Wang Shidan, Yang Donghan M, Rong Ruichen, Zhan Xiaowei, Fujimoto Junya, Liu Hongyu, Minna John, Wistuba Ignacio Ivan, Xie Yang, Xiao Guanghua
Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers (Basel). 2019 Oct 28;11(11):1673. doi: 10.3390/cancers11111673.
Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection.
In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer.
We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis.
With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.
准确的诊断和预后对于肺癌治疗方案的选择和规划至关重要。随着医学成像技术的迅速发展，病理学中的全切片成像（WSI）正成为一种常规临床程序。基于对病理图像进行准确高效分析的计算机辅助诊断存在需求与挑战的相互作用。近年来，人工智能，尤其是深度学习，在病理图像分析任务中展现出巨大潜力，如肿瘤区域识别、预后预测、肿瘤微环境特征描述以及转移检测。
在本综述中，我们旨在概述人工智能方法在病理图像分析中的当前及潜在应用，重点关注肺癌。
我们概述了肺癌病理图像分析中的当前挑战与机遇，讨论了近期可能对肺癌数字病理学产生影响的深度学习进展，并总结了深度学习算法在肺癌诊断和预后方面的现有应用。
随着技术的进步，数字病理学在肺癌患者护理方面可能具有巨大的潜在影响。我们指出了肺癌病理图像分析一些有前景的未来方向，包括多任务学习、迁移学习和模型解释。
