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人工智能在前列腺组织病理学中的应用:2021 年我们处于什么位置?

Artificial intelligence in prostate histopathology: where are we in 2021?

机构信息

Department of Pathology.

Department of Urology, Medical University of Vienna, Vienna, Austria.

出版信息

Curr Opin Urol. 2021 Jul 1;31(4):430-435. doi: 10.1097/MOU.0000000000000883.

Abstract

PURPOSE OF REVIEW

Artificial intelligence has made an entrance into mainstream applications of daily life but the clinical deployment of artificial intelligence-supported histological analysis is still at infancy. Recent years have seen a surge in technological advance regarding the use of artificial intelligence in pathology, in particular in the diagnosis of prostate cancer.

RECENT FINDINGS

We review first impressions of how artificial intelligence impacts the clinical performance of pathologists in the analysis of prostate tissue. Several challenges in the deployment of artificial intelligence remain to be overcome. Finally, we discuss how artificial intelligence can help in generating new knowledge that is interpretable by humans.

SUMMARY

It is evident that artificial intelligence has the potential to outperform most pathologists in detecting prostate cancer, and does not suffer from inherent interobserver variability. Nonetheless, large clinical validation studies that unequivocally prove the benefit of artificial intelligence support in pathology are necessary. Regardless, artificial intelligence may soon automate and standardize many facets of routine work, including qualitative (i.e. Gleason Grading) and quantitative measures (i.e. portion of Gleason Grades and tumor volume). For the near future, a model where pathologists are enhanced by second-review or real-time artificial intelligence systems appears to be the most promising approach.

摘要

目的综述

人工智能已进入日常生活的主流应用领域,但人工智能支持的组织学分析的临床应用仍处于起步阶段。近年来,人工智能在病理学中的应用技术取得了突飞猛进的发展,特别是在前列腺癌的诊断方面。

最近的发现

我们首先回顾了人工智能对病理学家分析前列腺组织的临床性能的影响。人工智能的部署仍存在一些挑战需要克服。最后,我们讨论了人工智能如何帮助生成可由人类解释的新知识。

总结

人工智能在检测前列腺癌方面明显优于大多数病理学家,并且不受固有观察者间变异性的影响。然而,仍需要进行大型的临床验证研究,明确证明人工智能支持在病理学中的益处。无论如何,人工智能可能很快将使包括定性(即 Gleason 分级)和定量测量(即 Gleason 分级和肿瘤体积的比例)在内的常规工作的许多方面实现自动化和标准化。在不久的将来,病理学家通过二次审查或实时人工智能系统得到增强的模式似乎是最有前途的方法。

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