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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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

Wei Tian^1,2, Siyuan Sun³, Bin Wu⁴

¹Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.

|September 4, 2023

Summary

A deep learning system (DLS) aids pathologists in classifying endocervical adenocarcinomas (EAC) using Silva

Keywords:

Deep learning system Endocervical adenocarcinomas Pattern-based classification system according to silva ResNet50 Whole slide images

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Area of Science:

Oncology
Pathology
Artificial Intelligence

Background:

Silva's pattern-based classification system (SPBC) improves clinical prognosis and management for endocervical adenocarcinomas (EAC).
Pathologist experience variations can lead to inconsistencies in SPBC application.
Standardized tools are needed to enhance SPBC's clinical practicality in EAC diagnosis and treatment.

Purpose of the Study:

To develop and validate a deep learning system (DLS) for standardized Silva pattern-based classification (SPBC) in EAC.
To assess the DLS performance in accurately classifying EAC patterns.

Main Methods:

A total of 90 patients with EAC were enrolled, with 63 in the training group and 27 in the validation group.
A deep learning system (DLS), specifically ResNet50, was utilized to create and validate prediction models for SPBC.
Area under the receiver operating characteristic curve (AUC) was calculated to evaluate model performance.

Main Results:

ResNet50 achieved an overall accuracy of 74.36% in Silva pattern classification.
Specific accuracies for patterns A, B, and C were 63.64%, 55.56%, and 89.47%, respectively.
ResNet50 achieved AUC values of 0.69 for pattern A, 0.58 for pattern B, and 0.91 for pattern C on the test set.

Conclusions:

A DLS for SPBC was successfully established.
The developed DLS shows potential to assist pathologists in the accurate classification of EAC.
This AI tool can help standardize SPBC, improving diagnostic consistency.