Segmentation and Grade Prediction of Colon Cancer Digital Pathology Images Across Multiple Institutions

• Published in: Cancers Vol. 11; no. 11; p. 1700
• Main Authors: Rathore, Saima, Iftikhar, Muhammad Aksam, Chaddad, Ahmad, Niazi, Tamim, Karasic, Thomas, Bilello, Michel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.11.2019; MDPI
• Subjects: Accuracy; Algorithms; Automation; Biopsy; Classification; Colon cancer; Colorectal cancer; Computer applications; Discordance; Glands; Histology; Histopathology; Malignancy; Medical diagnosis; Morphology; Neural networks; Pathology; Segmentation; Support vector machines; colon cancer grading; colon cancer detection; gland segmentation; hierarchical classification
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers11111700

Distinguishing benign from malignant disease is a primary challenge for colon histopathologists. Current clinical methods rely on qualitative visual analysis of features such as glandular architecture and size that exist on a continuum from benign to malignant. Consequently, discordance between histopathologists is common. To provide more reliable analysis of colon specimens, we propose an end-to-end computational pathology pipeline that encompasses gland segmentation, cancer detection, and then further breaking down the malignant samples into different cancer grades. We propose a multi-step gland segmentation method, which models tissue components as ellipsoids. For cancer detection/grading, we encode cellular morphology, spatial architectural patterns of glands, and texture by extracting multi-scale features: (i) Gland-based: extracted from individual glands, (ii) local-patch-based: computed from randomly-selected image patches, and (iii) image-based: extracted from images, and employ a hierarchical ensemble-classification method. Using two datasets (Rawalpindi Medical College (RMC), = 174 and gland segmentation (GlaS), = 165) with three cancer grades, our method reliably delineated gland regions (RMC = 87.5%, GlaS = 88.4%), detected the presence of malignancy (RMC = 97.6%, GlaS = 98.3%), and predicted tumor grade (RMC = 98.6%, GlaS = 98.6%). Training the model using one dataset and testing it on the other showed strong concordance in cancer detection (Train RMC - Test GlaS = 94.5%, Train GlaS - Test RMC = 93.7%) and grading (Train RMC - Test GlaS = 95%, Train GlaS - Test RMC = 95%) suggesting that the model will be applicable across institutions. With further prospective validation, the techniques demonstrated here may provide a reproducible and easily accessible method to standardize analysis of colon cancer specimens.