Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163278
Title: A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer
Authors: Ho, Cowan
Zhao, Zitong
Chen, Xiu Fen
Sauer, Jan
Saraf, Sahil Ajit
Jialdasani, Rajasa
Taghipour, Kaveh
Sathe, Aneesh
Khor, Li-Yan
Lim, Kiat-Hon
Leow, Wei-Qiang
Keywords: Science::Biological sciences
Science::Medicine
Issue Date: 2022
Source: Ho, C., Zhao, Z., Chen, X. F., Sauer, J., Saraf, S. A., Jialdasani, R., Taghipour, K., Sathe, A., Khor, L., Lim, K. & Leow, W. (2022). A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer. Scientific Reports, 12(1), 2222-. https://dx.doi.org/10.1038/s41598-022-06264-x
Journal: Scientific Reports 
Abstract: Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive's unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists' annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into 'low risk' (benign, inflammation) and 'high risk' (dysplasia, malignancy) categories. We further trained the composite AI-model's performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.
URI: https://hdl.handle.net/10356/163278
ISSN: 2045-2322
DOI: 10.1038/s41598-022-06264-x
Rights: © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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