Skip to main content

Advertisement

SpringerLink
Log in

Machine learning to predict the cancer-specific mortality of patients with primary non-metastatic invasive breast cancer

  • Original Article
  • Published:
Surgery Today Aims and scope Submit manuscript

Abstract

Purpose

We used five machine-learning algorithms to predict cancer-specific mortality after surgical resection of primary non-metastatic invasive breast cancer.

Methods

This study was a secondary analysis of data for 1661 women with primary non-metastatic invasive breast cancer. The overall patient population was divided into a training group and a test group at a ratio of 8:2 and python was used for machine learning to establish the prognosis model.

Results

The machine-learning Gbdt algorithm for cancer-specific death caused by various factors showed the five most important factors, ranked from high to low as follows: the number of regional lymph node metastases, LDH, triglyceride, plasma fibrinogen, and cholesterol. Among the five algorithm models in the test group, the highest accuracy rate was by DecisionTree (0.841), followed by the gbm algorithm (0.838). Among the five algorithms, the AUC values from high to low were GradientBoosting (0.755), gbm (0.755), Logistic (0.733), Forest (0.715), and DecisionTree (0.677).

Conclusion

Machine learning can predict cancer-specific mortality after surgery for patients with primary non-metastatic invasive breast.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet (London, England). 2012;379:432–44.

    Article  CAS  Google Scholar 

  2. Di LA, Jerusalem G, Petruzelka L, Torres R, Bondarenko IN, Khasanov R, et al. Final overall survival: fulvestrant 500 mg vs 250 mg in the randomized CONFIRM trial. J Natl Cancer Inst. 2014;106:djt337.

    Article  Google Scholar 

  3. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA. 2011;61:69–90.

  4. Acs G, Paragh G, Chuang S-T, Laronga C, Zhang PJ. The presence of micropapillary features and retraction artifact in core needle biopsy material predicts lymph node metastasis in breast carcinoma. Am J Surg Pathol. 2009;33:202–10.

    Article  Google Scholar 

  5. Shah TS, Kaag M, Raman JD, Chan W, Tran T, Kunchala S, et al. Clinical significance of prominent retraction clefts in invasive urothelial carcinoma. Hum Pathol. 2017;61:90–6.

    Article  Google Scholar 

  6. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319:1317–8.

    Article  Google Scholar 

  7. Delahanty RJ, Kaufman D, Jones SS. Development and evaluation of an automated machine learning algorithm for in-hospital mortality risk adjustment among critical care patients. Crit Care Med. 2018;46:e481–8.

    Article  Google Scholar 

  8. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216–9.

    Article  Google Scholar 

  9. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed health Inf. 2017;21:4–21.

    Article  Google Scholar 

  10. Sarkans U, Gostev M, Athar A, Behrangi E, Melnichuk O, Ali A, et al. The BioStudies database-one stop shop for all data supporting a life sciences study. Nucleic Acids Res. 2018;46:D1266–70.

    Article  CAS  Google Scholar 

  11. Viale G. Breast cancer. Lancet. 2005;365:1727–41.

    Article  Google Scholar 

  12. Li L, Chen L-Z. Factors influencing axillary lymph node metastasis in invasive breast cancer. Asian Pac J Cancer Prev. 2012;13:251–4.

    Article  Google Scholar 

  13. Fisher B, Bauer M, Wickerham DL, Redmond CK, Fisher ER, Cruz AB, et al. Relation of number of positive axillary nodes to the prognosis of patients with primary breast cancer. NSABP Update Cancer. 1983;52:1551–7.

    CAS  PubMed  Google Scholar 

  14. Fisher B, Jeong J-H, Anderson S, Bryant J, Fisher ER, Wolmark N. Twenty-five-year follow-up of a randomized trial comparing radical mastectomy, total mastectomy, and total mastectomy followed by irradiation. N Engl J Med. 2002;347:567–75.

    Article  Google Scholar 

  15. Voordeckers M, Vinh-Hung V, Van DSJ, Lamote J, Storme G. The lymph node ratio as prognostic factor in node-positive breast cancer. Radiother Oncol J Eur Soc Ther Radiol Oncol. 2004;70:225–30.

    Article  Google Scholar 

  16. Vinnicombe S, Pinto PSM, McCormack VA, Shiel S, Perry N, Dos SSIM. Full-field digital versus screen-film mammography: comparison within the UK breast screening program and systematic review of published data. Radiology. 2009;251:347–58.

    Article  Google Scholar 

  17. Hong YC. Serum high-density lipoprotein cholesterol and breast cancer risk by menopausa status, body mass index, and hormonal receptor in Korea. Cancer Epidemiol Biomarkers Prev. 2009;18:508–15.

    Article  Google Scholar 

  18. Lin X, Hong S, Huang J, Chen Y, Chen Y, Wu Z. Plasma apolipoprotein A1 levels at diagnosis are independent prognostic factors in invasive ductal breast cancer. Discov Med. 2017;23:247–58.

    PubMed  Google Scholar 

  19. Carlson SE. An empirically derived dietary pattern associated with breast cancer risk is validated in a nested case-control cohort from a randomized primary prevention trial. Clin Nutr Espen. 2017;17:8–17.

    Article  Google Scholar 

  20. Hux JE. Diabetes mellitus and breast cancer: a retrospective population-based cohort study. Breast Cancer Res Treat. 2006;98:349–56.

    Article  Google Scholar 

  21. Cook R. Serum lactate dehydrogenase (LDH) is a significant prognostic variable for survival in patients with metastatic breast cancer-a multivariate analysis. Eur J Cancer Suppl. 2008;6:189.

    Google Scholar 

  22. Kamby C, Bruun Rasmussen B, Kristensen B. Prognostic indicators of metastatic bone disease in human breast cancer. Cancer. 1991;68:2045–50.

    Article  CAS  Google Scholar 

  23. Ryberg M, Nielsen D, Osterlind K, Andersen PK, Skovsgaard T, Dombernowsky P. Predictors of central nervous system metastasis in patients with metastatic breast cancer. A competing risk analysis of 579 patients treated with epirubicin-based chemotherapy. Breast Cancer Res Treat. 2005;91:217–25.

    Article  Google Scholar 

  24. Kołodziejczyk J, Ponczek MB. The role of fibrinogen, fibrin and fibrin(ogen) degradation products (FDPs) in tumor progression. Contemp Oncol. 2013;17:113–9.

    Google Scholar 

  25. Altiay G, Ciftci A, Demir M, Kocak Z, Sut N, Tabakoglu E, et al. High plasma D-dimer level is associated with decreased survival in patients with lung cancer. Clin Oncol. 2007;19:494–8.

    Article  CAS  Google Scholar 

  26. Takeuchi H, Ikeuchi S, Kitagawa Y, Shimada A, Oishi T, Isobe Y, et al. Pretreatment plasma fibrinogen level correlates with tumor progression and metastasis in patients with squamous cell carcinoma of the esophagus. J Gastroenterol Hepatol. 2007;22:2222–7.

    Article  CAS  Google Scholar 

  27. Matsuda S, Takeuchi H, Fukuda K, Nakamura R, Takahashi T, Wada N. Clinical significance of plasma fibrinogen level as a predictive marker for postoperative recurrence of esophageal squamous cell carcinoma in patients receiving neoadjuvant treatment. Dis Esophagus. 2014;27:654–61.

    Article  CAS  Google Scholar 

  28. Fujii T, Tokuda S, Nakazawa Y, Kurozumi S, Obayashi S, Yajima R, et al. Implications of low serum albumin as a prognostic factor of long-term outcomes in patients with breast cancer. In Vivo. 2020;34:2033–6.

    Article  CAS  Google Scholar 

  29. Takaaki F, Reina Y, Takahiro T, Toshinaga S, Hiroki M, Satoru Y, et al. Serum albumin and prealbumin do not predict recurrence in patients with breast cancer. Anticancer Res. 2014;34:3775–9.

    Google Scholar 

  30. Chung L, Moore K, Phillips L, Boyle FM, Marsh DJ, Baxter RC, et al. Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer. Breast Cancer Res. 2014;16:R63.

    Article  Google Scholar 

  31. Zhou T, He X, Fang W, Zhan J, Hong S, Qin T, et al. Pretreatment albumin/globulin ratio predicts the prognosis for small-cell lung cancer. Medicine (Baltimore). 2016;95(12):e3097.

    Article  CAS  Google Scholar 

  32. Liu X, Meng QH, Ye Y, Hildebrandt MA, Gu J, Wu X. Prognostic significance of pretreatment serum levels of albumin, LDH and total bilirubin in patients with non-metastatic breast cancer. Carcinogenesis. 2015;36(2):243–8.

    Article  Google Scholar 

  33. Xue F, Lin F, Yin M, Feng N, Zhang X, Cui YG, et al. Preoperative albumin/globulin ratio is a potential prognosis predicting biomarker in patients with resectable gastric cancer. Turk J Gastroenterol. 2017;28(6):439–45.

    Article  Google Scholar 

  34. Yang S, He X, Liu Y, Ding X, Jiang H, Tan Y, et al. Prognostic Significance of serum uric acid and gamma-glutamyltransferase in patients with advanced gastric cancer. Dis Markers. 2019;2019:1415421.

    PubMed  PubMed Central  Google Scholar 

  35. Yue C-F, Feng P-N, Yao Z-R, Yu X-G, Lin W-B, Qian Y-M, et al. High serum uric acid concentration predicts poor survival in patients with breast cancer. Clin Chim Acta. 2017;473:160–5.

    Article  CAS  Google Scholar 

  36. Fisher ER, Wang J, Bryant J, Fisher B, Mamounas E, Wolmark N. Pathobiology of preoperative chemotherapy: findings from the National Surgical Adjuvant Breast and Bowel (NSABP) protocol B-18. Cancer. 2002;95(4):681.

    Article  Google Scholar 

  37. Hortobagyi GN. Comprehensive management of locally advanced breast cancer. Cancer. 1990;66(Supplement S14):1387.

    Article  CAS  Google Scholar 

  38. von MG, Huang C-S, Mano MS, Loibl S, Mamounas EP, Untch M, et al. Trastuzumab emtansine for residual invasive HER2-positive breast cancer. N Engl J Med. 2019;380:617–28.

  39. Caponio R, Ciliberti MP, Graziano G, Necchia R, Scognamillo G, Pascali A, et al. Waiting time for radiation therapy after breast-conserving surgery in early breast cancer: a retrospective analysis of local relapse and distant metastases in 615 patients. Eur J Med Res. 2016;21:32.

    Article  Google Scholar 

  40. Zhang W-W, Wu S-G, Sun J-Y, Li F-Y, He Z-Y. Long-term survival effect of the interval between mastectomy and radiotherapy in locally advanced breast cancer]. Cancer Manag Res. 2018;10:2047–54.

    Article  CAS  Google Scholar 

  41. Wen J, Ye F, Li S, Huang X, Yang L, Xiao X, et al. The Practicability of a Novel Prognostic Index (PI) Model and Comparison with Nottingham Prognostic Index (NPI) in Stages I–III breast cancer patients undergoing surgical treatment. PLoS ONE. 2015;10:e0143537.

    Article  Google Scholar 

Download references

Acknowledgements

We thank the BioStudies database (public database) for including and providing Professor Xie's original data [41].

Funding

None.

Author information

Authors and Affiliations

  1. Department of Anesthesiology, Pain, and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China

    Cheng-Mao Zhou, Qiong Xue, Ying Wang, Jianhuaa Tong, Muhuo Ji & Jian-Jun Yang

Authors
  1. Cheng-Mao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiong Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianhuaa Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muhuo Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian-Jun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors provided critical feedback and helped shape the research, analysis, and manuscript.

Corresponding authors

Correspondence to Cheng-Mao Zhou or Jian-Jun Yang.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Availability of data and material

Data are available at the BioStudies database: https://www.ebi.ac.uk/biostudies/studies?query=S-EPMC4658156, accession number: S-EPMC4658156.

Ethics approval and consent to participate

This was a secondary analysis using data from the BioStudies database, which is a public database.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 2593 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, CM., Xue, Q., Wang, Y. et al. Machine learning to predict the cancer-specific mortality of patients with primary non-metastatic invasive breast cancer. Surg Today 51, 756–763 (2021). https://doi.org/10.1007/s00595-020-02170-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00595-020-02170-9

Keywords

Search

Navigation