@article{66256c9c3b384bf1a533f8a3e9a5bd13,
title = "Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy",
keywords = "Quantitative imaging analysis, Radiomics, Esophageal cancer, Concurrent chemoradiotherapy, Clinical outcomes, Methodological assessment, PATHOLOGICAL COMPLETE RESPONSE, TEXTURE ANALYSIS, F-18-FDG PET, PREOPERATIVE CHEMORADIOTHERAPY, NEOADJUVANT CHEMORADIOTHERAPY, RADIATION PNEUMONITIS, GENETIC-VARIANTS, TUMOR RESPONSE, RADIOMICS, FEATURES",
author = "Z.W. Shi and Z. Zhang and Z.Y. Liu and L.J. Zhao and Z.X. Ye and A. Dekker and L. Wee",
year = "2021",
month = dec,
day = "23",
doi = "10.1007/s00259-021-05658-9",
language = "English",
journal = "European Journal of Nuclear Medicine and Molecular Imaging",
issn = "1619-7070",
publisher = "Springer, Cham",
}