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Research Article

An algorithm for expanding the TNM staging system

    Dechang Chen

    Department of Preventive Medicine & Biostatistics, The Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA

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    ,
    Matthew T Hueman

    Surgical Oncology, John P Murtha Cancer Center, Walter Reed National Military Medical Center, 8901 Wisconsin Ave., Bethesda, MD 20889, USA

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    ,
    Donald E Henson

    *Author for correspondence:

    E-mail Address: donald.henson.ctr@usuhs.edu

    Department of Preventive Medicine & Biostatistics, The Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA

    Department of Surgery, The Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA

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    &
    Arnold M Schwartz

    Department of Pathology, The George Washington University Medical Center, Washington, DC 20037, USA

    Department of Surgery, The George Washington University Medical Center, Washington, DC 20037, USA

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    Published Online:24 Feb 2016https://doi.org/10.2217/fon.16.5

    Abstract

    Aim: We describe a new method to expand the tumor, lymph node, metastasis (TNM) staging system using a clustering algorithm. Cases of breast cancer were used for demonstration. Materials & methods: An unsupervised ensemble-learning algorithm was used to create dendrograms. Cutting the dendrograms produced prognostic systems. Results: Prognostic systems contained groups of patients with similar outcomes. The prognostic systems based on tumor size and lymph node status recapitulated the general structure of the TNM for breast cancer. The prognostic systems based on tumor size, lymph node status, histologic grade and estrogen receptor status revealed a more detailed stratification of patients when grade and estrogen receptor status were added. Conclusion: Prognostic systems from cutting the dendrogram have the potential to improve and expand the TNM.

    Papers of special note have been highlighted as: • of interest; •• of considerable interest

    References

    • 1 Burke HB, Henson DE. Criteria for prognostic factors and for an enhanced prognostic system. Cancer 72(10), 3131–3135 (1993).
      Medline CASGoogle Scholar
    • 2 Gimotty PA, Guerry D, Ming ME et al. Thin primary cutaneous malignant melanoma: a prognostic tree for 10-year metastasis is more accurate than American Joint Committee on Cancer staging. J. Clin. Oncol. 22(18), 3668–3676 (2004). • Develops a prognostic tree for thin invasive melanomas.
      MedlineGoogle Scholar
    • 3 Radespiel-Tröger M, Hohenberger W, Reingruber B. Improved prediction of recurrence after curative resection of colon carcinoma using tree-based risk stratification. Cancer 100(5), 958–967 (2004).
      MedlineGoogle Scholar
    • 4 Kattan MW, Reuter V, Motzer RJ, Katz J, Russo P. A postoperative prognostic nomogram for renal cell carcinoma. J. Urol. 166(1), 63–67 (2001). • Develops a nomogram for renal cell carcinoma.
      Medline CASGoogle Scholar
    • 5 Liang W, Zhang L, Jiang G et al. Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer. J. Clin. Oncol. 33(8), 861–869 (2015).
      MedlineGoogle Scholar
    • 6 Chen D, Xing K, Henson D, Sheng L, Schwartz AM, Cheng X. Developing prognostic systems of cancer patients by ensemble clustering. J. Biomed. Biotechnol. 2009, 632786 (2009). •• Introduces the Ensemble Algorithm of Clustering Cancer Data.
      MedlineGoogle Scholar
    • 7 Qi R, Wu D, Sheng L et al. On an ensemble algorithm for clustering cancer patient data. BMC Syst. Biol. 7(Suppl. 4), S9 (2013). • Studies the effect of parameters in the Ensemble Algorithm of Clustering Cancer Data.
      MedlineGoogle Scholar
    • 8 Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95(25), 14863–14868 (1998).
      Medline CASGoogle Scholar
    • 9 Alizadeh AA, Eisen MB, Davis RE et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769), 503–511 (2000).
      Medline CASGoogle Scholar
    • 10 The Surveillance, Epidemiology, and End Results Program of the National Cancer Institute. www.seer.cancer.gov.
      Google Scholar
    • 11 Henson DE, Ries L, Freedman LS, Carriaga M. Relationship among outcome, stage of disease, and histologic grade for 22,616 cases of breast cancer. Cancer 68(10), 2142–2149 (1991).
      Medline CASGoogle Scholar
    • 12 Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A. Cancer Staging Manual (7th Edition). Springer, New York, NY, USA (2010).
      Google Scholar
    • 13 The R Project for Statistical Computing. www.r-project.org.
      Google Scholar
    • 14 Klein JP, Moeschberger ML. Survival Analysis: Techniques for Censored and Truncated Data (2nd Edition). Springer, New York, NY, USA (2005). •• Provides an excellent treatment of various topics in survival analysis.
      Google Scholar
    • 15 Kaufman L, Rousseeuw P. Finding Groups in Data: an Introduction to Cluster Analysis. Wiley, New York, NY, USA (1990). •• Describes the well-known Partitioning Around Medoids clustering procedure.
      Google Scholar
    • 16 Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning (2nd Edition). Springer, New York, NY, USA (2013). •• Presents a well-organized exploration of key ideas in unsupervised learning.
      Google Scholar
    • 17 Kaplan E L, Meier P. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457–481 (1958).
      Google Scholar
    • 18 Everitt BS, Landau S, Leese M, Stahl D. Cluster Analysis (5th Edition). Wiley, NJ, USA (2011).
      Google Scholar
    • 19 Schwartz AM, Henson DE, Chen D, Rajamarthandan S. Histologic grade remains a prognostic factor for breast cancer regardless of the number of positive lymph nodes and tumor size: a study of 161 708 cases of breast cancer from the SEER program. Arch. Pathol. Lab. Med. 138(8), 1048–1052 (2014).
      MedlineGoogle Scholar
    • 20 Henson DE, Schwartz AM, Chen D, Wu D. The clinical implications of integrating additional prognostic factors into the TNM. J. Surg. Oncol. 109(5), 391–394 (2014).
      MedlineGoogle Scholar
    • 21 Wu D, Yang C, Wong S, Meyerle J, Zhang B, Chen D. An examination of TNM staging of melanoma by a machine learning algorithm. In: Proceedings of 2012 International Conference on Computerized Healthcare. Institute of Electrical and Electronics Engineers, NY, USA, 120–126 (2012).
      Google Scholar