In Seminars in cancer biology
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically require single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in the clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in oncology image-recognition tasks assisting physicians in accurate diagnosis. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics which provides 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 upon routine clinical radiological scans and whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesize the general framework of molecular intelligent diagnostics beyond standard techniques. Then we summarize 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. However, despite that, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
Shao Jun, Ma Jiechao, Zhang Qin, Li Weimin, Wang Chengdi
2023-Feb-17
Gene mutation, artificial intelligence, multimodal data, precision oncology, radiology imaging
