Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine.

Journal: International journal of oncology
Published Date:

Abstract

Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic and novel drug predictive assays to inform patient selection and stratification for disease treatment. Accumulating evidence suggests that a combination of cancer pathology and artificial intelligence (AI) can meet this requirement. In the present review, the past, present and emerging integrations of AI into cancer pathology were comprehensively reviewed, which were divided into four main groups to highlight the roles of AI‑integrated cancer pathology in precision medicine. Furthermore, the unsolved problems and future challenges in AI‑integrated cancer pathology were also discussed. It was found that, although AI‑integrated cancer pathology could enable the amalgamation of complex morphological phenotypes with the multi‑omics datasets that drove precision medicine, synergies of cancer pathology with other medical tools could be more promising for the clinic when making an accurate and rapid decision in personalized treatments for patients. It was hypothesized by the authors that exploring the potential advantages of the multimodal integration of cancer pathology, imaging‑omics, protein‑omics and other‑omics, as well as clinical data to decide upon appropriate management and improve patient outcomes may be the most challenging issue of cancer precision medicine in the future.

Authors

  • Boan Lai
    Department of Pathology, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510150, P.R. China.
  • Jianjiang Fu
    Department of Pathology, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510150, P.R. China.
  • Qingxin Zhang
    Department of Pathology, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510150, P.R. China.
  • Nan Deng
    1 Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center , Los Angeles, California.
  • Qingping Jiang
    Department of Pathology, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510150, P.R. China.
  • Juan Peng
    State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China.