A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning

Twenty-five percent of cervical cancers are classified as endocervical adenocarcinomas (EACs), which comprise a highly heterogeneous group of tumors. A histopathologic risk stratification system known as the Silva pattern system was developed based on morphology. However, accurately classifying such patterns can be challenging. The study objective was to develop a deep learning pipeline (Silva3-AI) that automatically analyzes whole slide image –based histopathologic images and identifies Silva patterns with high accuracy.

Source: American Journal of Pathology - February 19, 2024 Category: Pathology Authors: Qingqing Liu, Xiaofang Zhang, Xuji Jiang, Chunyan Zhang, Jiamei Li, Xuedong Zhang, Jingyan Yang, Ning Yu, Yongcun Zhu, Jing Liu, Fengxiang Xie, Yawen Li, Yiping Hao, Yuan Feng, Qi Wang, Qun Gao, Wenjing Zhang, Teng Zhang, Taotao Dong, Baoxia Cui Tags: Regular article Source Type: research