title: Automatic annotation of radiological observations in liver CT images.
abstract: We aim to predict radiological observations using computationally-derived imaging features extracted from computed tomography (CT) images. We created a dataset of 79 CT images containing liver lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Traditional logistic regression was compared to L(1)-regularized logistic regression (LASSO) in order to predict the radiological observations using computational features. The approach was evaluated by leave one out cross-validation. Informative radiological observations such as lesion enhancement, hypervascular attenuation, and homogeneous retention were predicted well by computational features. By exploiting relationships between computational and semantic features, this approach could lead to more accurate and efficient radiology reporting.
authors.affiliation: Biomedical Informatics Training Program, Stanford University, CA, USA.
authors.forename: Francisco
authors.initials: F
authors.lastname: Gimenez
authors.forename: Jiajing
authors.initials: J
authors.lastname: Xu
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.forename: Tiffany
authors.initials: T
authors.lastname: Liu
authors.forename: Christopher
authors.initials: C
authors.lastname: Beaulieu
authors.forename: Daniel
authors.initials: D
authors.lastname: Rubin
authors.forename: Sandy
authors.initials: S
authors.lastname: Napel
mesh_terms: D019540:Area Under Curve; D001185:Artificial Intelligence; D019295:Computational Biology; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging*; D008107:Liver Diseases / Q000000981:diagnostic imaging*; D016015:Logistic Models; D012660:Semantics; D014057:Tomography, X-Ray Computed; D018875:Vocabulary, Controlled