Harnessing artificial intelligence for prostate cancer management
Lingxuan Zhu, Jiahua Pan, Weiming Mou, Longxin Deng, Yinjie Zhu, Yanqing Wang, Gyan Pareek, Elias Hyams, Benedito A. Carneiro, Matthew J. Hadfield, Wafik S. El‐Deiry, Tao Yang, Tao Tan, Tong Tong, Na Ta, Yan Zhu, Yisha Gao, Yancheng Lai, Liang Cheng, Rui Chen, Wei Xue
Abstract
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.