Обзорная статья

Artificial intelligence in cancer imaging: Clinical challenges and applications

Wenya Linda BiAssistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer Institute Harvard Medical School Boston MAAhmed HosnyResearch Scientist, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer Institute Harvard Medical School Boston MAMatthew B. SchabathAssociate Member, Department of Cancer Epidemiology H. Lee Moffitt Cancer Center and Research Institute Tampa FLMaryellen L. GigerProfessor of Radiology, Department of Radiology University of Chicago Chicago ILNicolai J. BirkbakResearch Associate, The Francis Crick Institute London United KingdomAlireza MehrtashResearch Assistant, Department of Electrical and Computer Engineering University of British Columbia Vancouver BC CanadaTavis AllisonResearch Assistant, Department of Radiology Columbia University College of Physicians and Surgeons New York NYOmar ArnaoutDana-Farber Cancer InstituteChristopher AbboshResearch Fellow, The Francis Crick Institute London United KingdomIan F. DunnAssociate Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer Institute Harvard Medical School Boston MARaymond H. MakAssociate Professor, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer Institute Harvard Medical School Boston MARulla M. TamimiAssociate Professor, Department of Medicine Brigham and Women’s Hospital, Dana‐Farber Cancer Institute, Harvard Medical School Boston MAClare M. TempanyProfessor of Radiology, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer Institute Harvard Medical School Boston MACharles SwantonProfessor, The Francis Crick Institute London United KingdomUdo HoffmannProfessor of Radiology, Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MALawrence H. SchwartzChair, Department of Radiology New York Presbyterian Hospital New York NYRobert J. GilliesProfessor of Radiology, Department of Cancer Physiology H. Lee Moffitt Cancer Center and Research Institute Tampa FLRaymond Y. HuangAssistant Professor, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer Institute Harvard Medical School Boston MAHugo J.W.L. AertsAssociate Professor, Departments of Radiation Oncology and Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer Institute Harvard Medical School Boston MA

2019en

ABI

Аннотация

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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Идентификаторы

DOI: 10.3322/caac.21552

Цитирования и источники

Цитирований: 3Использованных источников: 0

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