Evaluation Factors Affecting on Recurrence, Metastasis, and Survival of Breast Cancer in Iranian Women by Multi-State Model Approach
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Abstract
Background: We used the multistate model to investigate how prognostic factors of breast cancer are seen to affect the disease process.
Methods: This cohort study was conducted at Motamed Cancer Institute of Tehran, Iran on 2363 breast cancer patients admitted from 1978 to 2017, and they were followed up until 2018. We applied the multistate models, including four states: diagnosis, recurrence, metastasis, and final absorbing mortality state.
Results: Age over 50 years, positive lymph nodes and tumor size intensified the hazard of transition from diagnosis to metastasis (P=0.002, P<0.001 and P=0.001 respectively) and they also intensified the hazard of transition from diagnosis to mortality (P=0.010, P<0.001 and P<0.001 respectively). At the same time, the educational level decreased the hazard of mentioned transitions (P<0.001). Positive estrogen receptors reduced the hazard of transition from diagnosis to metastasis (P=0.007) and positive lymph nodes also intensified the hazard of transition from metastasis to mortality (P=0.040). Tumor size had an increasing role in the transitions from diagnosis to recurrence, recurrence to metastasis, and metastasis to mortality (P=0.014, P=0.018 and P=0.002 respectively).
Conclusion: Multistate model presented the detailed effects of prognostic factors on progression of breast cancer. Implementing early diagnosis strategies and providing informational programs, especially in younger ages and lower educational level patients may be helpful in reducing the hazard of transition to higher states of breast cancer and increasing the survival of Iranian women with breast cancer by controlling tumor size growth, lymph nodes involvements and estrogen receptor status.
1. Miller KD, Goding Sauer A, Ortiz AP, et al (2018). Cancer statistics for hispanics/latinos, 2018. CA Cancer J Clin, 68 (6):425-45.
2. McKinney SM, Sieniek M, Godbole V, et al (2020). International evaluation of an AI system for breast cancer screening. Nature, 577 (7788):89-94.
3. Sun Y-S, Zhao Z, Yang Z-et al (2017). Risk factors and preventions of breast cancer. Int J Biol Sci, 13 (11):1387-97.
4. Roshandel G, Ghanbari-Motlagh A, Partovipour E, et al (2019). Cancer incidence in Iran in 2014: results of the Iranian National Population-based Cancer Registry. Cancer Epidemiol, 61:50-58.
5. Alizadeh M, Ghojazadeh M, Piri R, et al (2021). Age at Diagnosis of Breast Cancer in Iran: A Systematic Review and Meta-Analysis. Iran J Public Health, 50 (8):1564-76.
6. Nafissi N, Khayamzadeh M, Zeinali Z, et al (2018). Epidemiology and histopathology of breast cancer in Iran versus other Middle Eastern countries. Middle East J Cancer, 9:243-251.
7. Lu W, Jansen L, Post W, et al (2009). Impact on survival of early detection of isolated breast recurrences after the primary treatment for breast cancer: a meta-analysis. Breast Cancer Res Treat, 114 (3):403-12.
8. Anderson SJ, Wapnir I, Dignam JJ, et al (2009). Prognosis after ipsilateral breast tumor recurrence and locoregional recurrences in patients treated by breast-conserving therapy in five National Surgical Adjuvant Breast and Bowel Project protocols of node-negative breast cancer. J Clin Oncol, 27 (15):2466-73.
9. Harris E, Barry M, Kell MR (2013). Meta-analysis to determine if surgical resection of the primary tumour in the setting of stage IV breast cancer impacts on survival. Ann Surg Oncol, 20 (9):2828-34.
10. Spronk I, Schellevis FG, Burgers JS, et al (2018). Incidence of isolated local breast cancer recurrence and contralateral breast cancer: a systematic review. Breast, 39:70-79.
11. Suva LJ, Griffin RJ, Makhoul I (2009). Mechanisms of bone metastases of breast cancer. Endocr Relat Cancer, 16 (3):703-13.
12. Lentzsch S, Reichardt P, Weber F, Budach V, Dörken B (1999). Brain metastases in breast cancer: prognostic factors and management. Eur J Cancer, 35 (4):580-5.
13. Cardoso F, Costa A, Norton L, et al (2012). 1st International consensus guidelines for advanced breast cancer (ABC 1). Breast, 21 (3):242-52.
14. Green S, Walter P, Kumar V, et al (1986). Human oestrogen receptor cDNA: sequence, expression and homology to v-erb-A. Nature, 320 (6058):134-39.
15. Putter H, van der Hage J, de Bock GH, et al CJ (2006). Estimation and prediction in a multi‐state model for breast cancer. Biom J, 48 (3):366-80.
16. Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: competing risks and multi‐state models. Stat Med, 26 (11):2389-430.
17. Van Den Hout A (2016). Multi-state survival models for interval-censored data. ed. CRC Press.
18. Cook RJ, Lawless JF (2018). Multistate models for the analysis of life history data. ed. CRC Press.
19. Gong Y, Zhang J, Ji P, Ling H, Hu X, Shao ZM (2018). Incidence proportions and prognosis of breast cancer patients with bone metastases at initial diagnosis. Cancer Med, 7 (8):4156-69.
20. Wang R, Zhu Y, Liu X, Liao X, He J, Niu L (2019). The Clinicopathological features and survival outcomes of patients with different metastatic sites in stage IV breast cancer. BMC Cancer, 19 (1):1091-12.
21. Jung EJ, Kim J-Y, Kim J-M, et al (2021). Positive estrogen receptor status is a poor prognostic factor in node-negative breast cancer: An observational study in Asian patients. Medicine (Baltimore), 100 (11):e25000.
22. Ieva F, Jackson CH, Sharples LD (2017). Multi-State modelling of repeated hospitalisation and death in patients with Heart Failure: the use of large administrative databases in clinical epidemiology. Stat Methods Med Res, 26 (3):1350-72.
23. Meira-Machado L, de Uña-Álvarez J, Cadarso-Suárez C, Andersen PK (2009). Multi-state models for the analysis of time-to-event data. Stat Methods Med Res, 18 (2):195-222.
24. Jackson CH (2011). Multi-state models for panel data: the msm package for R. J Stat Software, 38:1-28.
25. Andersen PK, Abildstrom SZ, Rosthøj S (2002). Competing risks as a multi-state model. Stat Methods Med Res, 11 (2):203-15.
26. Adami H-O, Malker B, Holmberg L, Persson I, Stone B (1986). The relation between survival and age at diagnosis in breast cancer. N Engl J Med, 315 (9):559-63.
27. Bonnier P, Romain S, Charpin C, et al (1995). Age as a prognostic factor in breast cancer: relationship to pathologic and biologic features. Int J Cancer, 62 (2):138-44.
28. Chen MT, Sun HF, Zhao Y, et al (2017). Comparison of patterns and prognosis among distant metastatic breast cancer patients by age groups: a SEER population-based analysis. Sci Rep, 7 (1):9254.
29. Abedi G, Janbabai G, Moosazadeh M, et al (2016). Survival rate of breast cancer in Iran: a meta-analysis. Asian Pac J Cancer Prev, 17 (10):4615-21.
30. Anders CK, Hsu DS, Broadwater G, et al (2008). Young age at diagnosis correlates with worse prognosis and defines a subset of breast cancers with shared patterns of gene expression. J Clin Oncol, 26 (20):3324-30.
31. Fredholm H, Eaker S, Frisell J, et al (2009). Breast cancer in young women: poor survival despite intensive treatment. PLoS One, 4 (11):e7695.
32. Osmani F, Hajizadeh E, Rasekhi A, Akbari ME (2018). Analyzing relationship between local and metastasis relapses with survival of patients with breast cancer: a study using joint frailty model. Int J Cancer Manag, 11 (12): e81783.
33. Carter CL, Allen C, Henson DE (1989). Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer, 63 (1):181-87.
34. Foerster M, McKenzie F, Zietsman A, et al (2021). Dissecting the journey to breast cancer diagnosis in sub‐Saharan Africa: Findings from the multicountry ABC‐DO cohort study. Int J Cancer, 148 (2):340-51.
35. Herndon JE, Kornblith AB, Holland JC, Paskett ED (2013). Effect of socioeconomic status as measured by education level on survival in breast cancer clinical trials. Psychooncology, 22 (2):315-23.
36. Yip CH, Rhodes A (2014). Estrogen and progesterone receptors in breast cancer. Future Oncol, 10 (14):2293-301.
37. Saha Roy S, Vadlamudi RK (2012). Role of estrogen receptor signaling in breast cancer metastasis. Int J Breast Cancer, 2012: 654698.
38. Michaelson JS, Silverstein M, Wyatt J, et al (2002). Predicting the survival of patients with breast carcinoma using tumor size. Cancer, 95 (4):713-23.
39. Movahedi M, Haghighat S, Khayamzadeh M, et al (2012). Survival rate of breast cancer based on geographical variation in Iran, a national study. Iran Red Crescent Med J, 14 (12):798-804.
40. Sopik V, Narod SA (2018). The relationship between tumour size, nodal status and distant metastases: on the origins of breast cancer. Breast Cancer Res Treat, 170 (3):647-656.
41. Mertens WC, Hilbert V, Makari‐Judson G (2004). Contralateral breast cancer: factors associated with stage and size at presentation. Breast J, 10 (4):304-12.
2. McKinney SM, Sieniek M, Godbole V, et al (2020). International evaluation of an AI system for breast cancer screening. Nature, 577 (7788):89-94.
3. Sun Y-S, Zhao Z, Yang Z-et al (2017). Risk factors and preventions of breast cancer. Int J Biol Sci, 13 (11):1387-97.
4. Roshandel G, Ghanbari-Motlagh A, Partovipour E, et al (2019). Cancer incidence in Iran in 2014: results of the Iranian National Population-based Cancer Registry. Cancer Epidemiol, 61:50-58.
5. Alizadeh M, Ghojazadeh M, Piri R, et al (2021). Age at Diagnosis of Breast Cancer in Iran: A Systematic Review and Meta-Analysis. Iran J Public Health, 50 (8):1564-76.
6. Nafissi N, Khayamzadeh M, Zeinali Z, et al (2018). Epidemiology and histopathology of breast cancer in Iran versus other Middle Eastern countries. Middle East J Cancer, 9:243-251.
7. Lu W, Jansen L, Post W, et al (2009). Impact on survival of early detection of isolated breast recurrences after the primary treatment for breast cancer: a meta-analysis. Breast Cancer Res Treat, 114 (3):403-12.
8. Anderson SJ, Wapnir I, Dignam JJ, et al (2009). Prognosis after ipsilateral breast tumor recurrence and locoregional recurrences in patients treated by breast-conserving therapy in five National Surgical Adjuvant Breast and Bowel Project protocols of node-negative breast cancer. J Clin Oncol, 27 (15):2466-73.
9. Harris E, Barry M, Kell MR (2013). Meta-analysis to determine if surgical resection of the primary tumour in the setting of stage IV breast cancer impacts on survival. Ann Surg Oncol, 20 (9):2828-34.
10. Spronk I, Schellevis FG, Burgers JS, et al (2018). Incidence of isolated local breast cancer recurrence and contralateral breast cancer: a systematic review. Breast, 39:70-79.
11. Suva LJ, Griffin RJ, Makhoul I (2009). Mechanisms of bone metastases of breast cancer. Endocr Relat Cancer, 16 (3):703-13.
12. Lentzsch S, Reichardt P, Weber F, Budach V, Dörken B (1999). Brain metastases in breast cancer: prognostic factors and management. Eur J Cancer, 35 (4):580-5.
13. Cardoso F, Costa A, Norton L, et al (2012). 1st International consensus guidelines for advanced breast cancer (ABC 1). Breast, 21 (3):242-52.
14. Green S, Walter P, Kumar V, et al (1986). Human oestrogen receptor cDNA: sequence, expression and homology to v-erb-A. Nature, 320 (6058):134-39.
15. Putter H, van der Hage J, de Bock GH, et al CJ (2006). Estimation and prediction in a multi‐state model for breast cancer. Biom J, 48 (3):366-80.
16. Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: competing risks and multi‐state models. Stat Med, 26 (11):2389-430.
17. Van Den Hout A (2016). Multi-state survival models for interval-censored data. ed. CRC Press.
18. Cook RJ, Lawless JF (2018). Multistate models for the analysis of life history data. ed. CRC Press.
19. Gong Y, Zhang J, Ji P, Ling H, Hu X, Shao ZM (2018). Incidence proportions and prognosis of breast cancer patients with bone metastases at initial diagnosis. Cancer Med, 7 (8):4156-69.
20. Wang R, Zhu Y, Liu X, Liao X, He J, Niu L (2019). The Clinicopathological features and survival outcomes of patients with different metastatic sites in stage IV breast cancer. BMC Cancer, 19 (1):1091-12.
21. Jung EJ, Kim J-Y, Kim J-M, et al (2021). Positive estrogen receptor status is a poor prognostic factor in node-negative breast cancer: An observational study in Asian patients. Medicine (Baltimore), 100 (11):e25000.
22. Ieva F, Jackson CH, Sharples LD (2017). Multi-State modelling of repeated hospitalisation and death in patients with Heart Failure: the use of large administrative databases in clinical epidemiology. Stat Methods Med Res, 26 (3):1350-72.
23. Meira-Machado L, de Uña-Álvarez J, Cadarso-Suárez C, Andersen PK (2009). Multi-state models for the analysis of time-to-event data. Stat Methods Med Res, 18 (2):195-222.
24. Jackson CH (2011). Multi-state models for panel data: the msm package for R. J Stat Software, 38:1-28.
25. Andersen PK, Abildstrom SZ, Rosthøj S (2002). Competing risks as a multi-state model. Stat Methods Med Res, 11 (2):203-15.
26. Adami H-O, Malker B, Holmberg L, Persson I, Stone B (1986). The relation between survival and age at diagnosis in breast cancer. N Engl J Med, 315 (9):559-63.
27. Bonnier P, Romain S, Charpin C, et al (1995). Age as a prognostic factor in breast cancer: relationship to pathologic and biologic features. Int J Cancer, 62 (2):138-44.
28. Chen MT, Sun HF, Zhao Y, et al (2017). Comparison of patterns and prognosis among distant metastatic breast cancer patients by age groups: a SEER population-based analysis. Sci Rep, 7 (1):9254.
29. Abedi G, Janbabai G, Moosazadeh M, et al (2016). Survival rate of breast cancer in Iran: a meta-analysis. Asian Pac J Cancer Prev, 17 (10):4615-21.
30. Anders CK, Hsu DS, Broadwater G, et al (2008). Young age at diagnosis correlates with worse prognosis and defines a subset of breast cancers with shared patterns of gene expression. J Clin Oncol, 26 (20):3324-30.
31. Fredholm H, Eaker S, Frisell J, et al (2009). Breast cancer in young women: poor survival despite intensive treatment. PLoS One, 4 (11):e7695.
32. Osmani F, Hajizadeh E, Rasekhi A, Akbari ME (2018). Analyzing relationship between local and metastasis relapses with survival of patients with breast cancer: a study using joint frailty model. Int J Cancer Manag, 11 (12): e81783.
33. Carter CL, Allen C, Henson DE (1989). Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer, 63 (1):181-87.
34. Foerster M, McKenzie F, Zietsman A, et al (2021). Dissecting the journey to breast cancer diagnosis in sub‐Saharan Africa: Findings from the multicountry ABC‐DO cohort study. Int J Cancer, 148 (2):340-51.
35. Herndon JE, Kornblith AB, Holland JC, Paskett ED (2013). Effect of socioeconomic status as measured by education level on survival in breast cancer clinical trials. Psychooncology, 22 (2):315-23.
36. Yip CH, Rhodes A (2014). Estrogen and progesterone receptors in breast cancer. Future Oncol, 10 (14):2293-301.
37. Saha Roy S, Vadlamudi RK (2012). Role of estrogen receptor signaling in breast cancer metastasis. Int J Breast Cancer, 2012: 654698.
38. Michaelson JS, Silverstein M, Wyatt J, et al (2002). Predicting the survival of patients with breast carcinoma using tumor size. Cancer, 95 (4):713-23.
39. Movahedi M, Haghighat S, Khayamzadeh M, et al (2012). Survival rate of breast cancer based on geographical variation in Iran, a national study. Iran Red Crescent Med J, 14 (12):798-804.
40. Sopik V, Narod SA (2018). The relationship between tumour size, nodal status and distant metastases: on the origins of breast cancer. Breast Cancer Res Treat, 170 (3):647-656.
41. Mertens WC, Hilbert V, Makari‐Judson G (2004). Contralateral breast cancer: factors associated with stage and size at presentation. Breast J, 10 (4):304-12.
| Files | ||
| Issue | Vol 52 No 10 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v52i10.13857 | |
| Keywords | ||
| Multistate models Breast cancer Transition probability Survival analysis | ||
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How to Cite
1.
Mousavi M, Hajizadeh E, Rasekhi A, Haghighat S. Evaluation Factors Affecting on Recurrence, Metastasis, and Survival of Breast Cancer in Iranian Women by Multi-State Model Approach. Iran J Public Health. 2023;52(10):2186-2195.
