Searching patterns in glands for predicting gastric cancer survival

This article presents an entire framework for analyzing survival-related gland features in gastric cancer images. This approach builds upon a previous automatic gland detection, which partitions the tissue into a set of primitive objects (glands) from a binarized version of the hematoxylin channel. Next, gland shape and nuclei are characterized using local and contextual features that include relationships between color or texture from glands and nuclei (5:120 features). A mutual information max-relevance-min-redundancy (mRMR) approach selects hundred features that correlate with patient survival "survival vs not survival (first year)". Finally, ten statistically significant features (test t-student, p < 0:05) were used to set a "one-year" survival. Evaluation was carried out in a set of fourteen cases diagnosed with pre-cancerous gastric lesions or cancer, under a leave-one-out scheme. Results showed an accuracy of 78.57% when predicting the patient survival (less or more than a year), using a QDA Linear & Quadratic Discriminant Analysis. This approach suggests there exist morphometric gland differences among cases with gastric related pathology.