Real-world assessment of quality-of-life in patients with breast cancer treated with adjuvant endocrine therapy.

Real-world assessment of quality-of-life in patients with breast cancer treated with adjuvant endocrine therapy - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Future Oncol . 2024 Jan 30;18(22):2425–2439. doi: 10.2217/fon-2021-1322 Real-world assessment of quality-of-life in patients with breast cancer treated with adjuvant endocrine therapy Tugba Akin Telli 1, * , Mehmet Akif Ozturk 1 , Ozkan Alan 1 , Rahib Hasanov 1 , Osman Kostek 1 , Rukiye Arikan 1 , Tugba Basoglu 1 , Serap Kaya 1 , Ozlem Ercelep 1 , Nalan Akgul Babacan 1 , Faysal Dane 1 , Perran Fulden Yumuk 1 Author information Article notes Copyright and License information 1 Department of Medical Oncology,Faculty of Medicine,Marmara University, Istanbul, Turkey * Author for correspondence: tugbaakintelli@gmail.com Received 2021 Oct 12; Accepted 2022 May 25; Collection date 2022. © 2022 Future Medicine Ltd PMC Copyright notice PMCID: PMC13235251  PMID: 35695547 Abstract Objective: The aim of this study was to investigate quality-of-life (QoL) in breast cancer (BC) patients treated with adjuvant endocrine therapy (AET). Methods: We designed a cross-sectional study of 233 BC patients treated with AET and used the Functional Assessment of Cancer Therapy – Breast questionnaire. Results: No significant difference was observed between endocrine agents. Duration of AET did not affect QoL. In the entire cohort, multivariate analysis determined age (p = 0.034) and switching treatment from tamoxifen to aromatase inhibitors (p = 0.049) as significant positive coefficients of QoL, while comorbidity (p = 0.072) tended to be associated with lower scores. Education level (p = 0.001) and chemotherapy (p = 0.04) were significant predictors of QoL in the tamoxifen group, while comorbidity (p = 0.04), surgery type (p = 0.02), radiotherapy (p = 0.006) and stage (p = 0.009) had a significant impact on QoL in aromatase inhibitors group. Conclusion: Evaluating the well-being of BC patients by QoL questionnaires is of great importance to identify particular subgroups that may require supportive care. Keywords: adjuvant endocrine therapy, breast cancer, FACT-B, FACT-G, quality-of-life Plain language summary Breast cancer (BC) remains the most common cancer among women worldwide. Hormone receptor-positive (estrogen receptor- and/or progesterone receptor-positive) BC represents 70% of all cases. Advances in the treatment of disease lead to improved patient survival. As a result, quality-of-life (QoL) becomes a major concern in clinical practice. This study aimed to assess the impact of socio-demographic, clinical and treatment-related factors on QoL among patients with BC treated with adjuvant endocrine therapy. We used the Functional Assessment of Cancer Therapy – Breast questionnaire to evaluate QoL. In the entire cohort, multivariate analysis determined age and switching treatment from tamoxifen to aromatase inhibitors to be significant positive coefficients of QoL, while comorbidity tended to be associated with lower scores. Education level and chemotherapy were significant determinants of QoL in the tamoxifen group, while comorbidity, surgery type, radiotherapy and disease stage had a significant impact on QoL in the aromatase inhibitor group. These findings can be utilized to identify certain subgroups that may need greater supportive care. Female breast cancer (BC) is the most common malignancy worldwide, with 2.3 million new cases overall in 2020. Despite advances in treatment strategies, it remains the fifth leading cause of cancer-related deaths across the globe [ 1 ]. Hormone receptor (HR)-positive (estrogen receptor [ER]- and/or progesterone receptor [PR]- positive) BC is the most common subtype, representing 70% of all BCs [ 2 ]. Early-stage disease without distant metastasis is considered potentially curable for about three out of four patients [ 3 ]. Adjuvant endocrine therapy (AET) is currently the therapeutic backbone of the management of HR-positive BC following surgery and/or adjuvant chemotherapy, and it has been demonstrated to improve clinical outcomes [ 4 ]. Since the benefit of AET in reducing relapse rates and deaths has been shown in previous clinical trials, current guidelines recommend endocrine therapy for at least 5 years in the adjuvant setting, taking into account the menopause status and preference of the patients, as well as adverse effects [ 5 ]. With the improved survival of BC patients, quality-of-life (QoL) has become a priority issue. Identifying the characteristics that contribute to a better QoL, which is actually related to prolonged survival, appears to be critical in BC survivors [ 6 ]. Cancer treatments are intended to improve QoL, yet the side effects of treatments sometimes result in health-related complaints. Regarding HR-positive BC, even though AET is generally thought to be well tolerated, long-term use is accompanied by potential side effects, which have a strong impact on QoL and patient compliance in clinical practice [ 7 ]. Likewise, physical and psychological sequelae of previous treatments show varying degrees of influence on QoL [ 8 , 9 ]. A number of studies also investigated the role of socio-demographic factors in QoL and showed that age, education level, marital status and income were key determinants of QoL in BC patients [ 10–12 ]. Given that several factors contribute to QoL, and that long-term usage of anti-hormonal agents in HR-positive BC have specific toxicity profiles, there is a need to highlight the long-term effect of AET in combination with socio-demographic and other clinical factors on QoL in this population. In order to assess QoL, we decided to use the Functional Assessment of Cancer Therapy – Breast (FACT-B) questionnaire, because this tool has been deemed to be a quick and well-validated multi-dimensional self-report questionnaire with subscales measuring physical, social, emotional and functional well-being and it contains additional concerns related to BC [ 13 ]. In this study, we aimed to assess socio-demographic, clinical and treatment-related factors as potential determinants of QoL among patients with BC treated with AET. Materials & methods Study population BC patients treated with AET (aromatase inhibitors [AIs] or tamoxifen with/without gonadotropin-releasing hormone [GnRH] agonists) in a university hospital medical oncology clinic in Istanbul, Turkey, were asked to participate in this study. More than 90% of the patients (93%) who were invited to participate in the study agreed. Those who showed interest in being part of the study were recruited consecutively after written consent was provided. Each participant was asked to complete the FACT-B questionnaire while they were waiting in the outpatient clinic for medical oncology consultation. A medical oncology fellow explained the study and how to complete the questionnaire. Patients completed the questionnaire on their own unless they requested assistance from a medical oncology fellow. At the time of the survey, all patients had been under treatment with AET for at least 3 months. The exclusion criteria were as follows: presence of metastatic disease, receiving AET for less than 3 months, inability to understand and answer the questions in the questionnaire due to illiteracy and being a non-Turkish speaker or emotionally distressed or ill on the day of the appointment. Instruments & data collection We used FACT-B to assess the QoL of BC patients treated with AET. The license for the validated and Turkish translated version of FACT-B was acquired from www.facit.org. It is a self-administered, 37-item questionnaire including two main parts: Functional Assessment of Cancer Therapy – General (FACT-G) and a BC-specific subscale (BCS). FACT-G consists of four domains: physical well-being (PWB, seven items), social/family well-being (SWB, seven items), emotional well-being (EWB, six items) and functional well-being (FWB, seven items). BCS is an additional section with ten items, designed specifically for the concerns of BC patients. All items are rated on the same response scale from 0 to 4: 0 = not at all, 1 = a little bit, 2 = somewhat, 3 = quite a bit and 4 = very much. FACT-B scoring guidelines are followed to obtain a score for each domain. Since some of the statements are negative, reversals are performed as indicated. Imputation of missing data may be acceptable only if more than half of the items are answered in subscales. The FACT-G total score is calculated by summing the scores of the four domains (PWB + SWB + EWB + FWB) and can range from 0 to 108. The FACT-B total score is obtained by summing the score of FACT-G and BCS and can range from 0 to 148. Moreover, in order to derive a FACT-B trial outcome index (TOI), summing the scores of PWB + FWB + BCS is mandatory and the total can range from 0 to 96. Eventually, a higher score defines a better QoL. Patients were also asked to complete a demographic questionnaire including a set of questions regarding age, comorbidities, education level, employment status, marital status, number of children and living arrangement. Clinicopathologic and treatment-related data were provided retrospectively from medical records. For this purpose, the Karnofsky Performance Status, date of diagnosis, stage at diagnosis, HR and HER2 status, type of surgery, management of the axilla, use of neoadjuvant/adjuvant chemotherapy, adjuvant radiotherapy, starting date of anti-hormonal treatment and type of AET were noted. Statistical analysis Descriptive statistics were used to summarize the socio-demographic and clinical characteristics and were expressed as medians (25–75 percentiles) for continuous variables and as percentages for categorical variables. The normality of data was tested by the Kolmogorov–Smirnov and Shapiro–Wilk tests and the parameters showed normal distribution. To compare the demographics and clinical characteristics between the tamoxifen and AI groups, a Mann–Whitney U test and a Chi-square/Fisher’s exact test were applied for continuous and categorical variables, respectively. The correlation between the duration of anti-hormonal therapy and FACT-B scales was calculated using Spearman’s rank correlation test. General linear regression analysis was primarily conducted to compare each scale of FACT-B between the tamoxifen and AI groups. Since the groups were not equal in terms of demographic and clinical characteristics, analyses were repeated in certain subgroups stratified by other treatment factors separately in tamoxifen and AI cohorts. The coefficient, standard error (SE) of the coefficient, R 2 (the higher the R 2 value, the better the model fit) and p-value were estimated. Then, univariate and multivariate linear regression analyses were computed in order to test the impact of socio-demographic, clinical and treatment-related factors on total FACT-B score. For this purpose, we created two models, one for the tamoxifen group and one for the AI group. p < 0.05 was accepted as the level of significance. Statistical analyses were carried out using SPSS version 22.0 (SPSS, Inc., IL, USA). Results Socio-demographic, clinical & treatment characteristics of the study population From August 2017 to September 2018, a total of 233 BC patients treated with AET were enrolled in the present study. All these patients were included in the final analysis, since none of them had missing values in the questionnaire beyond acceptable imputation shown in FACT-B scoring guidelines. The nonresponse rates were the highest for two items related to sex life (“I am satisfied with my sex life” and “I feel sexually attractive”), with 52.8 and 15.5%, respectively. Table 1 presents detailed socio-demographic and clinical characteristics of the entire study population and the relationship between anti-hormonal drugs and these characteristics. At the time of the interview, 101 patients were treated with tamoxifen and 132 were treated with AIs. Median age was 55 (range: 48–63) for the entire study population. Patients in the AI group (median age: 62 years; range: 57–70) were found to be significantly older than those in the tamoxifen group (median age: 46 years; range: 42–51; p < 0.001). Patients in the AI group had worse performance status (p = 0.003) and more comorbidities (p < 0.001) when compared with the tamoxifen group. Except employment status (p = 0.46), there were significant differences between the two groups regarding socio-demographic variables such as education (p = 0.01), marital status (p = 0.001), number of children (p = 0.001) and living arrangement (p = 0.001). Concerning the clinical factors of the patients, there seemed to be significant differences between the groups in terms of stage at diagnosis (p = 0.02), median ER positivity (p = 0.01), receiving chemotherapy (p < 0.001) and preferred chemotherapy regimens (p < 0.001). While stage 3 disease was observed more in the tamoxifen group (31.7 vs 17.4%), stage 1 disease was seen more in the AI group (28.8 vs 15.8%). Median ER expression was 80% (range: 70–90) in the tamoxifen group and 90% (range 80–95) in the AI group. While adjuvant/neoadjuvant chemotherapy was given to 93% of patients in the tamoxifen group, this ratio was only 64.4% in the AI group. Combination chemotherapy (four cycles of doxorubicin plus cyclophosphamide [AC] followed by four cycles of paclitaxel) was administered more in the tamoxifen group (74.3 vs 43.1%), whereas single-agent taxane was used more frequently in the AI group (6.8 vs 1.0%). Another notable difference was that the patients in the tamoxifen group received significantly more neoadjuvant treatment (29.7 vs 11.4%; p < 0.001). The tamoxifen group showed a tendency to include patients with HER2-positive BC with a borderline significance when compared with the AI group (36.6 vs 25%; p = 0.06). There was no significant difference between the groups regarding the surgery type, including management of the axilla and receiving radiotherapy. Also, the median duration of anti-hormonal therapy was similar in both groups (21.3 months vs 19.5 months; p = 0.79). Table 1. Socio-demographic and clinical characteristics of the study subjects. Characteristic All (n = 233) Tamoxifen (n = 101) Aromatase inhibitors (n = 132) p-value Age, years Median (IQR) 55 (48–63) 46 (42–51) 62 (57–70) <0.001 Karnofsky Performance Status, n (%) 100 90 80 70 209 (89.7) 14 (6.0) 8 (3.4) 2 (0.9) 99 (98.0) 2 (2.0) 0 0 110 (83.3) 12 (9.1) 8 (6.1) 2 (1.5) 0.003 Comorbidity, n (%) 98 (42.1) 24 (23.8) 74 (56.1) <0.001 Education, n (%) <High school High school University 159 (68.2) 49 (21.0) 25 (10.7) 66 (65.4) 20 (19.8) 15 (14.9) 93 (70.4) 29 (22.0) 10 (7.6) 0.01 Employment status, n (%) Unemployed Employed 165 (70.8) 68 (29.2) 69 (68.3) 32 (31.7) 96 (72.7) 36 (27.3) 0.46 Marital status, n (%) Single Married Divorced/widow 11 (4.7) 189 (81.1) 33 (14.2) 9 (8.9) 85 (84.2) 7 (6.9) 2 (1.5) 104 (78.8) 26 (19.7) 0.001 Children, n (%) 0 1 2 3 and above 14 (6.0) 22 (9.4) 99 (42.5) 98 (42.1) 9 (8.9) 11 (10.9) 53 (52.3) 28 (27.7) 5 (3.8) 11 (8.3) 46 (34.8) 70 (53.0) 0.001 Living arrangement, n (%) Live alone Live with family 16 (6.9) 217 (93.1) 1 (1.0) 100 (99.0) 15 (11.4) 117 (86.6) 0.001 Stage at diagnosis, n (%) DCIS Stage I Stage II Stage III 2 (0.9) 54 (23.2) 122 (52.4) 55 (23.6) 1 (1.0) 16 (15.8) 52 (51.5) 32 (31.7) 1 (0.8) 38 (28.8) 70 (53.0) 23 (17.4) 0.02 Hormone profile, median (IQR) ER positivity PR positivity 90 (70–90) 50 (5–90) 80 (70–90) 60 (10–90) 90 (80–95) 50 (5–85) 0.01 0.21 C-ERBB2 overexpression, n (%) 70 (30.0) 37 (36.6) 33 (25.0) 0.06 Surgery type, n (%) Mastectomy Mastectomy + SLND Mastectomy + ALND Lumpectomy + SLND Lumpectomy + ALND 2 (0.9) 45 (19.3) 60 (25.8) 98 (42.1) 28 (12.0) 0 21 (20.8) 24 (23.8) 42 (41.6) 14 (13.9) 2 (1.5) 24 (18.2) 36 (27.3) 56 (42.4) 14 (10.6) 0.64 Radiotherapy, n (%) Yes No 190 (81.5) 43 (18.5) 88 (87.1) 13 (12.9) 102 (77.3) 30 (22.7) 0.06 Chemotherapy, n (%) Yes No 179 (76.8) 54 (23.2) 94 (93.0) 7 (7.0) 85 (64.4) 47 (35.6) <0.001 Chemotherapy regimen, n (%) AC or EC or FEC AC then taxane Taxane only TC 31 (13.3) 132 (56.7) 10 (4.3) 6 (2.6) 13 (12.9) 75 (74.3) 1 (1.0) 5 (5.0) 18 (13.6) 57 (43.1) 9 (6.8) 1 (0.8) <0.001 Neoadjuvant treatment, n (%) 45 (19.3) 30 (29.7) 15 (11.4) <0.001 Duration of endocrine therapy Median (IQR) 20.1 (10.2–40.4) 21.3 (10.2–37.6) 19.5 (10.4–41.9) 0.79 Open in a new tab Values in bold are considered statistically significant. AC: Doxorubicin/cyclophosphamide; ALND: Axillary lymph node dissection; DCIS: Ductal carcinoma in situ ; EC: Epirubicin/cyclophosphamide; ER: Estrogen receptor; FEC: Fluorourocil/epirubicin/cyclophosphamide; IQR: Interquartile range; PR: Progesterone receptor; SLND: Sentinel lymph node dissection; TC: Docetaxel/cyclophosphamide. Comparison of QoL between patients treated with tamoxifen and those treated with AIs Using general linear regression analysis, we found no significant differences between patients treated with tamoxifen and those treated with AIs regarding any of the FACT-G domains (PWB, SWB, EWB, FWB), BCS, FACT-B and FACT-B TOI ( Table 2 ). Moreover, there was no correlation between the duration of anti-hormonal therapy and FACT-B scales (for FACT-G, FACT-B and FACT-B TOI, respectively: p = 0.16, r = 0.091; p = 0.22, r = 0.080; and p = 0.14, r = 0.0096). Table 2. Comparison of Functional Assessment of Cancer Therapy – Breast domain scores between all patients treated with tamoxifen and aromatase inhibitors. FACT-B scales Independent variable Coefficient SE p-value R 2 Physical well-being Tamoxifen vs AIs 0.094 0.667 0.888 0.001 Social/family well-being Tamoxifen vs AIs 0.117 0.691 0.866 0.001 Emotional well-being Tamoxifen vs AIs 0.747 0.604 0.217 0.007 Functional well-being Tamoxifen vs AIs -0.497 0.684 0.468 0.002 Breast cancer-specific subscale Tamoxifen vs AIs 0.813 0.919 0.377 0.003 FACT-G Tamoxifen vs AIs 0.502 1.917 0.794 0.001 FACT-B Tamoxifen vs AIs 1.308 2.542 0.607 0.001 FACT-B TOI Tamoxifen vs AIs 0.397 1.831 0.829 0.001 Open in a new tab AI: Aromatase inhibitor; FACT-B: Functional Assessment of Cancer Therapy – Breast; FACT-G: Functional Assessment of Cancer Therapy – General; SE: Standard error; TOI: Trial outcome index. Comparison of QoL between subgroups stratified by other treatment factors in patients treated with adjuvant tamoxifen We first compared the differences in QoL between patients who had undergone mastectomy and lumpectomy. No significant differences in FACT-B scales were observed between the two groups. Then, the association between axillary lymph node dissection and QoL was analyzed and there seemed to be no significant differences between the groups concerning any of the FACT-B scales. Adjuvant radiotherapy did not affect any of the domains, either. Negative coefficients indicated that patients who had received chemotherapy had significantly lower BCS (p = 0.019), FACT-G (p = 0.046), FACT-B (p = 0.017) and FACT-B TOI (p = 0.018) scores when than the patients who had not. Trastuzumab therapy was found to be associated with lower SWB scores (p = 0.029). Patients treated with GnRH agonists had significantly lower BCS (p = 0.004) and FACT-B TOI (p = 0.046) scores; however, GnRH agonists did not have an impact on other scales. Table 3 highlights the comparison of QoL between subgroups stratified by other treatment factors in patients receiving tamoxifen. Table 3. Comparison of Functional Assessment of Cancer Therapy – Breast domain scores between subgroups stratified by other treatment factors in patients treated with adjuvant tamoxifen. FACT-B scales Independent variable Coefficient SE p-value R 2 PWB MAS vs LUMP 0.029 0.992 0.770 0.009 SWB MAS vs LUMP -0.082 1.112 0.417 0.003 EWB MAS vs LUMP -0.029 0.921 0.772 0.007 FWB MAS vs LUMP 0.013 1.070 0.896 0.010 BCS MAS vs LUMP -0.039 1.440 0.702 0.009 FACT-G MAS vs LUMP -0.025 3.014 0.803 0.001 FACT-B MAS vs LUMP -0.033 3.942 0.741 0.009 FACT-B TOI MAS vs LUMP -0.005 2.763 0.962 0.010 PWB AXLND+ vs AXLND- 0.127 1.010 0.207 0.016 SWB AXLND+ vs AXLND- -0.146 1.132 0.146 0.021 EWB AXLND+ vs AXLND- 0.100 0.941 0.318 0.010 FWB AXLND+ vs AXLND- -0.022 1.098 0.824 0.010 BCS AXLND+ vs AXLND- 0.109 1.470 0.278 0.012 FACT-G AXLND+ vs AXLND- 0.009 3.093 0.930 0.010 FACT-B AXLND+ vs AXLND- 0.046 4.043 0.644 0.002 FACT-B TOI AXLND+ vs AXLND- 0.093 2.822 0.355 0.009 PWB RT- vs RT+ -0.155 1.455 0.122 0.024 SWB RT- vs RT+ 0.093 1.648 0.354 0.009 EWB RT- vs RT+ -0.064 1.365 0.522 0.004 FWB RT- vs RT+ 0.065 1.585 0.520 0.004 BCS RT- vs RT+ -0.052 2.137 0.605 0.007 FACT-G RT- vs RT+ -0.013 4.474 0.898 0.010 FACT-B RT- vs RT+ -0.029 5.852 0.776 0.009 FACT-B TOI RT- vs RT+ -0.057 4.093 0.570 0.003 PWB ChT- vs ChT+ -0.144 1.922 0.150 0.021 SWB ChT- vs ChT+ -0.102 2.172 0.310 0.010 EWB ChT- vs ChT+ -0.189 1.771 0.059 0.036 FWB ChT- vs ChT+ -0.160 2.067 0.111 0.026 BCS ChT- vs ChT+ -0.233 2.743 0.019 † 0.054 FACT-G ChT- vs ChT+ -0.199 5.782 0.046 † 0.040 FACT-B ChT- vs ChT+ -0.238 7.498 0.017 † 0.056 FACT-B TOI ChT- vs ChT+ -0.235 5.254 0.018 † 0.055 PWB Trastuzumab- vs trastuzumab+ -0.111 1.515 0.267 0.012 SWB Trastuzumab- vs trastuzumab+ -0.217 1.673 0.029 † 0.047 EWB Trastuzumab- vs trastuzumab+ 0.012 1.415 0.905 0.001 FWB Trastuzumab- vs trastuzumab+ -0.182 1.616 0.068 0.033 BCS Trastuzumab- vs trastuzumab+ -0.009 2.214 0.925 0.001 FACT-G Trastuzumab- vs trastuzumab+ -0.176 4.559 0.079 0.031 FACT-B Trastuzumab- vs trastuzumab+ -0.137 6.002 0.173 0.019 FACT-B TOI Trastuzumab- vs trastuzumab+ -0.114 4.216 0.255 0.013 PWB GnRH agonist- vs GnRH agonist+ -0.176 1.626 0.078 0.031 SWB GnRH agonist- vs GnRH agonist+ -0.046 1.854 0.649 0.002 EWB GnRH agonist- vs GnRH agonist+ -0.040 1.532 0.690 0.002 FWB GnRH agonist- vs GnRH agonist+ 0.035 1.780 0.730 0.001 BCS GnRH agonist- vs GnRH agonist+ -0.285 2.299 0.004 † 0.081 FACT-G GnRH agonist- vs GnRH agonist+ -0.075 5.003 0.458 0.006 FACT-B GnRH agonist- vs GnRH agonist+ -0.162 6.478 0.106 0.026 FACT-B TOI GnRH agonist- vs GnRH agonist+ -0.199 4.505 0.046 † 0.040 Open in a new tab † p < 0.05 was considered statistically significant. AXLND: Axillary lymph node dissection; BCS: Breast cancer-specific subscale; ChT: Chemotherapy; EWB: Emotional well-being; FACT-B: Functional Assessment of Cancer Therapy – Breast; FACT-G: Functional Assessment of Cancer Therapy – General; FWB: Functional well-being; GnRH: Gonadotropin-releasing hormone; LUMP: Lumpectomy; MAS: Mastectomy; PWB: Physical well-being; RT: Radiotherapy; SE: Standard error; SWB: Social/family well-being; TOI: Trial outcome index. Comparison of QoL between subgroups stratified by other treatment factors in patients treated with adjuvant AIs The QoL was similar between the groups who had undergone mastectomy and lumpectomy; additionally, axillary lymph node dissection did not affect the outcomes. Radiotherapy was found to be associated with lower EWB scores (p = 0.006) but however did not significantly affect other FACT-B domains. Administration of chemotherapy, trastuzumab or GnRH agonists had no detrimental impact on QoL. Anti-hormonal treatment was switched from tamoxifen to AIs due to the onset of menopause in 11 patients and in this group SWB and FACT-G scores were significantly higher (p = 0.017 and p = 0.025, respectively), while PWB and FACT-B scores tended to be higher without significance (p = 0.07 and p = 0.05, respectively). Table 4 presents the comparison of QoL between subgroups stratified by other treatment factors in patients receiving AIs. Table 4. Comparison of Functional Assessment of Cancer Therapy – Breast domain scores between subgroups stratified by other treatment factors in patients treated with adjuvant aromatase inhibitors. FACT-B scales Independent variable Coefficient SE p-value R 2 PWB MAS vs LUMP 0.042 0.897 0.636 0.006 SWB MAS vs LUMP 0.019 0.871 0.829 0.007 EWB MAS vs LUMP -0.170 0.787 0.051 0.021 FWB MAS vs LUMP 0.047 0.884 0.591 0.005 BCS MAS vs LUMP 0.034 1.187 0.701 0.007 FACT-G MAS vs LUMP -0.018 2.471 0.841 0.001 FACT-B MAS vs LUMP -0.001 3.315 0.991 0.008 FACT-B TOI MAS vs LUMP 0.049 2.438 0.576 0.005 PWB AXLND- vs AXLND+ 0.055 0.922 0.531 0.003 SWB AXLND- vs AXLND+ 0.001 0.897 0.991 0.008 EWB AXLND- vs AXLND+ -0.090 0.818 0.307 0.008 FWB AXLND- vs AXLND+ 0.014 0.911 0.877 0.008 BCS AXLND- vs AXLND+ 0.002 1.222 0.980 0.008 FACT-G AXLND- vs AXLND+ -0.007 2.543 0.941 0.008 FACT-B AXLND- vs AXLND+ -0.004 3.410 0.962 0.001 FACT-B TOI AXLND- vs AXLND+ 0.026 2.510 0.768 0.001 PWB RT- vs RT+ -0.071 1.066 0.420 0.005 SWB RT- vs RT+ 0.042 1.037 0.634 0.002 EWB RT- vs RT+ -0.239 0.923 0.006 † 0.057 FWB RT- vs RT+ -0.135 1.045 0.123 0.018 BCS RT- vs RT+ -0.113 1.405 0.198 0.013 FACT-G RT- vs RT+ -0.135 2.916 0.122 0.018 FACT-B RT- vs RT+ -0.141 3.908 0.106 0.020 FACT-B TOI RT- vs RT+ -0.130 2.882 0.137 0.017 PWB ChT- vs ChT+ -0.005 0.936 0.958 0.008 SWB ChT- vs ChT+ 0.064 0.906 0.467 0.004 EWB ChT- vs ChT+ -0.073 0.830 0.407 0.005 FWB ChT- vs ChT+ -0.067 0.921 0.447 0.004 BCS ChT- vs ChT+ -0.095 1.232 0.277 0.009 FACT-G ChT- vs ChT+ -0.024 2.575 0.785 0.001 FACT-B ChT- vs ChT+ -0.052 3.450 0.553 0.005 FACT-B TOI ChT- vs ChT+ -0.072 2.537 0.409 0.005 PWB Trastuzumab- vs trastuzumab+ -0.043 1.998 0.624 0.002 SWB Trastuzumab- vs trastuzumab+ -0.054 1.938 0.536 0.003 EWB Trastuzumab- vs trastuzumab+ -0.113 1.766 0.197 0.013 FWB Trastuzumab- vs trastuzumab+ 0.050 1.969 0.568 0.003 BCS Trastuzumab- vs trastuzumab+ -0.069 2.639 0.431 0.005 FACT-G Trastuzumab- vs trastuzumab+ -0.053 5.496 0.543 0.003 FACT-B Trastuzumab- vs trastuzumab+ -0.065 7.366 0.459 0.004 FACT-B TOI Trastuzumab- vs trastuzumab+ -0.032 5.433 0.712 0.001 PWB GnRH agonist- vs GnRH agonist+ 0.121 3.641 0.166 0.015 SWB GnRH agonist- vs GnRH agonist+ 0.000 3.560 0.997 0.001 EWB GnRH agonist- vs GnRH agonist+ 0.025 3.260 0.779 0.001 FWB GnRH agonist- vs GnRH agonist+ 0.110 3.596 0.211 0.012 BCS GnRH agonist- vs GnRH agonist+ 0.040 4.848 0.653 0.002 FACT-G GnRH agonist- vs GnRH agonist+ 0.093 9.989 0.289 0.009 FACT-B GnRH agonist- vs GnRH agonist+ 0.083 9.999 0.342 0.007 FACT-B TOI GnRH agonist- vs GnRH agonist+ 0.104 9.918 0.237 0.011 PWB Switch- vs Switch+ 0.158 1.601 0.070 0.025 SWB Switch- vs Switch+ 0.207 1.539 0.017 † 0.043 EWB Switch- vs Switch+ 0.075 1.437 0.393 0.006 FWB Switch- vs Switch+ 0.103 1.590 0.239 0.011 BCS Switch- vs Switch+ 0.073 2.139 0.406 0.005 FACT-G Switch- vs Switch+ 0.195 4.377 0.025 † 0.038 FACT-B Switch- vs Switch+ 0.171 5.897 0.050 0.029 FACT-B TOI Switch- vs Switch+ 0.130 4.369 0.136 0.017 Open in a new tab † p < 0.05 was considered statistically significant. AXLND: Axillary lymph node dissection; BCS: Breast cancer-specific subscale; ChT: Chemotherapy; EWB: Emotional well-being; FACT-B: Functional Assessment of Cancer Therapy – Breast; FACT-G: Functional Assessment of Cancer Therapy – General; FWB: Functional well-being; GnRH: Gonadotropin-releasing hormone; LUMP: Lumpectomy; MAS: Mastectomy; PWB: Physical well-being; RT: Radiotherapy; SE: Standard error; SWB: Social/family well-being; TOI: Trial outcome index. Univariate & multivariate linear regression analysis: the role of numerous parameters in FACT-B scores in patients treated with adjuvant tamoxifen & AIs In the entire cohort, switching treatment was positively associated with FACT-B score (p = 0.046). Multivariate analysis determined age (p = 0.034) and switching treatment (p = 0.049) to be significant positive coefficients of QoL, while the presence of comorbidity (p = 0.072) tended to be associated with lower scores. Table 5 presents univariate and multivariate models in the entire study population. Table 5. Univariate and multivariate linear regression analysis: the role of numerous parameters in Functional Assessment of Cancer Therapy – Breast scores in all patients. Determinant Univariate model Multivariate model β coefficient 95% CI p-value β coefficient 95% CI p-value Age 0.165 -0.062–0.39 0.15 0.26 0.015–0.5 0.034 † Comorbidity (no vs yes) -2.93 -7.95–2.08 0.25 -4.98 -10.4–0.44 0.072 Education level (≥high school vs others) 3.68 -4.32–11.7 0.36 Employment status (unemployed vs employed) 3.9 -3.3–11.1 0.28 Marital status (married vs unmarried) -1.14 -7.5–5.2 0.72 Children (n), (≥3 vs <3) 0.47 -1.5–2.45 0.64 Living arrangement (alone vs with family) -2.75 -12.5–7.06 0.58 Stage 1 vs others 4.1 -1.7–9.9 0.16 Stage 2 vs others -1.3 -6.3–3.65 0.6 Stage 3 vs others -2.3 -8.1–3.5 0.43 Trastuzumab (no vs yes) -4 -9.4–1.3 0.14 Surgery type (mastectomy vs lumpectomy) 0.6 -4.4–5.6 0.8 Management of axilla (AXLND+ vs AXLND-) 0.7 -4.4–5.8 0.78 Radiotherapy (no vs yes) -4.9 -11.3–1.4 0.13 Chemotherapy (no vs yes) -4.9 -10.7–1 0.1 Duration of endocrine therapy (≤12 vs >12 months) -2.2 -7.6–3.2 0.4 Gonadotropin-releasing hormone agonist (no vs yes) -6.7 -17.9–4.5 0.24 Switch treatment (no vs yes) 11.8 0.2–23.4 0.046 † 11.6 0.04–23.1 0.049 † Tamoxifen vs aromatase inhibitors 1.3 -3.7–6.3 0.6 Open in a new tab † p < 0.05 was considered statistically significant. AXLND: Axillary lymph node dissection. Concerning the patients in the tamoxifen group, increasing age (p = 0.03) and having fewer than three children (p = 0.07) were positively associated with FACT-B scores, while stage 1–2 disease (p = 0.02), trastuzumab therapy (p = 0.07) and administration of chemotherapy (p = 0.046) were negatively associated with FACT-B scores in the univariate model. Multivariate analysis determined education level (p = 0.001) and chemotherapy (p = 0.04) to be independent significant determinants of QoL; less educated patients were more likely to have higher FACT-B scores, and patients who received chemotherapy were more likely to have lower FACT-B scores. Table 6 outlines the univariate and multivariate models in the tamoxifen group. Table 6. Univariate and multivariate linear regression analysis: the role of numerous parameters in Functional Assessment of Cancer Therapy – Breast scores in patients treated with adjuvant tamoxifen. Determinant Univariate model Multivariate model β coefficient 95% CI p-value β coefficient 95% CI p-value Age 0.53 0.05–1.01 0.03 † Comorbidity (no vs yes) 2.24 -4.7–9.2 0.53 Education level (≥high school vs others) 3.8 -4.5–12.1 0.37 48.6 20.7–76.6 0.001 † Employment status (unemployed vs employed) -3.99 -3.05–11.04 0.26 Marital status (married vs unmarried) 1.88 -6.25–10 0.65 Children (n), (≥3 vs <3) 2.72 -0.21–5.65 0.07 Living arrangement (alone vs with family) -4.08 -34.1–25.9 0.79 Stage 1 vs others 5.07 -2.8–12.95 0.2 Stage 2 vs others 3.69 -2.21–9.6 0.22 Stage 3 vs others -7.5 -13.7– -1.3 0.02 † Trastuzumab (no vs yes) -5.52 -11.6–0.55 0.07 Surgery type (mastectomy vs lumpectomy) 0.75 -5.2–6.73 0.8 Management of axilla (AXLND+ vs AXLND-) 0.274 -5.86–6.41 0.93 Radiotherapy (no vs yes) -0.57 -9.4–8.3 0.9 Chemotherapy (no vs yes) -11.7 -23.2– -0.24 0.046 † -11.2 -22.0–-0.29 0.04 † Duration of endocrine therapy (≤12 vs <12 months) -1.29 -7.73–5.15 0.7 Gonadotropin-releasing hormone agonist (no vs yes) -3.73 -13.65–6.2 0.45 Open in a new tab † p < 0.05 was considered statistically significant. AXLND: Axillary lymph node dissection. In the AI group, the presence of comorbidity (p = 0.018) was negatively associated with FACT-B score in the univariate model, and stage 1–2 disease (p = 0.09) and switch treatment (p = 0.025) were positively associated with FACT-B scores. Multivariate analysis determined that comorbidity (p = 0.04), lumpectomy (p = 0.02) and radiotherapy (p = 0.006) were significant negative coefficients of QoL, and stage 1–2 disease (p = 0.009) was significantly associated with higher FACT-B scores. Patients whose treatment was switched from tamoxifen to AIs tended to have higher QoL (p = 0.07). Table 7 highlights the univariate and multivariate models in the AI cohort. Table 7. Univariate and multivariate linear regression analysis: the role of numerous parameters in Functional Assessment of Cancer Therapy – Breast scores in patients treated with adjuvant aromatase inhibitors. Determinant Univariate model Multivariate model β coefficient 95% CI p-value β coefficient 95% CI p-value Age -0.12 -0.42-0.18 0.44 Comorbidity (no vs yes) -5.85 -10.6– -1.04 0.018 † -4.96 -9.7– -0.21 0.04 † Education level (≥high school vs others) 3.5 -5.7–12.7 0.45 Employment status (unemployed vs employed) -4.87 -4.8–14.5 0.32 Marital status (married vs unmarried) -4.56 -6.42–5.51 0.88 Children (n), (≥3 vs <3) -0.55 -2.35–1.24 0.54 Living arrangement (alone vs with family) 0.22 -7.47–7.9 0.95 Stage 1 vs others 0.945 -4.4–6.3 0.73 Stage 2 vs others -4.0 -8.83–0.84 0.1 Stage 3 vs others 5.55 -0.81–11.9 0.09 9.84 2.5–17.10 0.009 † Trastuzumab (no vs yes) -1.3 -6.93–4.3 0.65 Surgery type (mastectomy vs lumpectomy) 0.497 -4.4–5.4 0.84 -7.73 -14.4– -1.1 0.02 † Management of axilla (AXLND+ vs AXLND-) -0.19 -5.22–4.84 0.94 Radiotherapy (no vs yes) -4.53 -10.3–1.23 0.12 -10.25 -17.58– -2.45 0.006 † Chemotherapy (no vs yes) -0.7 -5.8–4.4 0.78 Duration of endocrine therapy (≤12 vs >12 months) -2.7 -8–2.58 0.31 Switch treatment (no vs yes) 9.9 1.25–18.57 0.025 † 7.72 -0.88–16.3 0.07 Open in a new tab † p < 0.05 was considered statistically significant. AXLND: Axillary lymph node dissection. Discussion Advances in BC diagnosis and treatment strategies have significantly improved patients’ survival over time. On the other hand, this improvement requires particular attention to QoL achieved during and after treatment. So far, various definitions of QoL have been proposed; nevertheless, a precise, comprehensible and shared one still seems to be a long way off [ 14 ]. This elusive concept is changing over time and becoming more complex but enriched in terms of the different medical disciplines. However, QoL has been most generally defined as a multi-dimensional assessment of patients’ perceptions of their own physical, mental and social health, which are affected by the disease course [ 15 , 16 ]. The vast majority of cancer trials that are conducted to evaluate a new treatment include QoL assessments; in fact, due to its recognized significance, QoL is now considered a primary outcome in most clinical trials. Moreover, evidence suggests that QoL is associated with survival outcomes in patients with cancer [ 17 ]. Therefore, using QoL questionnaires to evaluate the well-being of patients following cancer treatment is of great importance not only in clinical trials but also in clinical practice. AET is the mainstay of treatment for HR-positive BC, as it has proven its efficacy in reducing recurrence rates and improving overall survival [ 18 ]. 5 years of AET has been the standard-of-care for a long time; however, strong evidence has indicated that a low but constant risk of recurrence and death remains up to 20 years [ 19 ]. Therefore, extended treatment approaches have recently come into prominence in clinical practice. On the other hand, toxicity has become an important aspect alongside these benefits. Osteoporosis, musculoskeletal symptoms (myalgia, arthralgia), depression, fatigue and menopausal-like symptoms such as hot flashes, excessive sweating, vaginal dryness and sexual dysfunction are adverse effects commonly seen with anti-hormonal treatments [ 20–22 ]. As expected, the prolongation of AET has brought exaggerated toxicity, leading to treatment discontinuation and nonadherence in recent trials [ 23 , 24 ]. Furthermore, the possible sequelae of earlier treatments such as surgery, chemotherapy and radiotherapy should be considered and addressed in a systematic manner [ 25 ]. Taken together, long-term survival rates of HR-positive BC and the necessity of long-term AET following earlier cancer treatments require better understanding of the physical and psychological distress of patients, which directly affects QoL. Patients whose QoL is adversely affected by AET will be less willing to continue the treatment. Besides clinical and treatment-related factors, socio-demographic parameters are well-recognized determinants of QoL [ 26 ]. Assessment of QoL in BC survivors may help to identify patients who may benefit from supportive interventions. Therefore, we conducted a cross-sectional study of 233 BC patients treated with AET in the Turkish population and utilized the FACT-B questionnaire to evaluate the impact of socio-demographic, clinical and treatment-related parameters on QoL in a multivariate model. Using a linear regression analysis, we compared the FACT-B domains of patients treated with two different groups of anti-hormonal drugs. Not surprisingly, the AI group consisted of older patients with more comorbidities and worse performance status. Moreover, some socio-demographic features differed between the groups, owing to differences between generations such as education level, marital status, number of children and living arrangement. Neoadjuvant treatment and combination chemotherapy regimens were administered more in the tamoxifen group on account of younger age and more advanced stage in contrast to the AI group. With respect to QoL, there were no significant differences between the groups in terms of any FACT-B scales. A similar result was obtained from the ATAC QoL trial, which found no significant difference in FACT-B TOI between 2 years of treatment with tamoxifen, anastrozole and the combination [ 27 ]. Interestingly, in the present study, duration of AET had no impact on QoL, which, in fact, differed from data provided by a numerous of clinical trials, as mentioned previously [ 23 , 24 ]. We believe that this finding is highly important as it may encourage clinicians for to prolong AET, as long-term toxicity appears to be a major concern. As this was not a randomized trial and based on real-world experience, we were aware of the fact that comparing two endocrine therapies would not be so fair owing to many differences in the two populations. As a result, all subsequent analyses in the tamoxifen and AI cohorts were carried out individually. The findings from subgroups stratified by other treatment factors such as surgical procedure, chemotherapy, radiotherapy, ovarian function suppression therapy and switch strategies were reported. Patients who had undergone mastectomy and lumpectomy were compared, and in none of the cohorts was there a statistically significant difference between groups regarding QoL. On the contrary, Chow et al. showed a statistically lower QoL in patients who had undergone mastectomy, as all FACT-B scales except SWB were found to be lower. The authors stated that poor QoL in relation to mastectomy may be attributed to body image and cosmetic appearance [ 28 ]. In the present study, management of the axilla also had no significant impact on QoL, in contrast with previous evidence [ 29 , 30 ]. This inconsistency could be explained by the fact that FACT-B does not have many specific questions focusing on the postoperative arm issues that patients suffer from. The only statement about arm morbidity is present in additional concerns: “One or both of my arms are swollen or tender.” Previous studies used different questionnaires to assess arm problems following BC surgery. Unlike radiotherapy, chemotherapy was found to be associated with lower BCS, FACT-G, FACT-B and FACT-B TOI scores, with a statistical significance in patients treated with tamoxifen. In contrast, chemotherapy showed no detrimental effect in patients receiving AIs, but radiotherapy was negatively associated with the EWB domain. In the study by Hamer et al. , a total of 1513 patients, including all stages of BC, were enrolled [ 31 ]. They mainly divided the whole population into cohorts based on stage and made comparisons under each stage. While chemotherapy was found to be associated with lower total QoL and BCS scores in patients with early-stage BC, patients receiving radiotherapy had significantly lower BCS scores only in the locally advanced stage. We also observed that patients receiving GnRH agonists combined with tamoxifen had significantly lower BCS scores. This also confirms previous findings in the literature. The addition of ovarian function suppression to tamoxifen or an AI may result in more evident adverse effects, negatively affecting treatment compliance in premenopausal patients and leading to early treatment discontinuation in about 20% of patients [ 32 ]. The switch from tamoxifen to AIs was found to be associated with significantly higher SWB and FACT-G scores, although none of the patients had adverse effects that led to switching treatment. However, in both the TEAM QoL study [ 33 ] and IES [ 34 ], the switch did not affect QoL. Following general linear regression analysis, we first performed univariate and multivariate regression analysis in the entire cohort. Then, we created two distinct models, one for the tamoxifen group and one for the AI group, in order to evaluate all significant parameters influencing FACT-B scores. In the multivariate regression analysis, age and switching treatment were found to be significant determinants of QoL in the entire study population. Type of AET did not have an impact on QoL. The presence of comorbidity tended to affect QoL negatively, with borderline significance. Education level and chemotherapy were found to be independent significant determinants of QoL in the tamoxifen group. Education level was inversely associated with QoL in the cohort. Evidence about education is unconvincing, with inconsistencies. A study from India reported that more educated patients and higher income ranked higher in QoL scores [ 35 ]; however, in a study similar to the present one, high education level affected QoL negatively [ 36 ]. While this finding may differ by country, a higher degree of education not only raises awareness of health issues but also improves patients’ understanding of disease itself. In the present study, it makes sense, then, that educated women were more conscious of their concerns, which influenced their scores on the questionnaire. Patients who received chemotherapy were more likely to have lower FACT-B scores in the tamoxifen group. When we compared the QoL between groups treated with AET and chemotherapy separately, we were aware of the fact that it was difficult to distinguish the effects of both treatments on each other. Numerous studies have investigated the long-term effects of chemotherapy. One of them reported prolonged fatigue with chemotherapy even up to 3 years in BC survivors [ 37 ]. On the other hand, Ferreira et al. compared the impact of AET and chemotherapy on QoL at 2 years after diagnosis in patients with early BC. Interestingly, they found a differential effect of the treatment by menopausal status; they demonstrated that chemotherapy deteriorated several QoL dimensions in premenopausal patients, while AET was associated with a persistent negative effect on QoL, especially in postmenopausal women [ 7 ]. Despite the fact that some earlier studies suggested that most of the deteriorating effect of chemotherapy resolved shortly after treatment completion [ 38 ], this study showed that it had a longer-lasting detrimental impact on QoL particularly in young women treated with tamoxifen. Therefore, this group should be considered to require more supportive care, such as close monitoring, physical activity suggestions and psychosocial interventions. In the group of patients treated with AIs, comorbidity, lumpectomy and radiotherapy were significant negative coefficients of QoL, and earlier disease stage (1–2) was significantly associated with higher FACT-B scores in the multivariate model. A growing body of evidence suggests that comorbidities have a confounding effect on QoL scores of BC patients [ 39 ]. These data emphasize the necessity to properly select patients with comorbidities for adjuvant treatments, since they appear to be more affected by interventions. The type of surgery had no effect on any of the outcomes in either the general linear regression analysis or the univariate model; however, in the multivariate model lumpectomy and radiotherapy significantly reduced QoL. In previous studies, mastectomy was found to be associated with worse QoL, owing to body image and cosmetic appearance [ 28 ]. These findings may be explained by a number of factors. First, because this is a population of patients treated with AIs who are generally older, cosmetic concerns may be pushed to the background. Second, the unpleasant effects of radiotherapy, which was applied as a result of breast-conserving surgery, could be the primary cause of poor QoL. Late effects of radiotherapy include skin thickening or fibrosis of the breast or chest wall, lymphedema, arm problems, pneumonitis and cardiovascular toxicity, all of which may have a negative impact on QoL [ 40 ]. Two studies investigated the predictive value of disease stage in terms of QoL, with varying results [ 36 ]. In this regard, the results of the present study can be explained by the fact that patients experience more symptoms in advanced stages. Furthermore, advanced stage is usually accompanied by chemotherapy, which may also negatively influence QoL. In the current study, two items (one in SWB and one in BCS) focusing specifically on sexual life satisfaction stand out with high rates of nonresponse (52.8 and 15.5%, respectively). These values correlate fairly well with the study of Raymond et al. , with nonresponse rates of 50.2 and 14.4%, respectively, for the two items [ 41 ]. Owing to cultural, social, historical and religious influences on human sexuality, many women have difficulty expressing themselves about this topic, often leading to lower participation rates. Even in some countries it may be considered a taboo. For instance, in an Indian study, almost none of the patients answered this item concerning sexual life [ 42 ]. Higher rates of nonresponse related to sexuality seem to be more prominent in Asian populations similar to Turkish society [ 41 ]. A number of potential limitations of the present study need to be considered when interpreting the results. The major limitation was the relatively small sample size, especially for some subgroups stratified by treatments. Comparing a large group with a small one (such as GnRH treatment or switch strategy) may have reduced the statistical power. Additionally, since this was a real-world comparison, there were significant differences between the tamoxifen and AI groups in terms of many socio-demographic and clinical characteristics, which indeed may affect QoL results. However, we tried to overcome this bias by performing analysis also in the tamoxifen and AI groups, separately. Finally, this work was designed as a cross-sectional study; therefore, the questionnaire was completed only once by the patients, which does not allow the detection of any changes in QoL. Conclusion The present study investigated the impact of socio-demographic, clinical and treatment-related parameters on the QoL of BC patients treated with AET in the Turkish population. It was designed to find a particular subgroup that may need greater supportive care related to the adverse effects of anti-hormonal drugs and to discover the long-term effects of standards of care such as surgical procedures, chemotherapy and radiotherapy. There was no significant difference between endocrine agents concerning QoL. Moreover, duration of AET did not adversely affect QoL, which, in fact, strongly favored extended treatment if indicated. Patients’ education level and chemotherapy were associated with QoL in patients treated with adjuvant tamoxifen, while comorbidity, type of surgery, radiotherapy and the stage of disease had a significant impact on QoL in patients treated with AIs. By enhancing the quality of evidence and creating a better understanding of QoL, personalized interventions for the particular needs of BC patients may be possible in the management of adjuvant treatment. As a result, we believe that our research adds substantially to the current knowledge concerning QoL in BC. Further studies with a larger population will need to be undertaken. Summary points. With the improved survival of breast cancer (BC) patients, quality-of-life (QoL) has become a priority issue. This was a cross-sectional study of 233 BC patients treated with adjuvant endocrine therapy (AET) in the Turkish population. The Functional Assessment of Cancer Therapy – Breast (FACT-B) questionnaire was used to assess QoL. Higher scores defined better QoL. Univariate and multivariate linear regression analyses were computed in order to test the impact of socio-demographic, clinical and treatment-related factors on total FACT-B scores. For this purpose, we created two models, one for a tamoxifen group and one for an aromatase inhibitor (AI) group. The results demonstrated no significant difference between patients treated with tamoxifen and those treated with AIs for any FACT-B domains. Moreover, duration of AET did not adversely affect QoL, which, in fact, strongly favored extended treatment if indicated. In the entire cohort, multivariate analysis determined age and switching treatment from tamoxifen to AIs to be significant positive coefficients of QoL, while comorbidity tended to be associated with lower scores. Education level and chemotherapy were significantly associated with QoL in patients treated with adjuvant tamoxifen, while comorbidity, type of surgery, radiotherapy and stage of disease had a significant impact on QoL in patients treated with AIs. To the best of our knowledge, this is the largest real-world study of QoL in BC patients treated with AET in the Turkish population. Acknowledgments The authors would like to thank all the patients who participated in this study. Author contributions TA Telli: data curation, writing – original draft preparation, writing – reviewing and editing; MA Ozturk: conceptualization, methodology, writing – reviewing and editing; O Alan: data curation, investigation; R Hasanov: data curation, investigation; O Kostek: formal analysis; R Arikan: data curation, investigation; T Basoglu: data curation, investigation; S Kaya: data curation, investigation; O Ercelep: data curation, investigation; NA Babacan: data curation; F Dane: supervision; PF Yumuk: supervision, writing – reviewing and editing Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript. Ethical conduct of research The study was approved by the Marmara University ethics committee (date of approval: 14 July 2017, protocol code: 09.2017.492). The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved. References Papers of special note have been highlighted as: •• of considerable interest 1. Sung H, Ferlay J, Siegel RLet al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021). [ DOI ] [ PubMed ] [ Google Scholar ] 2. Waks AG, Winer EP. Breast cancer treatment: a review. JAMA 321(3), 288–300 (2019). 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