Development and validation of AI-based pre-screening of large bowel biopsies

Abstract

Background Histopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. With the aims of facilitating diagnostic decision-making and improving the use of pathologists’ time, we developed an AI-based pre-screening tool that analyses whole slide images (WSIs) of large bowel biopsies to identify normal, inflammatory, and neoplastic biopsies.

Methods To learn the differential histological patterns from digitised WSIs of large bowel biopsy slides stained with Haematoxylin and Eosin (H&E), our proposed weakly supervised deep learning method uses only slide-level diagnostic labels and no detailed cell or region-level annotations. The proposed method was developed on an internal cohort of biopsy slides (n=5054) from a single laboratory labelled with corresponding diagnostic categories assigned by pathologists. Performance of the tool was evaluated on the internal development cohort (n=5054) in a cross-validation setting, and three external unseen cohorts (n=1536) for independent validation.

Findings The proposed tool demonstrates high degree of accuracy to assist with the pre-screening of large bowel biopsies, being able to identify neoplastic biopsies (AUROC = 0·993), inflammatory biopsies (AUROC = 0·966) and all abnormal biopsies (AUROC = 0·979). On the three independent validation cohorts, it achieves AUROC values of 0·943, 0·958 and 0·964 for the detection of abnormal biopsies. Analysis of saliency maps confirms the representation of disease heterogeneity in model predictions and their association with relevant histological features. Interestingly, after examining diagnostic discrepancies between the proposed AI tool and original diagnostic labels, a panel of pathologists found that the proposed tool correctly identified a number of abnormal slides that had been initially reported as normal.

Interpretations The proposed tool with its high sensitivity of detecting abnormal colorectal biopsies promises significant improvements in clinical workflow efficiency and assistance in diagnostic decision-making through pre-screening of normal biopsies.

Funding Innovate UK on behalf of UK Research and Innovation.

Competing Interest Statement

DS reports personal fees from Royal Philips, outside the submitted work. NR and FM report research funding from GlaxoSmithKline. NR is in receipt of research funding from AstraZeneca. SG, DS and NR are co-founders of Histofy Ltd. All other authors declare no competing interests.

Funding Statement

The PathLAKE Centre of Excellence for digital pathology and artificial intelligence is funded from the Data to Early Diagnosis and Precision Medicine strand of the HM Government's Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI, Grant ref: File Ref 104 689/application number 18 181).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was conducted under Health Research Authority National Research Ethics approval 15/NW/0843; IRAS 189 095 and the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) research ethics committee approval (REC reference 19/SC/0363, IRAS project ID 257 932, South Central - Oxford C Research Ethics Committee). Data collection and usage of the IMP Diagnostics dataset was performed in accordance with national legal and ethical standards applicable to this cohort.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

Original WSIs from University Hospitals Coventry and Warwickshire NHS Trust, East Suffolk and North Essex NHS Foundation Trust and South Warwickshire NHS Foundation Trust will be made available upon completion of the PathLAKE project. Relevant information on obtaining the data from the IMP cohort can be found in the original publication.

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