Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study

VERMORGEN, Sanne, Thijs GELTON, Peter BULT, Heidi V N KUSTERS-VANDEVELDE, Jitka HAUSNEROVÁ, Koen Van de VIJVER, Ben DAVIDSON, Ingunn Marie STEFANSSON, Loes F S KOOREMAN, Adelina QERIMI, Jutta HUVILA, Blake GILKS, Maryam SHAHI, Saskia ZOMER, Carla BARTOSCH, Johanna M A PIJNENBORG, Johan BULTEN, Francesco CIOMPI a Michiel SIMONS. Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study. Modern Pathology. New York: Elsevier, 2024, roč. 37, č. 2. ISSN 0893-3952. Dostupné z: https://dx.doi.org/10.1016/j.modpat.2023.100417.

Základní údaje

• Originální název: Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study
• Autoři: VERMORGEN, Sanne, Thijs GELTON, Peter BULT, Heidi V N KUSTERS-VANDEVELDE, Jitka HAUSNEROVÁ, Koen Van de VIJVER, Ben DAVIDSON, Ingunn Marie STEFANSSON, Loes F S KOOREMAN, Adelina QERIMI, Jutta HUVILA, Blake GILKS, Maryam SHAHI, Saskia ZOMER, Carla BARTOSCH, Johanna M A PIJNENBORG, Johan BULTEN, Francesco CIOMPI a Michiel SIMONS.
• Vydání: Modern Pathology, New York, Elsevier, 2024, 0893-3952.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Článek v odborném periodiku
• Stát vydavatele: Spojené státy
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Impakt faktor: Impact factor: 7.500 v roce 2022
• Organizační jednotka: Lékařská fakulta
• Doi: http://dx.doi.org/10.1016/j.modpat.2023.100417.
• UT WoS: 001163090800001
• Klíčová slova anglicky: classification; digital pathology; endometrial cancer; interobserver variability
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.