AI Article Synopsis

  • Twenty-five percent of cervical cancers are endocervical adenocarcinomas (EACs), characterized by a diverse range of tumors, which can be difficult to classify using current histopathological methods.
  • A new deep learning tool called Silva3-AI was created to automatically analyze histopathologic images and classify Silva patterns accurately, developed from data of 202 EAC patients and later tested on an additional 161 patients from various medical centers.
  • Silva3-AI demonstrated high accuracy in pattern classification, achieving scores comparable to experienced pathologists, and also provided visualization techniques, allowing for better understanding of tumor microenvironment variation.

Article Abstract

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. Initially, a total of 202 patients with EACs and histopathologic slides were obtained from Qilu Hospital of Shandong University for developing and internally testing the Silva3-AI model. Subsequently, an additional 161 patients and slides were collected from seven other medical centers for independent testing. The Silva3-AI model was developed using a vision transformer and recurrent neural network architecture, utilizing multi-magnification patches, and its performance was evaluated based on a class-specific area under the receiver-operating characteristic curve. Silva3-AI achieved a class-specific area under the receiver-operating characteristic curve of 0.947 for Silva A, 0.908 for Silva B, and 0.947 for Silva C on the independent test set. Notably, the performance of Silva3-AI was consistent with that of professional pathologists with 10 years' diagnostic experience. Furthermore, the visualization of prediction heatmaps facilitated the identification of tumor microenvironment heterogeneity, which is known to contribute to variations in Silva patterns.

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Source
http://dx.doi.org/10.1016/j.ajpath.2024.01.016DOI Listing

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