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Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides

Beuque, M; Magee, DR; Chatterjee, A; Woodruff, HC; Langley, RE; Allum, W; Nankivell, MG; ... Grabsch, HI; + view all (2023) Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides. Journal of Pathology Informatics , 14 , Article 100192. 10.1016/j.jpi.2023.100192. Green open access

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Abstract

Treatment of patients with oesophageal and gastric cancer (OeGC) is guided by disease stage, patient performance status and preferences. Lymph node (LN) status is one of the strongest prognostic factors for OeGC patients. However, survival varies between patients with the same disease stage and LN status. We recently showed that LN size from patients with OeGC might also have prognostic value, thus making delineations of LNs essential for size estimation and the extraction of other imaging biomarkers. We hypothesized that a machine learning workflow is able to: (1) find digital H&E stained slides containing LNs, (2) create a scoring system providing degrees of certainty for the results, and (3) delineate LNs in those images. To train and validate the pipeline, we used 1695 H&E slides from the OE02 trial. The dataset was divided into training (80%) and validation (20%). The model was tested on an external dataset of 826 H&E slides from the OE05 trial. U-Net architecture was used to generate prediction maps from which predefined features were extracted. These features were subsequently used to train an XGBoost model to determine if a region truly contained a LN. With our innovative method, the balanced accuracies of the LN detection were 0.93 on the validation dataset (0.83 on the test dataset) compared to 0.81 (0.81) on the validation (test) datasets when using the standard method of thresholding U-Net predictions to arrive at a binary mask. Our method allowed for the creation of an “uncertain” category, and partly limited false-positive predictions on the external dataset. The mean Dice score was 0.73 (0.60) per-image and 0.66 (0.48) per-LN for the validation (test) datasets. Our pipeline detects images with LNs more accurately than conventional methods, and high-throughput delineation of LNs can facilitate future LN content analyses of large datasets.

Type: Article
Title: Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jpi.2023.100192
Publisher version: https://doi.org/10.1016/j.jpi.2023.100192
Language: English
Additional information: © 2023 The Author(s). Published by Elsevier Inc. on behalf of Association for Pathology Informatics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Oesophageal cancer, Deep learning, Autodelineation, Explainability, Digital pathology, Lymph nodes
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10165789
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