Nomogram Identifies Age as the Most Important Predictor of Overall Survival in Oral Cavity Squamous Cell Cancer After Primary Surgery


Our goal was to determine the most important predictors and construct a nomogram for overall survival (OS) in oral cavity squamous cell cancer (OCSCC) treated with primary surgery followed by observation, adjuvant radiation or chemoradiation. Multivariable analysis was performed using Cox Proportional Hazard model of 9258 OCSCC patients from Surveillance, Epidemiology and End Results Program (SEER) database treated with surgery from 2003 to 2009. Potential predictors of OS were age, gender, race, tobacco use, oral cancer sub-sites, pathologic tumor stage and grade, pathologic nodal stage, extra-capsular invasion, clinical levels IV and V involvement, and adjuvant treatment selection. Weighted propensity scores for treatment were used to balance observed baseline characteristics between three treatment groups in order to reduce bias. Following primary surgery, patients underwent observation (56%), radiation alone (31%) or chemoradiation (13%). All tested predictors were statistically significant and included in our final nomogram. Most important predictor of OS was age, followed by pathologic tumor stage. SEER based-survival nomogram for OCSCC patients differs from published models derived from patients treated in a single or few academic treatment centers. An unexpected finding of patient age being the best OS predictor suggests that this factor may be more critical for the outcome than previously anticipated.

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

Fig. 1
Fig. 2


  1. 1.

    Bessell A, Glenny AM, Furness S et al (2011) Interventions for the treatment of oral and oropharyngeal cancers: surgical treatment. Cochrane Database Syst Rev 9(9):CD006205

    Google Scholar 

  2. 2. Accessed 19 Sept 2016

  3. 3.

    Ang KK, Trotti A, Brown BW et al (2001) Randomized trial addressing risk features and time factors of surgery plus radiotherapy in advanced head-and-neck cancer. Int J Radiat Oncol Biol Phys 51(3):571–578

    CAS  Article  Google Scholar 

  4. 4.

    Gross ND, Patel SG, Carvalho AL et al (2008) Nomogram for deciding adjuvant treatment after surgery for oral cavity squamous cell carcinoma. Head Neck 30(10):1352–1360

    Article  Google Scholar 

  5. 5.

    Montero PH, Yu C, Palmer FL, Patel PD, Ganly I, Shah JP, Shaha AR, Boyle JO, Kraus DH, Singh B, Wong RJ, Morris LG, Kattan MW, Patel SG (2014) Nomograms for preoperative prediction of prognosis in patients with oral cavity squamous cell carcinoma. Cancer 120(2):214–221.

    Article  PubMed  Google Scholar 

  6. 6.

    Kattan MW (2002) Nomograms. Introduction. Semin Urol Oncol 20:79–81

    PubMed  Google Scholar 

  7. 7.

    Stephenson AJ, Scardino PT, Kattan MW et al (2007) Predicting the outcome of salvage radiation therapy for recurrent prostate cancer after radical prostatectomy. J Clin Oncol 25(15):2035–2041

    Article  Google Scholar 

  8. 8.

    Stephenson AJ, Scardino PT, Eastham JA et al (2005) Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Clin Oncol 23(28):7005–7012

    Article  Google Scholar 

  9. 9.

    Stephenson AJ, Scardino PT, Eastham JA et al (2006) Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Natl Cancer Inst 98(10):715–717

    Article  Google Scholar 

  10. 10.

    Kattan MW, Zelefsky MJ, Kupelian PA, Scardino PT, Fuks Z, Leibel SA (2000) Pretreatment nomogram for predicting the outcome of three-dimensional conformal radiotherapy in prostate cancer. J Clin Oncol 18(19):3352–3359

    CAS  Article  Google Scholar 

  11. 11.

    Kattan MW (2003) Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preoperative application in prostate cancer. Curr Opin Urol 13(2):111–116

    Article  Google Scholar 

  12. 12.

    Rouzier R, Pusztai L, Delaloge S et al (2005) Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. J Clin Oncol 23(33):8331–8339

    Article  Google Scholar 

  13. 13.

    Ravdin PM (1995) A computer based program to assist in adjuvant therapy decisions for individual breast cancer patients. Bull Cancer 82(suppl 5):561s–564s

    PubMed  Google Scholar 

  14. 14.

    Ravdin PM (1996) A computer program to assist in making breast cancer adjuvant therapy decisions. Semin Oncol 23(1, suppl 2):43–50

    CAS  PubMed  Google Scholar 

  15. 15.

    Ravdin PM, Siminoff LA, Davis GJ et al (2001) Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 19(4):980–991

    CAS  Article  Google Scholar 

  16. 16.

    Olivotto IA, Bajdik CD, Ravdin PM et al (2005) Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 23(12):2716–2725

    Article  Google Scholar 

  17. 17.

    Brennan MF, Kattan MW, Klimstra D, Conlon K (2004) Prognostic nomogram for patients undergoing resection for adenocarcinoma of the pancreas. Ann Surg 240(2):293–298

    Article  Google Scholar 

  18. 18.

    Kattan MW, Karpeh MS, Mazumdar M, Brennan MF (2003) Postoperative nomogram for disease-specific survival after an R0 resection for gastric carcinoma. J Clin Oncol 21(19):3647–3650

    Article  Google Scholar 

  19. 19.

    Rosenbaum Paul R, Rubin Donald B (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55.

    Article  Google Scholar 

  20. 20.

    Pugliano FA, Piccirillo JF, Zequeira MR, Fredrickson JM, Perez CA, Simpson JR (1999) Symptoms as an index of biologic behavior in head and neck cancer. Otolaryngol Head Neck Surg 120:380–386

    CAS  Article  Google Scholar 

  21. 21.

    Cooper JS, Pajak TF, Forastiere A et al (1998) Precisely defining high-risk operable head and neck tumors based on RTOG #85–03 and #88–24: targets for postoperative radiochemotherapy? Head Neck 20:588–594

    CAS  Article  Google Scholar 

  22. 22.

    Ang KK, Trotti A, Brown BW et al (2001) Randomized trial addressing risk features and time factors of surgery plus radiotherapy in advanced head-and-neck cancer. Int J Radiat Oncol Biol Phys 51:571–578

    CAS  Article  Google Scholar 

  23. 23.

    Langendijk JA, Slotman BJ, Van der Waal I, Doornaert P, Berkof J, Leemans CR (2005) Risk-group definition by recursive partitioning analysis of patients with squamous cell head and neck carcinoma treated with surgery and postoperative radiotherapy. Cancer 104:1408–1417

    Article  Google Scholar 

  24. 24.

    de Baatenburg Jong RJ, Hermans J, Molenaar J, Briaire JJ, le Cessie S (2001) Prediction of survival in patients with head and neck cancer. Head Neck 23:718–724

    Article  Google Scholar 

  25. 25.

    van den Broek GB, Rasch CR, Pameijer FA et al (2004) Pretreatment probability model for predicting outcome after intraarterial chemoradiation for advanced head and neck carcinoma. Cancer 101:1809–1817

    Article  Google Scholar 

  26. 26.

    Lydiatt WM, Shah JP, Hoffman HT (2001) AJCC stage group- ings for head and neck cancer: should we look at alter- natives? A report of the head and neck sites task force. Head Neck 23:607–612

    CAS  Article  Google Scholar 

  27. 27.

    Qureshi MM, Shah BA, Mak KS, Salama A, Ezzat W, Giacalone NJ, Patel SA, Kamran SC, Kirke D, Jalisi S, Truong MT (2016) Determinants of survival in oral cavity cancer patients: an analysis of the National Cancer Data Base (NCDB). Int J Radiat Oncol Biol Phys 96(2S):E381.

    Article  Google Scholar 

  28. 28.

    van der Schroeff MP, Derks W, Hordijk GJ, de Leeuw RJ (2007) The effect of age on survival and quality of life in elderly head and neck cancer patients: a long-term prospective study. Eur Arch Otorhinolaryngol 264(4):415–422

    Article  Google Scholar 

  29. 29.

    Gleich LL, Collins CM, Gartside PS, Gluckman JL, Barrett WL, Wilson KM, Biddinger PW, Redmond KP (2003) Therapeutic decision making in stages III and IV head and neck squamous cell carcinoma. Arch Otolaryngol Head Neck Surg 129(1):26–35

    Article  Google Scholar 

  30. 30.

    Lassig AA, Lindgren BR, Fernandes P, Cooper S, Ardeshipour F, Schotzko C, Yueh B (2013) The effect of young age on outcomes in head and neck cancer. Laryngoscope 123(8):1896–1902.

    Article  PubMed  Google Scholar 

  31. 31. statistics/survival#P7RHYmby3K4APjTG.99. Accessed 21 Oct 2016

  32. 32.

    Giardino A, Gupta S, Olson E, Sepulveda K, Lenchik L, Ivanidze J, Rakow-Penner R, Patel MJ, Subramaniam RM, Ganeshan D (2017) Role of imaging in the era of precision medicine. Acad Radiol 24(5):639–649.

    Article  PubMed  Google Scholar 

  33. 33. Accessed 21 Oct 2016

  34. 34.

    Specht MC, Kattan MW, Gonen M, Fey J, Van Zee KJ (2005) Predicting nonsentinel node status after positive sentinel lymph biopsy for breast cancer: clinicians versus nomogram. Ann Surg Oncol 12:654–659

    Article  Google Scholar 

  35. 35.

    Spatz ES, Krumholz HM, Moulton BW (2016) The new era of informed consent getting to a reasonable-patient standard through shared decision making. JAMA 315(19):2063–2064.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Supriya Gupta.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gupta, S., Waller, J., Brown, J. et al. Nomogram Identifies Age as the Most Important Predictor of Overall Survival in Oral Cavity Squamous Cell Cancer After Primary Surgery. Indian J Otolaryngol Head Neck Surg 72, 160–168 (2020).

Download citation


  • Oral cavity
  • Squamous cell cancer
  • Nomogram
  • Survival
  • Age