Journal of Clinical Chemistry and Laboratory Medicine
Open Access
ISSN: 2736-6588
ISSN: 2736-6588
Department of Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea Seoul, Korea
Research Article
Deep Learning for Microsatellite Instability Prediction in Colorectal Cancer: Impact of Clinicopathologic Variables on Model Performance
Author(s): Meejeong Kim, Philip Chikontwe, Heounjeong Go, Jae Hoon Jeong, Su-Jin Shin, Sang Hyun Park* and Soo Jeong Nam*
Background: Microsatellite Instability (MSI) is a clinically significant subtype in colorectal cancer. Despite the
promising performance of deep learning techniques in digital pathology for clinical diagnosis, the impact of
clinicopathologic factors on the performance of these models has been largely overlooked.
Methodology: Using a total of 931 colorectal cancer Whole Slide Images (WSIs), we developed and verified a deep
learning algorithm and analyzed the WSI-level MSI probability and clinicopathologic variables.
Results: In both internal and external cohorts, our deep learning model achieved an Area Under the Receiver
Operating Curve (AUROC) of 0.901 and 0.908, respectively. The presence of a mucinous or a signet ring cell
carcinoma component enhanced the model’s ability to predict MSI (HR=19.73.. View More»
DOI:
10.35248/2736-6588.23.6.276