The investigators plan to develop a deep learning-based automatic interpretation model for Claudin18.2(CLDN18.2) using the institution's and multiple other centers' extensive pathological resources of digestive system adenocarcinomas. This study will not only strictly follow the latest domestic expert consensus and standards, but also aims to address current pain points in manual interpretation. It seeks to provide technical support for standardizing, objectifying, and streamlining CLDN18.2 testing, thereby advancing the application of precision medicine in the diagnosis and treatment of digestive system diseases. The project has clear clinical necessity and broad application prospects.
Study Type
OBSERVATIONAL
Enrollment
2,000
Nanfang Hospital, Southern Medical University
Guangzhou, Guangdong, China
Qianfoshan Hospital
Jinan, Shandong, China
Area under ROC curve (AUC)
Area under the curve
Time frame: Diagnostic evaluation will be performed within 1 week when the whole slide images(WSIs) are obtained.
Specificity
The true negative rate (TNR) of the diagnostic platform, which is the ratio between the number of negative individuals correctly categorized by platform and the total number of actual negative individuals (%).
Time frame: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained
Sensitivity
The true positive rate (TPR) of the diagnostic platform, which is the ratio between the number of positive individuals correctly categorized by platform and the total number of actual positive individuals (%).
Time frame: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained
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