Start date: 01-02-2022
End date: 31-07-2022
Endometrial cancer is one of the more frequent malignancies in women. Each year, 2000 women are diagnosed with endometrial cancer (EC) in the Netherlands, the majority of which are postmenopausal women with a mean age of 65 year at the time of diagnosis. An increased thickness of the endometrium and abnormal vaginal blood loss are often the first signs of the development of endometrium cancer. Sometimes, precancerous tissue (premalignancy) can be detected, and early treatment of this premalignancy can prevent the ultimate development of cancer, thus preventing the morbidity and mortality related to endometrial cancer. The histopathologic diagnosis of the pipelle sampled endometrium tissue guides further management of the patient. Pipelle sampling is a non-invasive method for biopsy, which is, therefore, often preferred in clinical screenings. The extracted cell tissue is, however, highly fragmented and pathologically less informative than, for example, a surgical resection. Additionally, a high percentage (90 - 95%) of the screenings yield benign tissue. A correct evaluation of the biopsy specimen, with 100% sensitivity, is therefore of paramount importance in reducing the workload of pathologists.
In this project, we will include all Radboudumc archival endometrial pipelle biopsies processed between October 2013 and April 2021, resulting in a total 3230 cases (using a PALGA search to find cases). From this set, we will select representative examples of the four main clinically relevant categories that will be addressed in this project, namely
- Normal endometrium
- Endometrial hyperplasia without atypia
- Endometrial hyperplasia with atypia
- Endometrial carcinoma.
On this set, pathologists will make manual annotations of relevant regions, which will be used for training and validation of AI algorithms. For training purposes, about 100 cases will be annotated with manual dense annotations of tissue types, while the rest of cases will be annotated with weak annotations extracted from the pathology reports. Eventually, the validation of developed algorithms will be performed on > 3,000 cases.
We will develop a segmentation model, trained using our novel Stratified Hard Negative Mining (SHNM) approach, where the most severe category will be used for slide-level diagnosis. This means that if malignant (the highest clinical importance) tissue is predicted to be present in the WSI, then this will determine the diagnosis. SHNM is a novel addition to Hard Negative Mining (HNM). Regular HNM will select false positives (FP) solely on a likelihood. This neglects the morphological value certain categories display. With SHNM the FP are first collected, including metadata (coordinates, low dimensional embedding, etc.). In the second stage, these FPs are clustered based on their encoded embeddings, which represent the morphologies of the cell tissue. This should aid the model in fine-tuning its ability to discriminate the difficult morphologies which are located on the boundaries between two categories.
The final algorithm will be made publicly available as a Docker container on https://grand-challenge.org/. This will allow pathologists and other interested parties to easily test against their own Whole-Slide images.