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Annals of Surgical Oncology

, Volume 13, Issue 8, pp 1113–1122 | Cite as

Support Vector Machine Learning Model for the Prediction of Sentinel Node Status in Patients With Cutaneous Melanoma

  • Simone Mocellin
  • Alessandro Ambrosi
  • Maria Cristina Montesco
  • Mirto Foletto
  • Giorgio Zavagno
  • Donato Nitti
  • Mario Lise
  • Carlo Riccardo Rossi
Article

Abstract

Background:

Currently, approximately 80% of melanoma patients undergoing sentinel node biopsy (SNB) have negative sentinel lymph nodes (SLNs), and no prediction system is reliable enough to be implemented in the clinical setting to reduce the number of SNB procedures. In this study, the predictive power of support vector machine (SVM)-based statistical analysis was tested.

Methods:

The clinical records of 246 patients who underwent SNB at our institution were used for this analysis. The following clinicopathologic variables were considered: the patient’s age and sex and the tumor’s histological subtype, Breslow thickness, Clark level, ulceration, mitotic index, lymphocyte infiltration, regression, angiolymphatic invasion, microsatellitosis, and growth phase. The results of SVM-based prediction of SLN status were compared with those achieved with logistic regression.

Results:

The SLN positivity rate was 22% (52 of 234). When the accuracy was ≥80%, the negative predictive value, positive predictive value, specificity, and sensitivity were 98%, 54%, 94%, and 77% and 82%, 41%, 69%, and 93% by using SVM and logistic regression, respectively. Moreover, SVM and logistic regression were associated with a diagnostic error and an SNB percentage reduction of (1) 1% and 60% and (2) 15% and 73%, respectively.

Conclusions:

The results from this pilot study suggest that SVM-based prediction of SLN status might be evaluated as a prognostic method to avoid the SNB procedure in 60% of patients currently eligible, with a very low error rate. If validated in larger series, this strategy would lead to obvious advantages in terms of both patient quality of life and costs for the health care system.

Keywords

Melanoma Sentinel node biopsy Support vector machine Sentinel node status Prognostic factors Predictive factors 

Notes

Acknowledgments

Supported by a grant from the Regione Veneto.

References

  1. 1.
    Morton DL, Wen DR, Wong JH, et al. Technical details of intraoperative lymphatic mapping for early stage melanoma. Arch Surg 1992; 127:392–9PubMedGoogle Scholar
  2. 2.
    Leong SP. Paradigm of metastasis for melanoma and breast cancer based on the sentinel lymph node experience. Ann Surg Oncol 2004; 11:192S–7SPubMedCrossRefGoogle Scholar
  3. 3.
    Tsao H, Atkins MB, Sober AJ. Management of cutaneous melanoma. N Engl J Med 2004; 351:998–1012PubMedCrossRefGoogle Scholar
  4. 4.
    Morton DL. Lymphatic mapping and sentinel lymphadenectomy for melanoma: past, present, and future. Ann Surg Oncol 2001; 8:22S–28SPubMedGoogle Scholar
  5. 5.
    McMasters KM, Reintgen DS, Ross MI, et al. Sentinel lymph node biopsy for melanoma: controversy despite widespread agreement. J Clin Oncol 2001; 19:2851–5PubMedGoogle Scholar
  6. 6.
    Takeuchi H, Morton DL, Kuo C, et al. Prognostic significance of molecular upstaging of paraffin-embedded sentinel lymph nodes in melanoma patients. J Clin Oncol 2004; 22:2671–80PubMedCrossRefGoogle Scholar
  7. 7.
    Mohrle M, Schippert W, Rassner G, et al. Is sentinel lymph node biopsy of therapeutic relevance for melanoma? Dermatology 2004; 209:5–13PubMedCrossRefGoogle Scholar
  8. 8.
    Fife K, Thompson JF. Lymph-node metastases in patients with melanoma: what is the optimum management? Lancet Oncol 2001; 2:614–21PubMedCrossRefGoogle Scholar
  9. 9.
    Caraco C, Celentano E, Lastoria S, et al. Sentinel lymph node biopsy does not change melanoma-specific survival among patients with Breslow thickness greater than four millimeters. Ann Surg Oncol 2004; 11:198S–202SCrossRefGoogle Scholar
  10. 10.
    Roka F, Kittler H, Cauzig P, et al. Sentinel node status in melanoma patients is not predictive for overall survival upon multivariate analysis. Br J Cancer 2005Google Scholar
  11. 11.
    Vuylsteke RJ, van Leeuwen PA, Statius Muller MG, et al. Clinical outcome of stage I/II melanoma patients after selective sentinel lymph node dissection: long-term follow-up results. J Clin Oncol 2003; 21:1057–65PubMedCrossRefGoogle Scholar
  12. 12.
    Shen J, Wallace AM, Bouvet M. The role of sentinel lymph node biopsy for melanoma. Semin Oncol 2002; 29:341–52PubMedCrossRefGoogle Scholar
  13. 13.
    McMasters KM. What good is sentinel lymph node biopsy for melanoma if it does not improve survival? Ann Surg Oncol 2004; 11:810–2PubMedCrossRefGoogle Scholar
  14. 14.
    Doting MH, Hoekstra HJ, Plukker JT, et al. Is sentinel node biopsy beneficial in melanoma patients? A report on 200 patients with cutaneous melanoma. Eur J Surg Oncol 2002; 28:673–8PubMedCrossRefGoogle Scholar
  15. 15.
    Thompson JF, Stretch JR, Uren RF, et al. Sentinel node biopsy for melanoma: where have we been and where are we going? Ann Surg Oncol 2004; 11:147S–51SPubMedCrossRefGoogle Scholar
  16. 16.
    Wrightson WR, Wong SL, Edwards MJ, et al. Complications associated with sentinel lymph node biopsy for melanoma. Ann Surg Oncol 2003; 10:676–80PubMedCrossRefGoogle Scholar
  17. 17.
    Hettiaratchy SP, Kang N, O’Toole G, et al. Sentinel lymph node biopsy in malignant melanoma: a series of 100 consecutive patients. Br J Plast Surg 2000; 53:559–62PubMedCrossRefGoogle Scholar
  18. 18.
    Cimmino VM, Brown AC, Szocik JF, et al. Allergic reactions to isosulfan blue during sentinel node biopsy—a common event. Surgery 2001; 130:439–42PubMedCrossRefGoogle Scholar
  19. 19.
    Roaten JB, Pearlman N, Gonzalez R, et al. Identifying risk factors for complications following sentinel lymph node biopsy for melanoma. Arch Surg 2005; 140:85–9PubMedCrossRefGoogle Scholar
  20. 20.
    Wasserberg N, Tulchinsky H, Schachter J, et al. Sentinel-lymph-node biopsy (SLNB) for melanoma is not complication-free. Eur J Surg Oncol 2004; 30:851–6PubMedCrossRefGoogle Scholar
  21. 21.
    Brobeil A, Cruse CW, Messina JL, et al. Cost analysis of sentinel lymph node biopsy as an alternative to elective lymph node dissection in patients with malignant melanoma. Surg Oncol Clin North Am 1999; 8:435–45, viiiGoogle Scholar
  22. 22.
    Agnese DM, Abdessalam SF, Burak WE Jr, et al. Cost-effectiveness of sentinel lymph node biopsy in thin melanomas. Surgery 2003; 134:542–7; discussion 547–8PubMedCrossRefGoogle Scholar
  23. 23.
    Estourgie SH, Nieweg OE, Kroon BB. High incidence of in-transit metastases after sentinel node biopsy in patients with melanoma. Br J Surg 2004; 91:1370–1PubMedCrossRefGoogle Scholar
  24. 24.
    Thomas JM, Clark MA. Selective lymphadenectomy in sentinel node-positive patients may increase the risk of local/in-transit recurrence in malignant melanoma. Eur J Surg Oncol 2004; 30:686–91PubMedCrossRefGoogle Scholar
  25. 25.
    Rossi CR, Mocellin S, Scagnet B, et al. The role of preoperative ultrasound scan in detecting lymph node metastasis before sentinel node biopsy in melanoma patients. J Surg Oncol 2003; 83:80–4PubMedCrossRefGoogle Scholar
  26. 26.
    Rossi CR, Scagnet B, Vecchiato A, et al. Sentinel node biopsy and ultrasound scanning in cutaneous melanoma: clinical and technical considerations. Eur J Cancer 2000; 36:895–900PubMedCrossRefGoogle Scholar
  27. 27.
    Hastie T, Tibshirani R. Generalized Additive Models. Chapman & Hall, 1990. Google Scholar
  28. 28.
    Vapnik V. Statistical Learning Theory. Wiley, 1998Google Scholar
  29. 29.
    Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer, 2001Google Scholar
  30. 30.
    Byvatov E, Schneider G. Support vector machine applications in bioinformatics. Appl Bioinformatics 2003; 2:67–77PubMedGoogle Scholar
  31. 31.
    Wagner JD, Gordon MS, Chuang TY, et al. Predicting sentinel and residual lymph node basin disease after sentinel lymph node biopsy for melanoma. Cancer 2000; 89:453–62PubMedCrossRefGoogle Scholar
  32. 32.
    Gershenwald JE, Thompson W, Mansfield PF, et al. Multi-institutional melanoma lymphatic mapping experience: the prognostic value of sentinel lymph node status in 612 stage I or II melanoma patients. J Clin Oncol 1999; 17:976–83PubMedGoogle Scholar
  33. 33.
    Cascinelli N, Belli F, Santinami M, et al. Sentinel lymph node biopsy in cutaneous melanoma: the WHO Melanoma Program experience. Ann Surg Oncol 2000; 7:469–74PubMedCrossRefGoogle Scholar
  34. 34.
    Essner R, Conforti A, Kelley MC, et al. Efficacy of lymphatic mapping, sentinel lymphadenectomy, and selective complete lymph node dissection as a therapeutic procedure for early-stage melanoma. Ann Surg Oncol 1999; 6:442–9PubMedCrossRefGoogle Scholar
  35. 35.
    Chao C, Martin RC II, Ross MI, et al. Correlation between prognostic factors and increasing age in melanoma. Ann Surg Oncol 2004; 11:259–64PubMedCrossRefGoogle Scholar
  36. 36.
    McMasters KM, Wong SL, Edwards MJ, et al. Factors that predict the presence of sentinel lymph node metastasis in patients with melanoma. Surgery 2001; 130:151–6PubMedCrossRefGoogle Scholar
  37. 37.
    Nguyen CL, McClay EF, Cole DJ, et al. Melanoma thickness and histology predict sentinel lymph node status. Am J Surg 2001; 181:8–11PubMedCrossRefGoogle Scholar
  38. 38.
    Mraz-Gernhard S, Sagebiel RW, Kashani-Sabet M, et al. Prediction of sentinel lymph node micrometastasis by histological features in primary cutaneous malignant melanoma. Arch Dermatol 1998; 134:983–7PubMedCrossRefGoogle Scholar
  39. 39.
    Cuellar FA, Vilalta A, Rull R, et al. Small cell melanoma and ulceration as predictors of positive sentinel lymph node in malignant melanoma patients. Melanoma Res 2004; 14:277–82PubMedCrossRefGoogle Scholar
  40. 40.
    Thompson JF. The Sydney Melanoma Unit experience of sentinel lymphadenectomy for melanoma. Ann Surg Oncol 2001; 8:44S–47SPubMedGoogle Scholar
  41. 41.
    Sondak VK, Taylor JM, Sabel MS, et al. Mitotic rate and younger age are predictors of sentinel lymph node positivity: lessons learned from the generation of a probabilistic model. Ann Surg Oncol 2004; 11:247–58PubMedCrossRefGoogle Scholar
  42. 42.
    Furey TS, Cristianini N, Duffy N, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000; 16:906–14PubMedCrossRefGoogle Scholar
  43. 43.
    Mocellin S, Provenzano M, Rossi CR, et al. DNA array-based gene profiling: from surgical specimen to the molecular portrait of cancer. Ann Surg 2005; 241:16–26PubMedGoogle Scholar
  44. 44.
    Machet L, Nemeth-Normand F, Giraudeau B, et al. Is ultrasound lymph node examination superior to clinical examination in melanoma follow-up? A monocentre cohort study of 373 patients. Br J Dermatol 2005; 152:66–70PubMedCrossRefGoogle Scholar
  45. 45.
    Hocevar M, Bracko M, Pogacnik A, et al. The role of preoperative ultrasonography in reducing the number of sentinel lymph node procedures in melanoma. Melanoma Res 2004; 14:533–6PubMedCrossRefGoogle Scholar
  46. 46.
    Bafounta ML, Beauchet A, Chagnon S, et al. Ultrasonography or palpation for detection of melanoma nodal invasion: a meta-analysis. Lancet Oncol 2004; 5:673–80PubMedCrossRefGoogle Scholar
  47. 47.
    Schmid-Wendtner MH, Dill-Muller D, Baumert J, et al. Lymph node metastases in patients with cutaneous melanoma: improvements in diagnosis by signal-enhanced color Doppler sonography. Melanoma Res 2004; 14:269–76PubMedCrossRefGoogle Scholar
  48. 48.
    Lean CL, Bourne R, Thompson JF, et al. Rapid detection of metastatic melanoma in lymph nodes using proton magnetic resonance spectroscopy of fine needle aspiration biopsy specimens. Melanoma Res 2003; 13:259–61PubMedCrossRefGoogle Scholar

Copyright information

© The Society of Surgical Oncology, Inc. 2006

Authors and Affiliations

  • Simone Mocellin
    • 1
    • 2
  • Alessandro Ambrosi
    • 1
  • Maria Cristina Montesco
    • 3
  • Mirto Foletto
    • 1
  • Giorgio Zavagno
    • 1
  • Donato Nitti
    • 1
  • Mario Lise
    • 1
  • Carlo Riccardo Rossi
    • 1
  1. 1.Clinica Chirurgica 2, Dipartimento di Scienze Oncologiche e ChirurgicheUniversità di PadovaPadovaItaly
  2. 2.Istituto Oncologico VenetoPadovaItaly
  3. 3.Pathology Section, Department of Oncological and Surgical SciencesUniversity of PadovaPadovaItaly

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