconsistent results obtained from the SEER database, it is reasonable to believe that a certain percentage of ACC cases do not represent pure ACC or may represent other histologic subtypes of breast cancer. The SEER database has several inherent limitations. These include incomplete information for a significant proportion of cases, particularly regarding clinically relevant variables such as tumor grade, stage, and the status of steroid receptors and HER2. Additionally, detailed information on specific treatment modalities and the duration of their use is often missing. In conclusion, the largest population-based cohort study of acinic cell carcinoma of the breast to date has confirmed its rarity, more aggressive clinical course, and poorer outcomes compared with conventional breast carcinoma NST. The molecular heterogeneity observed suggests a need for heightened awareness and improved diagnostics for this rare mammary malignancy. Abbreviations ACC – Acinic cell carcinoma AUC – Area Under the ROC Curve CI – Confidence interval ER – Estrogen receptor HR – Hormone receptors HRs – Hazard ratios IHC – Immunohistochemistry (immunohistochemical) NST – No special type PR – Progesterone receptor ROC – Receiver operating characteristic SEER – Surveillance, Epidemiology, and End Results TNM – Tumor, node, metastasis Declarations Author contributions Conceptualization, FS, SV. Data curation, FS, GRB, UG, EV. Formal analysis, FS, GRB, UG, EV, ZG, JL, SV. Funding acquisition, SV. Investigation, FS, GRB, UG, EV, SV. Methodology, FS, GRB, UG, EV, SV. Software, GRB, UG, EV. Supervision, SV. Roles/Writing—original draft, FS, GRB, SV. Writing—review & editing, FS, GRB, SV. Funding Open Access funding is provided by the Qatar National Library (QNL). Data availability The datasets from the study can be obtained from the corresponding author on reasonable request. Conflict of interest Una Glamoclija is an employee of Bosnalijek d.d., Sarajevo, Bosnia and Herzegovina. Other authors have no relevant financial or non-financial interests to disclose. Ethical approval Ethical approval was not required given that the SEER database is publicly available with fully anonymized patient data. The corresponding author also signed a SEER Research Data Agreement to use the data for research purposes (SEER ID: 17257-Nov2018, signed on October 24, 2019). Patient consent for publication Not applicable. References WHO Classification of Tumours: Breast Tumours. International Agency for Research on Cancer, Lyon, 2019 StataCorp. 2023 Stata Statistical Software: Release 18. 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Mol Oncol 4:192-208. doi: 10.1016/j.molonc.2010.04.004 Zhong F, Bi R, Yu B, Cheng Y, Xu X, Shui R, Yang W (2014) Carcinoma arising in microglandular adenosis of the breast: triple negative phenotype with variable morphology Int J Clin Exp Pathol 7:6149-6156 Additional Declarations The authors declare potential competing interests as follows: Una Glamoclija is an employee of Bosnalijek d.d., Sarajevo, Bosnia and Herzegovina. Other authors have no relevant financial or non-financial interests to disclose. Supplementary Files Supplementalfigure1.jpg Supplemental File 1: ROC for discrimination of the model of logistic regression. The logistic regression model's Receiver Operating Characteristic (ROC) curve shows the trade-off between sensitivity and specificity across different thresholds. The Area Under the ROC Curve (AUC) is 0.7202, which indicates the model’s performance in discriminating between acinic cell carcinoma (and invasive breast carcinoma (NST) is fair, with the Area Under the ROC Curve (AUC) of 0.7202, indicating a fair discrimination ability of the model. In other words, the model has a 72.02% chance of correctly differentiating a randomly chosen ACC case from a randomly chosen invasive breast carcinoma NST case. Supplementalfigure2.jpg Supplemental File 2: Link test, regression diagnostics for logistic regression. The link test shows the coefficient for the predicted values (_hat) is 1.572079 (p<0.0001), and for the squared predicted values (_hatsq) is 0.1446574 (p<0.0001), both are robust suggesting correct model specification, further supported by significant constant term (0.4695999, p<0.0001). 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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-4450431","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304665668,"identity":"cf521d56-01d0-41de-9785-cc072f8ab8ba","order_by":0,"name":"Faruk Skenderi","email":"","orcid":"","institution":"Sarajevo School of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Faruk","middleName":"","lastName":"Skenderi","suffix":""},{"id":304666016,"identity":"8a03367c-2f33-4b3f-bad4-27b72cdc230d","order_by":1,"name":"Giridhara Rathnaiah Babu","email":"","orcid":"","institution":"College of Medicine, QU Health, Qatar University","correspondingAuthor":false,"prefix":"","firstName":"Giridhara","middleName":"Rathnaiah","lastName":"Babu","suffix":""},{"id":304666017,"identity":"fc78b1af-7657-4bf7-a5c6-41fc6fbcf2da","order_by":2,"name":"Una Glamoclija","email":"","orcid":"","institution":"Department of Biochemistry and Clinical Analysis, University of Sarajevo Faculty of Pharmacy, Sarajevo, Bosnia and Herzegovina","correspondingAuthor":false,"prefix":"","firstName":"Una","middleName":"","lastName":"Glamoclija","suffix":""},{"id":304666018,"identity":"76909fd6-be07-4654-8e49-5be764cf45ed","order_by":3,"name":"Emir Veledar","email":"","orcid":"","institution":"Miller School of Medicine, University of Miami, Miami, Florida, United States of America","correspondingAuthor":false,"prefix":"","firstName":"Emir","middleName":"","lastName":"Veledar","suffix":""},{"id":304666019,"identity":"8e3c06e7-46a0-4dff-8c46-22768ddee5b3","order_by":4,"name":"Zoran Gatalica","email":"","orcid":"","institution":"Reference Medicine, Phoenix, Arizona, United States of America","correspondingAuthor":false,"prefix":"","firstName":"Zoran","middleName":"","lastName":"Gatalica","suffix":""},{"id":304666020,"identity":"5227b964-d18b-4158-bfa4-56a23292c5b5","order_by":5,"name":"Janez Lamovec","email":"","orcid":"","institution":"Institute of Oncology Ljubljana, Ljubljana, Slovenia","correspondingAuthor":false,"prefix":"","firstName":"Janez","middleName":"","lastName":"Lamovec","suffix":""},{"id":304666021,"identity":"50474fe0-69bc-458d-a06e-c2e32c410de1","order_by":6,"name":"Semir Vranic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYJCCAxCKsYHhA5BiYydFC+MMkBZmUqxj5gGTBFTJt3cnHq5gOJzPP7u58bPNr23yfMwMjB8+5uDWYnDm7IaDZxgOW864c7BZOrfvtmEbMwOz5MxteLRI5G442MCQZsBwI7GNObfnNiNQCxszLx4t8vPfQrTIg7RY9ty2J6iF4QYvSIuNgQFIC8OP24kEtRicATnMwMbA8EZis2Rvw+3kNmbGZrx+kW8/u/ljQ4WEgdyN9Icffvy5bTu/vfngh4/4HAaxC0oztoHJBkLqkcEfUhSPglEwCkbBSAEA38hRvPjDEUcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9743-7265","institution":"College of Medicine, QU Health, Qatar University","correspondingAuthor":true,"prefix":"","firstName":"Semir","middleName":"","lastName":"Vranic","suffix":""}],"badges":[],"createdAt":"2024-05-20 17:09:28","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4450431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4450431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56974312,"identity":"ed3be34e-876a-464b-a44c-44fc63a79eaf","added_by":"auto","created_at":"2024-05-23 01:52:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Kaplan-Meier survival analysis of ACC patients vs. patients with invasive breast carcinoma NST: \u003c/strong\u003eThe Kaplan-Meier survival plot indicates that the survival probability for patients with acinic cell carcinoma patients drops more sharply and earlier compared with the patients with invasive breast carcinoma NST (p=0.0006). The curve starts lower, suggesting a lower survival probability from the beginning, and declines more sharply, which indicates a higher hazard rate and, thus, a lower survival probability over time.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4450431/v1/ce0dae05717db05bd4b552c8.jpg"},{"id":56974309,"identity":"3f9d432a-d7c4-4e69-a407-b59d338c9055","added_by":"auto","created_at":"2024-05-23 01:52:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative Log-Log Survival Plot for the acinic cell carcinoma and invasive breast carcinoma NST cohorts: \u003c/strong\u003eThe log-log survival plot reveals steeper declines indicating periods of higher hazard, with the survival probability for ACC patients starting lower and decreasing more rapidly than for the invasive breast carcinoma NST patients.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4450431/v1/deb158766fbb59bccfd16144.jpg"},{"id":56974864,"identity":"38759070-73f2-4ee2-858d-4bab2deba10f","added_by":"auto","created_at":"2024-05-23 02:00:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1375927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4450431/v1/8d18a0cd-bf10-400b-8985-35b7ce4b1936.pdf"},{"id":56974310,"identity":"79d08ccf-03c6-4a93-a74c-964c34714d0d","added_by":"auto","created_at":"2024-05-23 01:52:33","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":176503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental File 1: \u003c/strong\u003eROC for discrimination of the model of logistic regression. The logistic regression model's Receiver Operating Characteristic (ROC) curve shows the trade-off between sensitivity and specificity across different thresholds. The Area Under the ROC Curve (AUC) is 0.7202, which indicates the model’s performance in discriminating between acinic cell carcinoma (and invasive breast carcinoma (NST) is fair, with the Area Under the ROC Curve (AUC) of 0.7202, indicating a fair discrimination ability of the model. In other words, the model has a 72.02% chance of correctly differentiating a randomly chosen ACC case from a randomly chosen invasive breast carcinoma NST case.\u003c/p\u003e","description":"","filename":"Supplementalfigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4450431/v1/70dc80c0bf67a66aac921778.jpg"},{"id":56974311,"identity":"2ab03c11-cd56-4b1c-b643-d6107bf1e47d","added_by":"auto","created_at":"2024-05-23 01:52:33","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":461591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental File 2: \u003c/strong\u003eLink test, regression diagnostics for logistic regression. The link test shows the coefficient for the predicted values (_hat) is 1.572079 (p\u0026lt;0.0001), and for the squared predicted values (_hatsq) is 0.1446574 (p\u0026lt;0.0001), both are robust suggesting correct model specification, further supported by significant constant term (0.4695999, p\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"Supplementalfigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4450431/v1/611456a4a2f8be813123833d.jpg"}],"financialInterests":"The authors declare potential competing interests as follows: Una Glamoclija is an employee of Bosnalijek d.d., Sarajevo, Bosnia and Herzegovina. Other authors have no relevant financial or non-financial interests to disclose.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAcinic Cell Carcinoma of The Breast: A Population-Based Clinicopathologic Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common malignancy among women worldwide and a leading cause of female cancer-related mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is a complex and heterogeneous disease, with various morphologies, molecular genomic, and clinical features [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Invasive breast carcinoma of no special type (NST) is the most common subtype of breast cancer, constituting\u0026thinsp;~\u0026thinsp;80% of all breast cancers. Special types, defined by \u0026gt;\u0026thinsp;90% of distinct and clinically relevant morphology, are a large and heterogeneous group of neoplasms, constituting the remaining\u0026thinsp;~\u0026thinsp;20% of all breast cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Among special types, salivary gland-type cancers are a distinct and very rare group, encompassing several peculiar entities, including acinic cell carcinoma (ACC). Most salivary gland-type breast cancers have a triple-negative phenotype [Estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2 negative], but their clinical course may be different from triple-negative breast carcinomas NST [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eACC is defined by its characteristic morphology (clear and/or granular epithelial cells, some of which may have intracytoplasmic zymogen granules, typically arranged in solid or microglandular arrangements) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The incidence of ACC in the breast is unknown but our comprehensive literature survey completed in 2022, revealed only 68 well-documented cases in the literature [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Since then, ~\u0026thinsp;35 additional ACC cases have been reported in the current literature (PubMed/MEDLINE literature survey completed in early May 2024). Similar to other salivary gland-type breast cancers, ACC usually exhibits a triple-negative phenotype, with rare cases with reported ER and PR positivity. Despite this, the clinical course of patients with ACC appears less aggressive compared with that of the NST subtype [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the current study, we explored the Surveillance, Epidemiology, and End Results (SEER) Program to analyze the clinicopathologic characteristics of ACC and compare them with those of invasive breast carcinoma NST.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; selection and cohorts\u003c/h2\u003e \u003cp\u003eWe utilized the SEER database to select our cohorts (ACC and a control cohort consisting of invasive breast carcinoma NST) using SEER*Stat V.8.4.0.1. The SEER database includes demographic data, tumor characteristics (such as tumor type, histologic grade, TNM stage (AJCC), tumor size, lymph node status, and the presence or absence of distant metastases), treatment options (surgery, chemotherapy, radiotherapy), and follow-up for vital status. The SEER database encompasses clinicopathologic data from 18 population-based cancer registries, covering approximately one-third of the American population [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTNM variables were described according to the seventh edition of the AJCC. The status of estrogen receptor (ER) or progesterone receptor (PR) was defined based on the result of immunohistochemical (IHC) analysis. In contrast, HER2 results were based on IHC and/or in situ hybridization tests. Based on the reported immunohistochemical expressions of ER and PR (hormonal receptors, [HR]), and HER2, all cases were classified into four molecular subtypes: HR+/HER2-, HR+/HER2+, HR-/HER2+, and HR-/HER2- [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor our study cohort, we selected the cases with histopathologically confirmed diagnoses of ACC between 2000 and 2018. For the control cohort, we selected the cases with histopathologically confirmed invasive breast carcinoma NST. Only cases with a complete treatment and follow-up were used. The study excluded all in situ carcinomas, mixed carcinomas (e.g., ductal or any other histotype combined with ACC), and other special types of breast cancers, including salivary gland-type breast cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData from the SEER database were analyzed to compare clinicopathologic parameters between ACC and invasive ductal carcinoma NST using chi-square (χ\u0026sup2;) tests for age and racial distributions, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Means and standard deviations were computed for continuous variables, while frequencies and percentages were calculated for categorical variables. Mortality differences by type were stratified by stage at diagnosis. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CI), adjusting for stage at diagnosis, age, year of diagnosis, lymph node status, and treatment regimen. Kaplan-Meier methods were used to generate survival curves and compare mortality by subtype, with significance assessed via the log-rank test. Follow-up began on the date of diagnosis (2000) and ended on the date of death, termination of health plan membership, or December 31, 2019. We ran a logistic regression analysis to compare the survival of patients with ACC to those with invasive breast carcinoma NST. The model included age, race, TNM staging, tumor grade, surgery, radiotherapy, chemotherapy, and hormonal status. Coefficients, standard errors, t-values, p-values, and 95% confidence intervals were calculated for each variable, with p-values less than 0.05 considered significant. A Receiver Operating Characteristic (ROC) curve was generated to evaluate the discrimination ability of the logistic regression model and a link test was performed to diagnose model specification in the logistic regression analysis. Kaplan-Meier survival analysis was performed to compare the survival probabilities of patients with ACC to those with invasive ductal carcinoma NST. The survival curves were generated and the differences between the groups were assessed using the log-rank test. The number of patients at risk was recorded at various time points throughout the analysis period. We used a log-log survival plot to compare the survival of patients to visualize the survival probability over time, indicating periods of higher hazard. We used the Stata version 18 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] for the analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eClinicopathologic characteristics of the acinic cell carcinoma cohort\u003c/h2\u003e\n\u003cp\u003eClinicopathologic and demographic characteristics of the ACC cohort are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Comparisons of the relevant clinicopathologic and demographic variables between ACC and the invasive breast carcinoma NST cohorts are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinicopathologic characteristics of acinic cell carcinoma of the breast cohort.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAge (levels)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFreq.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercent\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"10\" align=\"left\"\u003e\n\u003cp\u003e30\u0026ndash;34 years\u003c/p\u003e\n\u003cp\u003e35\u0026ndash;39 years\u003c/p\u003e\n\u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e\n\u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e\n\u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e\n\u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e\n\u003cp\u003e60\u0026ndash;64 years\u003c/p\u003e\n\u003cp\u003e65\u0026ndash;69 years\u003c/p\u003e\n\u003cp\u003e70\u0026ndash;74 years\u003c/p\u003e\n\u003cp\u003e85\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(86.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHistologic grading\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eGrade 1\u003c/p\u003e\n\u003cp\u003eGrade 2\u003c/p\u003e\n\u003cp\u003eGrade 3\u003c/p\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(36.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epT stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eT1a\u003c/p\u003e\n\u003cp\u003eT1b\u003c/p\u003e\n\u003cp\u003eT1c\u003c/p\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003cp\u003epTx\u003c/p\u003e\n\u003cp\u003eAny T, Mets\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(30.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epN stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003cp\u003eNX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epM stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eM0\u003c/p\u003e\n\u003cp\u003eM1\u003c/p\u003e\n\u003cp\u003eMX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(76.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(16.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAJCC TNM stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(43.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRadiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eNo radiotherapy\u003c/p\u003e\n\u003cp\u003eAdjuvant radiotherapy\u003c/p\u003e\n\u003cp\u003eThe sequence is unknown, but both were given\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(66.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(30.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNot performed\u003c/p\u003e\n\u003cp\u003eSurgery performed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(86.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003cp\u003eNo/Unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular subtype\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eHR-/HER2-\u003c/p\u003e\n\u003cp\u003eHR-/HER2+\u003c/p\u003e\n\u003cp\u003eHR+/HER2-\u003c/p\u003e\n\u003cp\u003eHR+/HER2+\u003c/p\u003e\n\u003cp\u003eRecode not available\u003c/p\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEstrogen receptor (ER)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBorderline/Unknown\u003c/p\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eProgesterone receptor (PR)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBorderline/Unknown\u003c/p\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHER-2/neu receptor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eBorderline/Unknown\u003c/p\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003cp\u003eRecode not available\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical outcome (cause of death)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAlive or dead of other cause\u003c/p\u003e\n\u003cp\u003eDead (attributable to this cancer)\u003c/p\u003e\n\u003cp\u003eDead (missing/unknown cause of death)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(76.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparative analysis of clinicopathologic parameters between acinic cell carcinoma and invasive ductal carcinoma NST cohorts.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eACC (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNST carcinoma (n\u0026thinsp;=\u0026thinsp;248076)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"15\" align=\"left\"\u003e\n\u003cp\u003e15\u0026ndash;19 years\u003c/p\u003e\n\u003cp\u003e20\u0026ndash;24 years\u003c/p\u003e\n\u003cp\u003e25\u0026ndash;29 years\u003c/p\u003e\n\u003cp\u003e30\u0026ndash;34 years\u003c/p\u003e\n\u003cp\u003e35\u0026ndash;39 years\u003c/p\u003e\n\u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e\n\u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e\n\u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e\n\u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e\n\u003cp\u003e60\u0026ndash;64 years\u003c/p\u003e\n\u003cp\u003e65\u0026ndash;69 years\u003c/p\u003e\n\u003cp\u003e70\u0026ndash;74 years\u003c/p\u003e\n\u003cp\u003e75\u0026ndash;79 years\u003c/p\u003e\n\u003cp\u003e80\u0026ndash;84 years\u003c/p\u003e\n\u003cp\u003e85\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1338 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3861 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7824 (3.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14464 (5.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21875 (8.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26400 (10.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30541 (12.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34193 (13.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35114 (14.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29308 (11.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19392 (7.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12550 (5.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11013 (4.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e\n\u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1685 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26005 (10.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27423 (11.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (86.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190217 (76.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2746 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHistologic grading\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eGrade 1\u003c/p\u003e\n\u003cp\u003eGrade 2\u003c/p\u003e\n\u003cp\u003eGrade 3\u003c/p\u003e\n\u003cp\u003eGrade 4 (anaplastic/undifferentiated)\u003c/p\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29098 (11.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57578 (23.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (36.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46110 (18.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115256 (46.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epT stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"15\" align=\"left\"\u003e\n\u003cp\u003eT0\u003c/p\u003e\n\u003cp\u003eT1a\u003c/p\u003e\n\u003cp\u003eT1b\u003c/p\u003e\n\u003cp\u003eT1c\u003c/p\u003e\n\u003cp\u003eT1mic\u003c/p\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003cp\u003eT4a\u003c/p\u003e\n\u003cp\u003eT4b\u003c/p\u003e\n\u003cp\u003eT4c\u003c/p\u003e\n\u003cp\u003eT4d\u003c/p\u003e\n\u003cp\u003eTis\u003c/p\u003e\n\u003cp\u003eTX Adjusted\u003c/p\u003e\n\u003cp\u003eAny T, Mets\u003c/p\u003e\n\u003cp\u003eBlank(s)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3273 (1.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7972 (3.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14915 (6.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e949 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (30.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12482 (5.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1977 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e640 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e309 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1488 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201845 (81.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epN stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003cp\u003eNX Adjusted\u003c/p\u003e\n\u003cp\u003eBlank(s)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30820 (12.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10341 (4.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1894 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1585 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1591 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201845 (81.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003epM stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eM0\u003c/p\u003e\n\u003cp\u003eM1\u003c/p\u003e\n\u003cp\u003eMX\u003c/p\u003e\n\u003cp\u003eBlank(s)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (76.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43593 (17.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e623 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201845 (81.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAJCC TNM stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"11\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003cp\u003eIIA\u003c/p\u003e\n\u003cp\u003eIIB\u003c/p\u003e\n\u003cp\u003eIIIA\u003c/p\u003e\n\u003cp\u003eIIIB\u003c/p\u003e\n\u003cp\u003eIIIC\u003c/p\u003e\n\u003cp\u003eIIINOS\u003c/p\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003cp\u003eUNK Stage\u003c/p\u003e\n\u003cp\u003eBlank(s)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22746 (9.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (43.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10677 (4.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4967 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2429 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e884 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1020 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1446 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201845 (81.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRadiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eIntraoperative radiotherapy with other radiotherapy before/after surgery\u003c/p\u003e\n\u003cp\u003eIntraoperative radiotherapy\u003c/p\u003e\n\u003cp\u003eNo radiotherapy\u003c/p\u003e\n\u003cp\u003eAdjuvant radiotherapy\u003c/p\u003e\n\u003cp\u003eNeoadjuvant and adjuvant radiotherapy\u003c/p\u003e\n\u003cp\u003eNeoadjuvant radiotherapy\u003c/p\u003e\n\u003cp\u003eThe sequence is unknown, but both were given\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e436 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2033 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (66.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122448 (49.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (30.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122125 (49.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e388 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e488 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurgery both before and after radiation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot performed, a patient died before the recommended surgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e260 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot performed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18309 (7.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot recommended, contraindicated due to other conditions; autopsy only (1973\u0026ndash;2002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1324 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecommended but not performed, patient refused\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2232 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecommended but not performed, unknown reason\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e835 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecommended, unknown if performed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2224 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurgery performed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (86.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e222388 (89.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown; death certificate; or autopsy only (2003+)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e504 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003cp\u003eNo/Unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99123 (40.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148953 (60.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular subtype\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eHR-/HER2-\u003c/p\u003e\n\u003cp\u003eHR-/HER2+\u003c/p\u003e\n\u003cp\u003eHR+/HER2-\u003c/p\u003e\n\u003cp\u003eHR+/HER2+\u003c/p\u003e\n\u003cp\u003eRecode not available\u003c/p\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27864 (11.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11748 (4.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168077 (67.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28179 (11.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12208 (4.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEstrogen receptor (ER)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBorderline/Unknown\u003c/p\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4699 (1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43556 (17.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199821 (80.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eProgesterone receptor (PR)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBorderline/Unknown\u003c/p\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5093 (2.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68487 (27.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (50.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174496 (70.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHER-2/neu receptor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eBorderline/Unknown\u003c/p\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003cp\u003eRecode not available\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11884 (4.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196159 (79.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (16.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40033 (16.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical outcome (cause of death)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAlive or dead of other cause\u003c/p\u003e\n\u003cp\u003eDead (attributable to this cancer)\u003c/p\u003e\n\u003cp\u003eDead (missing/unknown cause of death)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23(76.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e231137 (93.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16461 (6.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e463 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFrom the SEER database, 30 ACC cases and approximately 248,000 invasive breast carcinoma NST cases were retrieved from 2000 to 2018, indicating a frequency of ACC at about 0.01%. All ACC patients were women, with no cases found in men. 63% of ACC patients were between 45 and 69 years old. There was no significant age difference between ACC patients and those with invasive breast carcinoma NST (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.63). Similar to invasive breast carcinoma NST patients, ACC predominantly affected the white population (86.7%). The two cohorts differed in most clinicopathologic variables, including histologic grade, tumor size (pT), axillary lymph node status (pN), and the presence or absence of distant metastases (pM) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e44% of ACC cases were high-grade (G3) carcinomas, predominantly presenting as early-stage breast cancers (pT1-2 and N0, 66.6%). Surgery was the primary treatment modality for the majority (86.7%) of ACC cases, while adjuvant radiotherapy and chemotherapy were administered to 33% and 46.6% of patients, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eMolecular characteristics of the acinic cell carcinoma cohort\u003c/h2\u003e\n\u003cp\u003eThe status of HR and HER2 receptors, used as surrogates for molecular classification in the ACC cohort, was available for 13 patients. Four cases were triple-negative (HR-/HER2-), two were of the HER2 subtype (HR-/HER2+), and the remaining seven were luminal cancers. Of the luminal cases, four were likely luminal A (HR+/HER2-) and three were luminal B/HER2\u0026thinsp;+\u0026thinsp;ACC (HR+/HER2+) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eSurvival and outcome of ACC patients\u003c/h2\u003e\n\u003cp\u003eThe median survival time for ACC patients was only 19 months compared with 48 months for invasive breast carcinoma NST patients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean follow-up for 21.4 months for the ACC cohort vs. 52.2 months for the control. The corresponding year-wise survival rates for ACC patients in the risk table below the Kaplan-Meier plot indicated a dramatic decrease in the number of at-risk ACC patients over time, starting with 30 and decreasing rapidly, compared to a slower decline in the invasive breast carcinoma NST cohort (p\u0026thinsp;=\u0026thinsp;0.0006). The survival plot showed that the log-log survival curves for NST patients (blue line) demonstrated higher survival probability over time than ACC patients (red line) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) The steeper decline in the survival curve for ACC patients indicates a significantly lower survival rate than for patients with invasive breast carcinomas NST. The log odds of death in patients with ACC were 4.5 times higher than in patients with invasive breast carcinoma NST (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Age, race, histologic grading, and treatment types (surgery, radiotherapy, chemotherapy) had significant coefficients, indicating that they are important predictors of survival in ACC patients (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The negative coefficients for age, race, and grading indicated that higher values of these variables were associated with decreased survival, while the positive coefficients for the treatment types suggested that receiving these treatments was associated with improved survival outcomes. The overall model was highly significant (chi-square statistic) and had a good model fit (Akaike and Bayesian information criteria), suggesting the regression model was well-suited for predicting survival (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The regression diagnostics revealed an area under the curve of 0.76, suggesting that the model was reasonably accurate at discrimination (Supplemental File 1). The link test showed the predicted values from the model were statistically significant (Supplemental File 2), indicating that the model's predicted values are an important predictor of the outcome.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic regression comparing survival of the patients with acinic cell carcinoma compared with the patients with invasive ductal carcinoma NST patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCoef.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSt. Error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003et-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e[95% CI]\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSig.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcinic cell carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3.649\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.579\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.563\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8.521\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e84.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.283\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRace\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.951\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-5.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.933\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTNM staging\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e94.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.337\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHistologic grading\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e55.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-17.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.616\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadiotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.773\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-16.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.749\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHormonal status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.826\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-31.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.817\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.836\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-49.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMean dependent variable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eSD dependent variable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePseudo r-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eThe number of obs.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e248106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eChi-square\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e26292.782\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAkaike crit. (AIC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e147944.580\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBayesian crit. (BIC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e148048.796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"12\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;.1\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e$ Derived EOD 2018 Stage Group (2018+)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSalivary gland-type tumors of the breast are rare primary mammary malignancies, consisting of six well-defined entities that typically exhibit triple-negative phenotypes and have clinically low to intermediate aggressive potential [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. ACC is one of the rarest, as confirmed in our study (~\u0026thinsp;0.01%). Our cohort, based on data from the SEER (2000\u0026ndash;2018), is the largest to date with 30 mammary ACC cases identified among over 248,000 invasive breast carcinomas NST. Despite similar treatment options, our data indicate that patients with mammary ACC have worse outcomes compared to those with the more common NST subtype of breast carcinoma. ACC patients had significantly shorter median survival times than IDC NST patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and faced a 4.5 times higher risk of death, indicating a substantially worse prognosis. Despite their heterogeneous molecular profile and subtypes, our study revealed that ACC behaves more like ER-negative and is similar to more aggressive subtypes of breast cancers, such as triple-negative breast carcinoma [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The year-by-year survival rates for ACC patients revealed a sharp decline in the number of at-risk patients over time, beginning around three years and dropping quickly. In contrast, the IDC NST group experienced a more gradual decline. This pattern aligns with the recurrence trend seen in patients with triple-negative breast cancer, where recurrence risk peaks around three years post-diagnosis and then swiftly decreases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Notably, one-third of ACC patients in our cohort presented with axillary lymph node metastases, partially explaining the more aggressive clinical course and poor outcomes in ACC patients. These findings contrast with previous data showing a low frequency of axillary lymph node metastases in patients with mammary ACC [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For instance, Zhong et al. (2014) reported no axillary metastases among the 11 ACC patients in their cohort [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother significant observation from the SEER database is the molecular heterogeneity among ACC cases. Although HR and HER2 status was available for only 13 cases, the study found that approximately 50% were ER-negative, and only four out of 13 had a triple-negative phenotype. This contrasts with most previous studies, which indicated a predominantly triple-negative phenotype, with around 10% of ACC cases being positive for ER and PR, and no HER2 positivity reported [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The only study reporting HER2 expression in mammary ACC is a recent publication by Ch\u0026rsquo;ng ES [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which also examined the SEER database for a different salivary gland-type breast cohort, including a subset of ACC cases. Given the nature of the SEER database, re-reviewing previously diagnosed ACC cases is not possible. However, due to the inconsistent results obtained from the SEER database, it is reasonable to believe that a certain percentage of ACC cases do not represent pure ACC or may represent other histologic subtypes of breast cancer.\u003c/p\u003e \u003cp\u003eThe SEER database has several inherent limitations. These include incomplete information for a significant proportion of cases, particularly regarding clinically relevant variables such as tumor grade, stage, and the status of steroid receptors and HER2. Additionally, detailed information on specific treatment modalities and the duration of their use is often missing.\u003c/p\u003e \u003cp\u003eIn conclusion, the largest population-based cohort study of acinic cell carcinoma of the breast to date has confirmed its rarity, more aggressive clinical course, and poorer outcomes compared with conventional breast carcinoma NST. The molecular heterogeneity observed suggests a need for heightened awareness and improved diagnostics for this rare mammary malignancy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACC \u0026ndash; Acinic cell carcinoma\u003c/p\u003e\n\u003cp\u003eAUC \u0026ndash;\u0026nbsp;Area Under the ROC Curve\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence interval\u003c/p\u003e\n\u003cp\u003eER \u0026ndash; Estrogen receptor\u003c/p\u003e\n\u003cp\u003eHR \u0026ndash; Hormone receptors\u003c/p\u003e\n\u003cp\u003eHRs \u0026ndash; Hazard ratios\u003c/p\u003e\n\u003cp\u003eIHC \u0026ndash; Immunohistochemistry (immunohistochemical)\u003c/p\u003e\n\u003cp\u003eNST \u0026ndash; No special type\u003c/p\u003e\n\u003cp\u003ePR \u0026ndash; Progesterone receptor\u003c/p\u003e\n\u003cp\u003eROC \u0026ndash; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSEER \u0026ndash; Surveillance, Epidemiology, and End Results\u003c/p\u003e\n\u003cp\u003eTNM \u0026ndash; Tumor, node, metastasis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, FS, SV. Data curation, FS, GRB, UG, EV. Formal analysis, FS, GRB, UG, EV, ZG, JL, SV. Funding acquisition, SV. Investigation, FS, GRB, UG, EV, SV. Methodology, FS, GRB, UG, EV, SV. Software, GRB, UG, EV. Supervision, SV. Roles/Writing\u0026mdash;original draft, FS, GRB, SV. Writing\u0026mdash;review \u0026amp; editing, FS, GRB, SV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Access funding is provided by the Qatar National Library (QNL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets from the study can be obtained from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUna Glamoclija is an employee of Bosnalijek d.d., Sarajevo, Bosnia and Herzegovina. Other authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required given that the SEER database is publicly available with fully anonymized patient data. The corresponding author also signed a SEER Research Data Agreement to use the data for research purposes (SEER ID: 17257-Nov2018, signed on October 24, 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO Classification of Tumours: Breast Tumours. International Agency for Research on Cancer, Lyon, 2019\u003c/li\u003e\n\u003cli\u003eStataCorp. 2023 Stata Statistical Software: Release 18. StataCorp LLC, College Station, TX, pp.\u003c/li\u003e\n\u003cli\u003eAjkunic A, Skenderi F, Shaker N, Akhtar S, Lamovec J, Gatalica Z, Vranic S (2022) Acinic cell carcinoma of the breast: A comprehensive review Breast 66:208-216. doi: 10.1016/j.breast.2022.10.012\u003c/li\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 74:229-263. doi: 10.3322/caac.21834\u003c/li\u003e\n\u003cli\u003eCh\u0026apos;ng ES (2024) Prognosis of primary breast salivary gland-type carcinoma: a propensity score-matching analysis with invasive carcinoma of no special type based on the SEER database for years 2010-2020 Breast Cancer 31:496-506. doi: 10.1007/s12282-024-01564-8\u003c/li\u003e\n\u003cli\u003eCserni G, Quinn CM, Foschini MP, Bianchi S, Callagy G, Chmielik E, Decker T, Fend F, Kovacs A, van Diest PJ, Ellis IO, Rakha E, Tot T, European Working Group For Breast Screening P (2021) Triple-Negative Breast Cancer Histological Subtypes with a Favourable Prognosis Cancers (Basel) 13. doi: 10.3390/cancers13225694\u003c/li\u003e\n\u003cli\u003eDent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, Lickley LA, Rawlinson E, Sun P, Narod SA (2007) Triple-negative breast cancer: clinical features and patterns of recurrence Clin Cancer Res 13:4429-4434. doi: 10.1158/1078-0432.CCR-06-3045\u003c/li\u003e\n\u003cli\u003eFoschini MP, Morandi L, Asioli S, Giove G, Corradini AG, Eusebi V (2017) The morphological spectrum of salivary gland type tumors of the breast Pathology 49:215-227. doi: 10.1016/j.pathol.2016.10.011\u003c/li\u003e\n\u003cli\u003ePiscuoglio S, Hodi Z, Katabi N, Guerini-Rocco E, Macedo GS, Ng CK, Edelweiss M, De Mattos-Arruda L, Wen HY, Rakha EA, Ellis IO, Rubin BP, Weigelt B, Reis-Filho JS (2015) Are acinic cell carcinomas of the breast and salivary glands distinct diseases? Histopathology 67:529-537. doi: 10.1111/his.12673\u003c/li\u003e\n\u003cli\u003eSkenderi F, Alahmad MAM, Tahirovic E, Alahmad YM, Gatalica Z, Vranic S (2022) HER2-positive apocrine carcinoma of the breast: a population-based analysis of treatment and outcome Breast Cancer Res Treat 193:523-533. doi: 10.1007/s10549-022-06578-4\u003c/li\u003e\n\u003cli\u003eVranic S, Bender R, Palazzo J, Gatalica Z (2013) A review of adenoid cystic carcinoma of the breast with emphasis on its molecular and genetic characteristics Hum Pathol 44:301-309. doi: 10.1016/j.humpath.2012.01.002\u003c/li\u003e\n\u003cli\u003eWeigelt B, Geyer FC, Reis-Filho JS (2010) Histological types of breast cancer: how special are they? Mol Oncol 4:192-208. doi: 10.1016/j.molonc.2010.04.004\u003c/li\u003e\n\u003cli\u003eZhong F, Bi R, Yu B, Cheng Y, Xu X, Shui R, Yang W (2014) Carcinoma arising in microglandular adenosis of the breast: triple negative phenotype with variable morphology Int J Clin Exp Pathol 7:6149-6156\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, special types, acinic cell carcinoma, survival, outcome","lastPublishedDoi":"10.21203/rs.3.rs-4450431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4450431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eAcinic cell carcinoma (ACC) of the breast is a very rare, primary salivary gland-type breast malignancy with ~\u0026thinsp;100 reported cases in the literature. Limited information about the clinical features and outcomes of the patients with ACC is available.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilized the Surveillance, Epidemiology, and End Results (SEER) database to identify ACC cases diagnosed between 2000 and 2018. For comparison, we also examined a cohort of invasive breast carcinomas of no special type (NST).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThirty ACC cases were identified among over 248,000 invasive breast carcinoma NST cases in the SEER database. Most ACC cases affected the White population (87%) and individuals over 50 years old (70%). ACCs were predominantly grade 3 carcinomas (44%), diagnosed at an early stage (AJCC TNM stages I and II, 67%). Hormone receptor (HR) and HER2 status were available for 13 cases, revealing molecular heterogeneity: HR-/HER2- (four cases, 31%), HR-/HER2+ (two cases, 15%), HR+/HER2- (four cases, 31%), and HR+/HER2+ (three cases, 23%). Surgery was the primary treatment modality for 26 out of 30 (86.7%) ACC patients, with chemotherapy and radiotherapy used in 46.7% and 33% of cases, respectively. The median survival time for ACC patients was 19 months compared to 48 months for invasive breast carcinoma NST patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Year-wise survival rates for ACC patients showed a dramatic decrease in the number of at-risk patients over time, starting at 30 months and decreasing rapidly, compared to a slower decline in the invasive breast carcinoma NST group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The log odds of death for ACC patients were significantly higher (by 4.5 times) than for invasive breast carcinoma NST patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating a substantially worse prognosis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAcinic cell carcinoma of the breast is a very rare (0.01%) primary breast malignancy. Despite its early clinical presentation, ACC of the breast appears to have a more aggressive clinical course and poorer clinical outcomes compared with conventional breast cancer.\u003c/p\u003e","manuscriptTitle":"Acinic Cell Carcinoma of The Breast: A Population-Based Clinicopathologic Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 01:52:28","doi":"10.21203/rs.3.rs-4450431/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89bfcdc3-3a19-45c1-8dd8-12dc0c46b768","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32165558,"name":"Oncology"}],"tags":[],"updatedAt":"2024-05-24T02:15:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 01:52:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4450431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4450431","identity":"rs-4450431","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}