Development and validation of a deep learning algorithm for pattern-based classification system of cervical cancer from pathological sections

Background: Multi-center research has demonstrated that adopting Silva's pattern-based classification system (SPBC) enhances the clinical prognosis and facilitates hierarchical management of patients with endocervical adenocarcinomas (EAC). However, inconsistencies in SPBC can arise due to variations in pathologists' experience levels. Thus, the implementation of standardized decision-making tools becomes crucial to enhance the practicality of SPBC in clinical diagnosis and treatment. Methods: We enrolled a total of 90 patients with EAC in this study, of which 63 were assigned to the training group, and the remaining 27 were allocated to the validation group. To create and validate the prediction models for SPBC, we utilized a deep learning system (DLS) and calculated the area under the receiver operating characteristic curve (AUC). Results: In Silva pattern classification, ResNet50 achieved an average accuracy of 74.36% (63.64% for pattern A, 55.56% for pattern B, and 89.47% for pattern C respectively). Moreover, in test set, ResNet50 achieved an AUC of 0.69 for pattern A, 0.58 for pattern B, and 0.91 for pattern C. Conclusions: We successfully established a DLS for SPBC, which holds the potential to aid pathologists in accurately classifying patients with EAC.