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Lymphoma triage from H&E using AI for improved clinical management

Lookup NU author(s): Dr Christopher BaconORCiD, Dr Christopher Carey

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Abstract

© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ. Aims: In routine diagnosis of lymphoma, initial non-specialist triage is carried out when the sample is biopsied to determine if referral to specialised haematopathology services is needed. This places a heavy burden on pathology services, causes delays and often results in over-referral of benign cases. We aimed to develop an automated triage system using artificial intelligence (AI) to enable more accurate and rapid referral of cases, thereby addressing these issues. Methods: A retrospective dataset of H&E-stained whole slide images (WSI) of lymph nodes was taken from Newcastle University Hospital (302 cases) and Manchester Royal Infirmary Hospital (339 cases) with approximately equal representation of the 3 most prevalent lymphoma subtypes: follicular lymphoma, diffuse large B-cell and classic Hodgkin's lymphoma, as well as reactive controls. A subset (80%) of the data was used for training, a further validation subset (10%) for model selection and a final non-overlapping test subset (10%) for clinical evaluation. Results: AI triage achieved multiclass accuracy of 0.828±0.041 and overall accuracy of 0.932±0.024 when discriminating between reactive and malignant cases. Its ability to detect lymphoma was equivalent to that of two haematopathologists (0.925, 0.950) and higher than a non-specialist pathologist (0.75) repeating the same task. To aid explainability, the AI tool also provides uncertainty estimation and attention heatmaps. Conclusions: Automated triage using AI holds great promise in contributing to the accurate and timely diagnosis of lymphoma, ultimately benefiting patient care and outcomes.

Publication metadata

Author(s): Tsakiroglou AM, Bacon CM, Shingleton D, Slavin G, Vogiatzis P, Byers R, Carey C, Fergie M

Publication type: Article

Publication status: Published

Journal: Journal of Clinical Pathology

Year: 2025

Volume: 78

Issue: 1

Pages: 28-33

Print publication date: 01/01/2025

Online publication date: 09/11/2023

Acceptance date: 18/10/2023

ISSN (print): 0021-9746

ISSN (electronic): 1472-4146

Publisher: BMJ Publishing Group

URL: https://doi.org/10.1136/jcp-2023-209186

DOI: 10.1136/jcp-2023-209186

PubMed id: 37945334

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