Davri Athena, Birbas Effrosyni, Kanavos Theofilos, Ntritsos Georgios, Giannakeas Nikolaos, Tzallas Alexandros T, Batistatou Anna
Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece.
Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece.
Diagnostics (Basel). 2022 Mar 29;12(4):837. doi: 10.3390/diagnostics12040837.
Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.
结直肠癌（CRC）是女性中第二常见的癌症，在男性中是第三常见的癌症，其发病率呈上升趋势。病理诊断辅以预后和预测生物标志物信息是个性化治疗的第一步。病理实验室中诊断工作量的增加，再加上报告的生物标志物评估中的内部和外部变异性，促使人们寻求可靠的基于机器的方法并将其纳入常规实践。最近，人工智能（AI）在医学领域取得了重大进展，显示出临床应用的潜力。在此，我们旨在系统回顾当前关于AI在CRC图像分析中的研究。在组织病理学中，基于深度学习（DL）的算法有潜力协助诊断、预测临床相关分子表型和微卫星不稳定性、识别与预后相关且与转移相关的组织学特征，以及评估肿瘤微环境的特定成分。
