Wu Yihan, Li Yi, Xiong Xiaomin, Liu Xiaohua, Lin Bo, Xu Bo
School of Medicine, Chongqing University, Chongqing, China.
Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China.
Front Oncol. 2023 Jul 19;13:1094869. doi: 10.3389/fonc.2023.1094869. eCollection 2023.
Colorectal cancer (CRC) is one of the most common malignancies, with the third highest incidence and the second highest mortality in the world. To improve the therapeutic outcome, the risk stratification and prognosis predictions would help guide clinical treatment decisions. Achieving these goals have been facilitated by the fast development of artificial intelligence (AI) -based algorithms using radiological and pathological data, in combination with genomic information. Among them, features extracted from pathological images, termed pathomics, are able to reflect sub-visual characteristics linking to better stratification and prediction of therapeutic responses. In this paper, we review recent advances in pathological image-based algorithms in CRC, focusing on diagnosis of benign and malignant lesions, micro-satellite instability, as well as prediction of neoadjuvant chemoradiotherapy and the prognosis of CRC patients.
结直肠癌（CRC）是最常见的恶性肿瘤之一，在全球发病率排名第三，死亡率排名第二。为了改善治疗效果，风险分层和预后预测有助于指导临床治疗决策。基于人工智能（AI）的算法结合放射学和病理学数据以及基因组信息的快速发展，促进了这些目标的实现。其中，从病理图像中提取的特征，即病理组学，能够反映与更好的分层和治疗反应预测相关的亚视觉特征。在本文中，我们综述了基于病理图像的算法在CRC中的最新进展，重点关注良性和恶性病变的诊断、微卫星不稳定性，以及新辅助放化疗的预测和CRC患者的预后。
