Searching patterns in glands for predicting gastric cancer survival

Ricardo Moncayo; Sunny Alfonso; Angel Y. Sánchez; Carlos A. Parra; Eduardo Romero

doi:10.1117/12.2550041

16 March 2020 Searching patterns in glands for predicting gastric cancer survival

Ricardo Moncayo, Sunny Alfonso, Angel Y. Sánchez, Carlos A. Parra, Eduardo Romero

Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 1132015 (2020) https://doi.org/10.1117/12.2550041
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

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.

Citation Download Citation

Ricardo Moncayo, Sunny Alfonso, Angel Y. Sánchez, Carlos A. Parra, and Eduardo Romero "Searching patterns in glands for predicting gastric cancer survival", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132015 (16 March 2020); https://doi.org/10.1117/12.2550041

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