Prediction of Microscopic Metastases in Patients with Metachronous Oligo-Metastases after Curative Treatment of Non-Small Cell Lung Cancer: A Microsimulation Study

Simple SummaryMany patients with metachronous oligo-metastases in non-small cell lung cancer have their recurrences surgically removed, although the 5-year recurrence-free survival of this group is 16%. This does not provide any benefit for patients with additional undetected metastases. Therefore, we aim to find patient characteristics that are predictive for having additional undetected microscopic metastases. Based on a theoretical approach, we identified the size and number of detected oligo-metastases, as well as the presence of symptoms that are the most important risk predictors.Metachronous oligo-metastatic disease is variably defined as one to five metastases detected after a disease-free interval and treatment of the primary tumour with curative intent. Oligo-metastases in non-small cell lung cancer (NSCLC) are often treated with curative intent. However additional metastases are often detected later in time, and the 5-year survival is low. Burdensome surgical treatment in patients with undetected metastases may be avoided if patients with a high versus low risk of undetected metastases can be separated. Because there is no clinical data on undetected metastases available, a microsimulation model of the development and detection of metastases in 100,000 hypothetical stage I NSCLC patients with a controlled primary tumour was constructed. The model uses data from the literature as well as patient-level data. Calibration was used for the unobservable model parameters. Metastases can be detected by a scheduled scan, or an unplanned scan when the patient develops symptoms. The observable information at time of detection is used to identify subgroups of patients with a different risk of undetectable metastases. We identified the size and number of detected oligo-metastases, as well as the presence of symptoms that are the most important risk predictors. Based on these predictors, patients could be divided into a low-risk and a high-risk group, having a model-based predicted probability of 8.1% and 89.3% to have undetected metastases, respectively. Currently, the model is based on a synthesis of the literature data and individual patient-level data that were not collected for the purpose of this study. Optimization and validation of the model is necessary to allow clinical usability. We describe the type of data that needs to be collected to update our model, as well as the design of such a validation study.