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Endometrial Carcinoma Detection in Pipelle biopsies

Introduction

Endometrial carcinoma (EC) is the third most common cause of death in cancers that only affect women. The most frequent treatment for endometrial carcinoma is the removal of the uterus (abdominal hysterectomy). This treatment is less radical than it seems, since the cancer often occurs in postmenopausal women, where the function of the uterus has become obsolete. However, the desire to preserve the uterus, both in postmenopausal and non-postmenopausal women, will require different treatment options. The outcome of treatment options such as radiation therapy, hormone therapy or chemotherapy will become more favorable with early diagnosis of the disease. The need for early diagnosis caused a series of innovations of endometrial sampling devices to decrease the invasiveness and increase the specificity and sensitivity. The most used device today is the Pipelle de Cornier, which has high sensitivity and specificity in diagnosing EC. This elongated suction device has been researched clinically, but to date no studies have investigated the potential for computed-aided automation. The way the Pipelle uses vacuum to extract tissue does not yield usable material in 20-30% of the cases. Combined with the vast amount of benign samples, since the non-invasiveness will have specialists opt for it faster, results in a high number of cases that are less relevant for a pathologist.

The aim of our study was, therefore, to investigate the feasibility of computer-aided screening systems on Pipelle sampled tissue and developing, to the best of our knowledge, the first deep learning system to address this problem. The overall aim of our project was to develop a computer system that can improve the diagnostic procedure of Pipelle sampled endometrial tissue to reduce the amount of benign and non-informative samples a pathologist would have to diagnose, such that the pathologist can focus on the difficult cases. An ensemble of the CLAM architecture, hosted on grand-challenge.org, was able to achieve an AUC of 0.9599 and a sensitivity of 100% at a false positive rate of 10%, potentially reducing the amount of work by 72.9%. 

Publishing of our methods and reader study is in progress.

Here, we release the test set used in our project, namely the n=91 whole-slide images used in the Slide-study, and implement an automatic evaluation script to allow researchers to submit their predictions on these 91 cases, and benchmark those against the opinion of 15 pathologists involved in the study, as well as with the reference standard based on the majority vote of their opinion.

Goal

The goal of this evaluation platform is to be a reference benchmark for algorithms that can predict endometrial carcinoma on whole-slide images of Pipelle sampled endometrial slides stained in H&E, based on the test data set used in our project. In line with the idea of challenges in computer vision and medical imaging, here we propose a test data set publicly shared with the scientific community, as well as a benchmarking platform that will allow all researchers to evaluate their algorithm on exactly the same data set and using exactly the same evaluation procedure.

Differently from most challenges in computer vision and medical imaging, we are solely releasing the test set used in our study, together with the evaluation procedure used in that study, implemented on this web platform. The training data is not released on this platform, and there are currently no plans to release it.

We envision this initiative to be a first step towards promoting research, development, and evaluation of artificial intelligence for the prediction of Pipelle biopsies in endometrial sampling.

How to use this platform

To assess the performance of your algorithm on the Slide-study data set used as a test set in our project, you should:

  1. download the test set from Zenodo at this link; additional details on the dataset can be found on the Data page of this website;
  2. run your algorithm on the n=91 whole-slide images of this test set
  3. store the probability for endometrial carcinoma in a CSV file following the details of the instructions on the Submission page
  4. upload your CSV file to this website via a submission; after that, you should be able to see your position on the leaderboard on this website and compare your results with ours.