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Cancer is a major public health problem across the globe due to which millions of deaths occur every year. In the United States, prostate cancer is the second leading cause of cancer- related deaths in men. The major causes of prostate cancer include increasing age, family history, diet, sexual behavior, and geographic location. Early detection of prostate cancer can effectively reduce the mortality rate. In the past, researchers have adopted various multimodal feature extracting strategies to extract diverse and comprehensive quantitative imaging features and employed machine learning methods to detect prostate cancer. However, existing techniques lack detailed analysis of the magnitude of relationship among different individual discriminatory features, which is very important to understand the dynamics of the disease. In this study, we extracted diverse morphological features to summarize the imaging profile of patients of prostate cancer imaging database and employed Bayesian network analysis approach to quantify the association between different features and the strength of the association. The features and the association between the features were, respectively, modeled as the nodes and the edges of the network. The strength of association between the nodes was computed using Pearson’s correlation, mutual Information and Kullback–Liebler methods. The strongest associations were found between multiple features: (Area $\to $ Equidiameter), (Area $\to $ Circulatory 2), (Circulatory $1\to $ (Elongatedness), (Circulatory $1\to $ Entropy), (Circulatory $1\to $ Max. Radius), and (Min. Radius $\to $ Eccentricity). Moreover, interaction impact among nodes and node force was also computed. This analysis will help in finding the features that are more dominant to establish the relationship and can further increase the detection performance.

Applying Bayesian Network Approach to Determine the Association Between Morphological Features Extracted from Prostate Cancer Images