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Article

Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning

1
Quantitative and Digital Pathology, School of Medicine, University of St Andrews, St Andrews KY16 9TF, UK
2
Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama 359-8513, Japan
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Indica Labs, Inc., 2469 Corrales Rd Bldg A-3, Corrales, NM 87048, USA
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Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto, Tokyo 135-8550, Japan
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Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto, Tokyo 135-8550, Japan
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Pathology Project for Molecular Targets, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto, Tokyo 135-8550, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Noel F.C.C. De Miranda
Cancers 2021, 13(7), 1615; https://doi.org/10.3390/cancers13071615
Received: 27 November 2020 / Accepted: 26 March 2021 / Published: 31 March 2021
(This article belongs to the Section Tumor Microenvironment)
Desmoplastic reaction (DR) has previously been shown to be a promising prognostic factor in colorectal cancer (CRC). However, its manual reporting can be subjective and consequently consistency of reporting might be affected. The aim of our study was to develop a deep learning algorithm that would facilitate the objective and standardised DR assessment. By applying this algorithm on a CRC cohort of 528 patients, we demonstrate how deep learning methodologies can be used for the accurate and reproducible reporting of DR. Furthermore, this study showed that the prognostic significance of DR was superior when assessed through the use of the deep learning classifier than when assessed manually. In this study, we demonstrate how the application of machine learning approaches can help by not only identifying complex patterns present within histopathological images in a standardised and reproducible manner, but also report a more accurate patient stratification.
The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (n = 41). When assessing the classifier’s performance on a test set of patient samples (n = 40), a Dice score of 0.87 for the segmentation of myxoid stroma was reported. The classifier was then applied to the full cohort of 528 stage II and III CRC cases, which was then divided into a training (n = 396) and a test set (n = 132). Automatically classed DR was shown to have superior prognostic significance over the manually classed DR in both the training and test cohorts. The findings demonstrated that deep learning algorithms could be applied to assist pathologists in the detection and classification of DR in CRC in an objective, standardised and reproducible manner. View Full-Text
Keywords: deep learning; image analysis; desmoplastic reaction; colorectal cancer; digital pathology deep learning; image analysis; desmoplastic reaction; colorectal cancer; digital pathology
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MDPI and ACS Style

Nearchou, I.P.; Ueno, H.; Kajiwara, Y.; Lillard, K.; Mochizuki, S.; Takeuchi, K.; Harrison, D.J.; Caie, P.D. Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning. Cancers 2021, 13, 1615. https://doi.org/10.3390/cancers13071615

AMA Style

Nearchou IP, Ueno H, Kajiwara Y, Lillard K, Mochizuki S, Takeuchi K, Harrison DJ, Caie PD. Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning. Cancers. 2021; 13(7):1615. https://doi.org/10.3390/cancers13071615

Chicago/Turabian Style

Nearchou, Ines P., Hideki Ueno, Yoshiki Kajiwara, Kate Lillard, Satsuki Mochizuki, Kengo Takeuchi, David J. Harrison, and Peter D. Caie. 2021. "Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning" Cancers 13, no. 7: 1615. https://doi.org/10.3390/cancers13071615

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