Clustering-based Pattern Discovery in Lung Cancer Treatments

Lung cancer is the leading cause of cancer death. More than 238,340 new cases of lung cancer patients are expected in 2023, with an estimation of more than 127,070 deaths. Choosing the correct treatment is an important element to enhance the probability of survival and to improve patient's quality of life. Cancer treatments might provoke secondary effects. These toxicities cause different health problems that impact the patient's quality of life. Hence, reducing treatments toxicities while maintaining or improving their effectiveness is an important goal that aims to be pursued from the clinical perspective. On the other hand, clinical guidelines include general knowledge about cancer treatment recommendations to assist clinicians. Although they provide treatment recommendations based on cancer disease aspects and individual patient features, a statistical analysis taking into account treatment outcomes is not provided here. Therefore, the comparison between clinical guidelines with treatment patterns found in clinical data, would allow to validate the patterns found, as well as discovering alternative treatment patterns. In this work, we have analyzed a dataset containing lung cancer patients information including patients' data, prescribed treatments and their outcomes. Using a Chi-square test and K-Modes clustering algorithm in combination with Pattern Discovery metrics we identify patterns, within the clusters, based on cancer stage and treatment outcomes. Obtained results are analyzed based on statistical and clinical relevance and compared with lung cancer clinical guidelines. The comparison reveals that all patterns found coincide with clinical guidelines recommendations, assessing the validity of the proposed method for pattern discovery in a clinical dataset.