Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

Abstract

In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either “malignant”, “other or benign” or “insufficient”. An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with “malignant” and “other or benign” areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being “malignant” or “other or benign”. Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either “malignant”, “other or benign” or “insufficient”. The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists’ workload.

Artificial Intelligence to Predict the BRAF V595E Mutation in Canine Urinary Bladder Urothelial Carcinomas

“…In this situation, the pathologist needs to have a high level of experience to reach a definite diagnosis, and the risk of missing small cancerous lesions exists. The power of AI for supporting the pathologist in detecting early and small cancer foci has been demonstrated for bladder and other cancers in humans [56][57][58]. Even though limited to a small series (n = 14) of BRAF-mutated UC, the present study confirms that AI was able to distinguish between malignant and adjacent benign urothelium in six cases.…”

A consolidated working classification of gastric cancer for histopathologists (Review)

“…Recent studies using artificial intelligence revealed that systems can be built to recognise molecular changes from H&E sections ( 65 , 66 ). Therefore, it is plausible that pathologists may eventually be able to recognise various molecular subtypes of GC from unique morphological features without the adjunctive help of the molecular data.…”

Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?

“…Future advances in digital pathology show a great deal of promise, but progress is slowed by various barriers, including ethical concerns and regulations [9]. In the near future, it is likely that this technology will be slowly introduced into the diagnostic process, aiding pathologists by analysing slides and prioritising those that the algorithm indicates contain disease [12,16,17]. The introduction of the Grand Challenges has fast-tracked research in this field, providing datasets and creating an environment for researchers to submit their work.…”