Publication details

Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study

Authors

VERMORGEN Sanne GELTON Thijs BULT Peter KUSTERS-VANDEVELDE Heidi V N HAUSNEROVÁ Jitka VIJVER Koen Van de DAVIDSON Ben STEFANSSON Ingunn Marie KOOREMAN Loes F S QERIMI Adelina HUVILA Jutta GILKS Blake SHAHI Maryam ZOMER Saskia BARTOSCH Carla PIJNENBORG Johanna M A BULTEN Johan CIOMPI Francesco SIMONS Michiel

Year of publication 2024
Type Article in Periodical
Magazine / Source Modern Pathology
MU Faculty or unit

Faculty of Medicine

Citation
Web https://www.sciencedirect.com/science/article/pii/S0893395223003228
Doi http://dx.doi.org/10.1016/j.modpat.2023.100417.
Keywords classification; digital pathology; endometrial cancer; interobserver variability
Description Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.

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