Zhu Min, Sali Rasoul, Baba Firas, Khasawneh Hamdi, Ryndin Michelle, Leveillee Raymond J, Hurwitz Mark D, Lui Kin, Dixon Christopher, Zhang David Y
Department of Computational Pathology, NovinoAI 1443 NE 4th Ave, Fort Lauderdale, FL 33304, USA.
Department of Radiation Oncology, Stanford University School of Medicine Stanford, CA 94305, USA.
Am J Clin Exp Urol. 2024 Aug 25;12(4):200-215. doi: 10.62347/JSAE9732. eCollection 2024.
Histopathology, which is the gold-standard for prostate cancer diagnosis, faces significant challenges. With prostate cancer ranking among the most common cancers in the United States and worldwide, pathologists experience an increased number for prostate biopsies. At the same time, precise pathological assessment and classification are necessary for risk stratification and treatment decisions in prostate cancer care, adding to the challenge to pathologists. Recent advancement in digital pathology makes artificial intelligence and learning tools adopted in histopathology feasible. In this review, we introduce the concept of AI and its various techniques in the field of histopathology. We summarize the clinical applications of AI pathology for prostate cancer, including pathological diagnosis, grading, prognosis evaluation, and treatment options. We also discuss how AI applications can be integrated into the routine pathology workflow. With these rapid advancements, it is evident that AI applications in prostate cancer go beyond the initial goal of being tools for diagnosis and grading. Instead, pathologists can provide additional information to improve long-term patient outcomes by assessing detailed histopathologic features at pixel level using digital pathology and AI. Our review not only provides a comprehensive summary of the existing research but also offers insights for future advancements.
组织病理学是前列腺癌诊断的金标准,但面临重大挑战。前列腺癌是美国和全球最常见的癌症之一,病理学家面临的前列腺活检数量不断增加。与此同时,精确的病理评估和分类对于前列腺癌治疗中的风险分层和治疗决策至关重要,这也增加了病理学家的挑战。数字病理学的最新进展使组织病理学中采用人工智能和学习工具成为可能。在这篇综述中,我们介绍了人工智能的概念及其在组织病理学领域的各种技术。我们总结了人工智能病理学在前列腺癌中的临床应用,包括病理诊断、分级、预后评估和治疗选择。我们还讨论了人工智能应用如何融入常规病理工作流程。随着这些快速进展,很明显,前列腺癌中的人工智能应用超出了最初作为诊断和分级工具的目标。相反,病理学家可以通过使用数字病理学和人工智能在像素水平评估详细的组织病理学特征,提供额外信息以改善患者长期预后。我们的综述不仅对现有研究进行了全面总结,还为未来的进展提供了见解。