Towards Treatment Patterns Validation in Lung Cancer Patients

Lung cancer is the leading cause of cancer death. From the estimation of cases that will be in 2021, more than 230,000 new cases are expected to be of lung cancer patients, with an estimation of more than 131,000 deaths. Improving the survival rates or the patient's quality of life is partially covered by a common element: treatments. Collective knowledge about cancer treatment recommendations is typically included in clinical guidelines, intended to optimize patient care and assist clinicians in lung cancer treatment. These guidelines define a set of treatment paths, where recommendations depend on cancer disease aspects and individual features for a concrete patient. Although oncologists are expected to follow clinical guidelines, the inter and intrapatients' variability of response to the possible treatment combinations, makes it necessary to personalize different treatment-patterns on certain cases. Additionally, clinical guidelines are not frequently updated with new findings or lack a consistent methodology when they are frequently updated. For that reason, the analysis of patterns on both patients treated following the standard of care, or outside it, would allow to validate clinical guidelines and identify potential new treatment recommendations. In this work, we have analysed whether actual treatments prescribed to lung cancer patients follow clinical guidelines or not. Using a machine learning method that provides as output association rules (Apriori), we identify patterns based on cancer stage. These preliminary results show that treatments patterns found mostly match with clinical guidelines recommendations, validating the information included in the consulted guidelines.