Natural Language Processing of Computed Tomography Reports to Label Metastatic Phenotypes With Prognostic Significance in Patients With Colorectal Cancer.

Pamela Ines Causa Andrieu Jennifer S Golia Pernicka Rona Yaeger Kaelan Lupton Karen Batch Farhana Zulkernine Amber L Simpson Michio Taya Lior GazitHuy NguyenKevin J Nicholas Natalie GangaiVaradan SevilimeduShannan M Dickinson Viktoriya Paroder David D B Bates Richard Kinh Gian Do

Published in: JCO clinical cancer informatics (2022)

NLP applied to a large radiology report database can identify clinically relevant metastatic phenotypes and be used to investigate new M1 substaging for CRC. Patients with three or more metastatic disease organs have the worst prognosis, with an OS of 1 year.

Keyphrases

squamous cell carcinoma
small cell lung cancer
computed tomography
magnetic resonance imaging
adverse drug
positron emission tomography
artificial intelligence
autism spectrum disorder
emergency department
magnetic resonance
machine learning