The Canada Lymph Node Score: A Feasibility Randomized Controlled Trial

For patients diagnosed with early stage Non-Small Cell Lung Cancer (NSCLC) on preoperative computerized tomography (CT) and positron emission tomography (PET) scans, surgical resection is usually the preferred method of treatment. However, to be eligible for surgery, current guidelines require that the cancer has not spread to the lymph nodes in the chest cavity. To evaluate these lymph nodes, the standard of care is to undergo an endobronchial ultrasound (EBUS) procedure, where all the visible lymph nodes in the chest are biopsied (sampled) with a needle. Unfortunately, these biopsies are often inconclusive, especially in patients who have no evidence of mediastinal lymph node spread on pre-operative imaging. Currently, the standard of care mandates that inconclusive biopsies should be repeated, either through another EBUS, or through more invasive procedures. Repeat inconclusive biopsies are oftentimes inconclusive as well; leading to a vicious cycle of inconclusive results, a delay in treatment, morbidity for the patient, and increased costs to the healthcare system. To circumvent this issue, the investigators have developed, validated and published a 4-point score, the Canada Lymph Node Score (CLNS), which uses four features observed during EBUS to predict whether the cancer has spread to the lymph nodes or not. Research has demonstrated that lymph nodes which appear benign on both CT and PET scan that also have a CLNS of ≤1/4 are almost certainly benign. As such, it is believed that these "triple normal" lymph do not require biopsy (or repeat biopsy). The investigators are challenging the current standard of care in lung cancer, which mandates that all the lymph nodes in the chest need to be biopsied (i.e. Systematic Sampling) before surgery, by proposing that triple normal lymph nodes can be omitted, and only those with cancer potential should be biopsied (i.e. Targeted Sampling).To prove this hypothesis, a randomized controlled trial comparing Systematic Sam