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BackgroundNon-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer and the most difficult to predict. When there are no distant metastases, the optimal therapy depends mainly on whether there are malignant lymph nodes in the mediastinum. Given the vigorous debate among specialists about which tests should be used, our goal was to determine the optimal sequence of tests for each patient.MethodsWe have built an influence diagram (ID) that represents the possible tests, their costs, and their outcomes. This model is equivalent to a decision tree containing millions of branches. In the first evaluation, we only took into account the clinical outcomes (effectiveness). In the second, we used a willingness-to-pay of € 30,000 per quality adjusted life year (QALY) to convert economic costs into effectiveness. We assigned a second-order probability distribution to each parameter in order to conduct several types of sensitivity analysis.ResultsTwo strategies were obtained using two different criteria. When considering only effectiveness, a positive computed tomography (CT) scan must be followed by a transbronchial needle aspiration (TBNA), an endobronchial ultrasound (EBUS), and an endoscopic ultrasound (EUS). When the CT scan is negative, a positron emission tomography (PET), EBUS, and EUS are performed. If the TBNA or the PET is positive, then a mediastinoscopy is performed only if the EBUS and EUS are negative. If the TBNA or the PET is negative, then a mediastinoscopy is performed only if the EBUS and the EUS give contradictory results. When taking into account economic costs, a positive CT scan is followed by a TBNA; an EBUS is done only when the CT scan or the TBNA is negative.This recommendation of performing a TBNA in certain cases should be discussed by the pneumology community because TBNA is a cheap technique that could avoid an EBUS, an expensive test, for many patients.ConclusionsWe have determined the optimal sequence of tests for the mediastinal staging of NSCLC by considering sensitivity, specificity, and the economic cost of each test. The main novelty of our study is the recommendation of performing TBNA whenever the CT scan is positive. Our model is publicly available so that different experts can populate it with their own parameters and re-examine its conclusions. It is therefore proposed as an evidence-based instrument for reaching a consensus.

Abstract-Bayesian networks and influence diagrams are probabilistic graphical models widely used for building diagnosisand decision-support expert systems. Explanation of both the model and the reasoning is important for debugging these models, for alleviating users' reluctance to accept their advice, and for using them as tutoring systems. This paper describes some explanation options for Bayesian networks and influence diagrams that have been implemented in Elvira and how they have been used for building medical models and for teaching probabilistic reasoning to pre-and post-graduate students.

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