A novel framework for prognostic factors identification of malignant mesothelioma through association rule mining

Malignant mesothelioma (MM) is a rare cancer type arising from mesothelial cells. The current clinical diagnosis is based on contrast-enhanced computed tomography, magnetic resonance imaging, and positron emission tomography that are either invasive or costly. The failure to diagnose malignantly can lead to an increased risk of multiple medical conditions, including cardiovascular diseases, emotional distress, anemia, and diabetes. To date, there is a limited number of prognostic factors that can be used for diagnosis. Most existing work has considered the MM disease as a classification task. In contrast, our study has initiated a knowledge extraction problem and proposed a machine learning-based framework. The performance status, age, and sex of patients are currently the most substantial clinical prognostic factors, but other histopathological and clinical prognostic factors are still unclear. This study aims to search for clinical prognostic, radiological, and histopathological factors in MM. In this study, the latest dataset from a public repository (UCI) has been utilised, including patients' medical, socio-economic, histopathological, and clinical factors. Association rule mining-based algorithms (Apriori and frequent pattern (FP) growth method) and feature selection techniques have been employed to extract significant features. The performance of the proposed framework has been evaluated based on support, confidence, and lift. We set the support, confidence, and lift between 0.5-1.0, 0.5-1.0, and 1.0-1.6 respectively. Our results showed five significant prognosis factors with the values for the identification of MM: Pleural lactate dehydrogenase >500 IU/L, C-reactive protein >10/mu L, pleural albumin<3/mu L, the presence of asbestos exposure and pleural effusion. In nearly all the experiments, the binary features were among the leading top five features in the list. The diagnosis of MM can be accessible through prognostic factors. Our proposed framework will help to diagnose the patients without expensive tests and painful procedures. The proposed framework may assist doctors, patients, medical practitioners, and other healthcare professionals for early diagnosis and better treatment of malignant mesothelioma through significant prognostic factors.

Automated summary

Malignant mesothelioma is a rare cancer type with significant importance in diagnosis. This study proposes a machine learning-based framework to extract effective features for diagnosing MM using clinical prognostic, radiological, and histopathological factors. Results indicate that pleural lactate dehydrogenase, C-reactive protein, pleural albumin, asbestos exposure, and pleural effusion are five important prognostic factors for MM.