Screening of normal endoscopic large bowel biopsies with artificial intelligence: a retrospective study
Authors: Graham Simon , Minhas Fayyaz , Bilal Mohsin , Ali Mahmoud , Tsang Yee Wah , Eastwood Mark , Wahab Noorul , Jahanifar Mostafa , Hero Emily , Dodd Katherine , Sahota Harvir , Wu Shaobin , Lu Wenqi , Azam Ayesha , Benes Ksenija , Nimir Mohammed , Hewitt Katherine , Bhalerao Abhir , Robinson Andrew , Eldaly Hesham , E Ahmed Raza Shan , Gopalakrishnan Kishore , Snead David , Rajpoot Nasir M. 
Published: October 2022

Objectives

Develop an interpretable AI algorithm to rule out normal large bowel endoscopic biopsies saving pathologist resources.

Design

Retrospective study.

Setting

One UK NHS site was used for model training and internal validation. External validation conducted on data from two other NHS sites and one site in Portugal.

Participants

6,591 whole-slides images of endoscopic large bowel biopsies from 3,291 patients (54% Female, 46% Male).

Main outcome measures

Area under the receiver operating characteristic and precision recall curves (AUC-ROC and AUC-PR), measuring agreement between consensus pathologist diagnosis and AI generated classification of normal versus abnormal biopsies.

Results

A graph neural network was developed incorporating pathologist domain knowledge to classify the biopsies as normal or abnormal using clinically driven interpretable features. Model training and internal validation were performed on 5,054 whole slide images of 2,080 patients from a single NHS site resulting in an AUC-ROC of 0.98 (SD=0.004) and AUC-PR of 0.98 (SD=0.003). The predictive performance of the model was consistent in testing over 1,537 whole slide images of 1,211 patients from three independent external datasets with mean AUC-ROC = 0.97 (SD=0.007) and AUC-PR = 0.97 (SD=0.005). Our analysis shows that at a high sensitivity threshold of 99%, the proposed model can, on average, reduce the number of normal slides to be reviewed by a pathologist by 55%. A key advantage of IGUANA is its ability to provide an explainable output highlighting potential abnormalities in a whole slide image as a heatmap overlay in addition to numerical values associating model prediction with various histological features. Example results with can be viewed online athttps://iguana.dcs.warwick.ac.uk/.

Conclusions

An interpretable AI model was developed to screen abnormal cases for review by pathologists. The model achieved consistently high predictive accuracy on independent cohorts showing its potential in optimising increasingly scarce pathologist resources and for achieving faster time to diagnosis. Explainable predictions of IGUANA can guide pathologists in their diagnostic decision making and help boost their confidence in the algorithm, paving the way for future clinical adoption.

What is already known on this topic

Increasing screening rates for early detection of colon cancer are placing significant pressure on already understaffed and overloaded histopathology resources worldwide and especially in the United Kingdom1.

Approximately a third of endoscopic colon biopsies are reported as normal and therefore require minimal intervention, yet the biopsy results can take up to 2-3 weeks2.

AI models hold great promise for reducing the burden of diagnostics for cancer screening but require incorporation of pathologist domain knowledge and explainability.

What this study adds

This study presents the first AI algorithm for rule out of normal from abnormal large bowel endoscopic biopsies with high accuracy across different patient populations.

For colon biopsies predicted as abnormal, the model can highlight diagnostically important biopsy regions and provide a list of clinically meaningful features of those regions such as glandular architecture, inflammatory cell density and spatial relationships between inflammatory cells, glandular structures and the epithelium.

The proposed tool can both screen out normal biopsies and act as a decision support tool for abnormal biopsies, therefore offering a significant reduction in the pathologist workload and faster turnaround times.