Work

Radiomic phenotype features predict pathological response in non-small cell lung cancer

Year: 2016

Type: article

Study design: Observational Study

Source: Radiotherapy and Oncology

DOI: 10.1016/j.radonc.2016.04.004

Authors: Thibaud P. Coroller, Vishesh Agrawal, Vivek Narayan, Ying Hou, Patrick Grossmann, Stephanie W. Lee, Raymond H. Mak, Hugo J.W.L. Aerts

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

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: Brigham and Women's Hospital, National Institutes of Health, National Cancer Institute

Awards: Genotype and Imaging Phenotype Biomarkers in Lung Cancer, Quantitative Radiomics System Decoding the Tumor Phenotype

OpenAlex ID: W2338137334

FWCI: 27.43

Cites: 42

Cited by: 324

Related to: 10