Sentinel Node Status Prediction by Four Statistical Models: Results From a Large Bi-Institutional Series (n = 1132)

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Abstract

Objective:

To improve selection for sentinel node (SN) biopsy (SNB) in patients with cutaneous melanoma using statistical models predicting SN status.

Summary Background Data:

About 80% of patients currently undergoing SNB are node negative. In the absence of conclusive evidence of a SNB-associated survival benefit, these patients may be over-treated. Here, we tested the efficiency of 4 different models in predicting SN status.

Methods:

The clinicopathologic data (age, gender, tumor thickness, Clark level, regression, ulceration, histologic subtype, and mitotic index) of 1132 melanoma patients who had undergone SNB at institutions in Italy and Australia were analyzed. Logistic regression, classification tree, random forest, and support vector machine models were fitted to the data. The predictive models were built with the aim of maximizing the negative predictive value (NPV) and reducing the rate of SNB procedures though minimizing the error rate.

Results:

After cross-validation logistic regression, classification tree, random forest, and support vector machine predictive models obtained clinically relevant NPV (93.6%, 94.0%, 97.1%, and 93.0%, respectively), SNB reduction (27.5%, 29.8%, 18.2%, and 30.1%, respectively), and error rates (1.8%, 1.8%, 0.5%, and 2.1%, respectively).

Discussion:

Using commonly available clinicopathologic variables, predictive models can preoperatively identify a proportion of patients (∼25%) who might be spared SNB, with an acceptable (1%–2%) error. If validated in large prospective series, these models might be implemented in the clinical setting for improved patient selection, which ultimately would lead to better quality of life for patients and optimization of resource allocation for the health care system.

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