Work

Artificial intelligence for clinical oncology

Year: 2021

Type: review

Source: Cancer Cell

DOI: 10.1016/j.ccell.2021.04.002

Authors: Benjamin H. Kann, Ahmed Hosny, Hugo J.W.L. Aerts

Institutions: Brigham and Women's Hospital, Harvard University, Dana-Farber Cancer Institute, Dana-Farber Brigham Cancer Center, Mass General Brigham, Maastricht University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: Radiological Society of North America, European Commission, National Institutes of Health, European Research Council, National Cancer Institute, National Institute of Dental and Craniofacial Research, H2020 European Research Council

Awards: G2400796240, Deep Learning for Automated Quantification of Radiographic Tumor Phenotypes, Unraveling the Complexities of Risk and Mechanism in Cancer, Genotype and Imaging Phenotype Biomarkers in Lung Cancer, G5024937287, Quantitative Radiomics System Decoding the Tumor Phenotype, The Boston Lung Cancer Survival Cohort, Development of an artificial intelligence-driven, imaging-based platform for pretreatment identification of extranodal extension in head and neck cancer

OpenAlex ID: W3157202711

FWCI: 4.321

Cites: 93

Cited by: 387

Related to: 10