  The diagnosis and monitoring of Castrate Resistant Prostate Cancer (CRPC) are
crucial for cancer patients, but the current models (such as P-NET) have
limitations in terms of parameter count, generalization, and cost. To address
the issue, we develop a more accurate and efficient Prostate Cancer patient
condition prediction model, named PR-NET. By compressing and optimizing the
network structure of P-NET, the model complexity is reduced while maintaining
high accuracy and interpretability. The PR-NET demonstrated superior
performance in predicting prostate cancer patient outcomes, outshining P-NET
and six other traditional models with a significant margin. In our rigorous
evaluation, PR-NET not only achieved impressive average AUC and Recall scores
of 0.94 and 0.83, respectively, on known data but also maintained robust
generalizability on five unknown datasets with a higher average AUC of 0.73 and
Recall of 0.72, compared to P-NET's 0.68 and 0.5. PR-NET's efficiency was
evidenced by its shorter average training and inference times, and its
gene-level analysis revealed 46 key genes, demonstrating its enhanced
predictive power and efficiency in identifying critical biomarkers for prostate
cancer. Future research can further expand its application domains and optimize
the model's performance and reliability.
