Georg J Wengert1, Pascal A T Baltzer2, Hubert Bickel2, Patrick Thurner2, Julia Breitenseher2, Mathias Lazar2, Matthias Pones2, Markus Peck-Radosavljevic3, Florian Hucke3, Ahmed Ba-Ssalamah2. 1. Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer-Guertel 18-20, 1090Vienna, Austria. Electronic address: georg.wengert@meduniwien.ac.at. 2. Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer-Guertel 18-20, 1090Vienna, Austria. 3. Department of Internal Medicine III, Division of Gastroenterology/Hepatology, Liver Cancer (HCC)-Study Group, Medical University of Vienna, Vienna, Austria.
Abstract
RATIONALE AND OBJECTIVES: This study aimed to investigate the potential of contrast-enhanced magnetic resonance imaging features to differentiate between mass-forming intrahepatic cholangiocellular carcinoma (ICC) and hepatocellular carcinoma (HCC) in cirrhotic livers. MATERIALS AND METHODS: This study, performed between 2001 and 2013, included 64 baseline magnetic resonance imaging examinations with pathohistologically proven liver cirrhosis, presenting with either ICC (n = 32) or HCC (n = 32) tumors. To distinguish ICC form HCC tumors, 20 qualitative single-lesion descriptors were evaluated by two readers, in consensus, and statistically classified using the chi-square automatic interaction detection (CHAID) methodology. Diagnostic performance was assessed by a receiver operating characteristic analysis. RESULTS: The CHAID algorithm identified three independent categorical lesion descriptors, including (1) liver capsular retraction; (2) progressive or persistent enhancement pattern or wash-out on the T1-weighted delayed phase; and (3) signal intensity appearance on T2-weighted images that could help to reliably differentiate ICC from HCC, which resulted in an AUC of 0.807, and a sensitivity and specificity of 68.8 and 90.6 (95% confidence interval 75.0-98.0), respectively. CONCLUSIONS: The proposed CHAID algorithm provides a simple and robust step-by-step classification tool for a reliable and solid differentiation between ICC and HCC tumors in cirrhotic livers.
RATIONALE AND OBJECTIVES: This study aimed to investigate the potential of contrast-enhanced magnetic resonance imaging features to differentiate between mass-forming intrahepatic cholangiocellular carcinoma (ICC) and hepatocellular carcinoma (HCC) in cirrhotic livers. MATERIALS AND METHODS: This study, performed between 2001 and 2013, included 64 baseline magnetic resonance imaging examinations with pathohistologically proven liver cirrhosis, presenting with either ICC (n = 32) or HCC (n = 32) tumors. To distinguish ICC form HCC tumors, 20 qualitative single-lesion descriptors were evaluated by two readers, in consensus, and statistically classified using the chi-square automatic interaction detection (CHAID) methodology. Diagnostic performance was assessed by a receiver operating characteristic analysis. RESULTS: The CHAID algorithm identified three independent categorical lesion descriptors, including (1) liver capsular retraction; (2) progressive or persistent enhancement pattern or wash-out on the T1-weighted delayed phase; and (3) signal intensity appearance on T2-weighted images that could help to reliably differentiate ICC from HCC, which resulted in an AUC of 0.807, and a sensitivity and specificity of 68.8 and 90.6 (95% confidence interval 75.0-98.0), respectively. CONCLUSIONS: The proposed CHAID algorithm provides a simple and robust step-by-step classification tool for a reliable and solid differentiation between ICC and HCC tumors in cirrhotic livers.
Authors: Tyler J Fraum; Roberto Cannella; Daniel R Ludwig; Richard Tsai; Muhammad Naeem; Maverick LeBlanc; Amber Salter; Allan Tsung; Anup S Shetty; Amir A Borhani; Alessandro Furlan; Kathryn J Fowler Journal: Eur Radiol Date: 2019-10-25 Impact factor: 5.315
Authors: Rong Xia; Amir M Boroujeni; Stephanie Shea; Yongsheng Pan; Raag Agrawal; Elhem Yousefi; M Isabel Fiel; M A Haseeb; Raavi Gupta Journal: Gastroenterology Res Date: 2019-11-21