Cell Reports Medicine · 2024 · 53 citations · 107 references
Prostate cancer is a common male malignancy, and its pathology review—critical for treatment decisions—is labor‑intensive and subjective, but digital pathology and whole‑slide imaging now enable AI applications. This review aims to highlight AI successes in detecting, grading, and prognosticating prostate cancer, propose AI–pathologist collaboration to reduce workload and aid treatment decisions, outline development challenges, and summarize publicly available datasets and open‑source codes to enable model comparison. The authors review AI applications in prostate cancer, describe the development process and challenges of AI pathology models, and compile publicly available datasets and open‑source code to support model comparison. The review compiles publicly available datasets and open‑source code, enabling researchers to compare model performance and advance future studies.
Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.
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