Journal of Health Sciences & Surveillance System

Prediction of Breast Cancer Metastasis Via Decision Tree Modeling

Document Type : Original Article

Authors

Tayeb Mohammadi 1, 2
Hadi Raeisi Shahraki 3
Jalal Poorolajal 4
Roya Najafi-Vosough 2
Khadijeh Najafi-Ghobadi 2
Javad Faradmal 2, 5

1 Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran

2 Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

3 Department of Epidemiology and Biostatistics, School of Health, Shahrekord University of Medical Sciences, Shahrekord, Iran

4 Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

5 Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

https://doi.org/10.30476/jhsss.2021.93316.1442
Abstract
Background: Nowadays, breast cancer (BC) metastasis is a nightmare for women and one of the main challenges among researchers worldwide. Unlike traditional statistical methods that are not able to handle and take into account the complexity of effects and existence of interactions among predictor variables, the decision trees can overcome these problems. This study aimed to predict and identify the main prognostic factors of BC metastasis status (binary response) using decision tree modeling.
Methods: This retrospective cohort study was conducted on 375 patients with BC who had registered with the Comprehensive Cancer Control Center from 1998 to 2013. Some demographic features related to the conditions of the Person’s disease and the type of treatment received were recorded. We applied a tree-based approach using the Gini index as the homogeneity criterion to explore the factors affecting metastasis occurrence in BC patients.
Results: The mean (SD) age of BC patients with and without metastasis was 55.7 (12.4) and 43.1 (7.2) years, respectively (P<0.001). The rate of metastasis was 33.3%. The five most important risk factors for metastasis of tumor proposed by tree diagram were age at diagnosis, grade of tumor, type of surgery, number of deliveries, and axillary surgery. The prediction accuracy of the proposed model was 84.3%, and its sensitivity and specificity were 66.4% and 93.2%, respectively.
Conclusion: Age at diagnosis was the most important factor for predicting breast cancer metastasis, so that breast cancer patients aged over 54 were at high risk of metastasis.

Keywords

Breast neoplasms
Neoplasm metastasis
Decision trees
Machine learning
Risk factors
  1. World Health Organization. Breast cancer: prevention and control. 2021. Available from: URL: https://www.who.int/news-room/fact-sheets/detail/breast-cancer.(Accessed 30 August 2021).
  2. Kolandoozan S, Sadjadi A, Radmard AR, Khademi H. Five common cancers in Iran. Arch Iran Med.2010; 13(2):143-6. PMID: 20187669.
  3. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network‐based classification of breast cancer metastasis. Mol. Syst. Biol. 2007; 3, 140. doi:10.1038/msb4100180.
  4. Weigelt B, Peterse JL, Van't Veer LJ. Breast cancer metastasis: markers and models. Nat Rev Cancer. 2005; 5(8):591-602. doi: 1038/nrc1670.
  5. Song, YY, Lu Y. Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiatry. 2015; 27(2):130-5. doi: 10.11919/j.issn.1002-0829.215044. PMID: 26120265; PMCID: PMC4466856.
  6. D’Ambrosio A, Aria M, Iorio C, Siciliano R. Regression trees for multivalued numerical response variables. Expert Syst. Appl. 2017; 69:21-8. doi: 10.1016/j.eswa.2016.10.021.
  7. T. Saleh, A. Attia and O. Shaker, "Studying combined breast cancer biomarkers using machine learning techniques," 2016 IEEE 14th International Symposium on SAMI, Herlany, Slovakia, 2016: 247-251. doi: 10.1109/SAMI.2016.7423015.
  8. Sysoev O, Bartoszek K, Ekström EC, Ekholm Selling K. PSICA: Decision trees for probabilistic subgroup identification with categorical treatments. Stat Med. 2019;38(22):4436-52. doi: 10.1002/sim.8308. PMID: 31246349; PMCID: PMC6771862.
  9. De'ath G, Fabricius KE. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology. 2000; 81(11):3178-92. doi: 1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2.
  10. Zhao Y, Zhang Y. Comparison of decision tree methods for finding active objects. Adv Space Res. 2008;41(12):1955-9. doi:10.1016/j.asr.2007.07.020.
  11. Poorolajal J, Nafissi N, Akbari ME, Mahjub H, Esmailnasab N, Babaee E. Breast Cancer Survival Analysis Based on Immunohistochemistry Subtypes (ER/PR/HER2): a Retrospective Cohort Study. Arch Iran Med. 2016;19(10):680-6. doi: 0161910/aim.003. PMID: 27743431.
  12. Weigelt B, Peterse JL, Van't Veer LJ. Breast cancer metastasis: markers and models. Nat Rev Cancer. 2005;5(8):591-602. doi: 10.1038/nrc1670. PMID: 16056258.
  13. Moon WK, Lee YW, Huang YS, Lee SH, Bae MS, Yi A. et al. Computer-aided prediction of axillary lymph node status in breast cancer using tumor surrounding tissue features in ultrasound images. Comput Methods Programs Biomed. 2017;146:143-50. doi: 10.1016/j.cmpb.2017.06.001. PMID: 28688484.
  14. Purushotham A, Shamil E, Cariati M, Agbaje O, Muhidin A, Gillett C, et al. Age at diagnosis and distant metastasis in breast cancer–a surprising inverse relationship. Eur J Cancer. 2014;50(10):1697-705. doi: 10.1016/j.ejca.2014.04.00.
  15. Watkins EJ. Overview of breast cancer.
    2019;32(10):13-7. doi: 10.1097/01.JAA.0000580524.95733.3d.
  16. Assi HA, Khoury KE, Dbouk H, Khalil LE, Mouhieddine TH, El Saghir NS. Epidemiology and prognosis of breast cancer in young women. J Thorac Dis. 2013 Jun;5 Suppl 1:S2-8. doi: 10.3978/j.issn.2072-1439.2013.05.24. PMID: 23819024; PMCID: PMC3695538.
  17. Tazhibi M, Fayaz M, Mokarian F. Detection of prognostic factors in metastatic breast cancer. J Res Med Sci. 2013;18(4):283-90 . PMID: 24124424; PMCID: PMC3793372.
  18. Lao C, Kuper-Hommel M, Elwood M, Campbell I, Lawrenson R. Metastatic relapse of stage I–III breast cancer in New Zealand. Cancer Causes Control. 2021;32(7):753-761. doi: 10.1007/s10552-021-01426-0. PMID: 33830387.
  19. Han L, Zhu Y, Liu Z, Yu T, He C, Jiang W. et al. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. Eur Radiol. 2019 ;29(7):3820-9. doi: 10.1007/s00330-018-5981-2. PMID: 30701328.
  20. Cui X, Wang N, Zhao Y, Chen S, Li S, Xu M. et al. Preoperative prediction of axillary lymph node metastasis in breast cancer using radiomics features of DCE-MRI. Sci Rep. 2019 ;9(1):2240. doi: 10.1038/s41598-019-38502-0. PMID: 30783148; PMCID: PMC6381163.
  21. Yu FH, Wang JX, Ye XH, Deng J, Hang J, Yang B. Ultrasound-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol. 2019;119:108658. doi: 10.1016/j.ejrad.2019.108658. PMID: 31521878.
  22. Tang Y, Yang CM, Su S, Wang WJ, Fan LP, Shu J. Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma. BMC Cancer. 2021;21(1):1268. doi: 10.1186/s12885-021-08947-6. PMID: 34819043; PMCID: PMC8611922.
  23. National breast cancer foundation. About Breast Cancer : Stages : Stage 3 (III) A, B, And C Breast Cancer Overview. Available from: URL: https://www.nationalbreastcancer.org/breast-cancer-stage-3. (Accessed 13 March 2023).
  24. Shahbazian H, Nouralizadeh M, Hosseini SM. A Retrospective study on Frequency of Modified Radical Mastectomy and Breast Conserving Surgery and its Association with Breast Cancer Stage in Southwest Iran. Int. J. Pharm. Res. Allied Sci. 2016;5(2): 208-211.
  25. Bettaieb I, Boukhris S, Adouni O, Zemni I, Goucha A, Jaidane O. et al. Breast Cancer in Elderly Women: Role of Tumor Characteristics in Predicting Axillary Lymph Node Metastasis. Eur J Surg Oncol. 2020;46(2):e36. doi: 10.1016/j.ejso.2019.11.058.
  26. Çiftci, F. The Role Of Breast-Conserving Surgery In The Treatment Of Early-Stage Breast Cancer. Dicle Tıp Dergisi. 2020;47(4):852-8. doi: 5798/dicletip.850378.
  27. Bhattacharjee, A. Comparison of Early Postoperative Outcome between Breast Conservative Surgery and Modified Radical Mastectomy: Bangladesh Perspective. European Journal of Surgical Oncology. 2020; 46(2): e35-6. doi: 10.1016/j.ejso.2019.11.056.
  28. Jääskeläinen A, Roininen N, Karihtala P, Jukkola A. High parity predicts poor outcomes in patients with luminal B-like (HER2 negative) early breast cancer: a prospective Finnish single-center study. Front Oncol. 2020;10:1470. doi: 10.3389/fonc.2020.01470. PMID: 32923400; PMCID: PMC7457016.
  29. Keyser EA, Staat BC, Fausett MB, Shields AD. Pregnancy-associated breast cancer. Rev Obstet Gynecol. 2012;5(2):94-9. PMID: 22866188; PMCID: PMC3410508.
  30. Najafi-Vosough R, Faradmal J, Tapak L, Alafchi B, Najafi-Ghobadi K, Mohammadi T. Prediction the survival of patients with breast cancer using random survival forests for competing risks. J Prev Med Hyg. 2022;63(2):E298-E303. doi: 10.15167/2421-4248/jpmh2022.63.2.2405. PMID: 35968067; PMCID: PMC9351408.
Journal of Health Sciences & Surveillance System
Volume 11, Issue 2
Spring 2023
Pages 296-301

Home

Guide for Authors

Submit Manuscript

Guide for Reviewers

Contact Us

Forms