Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology.

Journal: Seminars in cancer biology
Published Date:

Abstract

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.

Authors

  • Jun Shao
    Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Jiechao Ma
    InferVision, Beijing, 100020, China.
  • Qin Zhang
    Department of Burn, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Weimin Li
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Chengdi Wang
    Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.