Keywords:
Artificial Intelligence, Liver, Abdomen, CT, MR, Neural networks, Diagnostic procedure, Cancer
Authors:
J. Thüring1, O. Rippel1, C. Haarburger1, D. Merhof1, P. Schad1, P. Bruners1, C. K. Kuhl1, D. Truhn2; 1Aachen/DE, 2Cologne/DE
DOI:
10.26044/ecr2019/C-0355
Aims and objectives
Computer tomography (CT) is an established modality in the diagnosis and clinical management of patients with chronic liver disease [1;2] and it is described as a sensitive diagnostic tool for the assessment of morphological changes of liver [3-5].
To extend the value of image-based diagnosis,
recent studies investigated machine learning algorithms and their potential clinical application [6-9].
Building on this,
artificial neuronal networks have been employed to utilize implicit image information that might not be encompassed in dedicated human-made radiomic features [8;10].
The aims of this study were therefore to identify a radiomic imaging signature that allows univariate associations of image features and the Child-Pugh Score,
as a surrogate for liver cirrhosis.
Based on this,
predictive machine-learning models were implemented to evaluate imaging appearance for the prediction of the Child-Pugh Score and finally compare these results to those of experienced radiologists.