Can surgical oncologists reliably predict the likelihood for non-SLN metastases in breast cancer patients? Academic Article uri icon

Overview

abstract

  • BACKGROUND: In approximately 40% of the breast cancer patients with sentinel lymph node (SLN) metastases, additional nodal metastases are detected in the completion axillary lymph node dissection (cALND). The MSKCC nomogram can help to quantify a patient's individual risk for non-SLN metastases with fairly accurate predicted probability. The aim of this study was to compare the predictions of surgical oncologists for non-SLN metastases with nomogram results and to clarify the impact of nomogram results on clinical decision-making. METHODS: Questionnaires, containing patient scenarios, were sent to surgical oncologists involved in breast cancer care. The surgeon was asked to predict the probability for non-SLN metastases for the first five scenarios. For the remaining scenarios, the patient's actuarial likelihood, calculated by the nomogram, was supplied. The surgeon was asked whether or not (s)he would perform a cALND. The type of hospital and the surgeon's experience were registered. RESULTS: The concordance-index amounted to 0.78, indicating moderate concurrence between the surgical predictions and nomogram results. The intersurgeon variation was important. About 25% of the surgeons was influenced by nomogram information and decided in one or more patients to abandon the cALND. Neither the type of hospital nor experience influenced predicting abilities or the clinical decision-making process. CONCLUSION: Individual predictions of surgical oncologists for non-SLN metastases do not correlate well with the MSKCC nomogram. The distribution between intersurgeon predictions for one scenario is important. Therefore, the nomogram is superior to clinical estimations for predicting the likelihood for non-SLN metastases.

publication date

  • February 1, 2007

Research

keywords

  • Breast Neoplasms
  • Lymph Nodes

Identity

Scopus Document Identifier

  • 33846646490

Digital Object Identifier (DOI)

  • 10.1245/s10434-006-9150-5

PubMed ID

  • 17103070

Additional Document Info

volume

  • 14

issue

  • 2