Support Vector Machine Learning Model for the Prediction of Sentinel Node Status in Patients With Cutaneous Melanoma
- 142 Downloads
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.
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.
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.
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.
KeywordsMelanoma Sentinel node biopsy Support vector machine Sentinel node status Prognostic factors Predictive factors
Supported by a grant from the Regione Veneto.
- 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
- 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
- 27.Hastie T, Tibshirani R. Generalized Additive Models. Chapman & Hall, 1990. Google Scholar
- 28.Vapnik V. Statistical Learning Theory. Wiley, 1998Google Scholar
- 29.Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer, 2001Google Scholar