Histopathology remains the gold standard for disease diagnosis, yet faces challenges including pathologist shortages and diagnostic model limitations. This underscores the critical need to develop deep learning-based pathology foundation models integrating prospective imaging and clinical data. Such models would enhance diagnostic accuracy and efficiency, enabling tumor grading, histo-molecular classification, and intelligent chemotherapy guidance - ultimately optimizing clinical workflows. However, a critical gap remains: the absence of prospectively validated, pan-disease pathology foundation models. Developing clinically validated models is therefore imperative.
Study Type
OBSERVATIONAL
Enrollment
2,000
Nanfang Hospital, Southern Medical University
Guangzhou, Guangdong, China
Qianfoshan Hospital
Jinan, Shandong, China
RECRUITINGArea under ROC curve (AUC)
Area under the curve
Time frame: Diagnostic evaluation will be performed within 1 week when the 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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