Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging

Giulia Baldini; René Hosch; Cynthia S Schmidt; Katarzyna Borys; Lennard Kroll; Sven Koitka; Patrizia Haubold; Obioma Pelka; Felix Nensa; Johannes Haubold

doi:10.1097/RLI.0000000000001071

Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging

Invest Radiol. 2024 Mar 4. doi: 10.1097/RLI.0000000000001071. Online ahead of print.

Authors

Giulia Baldini¹, René Hosch, Cynthia S Schmidt, Katarzyna Borys, Lennard Kroll, Sven Koitka, Patrizia Haubold, Obioma Pelka, Felix Nensa, Johannes Haubold

Affiliation

¹ From the Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (G.B., R.H., K.B., L.K., S.K., F.N., J.H.); Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (G.B., R.H., C.S.S., K.B., L.K., S.K., O.P., F.N., J.H.); Institute for Transfusion Medicine, University Hospital Essen, Essen, Germany (C.S.S.); Department of Diagnostic and Interventional Radiology, Kliniken Essen-Mitte, Essen, Germany (P.H.); and Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany (O.P., F.N.).

PMID: 38436405
DOI: 10.1097/RLI.0000000000001071

Abstract

Objectives: Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT).

Materials and methods: This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs).

Results: For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively.

Conclusions: The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.