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Proceedings Paper

Searching patterns in glands for predicting gastric cancer survival
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Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132015 (16 March 2020); doi: 10.1117/12.2550041
Show Author Affiliations
Ricardo Moncayo, School of Medicine, Univ. Nacional de Colombia Bogotá (Colombia)
Sunny Alfonso, School of Medicine, Univ. Nacional de Colombia Bogotá (Colombia)
Angel Y. Sánchez, School of Medicine, Univ. Nacional de Colombia Bogotá (Colombia)
Carlos A. Parra, Univ. Nacional de Colombia - Bogotá (Colombia)
Eduardo Romero, School of Medicine, Univ. Nacional de Colombia Bogotá (Colombia)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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