A risk stratification model, based on lymph node characteristics, can confirm a negative classification of lung cancer with a high level of confidence. The model determines the true lack of lung cancer in lymph nodes based on adequate sampling with endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA).
Lung cancer treatment and prognosis is critically dependent on accurate staging that takes into account the extent to which cancer has spread from the primary lung tumor to other locations. Examination of lymph nodes containing lung cancer cells that have spread can be done by surgical removal, which historically is the standard practice, or by using the less invasive and more cost-effective technique of EBUS-TBNA.
The use of EBUS-TBNA is well established in several countries throughout the world and staging patients who have a positive result is defined. It is less clear how to stage and treat patients with negative EBUS-TBNA results.
Researchers at the University Hospital of South Manchester in the United Kingdom examined 329 lymph nodes that were classified as EBUS-TBNA negative from patients with pathologically diagnosed or, in a limited number of cases, clinically diagnosed lung cancer.
A total of 196 of the lymph nodes were used to derive a model based on lymph node radiologic and ultrasound characteristics for high or low risk. This was based on them actually being positive for lung cancer upon further evaluation. The model was then validated with the remaining 133 lymph nodes.
Results, published in the Journal of Thoracic Oncology (2014; doi:10.1097/JTO.0000000000000348), show that lymph nodes categorized as low risk by the model had between a 98% and 99% chance of being truly negative based on the validation and derivation sets, respectively. Lymph nodes categorized in the model as high risk of being truly malignant were 65% in both the validation and derivation sets following a false negative EBUS-TBNA classification.
The authors acknowledge that “radiological staging will never replace pathological staging, but in cases of negative or inadequate EBUS-TBNA sampling our study demonstrates that the combination of radiologic and ultrasound data posttest can stratify patients into low and high risk for nodal malignancy.”
The authors are committed to further data collection and analysis of the model. They conclude that “this risk stratification model provides a mechanism for lung cancer multidisciplinary teams to discuss the risk of false negative EBUS-TBNA sampling, which may ultimately assist in the decision making process for either further staging procedures or direct progression to treatment.”