Time to Breast Cancer Recurrence and Associated Predictors in Public Hospitals of Addis Ababa, Central Ethiopia: A Retrospective Cohort Study

Time to Breast Cancer Recurrence and Associated Predictors in Public Hospitals of Addis Ababa, Central Ethiopia: A Retrospective Cohort 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 Time to Breast Cancer Recurrence and Associated Predictors in Public Hospitals of Addis Ababa, Central Ethiopia: A Retrospective Cohort Study Yadessa Chala, Tesfaye Techane, Bonsisa Bekele, Tesfaye Girma, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5852483/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jun, 2025 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted 6 You are reading this latest preprint version Abstract Background Breast cancer recurrence is a significant concern when the disease returns following surgery. The time to recurrence and factors affecting it are not well studied in low-income countries. This study aimed to assess the time to recurrence and predictors of breast cancer among women treated in public hospitals of Addis Ababa, Ethiopia. Methods Retrospective cohort study was conducted from April 30 to May 30, 2024, among randomly selected 322 recorded cases. The study covered from September 11, 2018, to September 12, 2023. Data were collected by the Kobo toolbox and analyzed by Stata Version 15. The Cox proportional hazard model was used to identify predictor variables, with assumptions checked using the Schoenfeld residual/global test (0.79). Multi-collinearity was checked using the variance inflation factor (3.72). Variables with a P-value < 0.25 in bivariable analysis were entered into the final multivariable analysis. Variables with a P-value < 0.05 at 95% confidence level were considered independent predictors of recurrence. Result The recurrence-free survival (RFS) status at the median follow-up time was 87.5%. The incidence rate of breast cancer recurrence was 6.8 per 100 women years (95%CI = 5.34–8.13) follow-up. The 75%RFS time was 44 months (95CI%=40–48). The proportion of RFS survival at 24, 36, 48, and 60 months was 91.93%, 83.3%, and 67.7%, 61% respectively. Women aged 40 & below (AHR = 3.32; 95%CI: 1.80–5.88), Overweight (AHR = 1.95, 95%CI: 1.06–3.59), surgical margin positive (AHR = 2.1; 95%CI: 1.20–4.02), axillary node-positive (AHR = 1.98; 95%CI: 1.08–3.61) and comorbidity (AHR = 4.45, 95%CI: 2.39–8.30) were independent predictors for increased hazard of recurrence. Conclusion and Recommendation: This study confirms a substantial incidence of breast cancer recurrence, with identifiable predictors including comorbidity, age, overweight, positive axillary node status, lymph node involvement, and deep surgical margin. Targeted interventions aimed at improving patient understanding of recurrence risk, promoting adherence to treatment protocols, and fostering healthy lifestyle modifications are crucial for reducing recurrence rates. Breast cancer Ethiopia Predictors recurrence Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Breast cancer is a complex illness with many faces – different biological characteristics, a range of treatments, and varying outcomes for those diagnosed. At its core, it's about cells in the breast tissue that begin to grow and multiply uncontrollably, ultimately becoming cancerous ( 1 ). In 2020, breast cancer became the most commonly diagnosed cancer worldwide, surpassing lung cancer, with 2.3 million new cases—that's nearly 12% of all cancers diagnosed globally. Sadly, it's also the fifth leading cause of cancer-related deaths, claiming 685,000 lives each year ( 2 ). In Africa, and specifically in Ethiopia, breast cancer is the most prevalent cancer affecting people, both in terms of new diagnoses and deaths ( 3 ). Breast cancer represents a significant global health burden, impacting millions of women worldwide. It is the most commonly diagnosed cancer among women, and a leading cause of cancer-related mortality. This burden extends beyond mortality, encompassing substantial morbidity, including physical and psychological consequence from diagnosis and treatment. The economic impact is also considerable, with costs associated with screening, diagnosis, treatment, and long-term care placing a strain on healthcare systems and individuals ( 2 ). When someone is diagnosed with breast cancer, the treatment approach depends heavily on how far the cancer has progressed. For those with Stage I–III breast cancer, surgery is often the first step, either a mastectomy (removal of the breast) or breast-conserving surgery followed by radiation therapy ( 4 ). Stage IV breast cancer, where the cancer has spread to other parts of the body, is generally considered more difficult to cure and is usually managed with systemic therapy, such as chemotherapy or targeted drugs, although sometimes surgery might be considered for comfort or symptom relief ( 5 ). After initial treatment, therapies like radiation, hormone therapy, and chemotherapy can be used to help lower the chances of the cancer coming back and improve survival ( 6 ). Unfortunately, breast cancer can sometimes return after initial treatment—we call this recurrence ( 7 ). The risk is highest in the first couple of years after diagnosis, but it remains a possibility, albeit lower, for many years afterward, roughly between 2% and 5% annually from years five to twenty ( 8 ). Where the cancer returns can also vary, depending on the specific type of breast cancer. It might come back in the breast or chest wall area (local recurrence), spread to the bones (distant metastasis), or appear in nearby lymph nodes ( 1 ). This lingering possibility of recurrence can understandably make people feel that breast cancer is never truly "gone." Even with the best treatments and close monitoring, some people (5–10%) are diagnosed with metastatic disease right away, and another 20% will experience a recurrence later on ( 9 ). Sadly, when the cancer does return, it often behaves more aggressively ( 10 ). Looking at the numbers around the world, we see a significant difference in recurrence rates depending on where people live, reflecting differences in access to healthcare and resources. For example, studies have shown that within 5 years after surgery, recurrence rates were around 3.3% in Australia ( 6 ), 11.7% in the Netherlands ( 12 ), and 5.9% in South Korea ( 13 ). In countries with fewer resources, like Egypt and Iran, the rates were 4.2% ( 10 ) and 20.2% ( 11 ), respectively. We have very limited information from Africa, but one study in Ethiopia found a recurrence rate of 18.5% ( 14 ). Breast cancer recurrence is a key clinical challenge, influenced by patient factors like age and BMI, and tumor characteristics. Positive lymph node status, lympho-vascular invasion, high tumor grade, and hormone receptor negativity increase recurrence risk. Inadequate surgical margins also elevate local recurrence ( 15 , 18 ). This lack of data in Ethiopia shows us that we don't fully understand how often breast cancer comes back, or what factors might contribute to it. That's why this study is so important—we want to understand how long it takes for breast cancer to recur and identify any factors that might predict recurrence among women treated in public hospitals in Addis Ababa, Ethiopia. 2. Methods 2.1. Study design and setting An institution-based retrospective cohort study was conducted at Saint Paul’s Hospital Millennium Medical College (SPHMMC) and Tikur Anbessa Specialized Hospital (TASH) from April 30- May 30, 2024. The study was conducted at the largest tertiary care, specialized, referral, and teaching hospitals. There are 13 public hospitals in Addis Ababa, and four public hospitals have oncology treatment centers (Tikur Anbessa Specialized Hospital, St. Paul's Hospital Millennium Medical College, Ethiopian Public Health Institute, and Yekatit 12 Hospital Medical College). Tikur Anbessa Specialized Hospital and St. Paul's Hospital Millennium Medical College were selected by using simple random sampling. The St. Paul’s Hospital Millennium Medical College was established in Addis Ababa, the capital city of Ethiopia in 1968 by the late Emperor Haile Selassie. The college has more than 2800 clinical, academic administrative, and support staff that provide medical specialty services to patients who are referred from all over the country, teaching medicine and nursing students and doing basic and applied research. The College can accommodate more than 700 inpatient beds, but on a daily average, 1200 emergency and outpatient patients are seen. St. Pauls‘ Hospital Millenium Medical College oncology unit was established on August 1, 2018. It was the second hospital offering cancer treatment in the country ( 15 ). Tikur Anbessa Specialized Hospital is the largest tertiary care, specialized, referral, and teaching hospital in the country that is owned by the government and established in 1973. TASH has 51 specialty outpatient clinics, serving 500,000 patients annually ( 14 ). 2.2. Sample size determination As the study was a cohort study, the sample size needed for acquiring statistically significant results was determined using a two-population proportion formula. Therefore, the sample size was calculated by taking into account the predictor variables and using open epiInfo version 7.2.6 statistical package ( 16 ). Among predictor variables, lymph node status is chosen as the main predictor variable of breast cancer recurrence during the 6 years of follow-up since it was considered to give the optimal sample size and most significant result. In this regard, with a 5% level of significance (two-sided), a power of 80%, and a ratio of unexposed to exposed of 1:1, the estimated proportion of recurrence in Ethiopia was taken at 10% for the non-exposed group ( negative lymph node status) and 22.4% for the exposed group ( positive lymph node status) ( 14 ) (Table 1 ). However, in practice getting 153 patients their positive lymph nodes was difficult and the rest were from negative lymph nodes. Thus, the total sample size was 306. Finally, by adding 10% for incomplete data, the final sample size required was 337. Table 1 sample size calculation for time to recurrence and predictors of breast cancer recurrence among patients treated in a public hospital, Addis Ababa, Ethiopia 2024 by using open epi version 7.2.6 Software. Assumptions Major predictors variable Sample size by Fleiss with CC Formula Total sample size Two-sided significance level : 0.05 Lymph node status (positive exposed; negative unexposed) Number of exposed = 153 306 Power : 80 Ratio of sample size : 1:1 % of Unexposed with Outcome 10 Number of unexposed = 153 % of exposed with Outcome 22.4 Hazard ratio 2.6 Relative risk 2.24 2.3. Subjects All medical records with breast cancer who had breast surgery in a selected public hospital in Addis Ababa, from September 11st 2018 to September 12, 2023. All medical records of breast cancer patients who had breast surgery in the SPHMMC and TASH hospitals from September 11 2018 to September 12 2023 were included in the study. 2.4. Data collection A data extraction tool was developed from related literature to collect information from patients' medical records. Socio-demographic, clinic-pathological, and treatment-related factors that are supposed to be predictors for breast cancer recurrence were extracted from the patient's medical records by using the Kobo toolbox. Data were collected by four BSc nurses and supervised by two MSc Oncology nurses. Before the data was collected, 5% ( 17 ) of the total sample size underwent a pretest to ensure that the questionnaires were clear and easy to access on the chart. After pre-testing the checklist, Cronbach's Alpha was calculated by using Stata version 15 to test the internal consistency (reliability) of the item and the result was 0.79. Study variables are identified based on similar studies and a data extraction tool was developed by the information available in the patient's medical record at the cancer treatment center. Training on how to collect data by the Kobo toolbox was given to data collectors and supervisors before one day of data collection. Senior experts in the area of study for content validity examined the data extraction tool. The entire data collection process was closely supervised by the supervisors and principal investigator. Supervisors checked the ID of the patient with the registry code whether clinically matched (at diagnosis stage I-III) or not. 2.5. Data analysis Data were entered, checked, and arranged in Kobo toolbox software. After Coding, editing, and cleaning analysis were done in Stata Vers.15. To summarize the cohort's characteristics, descriptive statistics such as frequency tables, life tables, graphs, median, and Inter quartile range were used. The incidence density rate was computed throughout the study. The survival time was estimated using the Kaplan–Meier survival curve. Log-rank test was performed for the presence of any differences in time to recurrence among different categorical variables. Those variables that the test statistics & Kaplan-Meier analysis displayed as a significant difference in RFS function among categorical variables are considered as having significant evidence of differences in time to recurrence. The Cox proportional hazard model was used to identify the predictor variables. The Cox-proportional hazard model assumption was checked using the Schoenfeld residual/global test (0.79). Overall the fitness' of the proportional hazard model was assessed by using the Cox Snell residual graph. Multi-co linearity was checked using the variance inflation factor (3.72). Variables with a P-value < 0.25 in the bivariable analysis were entered into the final multivariable analysis. Variables with a P-value < 0.05 at a 95% confidence level were considered independent predictors of recurrence. 3. Results 3.1. Socio-demographic traits of the respondents Out of 337 study participants of this study, 322 complete record reviews were done. This makes the response rate of the study 96%. The median age at diagnosis was 43 years (Inter-quartile range: 34–51). Of the 322 study participants, 162(50.31%) were diagnosed at age 40 and above. One hundred sixty-two (81.37%) were married, and 195 (60.56%) were from rural areas (Table 2 ). Table 2 Socio-demographic characteristics of breast cancer patients who had surgical treatment in a selected public hospital, Addis Ababa, Ethiopia, 2024(n = 322) Variables Category Frequency, (%) Outcome Recurrence N (%) Censored N (%) Age at dx =>40 < 40 190 (59.01) 132 (40.99) 31 (16.31) 32(24.24) 159 (83.69) 100(75.76) Marital status Single Married Widowed Divorced 42 (13.04) 162 (81.37) 11 (3.42) 7 (2.17) 6(14.28) 57(35.18) 36 (85.72) 205(64.82) 11(100) 7(100) Residence Urban Rural 195 (60.56) 127 (39.44) 35(17.94) 28(22.04) 160(82.06) 99 (77.96) 3.2. Baseline clinical, pathological, and treatment characteristics of the study participants Eighty-six (26.71%) women had preexisting comorbidity at the time of diagnosis, 152 (47.20%) cases were in clinical stage III cancer; and 135(41.93%) were moderately differentiated (grade II) histologic cancer cases at a time of diagnosis. Out of a total of 151 (46.8%), women were overweight during diagnosis. Invasive carcinoma was a commonest histologic type; accounting for 256(79.50%) of all cases. One hundred seventy-one (53.11%) cases had positive lymph involvement of two or above, and 160 (39.69%) cases had tumor size of 2 to 5 cm at the time of diagnosis. Ninety-eight (30.43%) cases had involved surgical margin status. About 157 (48.76%) women had positive axillary node status. One hundred sixty-one (50%), and 147(45.65%) of study participants had positive estrogen and progesterone receptors respectively (Table 3 ). Table 3 Clinical and pathological characteristics of breast cancer patients who had surgical treatment in a selected public hospital, Addis Ababa, Ethiopia, 2024 (n = 322) Variables Category Frequency, (%) Outcome status Recurrence N (%) Censored N (%) Comorbidity Yes No 86 (26.71) 236 (73.29) 40(46.51) 23(9.74) 46(53.49) 213(90.26) BMI at diagnosis Normal weight Overweight 171 (53.11) 151 (46.89) 16(9.35) 47(31.12) 155(90.65) 104(68.98) Laterality Left Right 147(45.65) 175(54.35) 28(19.05) 35( 20 ) 119(80.95) 140(80) Cancer stage at diagnosis I II III 30 (9.32) 140 (43.48) 152 (47.20) 2(6.66) 14( 10 ) 47(30.92) 28(93.34) 126(90) 105(69.08) Histological grade Grade I Grade II Grade III 65 (20.19) 135 (41.93) 122 (37.89) 5(7.70) 18(13.33) 40(37.78) 60(92.30) 117(86.67) 82(62.22) Histological type Noninvasive Invasive 66 (20.50) 256 (79.50) 4(6.06) 59(23.04) 62(93.94) 197(86.96) Surgical margin Free Involved 224 (69.57) 98 (30.43) 25(11.16) 38(38.78) 199(88.84) 60(61.22) Number of + lymph nodes involved = 2 151 (43.89) 171 (53.11) 16(10.60) 47(27.48) 135(89.40) 124(72.52) Axillary node status Negative Positive 165 (51.24) 157 (48.76 ) 17(10.30) 46(29.29) 148(89.70) 111(70.71) Tumor size at diagnosis 5cm 36 (11.18) 160 (39.69) 126 (29.13) 3(8.33) 24(15.00) 36(28.57) 33(91.67) 136(75.00) 90(71.43) Estrogen receptors Positive Negative Not determined 161 (50.00) 129 40.06) 32 (9.94) 35(21.74) 26(20.16) 2(6.25) 126(78.26) 103(79.84) 30(93.75) Progesterone receptor Positive Negative Not determined 147 (45.65) 142 (44.10) 33 (10.25) 25(17.00) 36(25.35) 2(6.06) 122(83.00) 106(74.65) 31(93.94) According to this study, about 55.28% of women's surgery was done after 30 days from the date of diagnosis. Three hundred eight (95.56%) cases had undergone Modified Radical Mastectomy. Three hundred forty (81%) women were using adjuvant chemotherapy and out of these ACT is the most common chemotherapy used as an Adjuvant regimen 173(53.7%). About Two hundred fourteen (66.46%) were using hormone therapy. Out of 214, about 148 (69.16) women who were diagnosed with early-stage breast cancer used the Tamoxifen regimen of hormonal therapy during these follow-ups (Table 4 ). Table 4 Treatment characteristics of breast cancer patients who had surgical treatment in selected public hospitals Addis Ababa, Ethiopia, 2024 (n = 322) Variables Category Frequency,% Recurrence(63) N (%) Censored (259) N (%) Duration from diagnosis to surgery 30 days 144 (44.72) 178 (55.28) 23 (15.97) 40 (22.47) 121(84.03) 138(77.53) Neo-adjuvant chemotherapy use Yes No 38(11.80) 284(88.20) 11 (28.94) 52 (19.79) 27(71.06) 232(80.21) Adjuvant chemotherapy use Yes No 288 (89.44) 34 (10.56) 57(19.79) 6(17.65) 231(80.21) 28(72.35) Chemotherapy regimen used ACT AC Paclitaxel 173 (53.7) 75 (23.3) 40 (12.4) 36(20.80) 12 (16.00) 9 (22.50) 137(79.20) 63(84.00) 31(77.50) Use of hormonal therapy Yes No 214 (66.46) 108 (33.54) 43(20.09) 20(18.51) 171(79.91) 88 (81.49) A regimen of hormonal therapy Tamoxifen Anastrazole 148 (69.16) 66 (30.84) 27(18.24) 16(24.24) 121 (81.76) 50 (75.76) 3.3. Overall status of breast cancer patients In this study, 322 women patients with breast cancer who underwent surgery were followed retrospectively. The median follow-up time was 33 months, with a minimum and maximum follow-up time of 5 and 60 months, respectively. The recurrence-free survival status at the median follow-up time was 85.73% (95%CI = 80.8%-89.4%). In this study 63 (19.56%) patients developed recurrence (Fig. 1 ). 3.4. Incidence of breast cancer recurrence The overall incidence rate of breast cancer recurrence in the cohort during the 919 person-years of observation was 6.8 per 100 person-years (95%CI = 5.35–8.14) follow-up. In this study, the 75% recurrence-free survival (RFS) time was 44 months (95%CI = 40–48.00). The estimated overall RFS survival at 24, 36, 48, and 60 months was 91.93%, 83.3%, and 67.7%, 61% respectively (Fig. 4 ). There were 32(50.79%) distant recurrences & 31 (49.21%) loco regional recurrences; and regarding the site of recurrence, axillary and opposite breast recurrence for loco regional type, and lung & chest wall for the distant recurrence were the commonest sites (Fig. 2 ). 3.5. Comparison of time to breast cancer recurrence among categorical variables Log-rank test was performed to determine the presence of a significant difference in recurrence rate among categorical variables such as;-Age of patients at diagnosis, Preexisting comorbidity status, Surgical margin status, Clinical stage of cancer, and Axillary node status at 5% level of significance (Table 5 ). The Log-rank (LR) test has shown that women who were age 40 and below, and those above 40 years at diagnosis had differences in their time recurrence-free survival time (P value for LR test = 0.041). Also, the recurrence-free survival time was different among women who had preexisting comorbidity and those with no preexisting comorbidity at baseline (P value for LR test < 0.001). In addition, patients who were diagnosed with clinical stage III cancer have a shorter median time to recurrence compared to those who were presented with clinical stage I and II cancer (P value for LR < 0.001). The graph of the log displayed that there was a difference in recurrence-free survival time among women presented with histologic grades I & II, and those with grade III. The median time to recurrence is longer in grades I and II than in histological grade III, and this difference was significant at a log-rank test P value of < 0.001. Additionally, the median time to recurrence was shorter in women who had two or more lymph node involvement than those with less than two lymph node involvement (P value for LR-test = 0.0012) (Fig. 3 ). Table 5 Log-rank test for categorical independent variables among breast cancer recurrence patients who had surgical treatment in Addis Ababa, Ethiopia, 2024. Variables Chi-square Df p-value Age 4.15 1 0.0417 Stage of cancer 24.82 2 <0.001 Comorbidity 49.11 1 < 0.001 Histological grade 24.91 2 < 0.001 Body mass index 18.98 1 0.001 Surgical margin 47.38 1 < 0.001 Lymph node 10.58 1 0.0012 Tumor size 12.15 1 0.0023 Axillary node status 19.54 1 < 0.001 3.6. Cox proportional hazard Assumption The Cox proportional hazard assumptions were checked statistically using a global test. All the covariates met the proportional hazard assumption, and the p-value over all Schoenfeld global tests was 0.7933 (Table 6 ). Table 6 Proportional Hazard assumption model of the Cox model for breast cancer patients who had surgical treatment in selected public hospitals in Addis Ababa, Ethiopia, 2024 Variables Rho Chi-square Df Pro > chi2 Age -0.05794 0.24 1 0.6262 Comorbidity -0.06379 0.30 1 0.5861 Cancer stage -0.12249 1.05 1 0.3066 BMI 0.06916 0.34 1 0.5587 Histological type of cancer -0.12654 1.06 1 0.3023 Histological grade 0.16340 2.12 1 0.1453 surgical margin status 0.12185 1.20 1 0.2742 Lymph node involvement 0.00868 0.00 1 0.9474 Axillary node status -0.17863 1.65 1 0.1993 Tumor size -0.04265 0.13 1 0.7220 Global test 6.26 10 0.7933 Model goodness-of-fit After fitting a multivariable Cox Proportional Hazard Model, the adequacy of the fitted model was evaluated using Cox Snell residuals. The hazard function follows the 45 0 -line, which approximately, indicates that the model fits the data well (Fig. 4 ). 3.7. Predictors of breast cancer recurrence Covariates selected for the final model were age at diagnosis, preexisting comorbidity, BMI at diagnosis, histologic grade, histological type of cancer, stage of cancer, surgical margin, axillary node status, number of positive lymph nodes involved, and histological type of cancer. According to the results of multivariate Cox proportional hazard analysis, women aged 40 & below were nearly 3 times more at risk of increased time to breast cancer recurrence than those with age above 40 years (AHR = 3.32; 95%CI: 1.8–5.88). Similarly, women who were presented with body mass index ≥ 25 mg/m 2 at diagnosis had nearly 2 times higher risk of developing recurrence than those who had body mass index < 25 mg/m 2 (AHR = 1.95;95%CI: 1.06–3.59). On the other hand, women who were surgical margin positive at diagnosis had 2 times higher risk of developing recurrence than those who were surgical margin negative (AHR = 2.1;95%CI: 1.20–4.02). In addition, women who had axillary node status positive were nearly 1.9 times at higher risk of developing recurrence than those with axillary nodes negative (AHR = 1.98;95%CI: 1.08–3.61). Similarly, the presence of preexisting comorbidity (AHR = 4.45; 95%CI: 2.39–8.30) was 4 times more at risk for recurrence than its counterpart (Table 7 ). Table 7 Bivariable and multivariable Proportional Cox Hazard regression analysis of predictors associated with breast cancer recurrence in patients who had surgical treatment in Addis Ababa, Ethiopia, 2024. Variables Category Outcome CHR with 95% CI AHR with 95%CI P-value Recurrence Cen- Sored Age at dx ≥ 40 < 40 31 32 159 100 1 1.66(1.01–2.72) 1 3.35(1.8 - 5.8) < 0.001*** Comorbidity Yes No 40 23 46 213 5.16(3.09–8.6) 1 4.77(2.39 - 8.30) 1 < 0.001*** BMI at diagnosis Normal weight Overweight 16 47 155 104 1 3.2 (1.8–5.7) 1 3.33(1.06 - 3.59) 0.032* Cancer stage at diagnosis I II III 2 14 47 28 126 105 1 1.18(0.26–5.21) 4.43 (1.07–18.2) 1 1.16(0.24 - 5.58) 2.26(0.47- 10.75) 0.851 0.302 Histological grade Grade I Grade II Grade III 5 18 40 62 116 81 1 1.7(.60 - 4.6) 5 (1.9 - 12.6) 1 0.93 (0.31–2.49) 0.96(0.31–2.76) 0.859 0.949 Histological type Noninvasive Invasive 4 59 62 197 1 4.77(1.72–13.21) 1 1.8(0.58–5.71) 0.303 Surgical margin Free Involved 25 38 199 60 1 5.08(3.0 − 8.5) 1 3.47(1.20–4.02) 0.011* Number of + lymph nodes involved < 2 ≥ 2 16 47 135 124 1 2.48(1.40 - 4.37) 1 1.4(.72 -2.61) 0.339 Axillary node status Negative Positive 17 46 148 111 1 3.26(1.86 5.69) 1 2.84 (1.09–3.60) 0.025* Tumor size at diagnosis 5cm 3 24 36 33 136 90 1 1.44(.433 - 4.79) 3.2(.98 - 10.12) 1 0.5(.13- 1.83) 0.7(0.19- 2.5) 0.306 0.635 NB: *=significant, *p < 0.05, **p < 0.01 and ***p < 0.001 CHR = crude hazard ratio , AHR = adjusted hazard ratio , BMI = body mass index , 1 = references 4. Discussion This study investigated time to recurrence and predictors among women with breast cancer treated in selected public hospitals in Addis Ababa, Ethiopia. Our findings discovered a breast cancer recurrence incidence rate of 6.8 (95% CI: 5.35–8.14) per 100 person-years, with a median recurrence-free survival (RFS) of 85.73%. The 75% RFS time was 44 months, with estimated RFS proportions of 91.9%, 83%, 67%, and 61% at 24, 36, 48, and 60 months, respectively. Independent predictors of recurrence included age ≤ 40 years, BMI (overweight), positive surgical margins, positive axillary node status, and pre-existing comorbidities. The observed incidence rate is comparable with to findings from Addis Ababa (6.5%) ( 14 ) and South Korea (5.9%) ( 13 ), but higher than those reported in Australia (3.3%) ( 17 ) and Egypt (4.2%) ( 10 ), and lower than that reported in the Netherlands (11.9%) ( 12 ). These variations may be attributed to differences in patient populations, including stage at diagnosis. Our study population likely included a higher proportion of patients diagnosed at later stages, which is strongly associated with increased recurrence risk ( 14 , 18 ). The 75% RFS time of 44 months is consistent with findings from the USA (48 months) ( 19 ), Thailand (45.43 months) ( 6 ), and South Korea (47 months) ( 13 ), but higher than reports from France ( 20 ) and Addis Ababa ( 14 ). This difference might be related to the high proportion (98%) of our participants undergoing modified radical mastectomy, a surgical approach proven effective in recurrence prevention ( 20 ). Our study demonstrated estimated RFS rates of 97.19%, 91.7%, 83.3%, 67.07%, and 61.24% at 1, 2, 3, 4, and 5 years, respectively. The RFS rates for the first three years are comparable with those reported in Iran (2.5-year RFS: 86%) ( 11 ) and Addis Ababa (2-year RFS: 91.5%; 3-year RFS: 82.4%) ( 14 ). However, our 5-year RFS rate is higher than that reported in Addis Ababa (50.5% at 5 years and 28.5% at 6 years) ( 14 ) and more in line with findings from Iran (82.5% at 5 years) ( 11 ) and the Netherlands (88.4% at 4 years) ( 12 ). This seemingly paradoxical finding of higher long-term survival despite likely later-stage diagnoses in our population warrants further investigation. It may be partially explained by the high prevalence of two or more lymph node involvements in our cohort, as this has been associated with poorer RFS in other studies ( 10 , 18 , 21 ). Consistent with previous research ( 25 , 26 ), our analysis identified age ≤ 40 years as a significant predictor of recurrence, with these women having a three times higher risk compared to those over 40. The 5-year RFS was 63% for younger women and 60% for older women. This increased risk in younger women may be linked to more aggressive tumor biology, including a higher prevalence of estrogen receptor/progesterone receptor-negative, HER2-positive, and triple-negative tumors ( 22 ), and presentation at more advanced stages, as observed in our study where a higher proportion of women ≤ 40 years presented with stage III disease. Preexisting comorbidity were also associated with a four-fold increased recurrence risk, potentially due to metabolic impairments interfering with treatment response (21, 43). Overweight at diagnosis nearly doubled the risk of recurrence, consistent with the established link between overweight, chronic inflammation, and breast cancer progression ( 24 ). As expected, positive surgical margins and positive axillary node status were also significant predictors of recurrence, aligning with findings from other studies ( 10 , 21 , 23 , 24 ). Lymph node involvement is a strong indicator of potential disease spread and future recurrence. The association of these factors with recurrence highlights the importance of achieving negative surgical margins and thorough axillary staging. While histologic grade and clinical stage were not independently analyzed in this study, previous research, including studies conducted in Ethiopia ( 14 , 18 ), has consistently identified poorly differentiated histologic grade (grade III) and advanced clinical stage (stage III) as predictors of recurrence. These findings are likely related to delayed tumor detection and diagnosis. Limitation and strength of the study We couldn't include some behavioral factors in our study because the patient charts didn't have all the necessary information. Also, because we had to exclude incomplete charts, our data might not fully represent the whole population we were studying. However, it's important to note that this study employed a large sample size from a major referral hospital, providing valuable insights into breast cancer recurrence patterns within this specific population. Conclusions Overall the incidence rate of breast cancer recurrence was high. Comorbidity, Age less than 40 years, overweight, axillary node status positive, more number of lymph nodes involved and deep surgical margin were predictors variables with higher recurrence rates. These findings underline the need for targeted interventions and improved post-treatment surveillance. Declarations Ethical approval and consent to participate The ethical clearance was obtained from SPHMMC the institutional review board (IRB) (Ref. No;-Pm 23/1138). Then a support letter was submitted to the respective hospital authorities and permission was granted. Finally, the questionnaire was kept locked after the data entry. Consent for publishing ‘Not Applicable’ Competing interests There are no competing interests among the authors. Availability of data and materials Data and materials are available and can be shared by the corresponding author. Funding No funding was obtained for this study. Authors’ contributions Design and conception of the study: YC, TT, TG, WF, and AD; Performed the study: WF, AD, YC, TG, TT, GA, CA, and BT; Data analysis and interpretation: AD, WF, KM, BB, AW, MA, and TA; Writing of the manuscript: AD, YC; All the authors have read and approved the final manuscript. Acknowledgment We acknowledge St. Paul’s Hospital Millennium Medical College, Oromia Regional Health Bureau, Madda Walabu University Goba referral hospital, and Adama Hospital Medical College. Authors’ Information YC: Madda Walabu University Goba referral Hospital, Goba, Ethiopia; E-mail: [email protected] TT: St. Paul’s Hospital Millennium Medical College, Department of Oncology, Addis Ababa, Ethiopia; E-mail: [email protected] BB: St. Paul’s Hospital Millennium Medical College, Department of Oncology, Addis Ababa, Ethiopia; E-mail:Email: [email protected] TG: St. Paul’s Hospital Millennium Medical College, Department of Oncology, Addis Ababa, Ethiopia; E-mail: [email protected] WF: Adama Hospital Medical College, Department of Oncology, Adama, Ethiopia; E-mail: [email protected] CA: Early Start, School of Education, University of Wollongong, Wollongong, Australia, E-mail: [email protected] KM: Adama Hospital Medical College, Department of Internal Medicine, Adama, Ethiopia; E-mail: [email protected] TA: Adama Hospital Medical College, Department of General Surgery, Adama, Ethiopia; E-mail: [email protected] BT: Adama Public Health Research and Referral Laboratory Center, Adama, Ethiopia; E-mail: [email protected] GA: Adama Hospital Medical College, Department of Public Health, Ethiopia; E-mail: [email protected] AW: Adama Hospital Medical College, Department of Internal Medicine, Adama, Ethiopia; E-mail: [email protected] MA: Adama Hospital Medical College, Department of Oncology, Ethiopia; E-mail: [email protected] AD : Adama Hospital Medical College, Department of Public Health, Ethiopia; Mobile phone: +251911069074; E-mail: [email protected] References Wu X, Baig A, Kasymjanova G, Kafi K, Holcroft C, Mekouar H, et al. Pattern of Local Recurrence and Distant Metastasis in Breast Cancer By Molecular Subtype. Cureus. 2016 Dec 10; Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209–49. Sharma R, Aashima, Nanda M, Fronterre C, Sewagudde P, Ssentongo AE, et al. Mapping Cancer in Africa: A Comprehensive and Comparable Characterization of 34 Cancer Types Using Estimates From GLOBOCAN 2020. Front Public Heal. 2022 Apr 25;10. Trayes KP, Cokenakes SE. Treatment Cancer Breast. Am Fam Physician. 2021;104(2):171–8. Horani M, Abdel-Razeq H. Treatment options for patients with hormone receptor-positive, HER2-negative advanced-stage breast cancer: maintaining cyclin-dependent kinase 4/6 inhibitors beyond progression. Front Oncol. 2023;13(October):1–8. Wangchinda P, Ithimakin S. Factors that predict recurrence later than 5 years after initial treatment in operable breast cancer. World J Surg Oncol. 2016 Aug 24;14(1). Cossetti RJD, Tyldesley SK, Speers CH, Zheng Y, Gelmon KA. Comparison of breast cancer recurrence and outcome patterns between patients treated from 1986 to 1992 and from 2004 to 2008. J Clin Oncol. 2014 Nov 24;33(1):65–73. Metzger-Filho O, Sun Z, Viale G, Price KN, Crivellari D, Snyder RD, et al. Patterns of recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: Results from international breast cancer study group trials VIII and IX. J Clin Oncol. 2013 Sep 1;31(25):3083–90. DeSantis C, Siegel R, Bandi P, Jemal A. Breast cancer statistics, 2011. CA Cancer J Clin. 2011 Nov;61(6):408–18. Elsayed M, Alhussini M, Basha A, Awad AT. Analysis of loco-regional and distant recurrences in breast cancer after conservative surgery. World J Surg Oncol. 2016;(1). Kheradmand AA, Ranjbarnovin N, Khazaeipour Z. Postmastectomy locoregional recurrence and recurrence-free survival in breast cancer patients. World J Surg Oncol. 2010 Apr 17;8. Franken B, de Groot MR, Mastboom WJB, Vermes I, van der Palen J, Tibbe AGJ, et al. Circulating tumor cells, disease recurrence and survival in newly diagnosed breast cancer. Breast Cancer Res. 2012 Oct 22;14. Choi YJ, Shin YD, Song YJ. Comparison of ipsilateral breast tumor recurrence after breast-conserving surgery between ductal carcinoma in situ and invasive breast cancer. World J Surg Oncol. 2016 Apr 27;(1). Shiferaw WS, Aynalem YA, Yirga Akalu T, Demelew TM. Incidence and Predictors of Recurrence among Breast Cancer Patients in Black Lion Specialized Hospital Adult Oncology Unit, Addis Ababa, Ethiopia: Retrospective Follow-up Study with Survival Analysis. J Cancer Prev [Internet]. 2020;25(2):111–8. Available from: https://doi.org/10.15430/JCP.2020 Ren Z, Li Y, Hameed O, Siegal GP, Wei S. Prognostic factors in patients with metastatic breast cancer at the time of diagnosis. Pathol Res Pract [Internet]. 2014;210(5):301–6. Available from: http://dx.doi.org/10.1016/j.prp.2014.01.008 Colleoni M, Sun Z, Price KN, Karlsson P, Forbes JF, Thürlimann B, et al. Annual hazard rates of recurrence for breast cancer during 24 years of follow-up: Results from the international breast cancer study group trials I to V. J Clin Oncol. 2016 Mar 20;34(9):927–35. Lowery AJ, Kell MR, Glynn RW, Kerin MJ, Sweeney KJ. Locoregional recurrence after breast cancer surgery: A systematic review by receptor phenotype. Vol. 133, Breast Cancer Research and Treatment. 2012. Lafourcade A, His M, Baglietto L, Boutron-Ruault MC, Dossus L, Rondeau V. Factors associated with breast cancer recurrences or mortality and dynamic prediction of death using history of cancer recurrences: The French E3N cohort. BMC Cancer. 2018 Feb 9;18(1). He XM, Zou DH. The association of young age with local recurrence in women with early-stage breast cancer after breast-conserving therapy: A meta-analysis. Sci Rep. 2017 Dec 1;7(1). Crozier JA, Moreno-Aspitia A, Ballman K V., Dueck AC, Pockaj BA, Perez EA. Effect of body mass index on tumor characteristics and disease-free survival in patients from the HER2-positive adjuvant trastuzumab trial N9831. Cancer. 2013 Jul 1;119(13):2447–54. Tonellotto F, Bergmann A, de Souza Abrahão K, de Aguiar SS, Bello MA, Thuler LCS. Impact of Number of Positive Lymph Nodes and Lymph Node Ratio on Survival of Women with Node-Positive Breast Cancer. Eur J Breast Heal. 2019 Apr 1;15(2):76–84. American Cancer Society. Breast Cancer: Treating Breast Cancer. Am Cancer Soc [Internet]. 2019;1–120. Available from: https://www.cancer.org/cancer/breast-cancer/treatment.html Sh Mutlak N, Ramiz Al-Mukhtar F, Nabeel Al-Dawoodi FS, Tharwat Sulaiman CI. Recurrent Breast Cancer Following Modified Radical Mastectomy and Risk Factors. Vol. 54, J Fac Med Baghdad Baghdad. 2012. Teferi D, Dadi D, Hassen I, Teklemariam B, Yesufe A. Empowering Catchment Health Center to Deliver Comprehensive and Safe Obstetric Care Near Clients' Home: An Institutional Experience in Ethiopia. Clin Audit. 2024; Volume 16:29–37. Zhao C, Hu W, Xu Y, Wang D, Wang Y, Lv W, et al. Current Landscape: The Mechanism and Therapeutic Impact of Obesity for Breast Cancer. Front Oncol. 2021;11(July):1–20. Bundred J, Michael S, Bowers S, Barnes N, Jauhari Y, Plant D, et al. Do surgical margins matter after mastectomy? A systematic review. Eur J Surg Oncol [Internet]. 2020;46(12):2185–94. Available from: https://doi.org/10.1016/j.ejso.2020.08.015 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Jun, 2025 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted Editorial decision: Accepted 23 Mar, 2025 Reviews received at journal 22 Mar, 2025 Reviewers agreed at journal 22 Mar, 2025 Reviewers invited by journal 19 Mar, 2025 Submission checks completed at journal 17 Mar, 2025 First submitted to journal 17 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5852483","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431281834,"identity":"506507b9-5b35-46fd-8e3b-e74b9a994626","order_by":0,"name":"Yadessa Chala","email":"","orcid":"","institution":"Madda Walabu university Goba referral hospital","correspondingAuthor":false,"prefix":"","firstName":"Yadessa","middleName":"","lastName":"Chala","suffix":""},{"id":431281836,"identity":"0fbbd5fd-3aac-4f4d-b330-a4e4e57be569","order_by":1,"name":"Tesfaye Techane","email":"","orcid":"","institution":"Saint Paul Hospital Millennium Medical 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Hospital Medical College","correspondingAuthor":true,"prefix":"","firstName":"Alem","middleName":"","lastName":"Deksisa","suffix":""}],"badges":[],"createdAt":"2025-01-18 03:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5852483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5852483/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00432-025-06181-2","type":"published","date":"2025-06-12T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79093495,"identity":"4dc318ff-5c19-4af8-aafc-740e2c3d7f1b","added_by":"auto","created_at":"2025-03-24 10:33:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20626,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival status of breast cancer patients who underwent surgery in selected public hospitals of Addis Ababa, Ethiopia, 2024.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5852483/v1/d84bf8cab5a2cc52d4387182.png"},{"id":79094745,"identity":"a16dfc92-403a-4dda-806f-49f2606d08a6","added_by":"auto","created_at":"2025-03-24 10:41:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18252,"visible":true,"origin":"","legend":"\u003cp\u003eAn overall Kaplan-Meier analysis of recurrence-free survival of breast cancer patients who had surgery at a selected public hospital, Addis Ababa, Ethiopia, 2024\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5852483/v1/0f08c4852dea76defa040415.png"},{"id":79093511,"identity":"f4ab6d49-f57b-48c9-908c-f3d799d5f2a5","added_by":"auto","created_at":"2025-03-24 10:33:24","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":524256,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier recurrence-free survival function among different groups of breast cancer patients with log-rank test P-value by Preexisting comorbidity status (P\u0026lt;0.001) (A), Surgical margin status (P\u0026lt;0.001) (B), Clinical stage of cancer (P\u0026lt; 0.001) (C), and Axillary node status (P\u0026lt;0.001) (D) at selected public hospital Addis Ababa Ethiopia, 2024.\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5852483/v1/4e17afc7b1d74e0f7d062fb9.jpeg"},{"id":79093496,"identity":"6994de6a-a8eb-4c9f-b458-780a56243055","added_by":"auto","created_at":"2025-03-24 10:33:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10205,"visible":true,"origin":"","legend":"\u003cp\u003eCox Snell residual test for the model's overall fitness.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5852483/v1/7debb80d0b385704bc1d9f78.png"},{"id":84726572,"identity":"8cc5ef49-8d86-4381-be0a-e30388f1d2b1","added_by":"auto","created_at":"2025-06-16 16:07:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2090481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5852483/v1/6bd39607-0bfe-40f3-a081-dfdd08b2ca66.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time to Breast Cancer Recurrence and Associated Predictors in Public Hospitals of Addis Ababa, Central Ethiopia: A Retrospective Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer is a complex illness with many faces \u0026ndash; different biological characteristics, a range of treatments, and varying outcomes for those diagnosed. At its core, it's about cells in the breast tissue that begin to grow and multiply uncontrollably, ultimately becoming cancerous (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2020, breast cancer became the most commonly diagnosed cancer worldwide, surpassing lung cancer, with 2.3\u0026nbsp;million new cases\u0026mdash;that's nearly 12% of all cancers diagnosed globally. Sadly, it's also the fifth leading cause of cancer-related deaths, claiming 685,000 lives each year (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Africa, and specifically in Ethiopia, breast cancer is the most prevalent cancer affecting people, both in terms of new diagnoses and deaths (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBreast cancer represents a significant global health burden, impacting millions of women worldwide. It is the most commonly diagnosed cancer among women, and a leading cause of cancer-related mortality. This burden extends beyond mortality, encompassing substantial morbidity, including physical and psychological consequence from diagnosis and treatment. The economic impact is also considerable, with costs associated with screening, diagnosis, treatment, and long-term care placing a strain on healthcare systems and individuals (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen someone is diagnosed with breast cancer, the treatment approach depends heavily on how far the cancer has progressed. For those with Stage I\u0026ndash;III breast cancer, surgery is often the first step, either a mastectomy (removal of the breast) or breast-conserving surgery followed by radiation therapy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Stage IV breast cancer, where the cancer has spread to other parts of the body, is generally considered more difficult to cure and is usually managed with systemic therapy, such as chemotherapy or targeted drugs, although sometimes surgery might be considered for comfort or symptom relief (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). After initial treatment, therapies like radiation, hormone therapy, and chemotherapy can be used to help lower the chances of the cancer coming back and improve survival (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnfortunately, breast cancer can sometimes return after initial treatment\u0026mdash;we call this recurrence (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The risk is highest in the first couple of years after diagnosis, but it remains a possibility, albeit lower, for many years afterward, roughly between 2% and 5% annually from years five to twenty (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Where the cancer returns can also vary, depending on the specific type of breast cancer. It might come back in the breast or chest wall area (local recurrence), spread to the bones (distant metastasis), or appear in nearby lymph nodes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This lingering possibility of recurrence can understandably make people feel that breast cancer is never truly \"gone.\" Even with the best treatments and close monitoring, some people (5\u0026ndash;10%) are diagnosed with metastatic disease right away, and another 20% will experience a recurrence later on (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Sadly, when the cancer does return, it often behaves more aggressively (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLooking at the numbers around the world, we see a significant difference in recurrence rates depending on where people live, reflecting differences in access to healthcare and resources. For example, studies have shown that within 5 years after surgery, recurrence rates were around 3.3% in Australia (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), 11.7% in the Netherlands (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and 5.9% in South Korea (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In countries with fewer resources, like Egypt and Iran, the rates were 4.2% (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and 20.2% (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), respectively. We have very limited information from Africa, but one study in Ethiopia found a recurrence rate of 18.5% (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Breast cancer recurrence is a key clinical challenge, influenced by patient factors like age and BMI, and tumor characteristics. Positive lymph node status, lympho-vascular invasion, high tumor grade, and hormone receptor negativity increase recurrence risk. Inadequate surgical margins also elevate local recurrence (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis lack of data in Ethiopia shows us that we don't fully understand how often breast cancer comes back, or what factors might contribute to it. That's why this study is so important\u0026mdash;we want to understand how long it takes for breast cancer to recur and identify any factors that might predict recurrence among women treated in public hospitals in Addis Ababa, Ethiopia.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design and setting\u003c/h2\u003e \u003cp\u003eAn institution-based retrospective cohort study was conducted at Saint Paul\u0026rsquo;s Hospital Millennium Medical College (SPHMMC) and Tikur Anbessa Specialized Hospital (TASH) from April 30- May 30, 2024. The study was conducted at the largest tertiary care, specialized, referral, and teaching hospitals. There are 13 public hospitals in Addis Ababa, and four public hospitals have oncology treatment centers (Tikur Anbessa Specialized Hospital, St. Paul's Hospital Millennium Medical College, Ethiopian Public Health Institute, and Yekatit 12 Hospital Medical College). Tikur Anbessa Specialized Hospital and St. Paul's Hospital Millennium Medical College were selected by using simple random sampling.\u003c/p\u003e \u003cp\u003eThe St. Paul\u0026rsquo;s Hospital Millennium Medical College was established in Addis Ababa, the capital city of Ethiopia in 1968 by the late Emperor Haile Selassie. The college has more than 2800 clinical, academic administrative, and support staff that provide medical specialty services to patients who are referred from all over the country, teaching medicine and nursing students and doing basic and applied research. The College can accommodate more than 700 inpatient beds, but on a daily average, 1200 emergency and outpatient patients are seen. St. Pauls\u0026lsquo; Hospital Millenium Medical College oncology unit was established on August 1, 2018. It was the second hospital offering cancer treatment in the country (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTikur Anbessa Specialized Hospital is the largest tertiary care, specialized, referral, and teaching hospital in the country that is owned by the government and established in 1973. TASH has 51 specialty outpatient clinics, serving 500,000 patients annually (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample size determination\u003c/h2\u003e \u003cp\u003eAs the study was a cohort study, the sample size needed for acquiring statistically significant results was determined using a two-population proportion formula. Therefore, the sample size was calculated by taking into account the predictor variables and using open epiInfo version 7.2.6 statistical package (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Among predictor variables, lymph node status is chosen as the main predictor variable of breast cancer recurrence during the 6 years of follow-up since it was considered to give the optimal sample size and most significant result. In this regard, with a 5% level of significance (two-sided), a power of 80%, and a ratio of unexposed to exposed of 1:1, the estimated proportion of recurrence in Ethiopia was taken at 10% for the non-exposed group ( negative lymph node status) and 22.4% for the exposed group ( positive lymph node status) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, in practice getting 153 patients their positive lymph nodes was difficult and the rest were from negative lymph nodes. Thus, the total sample size was 306. Finally, by adding 10% for incomplete data, the final sample size required was 337.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esample size calculation for time to recurrence and predictors of breast cancer recurrence among patients treated in a public hospital, Addis Ababa, Ethiopia 2024 by using open epi version 7.2.6 Software.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAssumptions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor predictors variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size by Fleiss with CC Formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal sample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTwo-sided significance level\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLymph node status (positive exposed; negative unexposed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of exposed\u0026thinsp;=\u0026thinsp;153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePower\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRatio of sample size\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% of Unexposed with Outcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNumber of unexposed\u0026thinsp;=\u0026thinsp;153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% of exposed with Outcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHazard ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelative risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Subjects\u003c/h2\u003e \u003cp\u003eAll medical records with breast cancer who had breast surgery in a selected public hospital in Addis Ababa, from September 11st 2018 to September 12, 2023. All medical records of breast cancer patients who had breast surgery in the SPHMMC and TASH hospitals from September 11 2018 to September 12 2023 were included in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data collection\u003c/h2\u003e \u003cp\u003eA data extraction tool was developed from related literature to collect information from patients' medical records. Socio-demographic, clinic-pathological, and treatment-related factors that are supposed to be predictors for breast cancer recurrence were extracted from the patient's medical records by using the Kobo toolbox. Data were collected by four BSc nurses and supervised by two MSc Oncology nurses.\u003c/p\u003e \u003cp\u003eBefore the data was collected, 5% (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) of the total sample size underwent a pretest to ensure that the questionnaires were clear and easy to access on the chart. After pre-testing the checklist, Cronbach's Alpha was calculated by using Stata version 15 to test the internal consistency (reliability) of the item and the result was 0.79. Study variables are identified based on similar studies and a data extraction tool was developed by the information available in the patient's medical record at the cancer treatment center. Training on how to collect data by the Kobo toolbox was given to data collectors and supervisors before one day of data collection. Senior experts in the area of study for content validity examined the data extraction tool. The entire data collection process was closely supervised by the supervisors and principal investigator. Supervisors checked the ID of the patient with the registry code whether clinically matched (at diagnosis stage I-III) or not.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data analysis\u003c/h2\u003e \u003cp\u003eData were entered, checked, and arranged in Kobo toolbox software. After Coding, editing, and cleaning analysis were done in Stata Vers.15. To summarize the cohort's characteristics, descriptive statistics such as frequency tables, life tables, graphs, median, and Inter quartile range were used. The incidence density rate was computed throughout the study. The survival time was estimated using the Kaplan\u0026ndash;Meier survival curve. Log-rank test was performed for the presence of any differences in time to recurrence among different categorical variables. Those variables that the test statistics \u0026amp; Kaplan-Meier analysis displayed as a significant difference in RFS function among categorical variables are considered as having significant evidence of differences in time to recurrence. The Cox proportional hazard model was used to identify the predictor variables. The Cox-proportional hazard model assumption was checked using the Schoenfeld residual/global test (0.79). Overall the fitness' of the proportional hazard model was assessed by using the Cox Snell residual graph. Multi-co linearity was checked using the variance inflation factor (3.72). Variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in the bivariable analysis were entered into the final multivariable analysis. Variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at a 95% confidence level were considered independent predictors of recurrence.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Socio-demographic traits of the respondents\u003c/h2\u003e \u003cp\u003eOut of 337 study participants of this study, 322 complete record reviews were done. This makes the response rate of the study 96%. The median age at diagnosis was 43 years (Inter-quartile range: 34\u0026ndash;51). Of the 322 study participants, 162(50.31%) were diagnosed at age 40 and above. One hundred sixty-two (81.37%) were married, and 195 (60.56%) were from rural areas (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of breast cancer patients who had surgical treatment in a selected public hospital, Addis Ababa, Ethiopia, 2024(n\u0026thinsp;=\u0026thinsp;322)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrequency, (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensored\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at dx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u0026gt;40\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190 (59.01)\u003c/p\u003e \u003cp\u003e132 (40.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (16.31)\u003c/p\u003e \u003cp\u003e32(24.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159 (83.69)\u003c/p\u003e \u003cp\u003e100(75.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003cp\u003eMarried\u003c/p\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (13.04)\u003c/p\u003e \u003cp\u003e162 (81.37)\u003c/p\u003e \u003cp\u003e11 (3.42)\u003c/p\u003e \u003cp\u003e7 (2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(14.28)\u003c/p\u003e \u003cp\u003e57(35.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (85.72)\u003c/p\u003e \u003cp\u003e205(64.82)\u003c/p\u003e \u003cp\u003e11(100)\u003c/p\u003e \u003cp\u003e7(100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (60.56)\u003c/p\u003e \u003cp\u003e127 (39.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(17.94)\u003c/p\u003e \u003cp\u003e28(22.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160(82.06)\u003c/p\u003e \u003cp\u003e99 (77.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Baseline clinical, pathological, and treatment characteristics of the study participants\u003c/h2\u003e \u003cp\u003eEighty-six (26.71%) women had preexisting comorbidity at the time of diagnosis, 152 (47.20%) cases were in clinical stage III cancer; and 135(41.93%) were moderately differentiated (grade II) histologic cancer cases at a time of diagnosis. Out of a total of 151 (46.8%), women were overweight during diagnosis. Invasive carcinoma was a commonest histologic type; accounting for 256(79.50%) of all cases. One hundred seventy-one (53.11%) cases had positive lymph involvement of two or above, and 160 (39.69%) cases had tumor size of 2 to 5 cm at the time of diagnosis. Ninety-eight (30.43%) cases had involved surgical margin status. About 157 (48.76%) women had positive axillary node status. One hundred sixty-one (50%), and 147(45.65%) of study participants had positive estrogen and progesterone receptors respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and pathological characteristics of breast cancer patients who had surgical treatment in a selected public hospital, Addis Ababa, Ethiopia, 2024 (n\u0026thinsp;=\u0026thinsp;322)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrequency, (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eOutcome status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRecurrence\u003c/b\u003e\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCensored\u003c/b\u003e\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (26.71)\u003c/p\u003e \u003cp\u003e236 (73.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(46.51)\u003c/p\u003e \u003cp\u003e23(9.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e46(53.49)\u003c/p\u003e \u003cp\u003e213(90.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171 (53.11)\u003c/p\u003e \u003cp\u003e151 (46.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(9.35)\u003c/p\u003e \u003cp\u003e47(31.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e155(90.65)\u003c/p\u003e \u003cp\u003e104(68.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147(45.65)\u003c/p\u003e \u003cp\u003e175(54.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(19.05)\u003c/p\u003e \u003cp\u003e35(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e119(80.95)\u003c/p\u003e \u003cp\u003e140(80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer stage at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003cp\u003eII\u003c/p\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (9.32)\u003c/p\u003e \u003cp\u003e140 (43.48)\u003c/p\u003e \u003cp\u003e152 (47.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(6.66)\u003c/p\u003e \u003cp\u003e14(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e47(30.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e28(93.34)\u003c/p\u003e \u003cp\u003e126(90)\u003c/p\u003e \u003cp\u003e105(69.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade I\u003c/p\u003e \u003cp\u003eGrade II\u003c/p\u003e \u003cp\u003eGrade III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (20.19)\u003c/p\u003e \u003cp\u003e135 (41.93)\u003c/p\u003e \u003cp\u003e122 (37.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(7.70)\u003c/p\u003e \u003cp\u003e18(13.33)\u003c/p\u003e \u003cp\u003e40(37.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e60(92.30)\u003c/p\u003e \u003cp\u003e117(86.67)\u003c/p\u003e \u003cp\u003e82(62.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNoninvasive\u003c/p\u003e \u003cp\u003eInvasive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (20.50)\u003c/p\u003e \u003cp\u003e256 (79.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(6.06)\u003c/p\u003e \u003cp\u003e59(23.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e62(93.94)\u003c/p\u003e \u003cp\u003e197(86.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree\u003c/p\u003e \u003cp\u003eInvolved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (69.57)\u003c/p\u003e \u003cp\u003e98 (30.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(11.16)\u003c/p\u003e \u003cp\u003e38(38.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e199(88.84)\u003c/p\u003e \u003cp\u003e60(61.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of +\u0026thinsp;lymph nodes involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003cp\u003e\u0026gt;= 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (43.89)\u003c/p\u003e \u003cp\u003e171 (53.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(10.60)\u003c/p\u003e \u003cp\u003e47(27.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e135(89.40)\u003c/p\u003e \u003cp\u003e124(72.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxillary node status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (51.24)\u003c/p\u003e \u003cp\u003e157 (48.76 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(10.30)\u003c/p\u003e \u003cp\u003e46(29.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e148(89.70)\u003c/p\u003e \u003cp\u003e111(70.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 cm\u003c/p\u003e \u003cp\u003e2\u0026ndash;5 cm\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (11.18)\u003c/p\u003e \u003cp\u003e160 (39.69)\u003c/p\u003e \u003cp\u003e126 (29.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(8.33)\u003c/p\u003e \u003cp\u003e24(15.00)\u003c/p\u003e \u003cp\u003e36(28.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e33(91.67)\u003c/p\u003e \u003cp\u003e136(75.00)\u003c/p\u003e \u003cp\u003e90(71.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstrogen receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003eNot determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (50.00)\u003c/p\u003e \u003cp\u003e129 40.06)\u003c/p\u003e \u003cp\u003e32 (9.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(21.74)\u003c/p\u003e \u003cp\u003e26(20.16)\u003c/p\u003e \u003cp\u003e2(6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e126(78.26)\u003c/p\u003e \u003cp\u003e103(79.84)\u003c/p\u003e \u003cp\u003e30(93.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesterone receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003eNot determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (45.65)\u003c/p\u003e \u003cp\u003e142 (44.10)\u003c/p\u003e \u003cp\u003e33 (10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(17.00)\u003c/p\u003e \u003cp\u003e36(25.35)\u003c/p\u003e \u003cp\u003e2(6.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e122(83.00)\u003c/p\u003e \u003cp\u003e106(74.65)\u003c/p\u003e \u003cp\u003e31(93.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to this study, about 55.28% of women's surgery was done after 30 days from the date of diagnosis. Three hundred eight (95.56%) cases had undergone Modified Radical Mastectomy. Three hundred forty (81%) women were using adjuvant chemotherapy and out of these ACT is the most common chemotherapy used as an Adjuvant regimen 173(53.7%). About Two hundred fourteen (66.46%) were using hormone therapy. Out of 214, about 148 (69.16) women who were diagnosed with early-stage breast cancer used the Tamoxifen regimen of hormonal therapy during these follow-ups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatment characteristics of breast cancer patients who had surgical treatment in selected public hospitals Addis Ababa, Ethiopia, 2024 (n\u0026thinsp;=\u0026thinsp;322)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency,%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecurrence(63)\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensored (259)\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration from diagnosis to surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; =30 days\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (44.72)\u003c/p\u003e \u003cp\u003e178 (55.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (15.97)\u003c/p\u003e \u003cp\u003e40 (22.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121(84.03)\u003c/p\u003e \u003cp\u003e138(77.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeo-adjuvant chemotherapy use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(11.80)\u003c/p\u003e \u003cp\u003e284(88.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (28.94)\u003c/p\u003e \u003cp\u003e52 (19.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(71.06)\u003c/p\u003e \u003cp\u003e232(80.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant chemotherapy use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288 (89.44)\u003c/p\u003e \u003cp\u003e34 (10.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(19.79)\u003c/p\u003e \u003cp\u003e6(17.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e231(80.21)\u003c/p\u003e \u003cp\u003e28(72.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy regimen used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003cp\u003eAC\u003c/p\u003e \u003cp\u003ePaclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173 (53.7)\u003c/p\u003e \u003cp\u003e75 (23.3)\u003c/p\u003e \u003cp\u003e40 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(20.80)\u003c/p\u003e \u003cp\u003e12 (16.00)\u003c/p\u003e \u003cp\u003e9 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137(79.20)\u003c/p\u003e \u003cp\u003e63(84.00)\u003c/p\u003e \u003cp\u003e31(77.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of hormonal therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (66.46)\u003c/p\u003e \u003cp\u003e108 (33.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(20.09)\u003c/p\u003e \u003cp\u003e20(18.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171(79.91)\u003c/p\u003e \u003cp\u003e88 (81.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA regimen of hormonal therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTamoxifen\u003c/p\u003e \u003cp\u003eAnastrazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (69.16)\u003c/p\u003e \u003cp\u003e66 (30.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(18.24)\u003c/p\u003e \u003cp\u003e16(24.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (81.76)\u003c/p\u003e \u003cp\u003e50 (75.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Overall status of breast cancer patients\u003c/h2\u003e \u003cp\u003eIn this study, 322 women patients with breast cancer who underwent surgery were followed retrospectively. The median follow-up time was 33 months, with a minimum and maximum follow-up time of 5 and 60 months, respectively. The recurrence-free survival status at the median follow-up time was 85.73% (95%CI\u0026thinsp;=\u0026thinsp;80.8%-89.4%). In this study 63 (19.56%) patients developed recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Incidence of breast cancer recurrence\u003c/h2\u003e \u003cp\u003eThe overall incidence rate of breast cancer recurrence in the cohort during the 919 person-years of observation was 6.8 per 100 person-years (95%CI\u0026thinsp;=\u0026thinsp;5.35\u0026ndash;8.14) follow-up. In this study, the 75% recurrence-free survival (RFS) time was 44 months (95%CI\u0026thinsp;=\u0026thinsp;40\u0026ndash;48.00). The estimated overall RFS survival at 24, 36, 48, and 60 months was 91.93%, 83.3%, and 67.7%, 61% respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There were 32(50.79%) distant recurrences \u0026amp; 31 (49.21%) loco regional recurrences; and regarding the site of recurrence, axillary and opposite breast recurrence for loco regional type, and lung \u0026amp; chest wall for the distant recurrence were the commonest sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Comparison of time to breast cancer recurrence among categorical variables\u003c/h2\u003e \u003cp\u003eLog-rank test was performed to determine the presence of a significant difference in recurrence rate among categorical variables such as;-Age of patients at diagnosis, Preexisting comorbidity status, Surgical margin status, Clinical stage of cancer, and Axillary node status at 5% level of significance (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The Log-rank (LR) test has shown that women who were age 40 and below, and those above 40 years at diagnosis had differences in their time recurrence-free survival time (P value for LR test\u0026thinsp;=\u0026thinsp;0.041). Also, the recurrence-free survival time was different among women who had preexisting comorbidity and those with no preexisting comorbidity at baseline (P value for LR test\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, patients who were diagnosed with clinical stage III cancer have a shorter median time to recurrence compared to those who were presented with clinical stage I and II cancer (P value for LR\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The graph of the log displayed that there was a difference in recurrence-free survival time among women presented with histologic grades I \u0026amp; II, and those with grade III. The median time to recurrence is longer in grades I and II than in histological grade III, and this difference was significant at a log-rank test P value of \u0026lt;\u0026thinsp;0.001. Additionally, the median time to recurrence was shorter in women who had two or more lymph node involvement than those with less than two lymph node involvement (P value for LR-test\u0026thinsp;=\u0026thinsp;0.0012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLog-rank test for categorical independent variables among breast cancer recurrence patients who had surgical treatment in Addis Ababa, Ethiopia, 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage of cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph node\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAxillary node status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Cox proportional hazard Assumption\u003c/h2\u003e \u003cp\u003eThe Cox proportional hazard assumptions were checked statistically using a global test. All the covariates met the proportional hazard assumption, and the p-value over all Schoenfeld global tests was 0.7933 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProportional Hazard assumption model of the Cox model for breast cancer patients who had surgical treatment in selected public hospitals in Addis Ababa, Ethiopia, 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePro\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological type of cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esurgical margin status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph node involvement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAxillary node status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.17863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.7933\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModel goodness-of-fit\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter fitting a multivariable Cox Proportional Hazard Model, the adequacy of the fitted model was evaluated using Cox Snell residuals. The hazard function follows the 45\u003csup\u003e0\u003c/sup\u003e-line, which approximately, indicates that the model fits the data well (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Predictors of breast cancer recurrence\u003c/h2\u003e \u003cp\u003eCovariates selected for the final model were age at diagnosis, preexisting comorbidity, BMI at diagnosis, histologic grade, histological type of cancer, stage of cancer, surgical margin, axillary node status, number of positive lymph nodes involved, and histological type of cancer. According to the results of multivariate Cox proportional hazard analysis, women aged 40 \u0026amp; below were nearly 3 times more at risk of increased time to breast cancer recurrence than those with age above 40 years (AHR\u0026thinsp;=\u0026thinsp;3.32; 95%CI: 1.8\u0026ndash;5.88). Similarly, women who were presented with body mass index\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25 mg/m\u003csup\u003e2\u003c/sup\u003e at diagnosis had nearly 2 times higher risk of developing recurrence than those who had body mass index\u0026thinsp;\u0026lt;\u0026thinsp;25 mg/m\u003csup\u003e2\u003c/sup\u003e (AHR\u0026thinsp;=\u0026thinsp;1.95;95%CI: 1.06\u0026ndash;3.59). On the other hand, women who were surgical margin positive at diagnosis had 2 times higher risk of developing recurrence than those who were surgical margin negative (AHR\u0026thinsp;=\u0026thinsp;2.1;95%CI: 1.20\u0026ndash;4.02). In addition, women who had axillary node status positive were nearly 1.9 times at higher risk of developing recurrence than those with axillary nodes negative (AHR\u0026thinsp;=\u0026thinsp;1.98;95%CI: 1.08\u0026ndash;3.61). Similarly, the presence of preexisting comorbidity (AHR\u0026thinsp;=\u0026thinsp;4.45; 95%CI: 2.39\u0026ndash;8.30) was 4 times more at risk for recurrence than its counterpart (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariable and multivariable Proportional Cox Hazard regression analysis of predictors associated with breast cancer recurrence in patients who had surgical treatment in Addis Ababa, Ethiopia, 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCHR with 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAHR with 95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCen-\u003c/p\u003e \u003cp\u003eSored\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at dx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e40\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.66(1.01\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3.35(1.8 - 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.16(3.09\u0026ndash;8.6)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.77(2.39 - 8.30)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155\u003c/p\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3.2 (1.8\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3.33(1.06 - 3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.032*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer stage at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003cp\u003eII\u003c/p\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e126\u003c/p\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.18(0.26\u0026ndash;5.21)\u003c/p\u003e \u003cp\u003e4.43 (1.07\u0026ndash;18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.16(0.24 - 5.58)\u003c/p\u003e \u003cp\u003e2.26(0.47- 10.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade I\u003c/p\u003e \u003cp\u003eGrade II\u003c/p\u003e \u003cp\u003eGrade III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003cp\u003e116\u003c/p\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.7(.60 - 4.6)\u003c/p\u003e \u003cp\u003e5 (1.9 - 12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.93 (0.31\u0026ndash;2.49)\u003c/p\u003e \u003cp\u003e0.96(0.31\u0026ndash;2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNoninvasive\u003c/p\u003e \u003cp\u003eInvasive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e4.77(1.72\u0026ndash;13.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.8(0.58\u0026ndash;5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree\u003c/p\u003e \u003cp\u003eInvolved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199\u003c/p\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e5.08(3.0 \u0026minus;\u0026thinsp;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3.47(1.20\u0026ndash;4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of +\u0026thinsp;lymph nodes involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2.48(1.40 - 4.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.4(.72 -2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxillary node status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3.26(1.86 5.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2.84 (1.09\u0026ndash;3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 cm\u003c/p\u003e \u003cp\u003e2\u0026ndash;5 cm\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e136\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.44(.433 - 4.79)\u003c/p\u003e \u003cp\u003e3.2(.98 - 10.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.5(.13- 1.83)\u003c/p\u003e \u003cp\u003e0.7(0.19- 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNB: *=significant, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e \u003cb\u003eCHR\u003c/b\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;crude hazard ratio\u003c/em\u003e, \u003cb\u003eAHR\u003c/b\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;adjusted hazard ratio\u003c/em\u003e, \u003cb\u003eBMI\u003c/b\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;body mass index\u003c/em\u003e, \u003cb\u003e1\u003c/b\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;references\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study investigated time to recurrence and predictors among women with breast cancer treated in selected public hospitals in Addis Ababa, Ethiopia. Our findings discovered a breast cancer recurrence incidence rate of 6.8 (95% CI: 5.35–8.14) per 100 person-years, with a median recurrence-free survival (RFS) of 85.73%. The 75% RFS time was 44 months, with estimated RFS proportions of 91.9%, 83%, 67%, and 61% at 24, 36, 48, and 60 months, respectively. Independent predictors of recurrence included age ≤ 40 years, BMI (overweight), positive surgical margins, positive axillary node status, and pre-existing comorbidities.\u003c/p\u003e\u003cp\u003eThe observed incidence rate is comparable with to findings from Addis Ababa (6.5%) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and South Korea (5.9%) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), but higher than those reported in Australia (3.3%) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and Egypt (4.2%) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and lower than that reported in the Netherlands (11.9%) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These variations may be attributed to differences in patient populations, including stage at diagnosis. Our study population likely included a higher proportion of patients diagnosed at later stages, which is strongly associated with increased recurrence risk (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 75% RFS time of 44 months is consistent with findings from the USA (48 months) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), Thailand (45.43 months) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and South Korea (47 months) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), but higher than reports from France (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and Addis Ababa (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This difference might be related to the high proportion (98%) of our participants undergoing modified radical mastectomy, a surgical approach proven effective in recurrence prevention (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study demonstrated estimated RFS rates of 97.19%, 91.7%, 83.3%, 67.07%, and 61.24% at 1, 2, 3, 4, and 5 years, respectively. The RFS rates for the first three years are comparable with those reported in Iran (2.5-year RFS: 86%) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and Addis Ababa (2-year RFS: 91.5%; 3-year RFS: 82.4%) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, our 5-year RFS rate is higher than that reported in Addis Ababa (50.5% at 5 years and 28.5% at 6 years) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and more in line with findings from Iran (82.5% at 5 years) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and the Netherlands (88.4% at 4 years) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This seemingly paradoxical finding of higher long-term survival despite likely later-stage diagnoses in our population warrants further investigation. It may be partially explained by the high prevalence of two or more lymph node involvements in our cohort, as this has been associated with poorer RFS in other studies (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsistent with previous research (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), our analysis identified age ≤ 40 years as a significant predictor of recurrence, with these women having a three times higher risk compared to those over 40. The 5-year RFS was 63% for younger women and 60% for older women. This increased risk in younger women may be linked to more aggressive tumor biology, including a higher prevalence of estrogen receptor/progesterone receptor-negative, HER2-positive, and triple-negative tumors (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and presentation at more advanced stages, as observed in our study where a higher proportion of women ≤ 40 years presented with stage III disease.\u003c/p\u003e \u003cp\u003ePreexisting comorbidity were also associated with a four-fold increased recurrence risk, potentially due to metabolic impairments interfering with treatment response (21, 43). Overweight at diagnosis nearly doubled the risk of recurrence, consistent with the established link between overweight, chronic inflammation, and breast cancer progression (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs expected, positive surgical margins and positive axillary node status were also significant predictors of recurrence, aligning with findings from other studies (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Lymph node involvement is a strong indicator of potential disease spread and future recurrence. The association of these factors with recurrence highlights the importance of achieving negative surgical margins and thorough axillary staging.\u003c/p\u003e \u003cp\u003eWhile histologic grade and clinical stage were not independently analyzed in this study, previous research, including studies conducted in Ethiopia (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), has consistently identified poorly differentiated histologic grade (grade III) and advanced clinical stage (stage III) as predictors of recurrence. These findings are likely related to delayed tumor detection and diagnosis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitation and strength of the study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe couldn't include some behavioral factors in our study because the patient charts didn't have all the necessary information. Also, because we had to exclude incomplete charts, our data might not fully represent the whole population we were studying.\u003c/p\u003eHowever, it's important to note that this study employed a large sample size from a major referral hospital, providing valuable insights into breast cancer recurrence patterns within this specific population.\u003cp\u003e\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eOverall the incidence rate of breast cancer recurrence was high. Comorbidity, Age less than 40 years, overweight, axillary node status positive, more number of lymph nodes involved and deep surgical margin were predictors variables with higher recurrence rates. These findings underline the need for targeted interventions and improved post-treatment surveillance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical clearance was obtained from SPHMMC the institutional review board (IRB) (Ref. No;-Pm 23/1138). Then a support letter was submitted to the respective hospital authorities and permission was granted. Finally, the questionnaire was kept locked after the data entry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publishing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Not Applicable\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThere are no competing interests among the authors.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eData and materials are available and can be shared by the corresponding author.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding was obtained for this study.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eDesign and conception of the study: YC, TT, TG, WF, and AD; Performed the study: WF, AD, YC, TG, TT, GA, CA, and BT; Data analysis and interpretation: AD, WF, KM, BB, AW, MA, and TA; Writing of the manuscript: AD, YC; All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgment\u003c/p\u003e\n\u003cp\u003eWe acknowledge St. Paul\u0026rsquo;s Hospital Millennium Medical College, Oromia Regional Health Bureau, Madda Walabu University Goba referral hospital, and Adama Hospital Medical College.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; Information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYC:\u003c/strong\u003e Madda Walabu University Goba referral Hospital, Goba, Ethiopia; E-mail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTT:\u003c/strong\u003e St. Paul\u0026rsquo;s Hospital Millennium Medical College, Department of Oncology, Addis Ababa, Ethiopia; E-mail:[email protected] \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBB: \u003c/strong\u003eSt. Paul\u0026rsquo;s Hospital Millennium Medical College, Department of Oncology, Addis Ababa, Ethiopia; E-mail:Email:[email protected] \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTG: \u003c/strong\u003eSt. Paul\u0026rsquo;s Hospital Millennium Medical College, Department of Oncology, Addis Ababa, Ethiopia; E-mail:[email protected] \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWF:\u003c/strong\u003e Adama Hospital Medical College, Department of Oncology, Adama, Ethiopia; E-mail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCA: \u003c/strong\u003eEarly Start, School of Education, University of Wollongong, Wollongong, Australia, E-mail: [email protected] \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKM:\u003c/strong\u003e Adama Hospital Medical College, Department of Internal Medicine, Adama, Ethiopia; E-mail: [email protected] \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTA: \u003c/strong\u003eAdama Hospital Medical College, Department of General Surgery, Adama, Ethiopia; E-mail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBT: \u003c/strong\u003eAdama Public Health Research and Referral Laboratory Center, Adama, Ethiopia; E-mail: \u003cu\[email protected]\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGA:\u003c/strong\u003e Adama Hospital Medical College, Department of Public Health, Ethiopia; E-mail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAW: \u003c/strong\u003eAdama Hospital Medical College, Department of Internal Medicine, Adama, Ethiopia; E-mail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMA: \u003c/strong\u003eAdama Hospital Medical College, Department of Oncology, Ethiopia; E-mail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAD\u003c/strong\u003e: Adama Hospital Medical College, Department of Public Health, Ethiopia; Mobile phone: +251911069074; E-mail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWu X, Baig A, Kasymjanova G, Kafi K, Holcroft C, Mekouar H, et al. Pattern of Local Recurrence and Distant Metastasis in Breast Cancer By Molecular Subtype. Cureus. 2016 Dec 10; \u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eSharma R, Aashima, Nanda M, Fronterre C, Sewagudde P, Ssentongo AE, et al. Mapping Cancer in Africa: A Comprehensive and Comparable Characterization of 34 Cancer Types Using Estimates From GLOBOCAN 2020. Front Public Heal. 2022 Apr 25;10. \u003c/li\u003e\n\u003cli\u003eTrayes KP, Cokenakes SE. Treatment Cancer Breast. Am Fam Physician. 2021;104(2):171\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eHorani M, Abdel-Razeq H. Treatment options for patients with hormone receptor-positive, HER2-negative advanced-stage breast cancer: maintaining cyclin-dependent kinase 4/6 inhibitors beyond progression. Front Oncol. 2023;13(October):1\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eWangchinda P, Ithimakin S. Factors that predict recurrence later than 5 years after initial treatment in operable breast cancer. World J Surg Oncol. 2016 Aug 24;14(1). \u003c/li\u003e\n\u003cli\u003eCossetti RJD, Tyldesley SK, Speers CH, Zheng Y, Gelmon KA. Comparison of breast cancer recurrence and outcome patterns between patients treated from 1986 to 1992 and from 2004 to 2008. J Clin Oncol. 2014 Nov 24;33(1):65\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eMetzger-Filho O, Sun Z, Viale G, Price KN, Crivellari D, Snyder RD, et al. Patterns of recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: Results from international breast cancer study group trials VIII and IX. J Clin Oncol. 2013 Sep 1;31(25):3083\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eDeSantis C, Siegel R, Bandi P, Jemal A. Breast cancer statistics, 2011. CA Cancer J Clin. 2011 Nov;61(6):408\u0026ndash;18. \u003c/li\u003e\n\u003cli\u003eElsayed M, Alhussini M, Basha A, Awad AT. Analysis of loco-regional and distant recurrences in breast cancer after conservative surgery. World J Surg Oncol. 2016;(1). \u003c/li\u003e\n\u003cli\u003eKheradmand AA, Ranjbarnovin N, Khazaeipour Z. Postmastectomy locoregional recurrence and recurrence-free survival in breast cancer patients. World J Surg Oncol. 2010 Apr 17;8. \u003c/li\u003e\n\u003cli\u003eFranken B, de Groot MR, Mastboom WJB, Vermes I, van der Palen J, Tibbe AGJ, et al. Circulating tumor cells, disease recurrence and survival in newly diagnosed breast cancer. Breast Cancer Res. 2012 Oct 22;14. \u003c/li\u003e\n\u003cli\u003eChoi YJ, Shin YD, Song YJ. Comparison of ipsilateral breast tumor recurrence after breast-conserving surgery between ductal carcinoma in situ and invasive breast cancer. World J Surg Oncol. 2016 Apr 27;(1). \u003c/li\u003e\n\u003cli\u003eShiferaw WS, Aynalem YA, Yirga Akalu T, Demelew TM. Incidence and Predictors of Recurrence among Breast Cancer Patients in Black Lion Specialized Hospital Adult Oncology Unit, Addis Ababa, Ethiopia: Retrospective Follow-up Study with Survival Analysis. J Cancer Prev [Internet]. 2020;25(2):111\u0026ndash;8. Available from: https://doi.org/10.15430/JCP.2020\u003c/li\u003e\n\u003cli\u003eRen Z, Li Y, Hameed O, Siegal GP, Wei S. Prognostic factors in patients with metastatic breast cancer at the time of diagnosis. Pathol Res Pract [Internet]. 2014;210(5):301\u0026ndash;6. Available from: http://dx.doi.org/10.1016/j.prp.2014.01.008\u003c/li\u003e\n\u003cli\u003eColleoni M, Sun Z, Price KN, Karlsson P, Forbes JF, Th\u0026uuml;rlimann B, et al. Annual hazard rates of recurrence for breast cancer during 24 years of follow-up: Results from the international breast cancer study group trials I to V. J Clin Oncol. 2016 Mar 20;34(9):927\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eLowery AJ, Kell MR, Glynn RW, Kerin MJ, Sweeney KJ. Locoregional recurrence after breast cancer surgery: A systematic review by receptor phenotype. Vol. 133, Breast Cancer Research and Treatment. 2012. \u003c/li\u003e\n\u003cli\u003eLafourcade A, His M, Baglietto L, Boutron-Ruault MC, Dossus L, Rondeau V. Factors associated with breast cancer recurrences or mortality and dynamic prediction of death using history of cancer recurrences: The French E3N cohort. BMC Cancer. 2018 Feb 9;18(1). \u003c/li\u003e\n\u003cli\u003eHe XM, Zou DH. The association of young age with local recurrence in women with early-stage breast cancer after breast-conserving therapy: A meta-analysis. Sci Rep. 2017 Dec 1;7(1). \u003c/li\u003e\n\u003cli\u003eCrozier JA, Moreno-Aspitia A, Ballman K V., Dueck AC, Pockaj BA, Perez EA. Effect of body mass index on tumor characteristics and disease-free survival in patients from the HER2-positive adjuvant trastuzumab trial N9831. Cancer. 2013 Jul 1;119(13):2447\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eTonellotto F, Bergmann A, de Souza Abrah\u0026atilde;o K, de Aguiar SS, Bello MA, Thuler LCS. Impact of Number of Positive Lymph Nodes and Lymph Node Ratio on Survival of Women with Node-Positive Breast Cancer. Eur J Breast Heal. 2019 Apr 1;15(2):76\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eAmerican Cancer Society. Breast Cancer: Treating Breast Cancer. Am Cancer Soc [Internet]. 2019;1\u0026ndash;120. Available from: https://www.cancer.org/cancer/breast-cancer/treatment.html\u003c/li\u003e\n\u003cli\u003eSh Mutlak N, Ramiz Al-Mukhtar F, Nabeel Al-Dawoodi FS, Tharwat Sulaiman CI. Recurrent Breast Cancer Following Modified Radical Mastectomy and Risk Factors. Vol. 54, J Fac Med Baghdad Baghdad. 2012. \u003c/li\u003e\n\u003cli\u003eTeferi D, Dadi D, Hassen I, Teklemariam B, Yesufe A. Empowering Catchment Health Center to Deliver Comprehensive and Safe Obstetric Care Near Clients\u0026apos; Home: An Institutional Experience in Ethiopia. Clin Audit. 2024; Volume 16:29\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eZhao C, Hu W, Xu Y, Wang D, Wang Y, Lv W, et al. Current Landscape: The Mechanism and Therapeutic Impact of Obesity for Breast Cancer. Front Oncol. 2021;11(July):1\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eBundred J, Michael S, Bowers S, Barnes N, Jauhari Y, Plant D, et al. Do surgical margins matter after mastectomy? A systematic review. Eur J Surg Oncol [Internet]. 2020;46(12):2185\u0026ndash;94. Available from: https://doi.org/10.1016/j.ejso.2020.08.015\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Breast, cancer, Ethiopia, Predictors, recurrence","lastPublishedDoi":"10.21203/rs.3.rs-5852483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5852483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer recurrence is a significant concern when the disease returns following surgery. The time to recurrence and factors affecting it are not well studied in low-income countries. This study aimed to assess the time to recurrence and predictors of breast cancer among women treated in public hospitals of Addis Ababa, Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRetrospective cohort study was conducted from April 30 to May 30, 2024, among randomly selected 322 recorded cases. The study covered from September 11, 2018, to September 12, 2023. Data were collected by the Kobo toolbox and analyzed by Stata Version 15. The Cox proportional hazard model was used to identify predictor variables, with assumptions checked using the Schoenfeld residual/global test (0.79). Multi-collinearity was checked using the variance inflation factor (3.72). Variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in bivariable analysis were entered into the final multivariable analysis. Variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at 95% confidence level were considered independent predictors of recurrence.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe recurrence-free survival (RFS) status at the median follow-up time was 87.5%. The incidence rate of breast cancer recurrence was 6.8 per 100 women years (95%CI\u0026thinsp;=\u0026thinsp;5.34\u0026ndash;8.13) follow-up. The 75%RFS time was 44 months (95CI%=40\u0026ndash;48). The proportion of RFS survival at 24, 36, 48, and 60 months was 91.93%, 83.3%, and 67.7%, 61% respectively. Women aged 40 \u0026amp; below (AHR\u0026thinsp;=\u0026thinsp;3.32; 95%CI: 1.80\u0026ndash;5.88), Overweight (AHR\u0026thinsp;=\u0026thinsp;1.95, 95%CI: 1.06\u0026ndash;3.59), surgical margin positive (AHR\u0026thinsp;=\u0026thinsp;2.1; 95%CI: 1.20\u0026ndash;4.02), axillary node-positive (AHR\u0026thinsp;=\u0026thinsp;1.98; 95%CI: 1.08\u0026ndash;3.61) and comorbidity (AHR\u0026thinsp;=\u0026thinsp;4.45, 95%CI: 2.39\u0026ndash;8.30) were independent predictors for increased hazard of recurrence.\u003c/p\u003e\u003ch2\u003eConclusion and Recommendation:\u003c/h2\u003e \u003cp\u003eThis study confirms a substantial incidence of breast cancer recurrence, with identifiable predictors including comorbidity, age, overweight, positive axillary node status, lymph node involvement, and deep surgical margin. Targeted interventions aimed at improving patient understanding of recurrence risk, promoting adherence to treatment protocols, and fostering healthy lifestyle modifications are crucial for reducing recurrence rates.\u003c/p\u003e","manuscriptTitle":"Time to Breast Cancer Recurrence and Associated Predictors in Public Hospitals of Addis Ababa, Central Ethiopia: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 10:33:18","doi":"10.21203/rs.3.rs-5852483/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-03-24T00:35:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-22T08:15:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137125814485425422013946443976194038943","date":"2025-03-22T07:30:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T00:35:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T03:25:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Research and Clinical Oncology","date":"2025-03-18T00:34:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"85de6697-8282-4207-8881-2045fb360603","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T16:02:51+00:00","versionOfRecord":{"articleIdentity":"rs-5852483","link":"https://doi.org/10.1007/s00432-025-06181-2","journal":{"identity":"journal-of-cancer-research-and-clinical-oncology","isVorOnly":false,"title":"Journal of Cancer Research and Clinical Oncology"},"publishedOn":"2025-06-12 15:57:13","publishedOnDateReadable":"June 12th, 2025"},"versionCreatedAt":"2025-03-24 10:33:18","video":"","vorDoi":"10.1007/s00432-025-06181-2","vorDoiUrl":"https://doi.org/10.1007/s00432-025-06181-2","workflowStages":[]},"version":"v1","identity":"rs-5852483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5852483","identity":"rs-5852483","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}