Sakamoto Taro, Furukawa Tomoi, Lami Kris, Pham Hoa Hoang Ngoc, Uegami Wataru, Kuroda Kishio, Kawai Masataka, Sakanashi Hidenori, Cooper Lee Alex Donald, Bychkov Andrey, Fukuoka Junya
Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan.
Transl Lung Cancer Res. 2020 Oct;9(5):2255-2276. doi: 10.21037/tlcr-20-591.
The emergence of whole slide imaging technology allows for pathology diagnosis on a computer screen. The applications of digital pathology are expanding, from supporting remote institutes suffering from a shortage of pathologists to routine use in daily diagnosis including that of lung cancer. Through practice and research large archival databases of digital pathology images have been developed that will facilitate the development of artificial intelligence (AI) methods for image analysis. Currently, several AI applications have been reported in the field of lung cancer; these include the segmentation of carcinoma foci, detection of lymph node metastasis, counting of tumor cells, and prediction of gene mutations. Although the integration of AI algorithms into clinical practice remains a significant challenge, we have implemented tumor cell count for genetic analysis, a helpful application for routine use. Our experience suggests that pathologists often overestimate the contents of tumor cells, and the use of AI-based analysis increases the accuracy and makes the tasks less tedious. However, there are several difficulties encountered in the practical use of AI in clinical diagnosis. These include the lack of sufficient annotated data for the development and validation of AI systems, the explainability of black box AI models, such as those based on deep learning that offer the most promising performance, and the difficulty in defining the ground truth data for training and validation owing to inherent ambiguity in most applications. All of these together present significant challenges in the development and clinical translation of AI methods in the practice of pathology. Additional research on these problems will help in resolving the barriers to the clinical use of AI. Helping pathologists in developing knowledge of the working and limitations of AI will benefit the use of AI in both diagnostics and research.
全切片成像技术的出现使得病理诊断能够在电脑屏幕上进行。数字病理学的应用正在不断扩展，从支持病理学家短缺的偏远机构到包括肺癌诊断在内的日常诊断中的常规使用。通过实践和研究，已经建立了大型数字病理学图像存档数据库，这将有助于开发用于图像分析的人工智能（AI）方法。目前，在肺癌领域已经报道了几种AI应用；这些应用包括癌灶分割、淋巴结转移检测、肿瘤细胞计数以及基因突变预测。尽管将AI算法整合到临床实践中仍然是一项重大挑战，但我们已经实现了用于遗传分析的肿瘤细胞计数，这是一种有助于常规使用的应用。我们的经验表明，病理学家常常高估肿瘤细胞的含量，而基于AI的分析提高了准确性并使任务不那么繁琐。然而，在临床诊断中实际使用AI时会遇到一些困难。这些困难包括缺乏足够的标注数据用于AI系统的开发和验证、黑箱AI模型（如基于深度学习且性能最有前景的模型）的可解释性，以及由于大多数应用中存在固有的模糊性而难以定义用于训练和验证的真实数据。所有这些共同在病理学实践中AI方法的开发和临床转化方面带来了重大挑战。对这些问题的进一步研究将有助于解决AI临床应用的障碍。帮助病理学家了解AI的工作原理和局限性将有利于AI在诊断和研究中的应用。
