Quantitation of cancer regions in microscopic low resolution histopathological colon tissue images to predict patient survival

Despite the generally excellent outcomes associated with early stage colon cancer treatment, a significant number of patients still develop recurrence and ultimately die from their disease. The standard tumor-node-metastasis (TNM) staging system cannot predict which patient will recur and will need additional therapy. This study aims to provide clinicians with a new computational tool based on quantitative analyses of histopathological images and a systems approach to accurately predict disease recurrence. We developed a set of advanced imaging algorithms including unsupervised dissection to automatically segment images into major histopathological components and to extract a broad spectrum of quantitative measurements from these components. Considering the complex interplay among various factors, a novel non-parametric random survival forest methodology was used to identify factors that most accurately predict the survival of colon cancer patients. Relative area and Haralick´s contrast features of the tumor necrosis region have been identified as the most statistically significant predictors of survival for early stage colon cancer patients.