Liao Jinzhuang, Li Xiaoying, Gan Yu, Han Shuangze, Rong Pengfei, Wang Wei, Li Wei, Zhou Li
Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Front Oncol. 2023 Jan 4;12:998222. doi: 10.3389/fonc.2022.998222. eCollection 2022.
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.
癌症是全球主要的医学问题。由于其高度异质性，对患有相同肿瘤的患者使用相同的药物或手术方法可能会有不同的疗效，这就导致需要更精确的肿瘤治疗方法和针对患者的个性化治疗。肿瘤的精准治疗至关重要，这使得迫切需要深入了解肿瘤所经历的变化，包括其基因、蛋白质和癌细胞表型的变化，以便为患者制定靶向治疗策略。基于大数据的人工智能（AI）可以提取海量数据背后隐藏的模式、重要信息和相应知识。例如，AI的机器学习和深度学习可用于挖掘基因组学、转录组学、蛋白质组学、放射组学、数字病理图像等数据中的深层次信息，这可以使临床医生综合全面地了解肿瘤。此外，AI可以从数据中发现新的生物标志物，以辅助肿瘤筛查、检测、诊断、治疗和预后预测，从而为个体患者提供最佳治疗并改善其临床结局。
