The goal of this predictive test is to prospectively test the performance of pre-developed artificial intelligence (AI) predictive model for predicting the time to castration resistance of prostate cancer. Investigators had developed this AI model based on deep learning algorithms in preliminary research, and it performed well in retrospective tests.
Patients are diagnosed with intermediate- to high-risk prostate cancer; undergo prostate biopsy
Patients only received endocrine therapy for prostate cancer;
Patients with complete clinical and pathological information.
Patients agree to participate in this diagnostic test.
Patients with other tumors and undergo systemic therapy .
The patient refused to participate in this diagnostic test.
Patients with prostate cancer, undergo prostate biopsy between Jan, 2026 and Dec 2026 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University are planned to be enrolled in this prospective predictive test. Histopathological slides of biopsy tissues of enrolled patients will be collected and digitised as whole-slide images (WSIs) for prospective validation of the AI model.
Cohort
Prospective
Artificial intelligence (AI)-based predictive model (developed)
Other interventionCollect pathological slides of prostate biopsy of the enrolled patients. Digitise these slides into whole-slide images (WSIs). Analyze the WSIs using the AI model to generate predictive results (within 12 months, between 12 to 24months or over 24 months). No intervention to patients would be performed in this predictive test study.
C-index (Concordance Index)
The proportion of all patient pairs in which the predicted outcome order matches the actual outcome order. It estimates the probability that the predicted results are consistent with the observed outcomes.
sensitivity
The output of the predictive model is divided into a binary variable using a 12-month threshold: TTCR \<12 months is considered a positive outcome, and TTCR ≥12 months is considered a negative outcome. Accordingly, patients with TTCR \<12 months are positive patients, and those with TTCR ≥12 months are negative patients. The number of correctly predicted positive slides (TTCR\<12 months), to be divided by the number of positive slides in total
specificity
The output of the predictive model is divided into a binary variable using a 12-month threshold: TTCR \<12 months is considered a positive outcome, and TTCR ≥12 months is considered a negative outcome. Accordingly, patients with TTCR \<12 months are positive patients, and those with TTCR ≥12 months are negative patients. The number of correctly predicted negative slides (TTCR≥12 months), to be divided by the number of negative slides in total
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