Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
AI Lab, Deepwise Healthcare, Beijing 100080, China.
Semin Cancer Biol. 2023 Jun;91:1-15. doi: 10.1016/j.semcancer.2023.02.006. Epub 2023 Feb 19.
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
癌症的个体化治疗策略通常依赖于分子生物学检测确定的基因改变。从历史上看，这些过程通常需要单基因测序、下一代测序或临床环境下经验丰富的病理学家对组织病理学幻灯片进行目视检查。在过去的十年中，人工智能 (AI) 技术的进步在协助医生准确诊断肿瘤影像学识别任务方面显示出了巨大的潜力。同时，AI 技术还可以实现对多模态数据（如放射学、组织学和基因组学）的整合，为精准治疗背景下的患者分层提供关键指导。鉴于对相当数量的患者来说，突变检测既昂贵又耗时，因此基于 AI 方法的常规临床放射学扫描或组织全切片图像来预测基因突变已成为实际临床实践中的一个热点问题。在这篇综述中，我们综合了超越标准技术的分子智能诊断的多模态整合 (MMI) 的一般框架。然后，我们总结了 AI 在预测常见癌症（肺癌、脑癌、乳腺癌和其他肿瘤类型）的突变和分子特征方面的新兴应用，这些应用涉及放射学和组织学成像。此外，我们得出结论，AI 技术在其在医学领域的实际应用中确实存在多个挑战，包括数据管理、特征融合、模型可解释性和实践规范。尽管存在这些挑战，但我们仍期待 AI 作为一种有潜力的决策支持工具在未来癌症治疗管理中为肿瘤学家提供帮助。
