Mélanie Chiaradia1, Maria-Louisa Izamis2, Alessandro Radaelli2, Warner Prevoo3, Geert Maleux4, Todd Schlachter5, Julie Mayer1, Alain Luciani6, Hicham Kobeiter7, Vania Tacher8. 1. Department of Radiology and Medical Imaging, Henri Mondor University Hospital, 51 Avenue du maréchal de Lattre de Tassigny, 94010 Creteil, France. 2. Philips Healthcare, Best, The Netherlands. 3. Department of Radiology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands; The Netherlands Katholieke Universiteit, Amsterdam, The Netherlands. 4. Department of Radiology, University Hospitals Leuven, Leuven, Belgium. 5. Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut. 6. Department of Radiology and Medical Imaging, Henri Mondor University Hospital, 51 Avenue du maréchal de Lattre de Tassigny, 94010 Creteil, France; Medical School, Université Paris Est Créteil, Créteil, France; Unité INSERM U 955, Equipe 18, Créteil, France. 7. Department of Radiology and Medical Imaging, Henri Mondor University Hospital, 51 Avenue du maréchal de Lattre de Tassigny, 94010 Creteil, France; Medical School, Université Paris Est Créteil, Créteil, France. 8. Department of Radiology and Medical Imaging, Henri Mondor University Hospital, 51 Avenue du maréchal de Lattre de Tassigny, 94010 Creteil, France; Medical School, Université Paris Est Créteil, Créteil, France; Unité INSERM U 955, Equipe 18, Créteil, France. Electronic address: vania.tacher@aphp.fr.
Abstract
PURPOSE: To evaluate the performance of automated feeder detection (AFD) software (EmboGuide; Philips Healthcare, Best, The Netherlands) on hepatocellular carcinoma (HCC) tumors during transarterial chemoembolization. MATERIALS AND METHODS: Forty-four first-time transarterial chemoembolization patients (37 men; mean age, 62 ± 11 years) were enrolled between May 2012 and July 2013. A total of 86 HCC lesions were treated (2.0 ± 1.4 lesions per patient; 27.6 ± 15.9 mm maximum diameter). One hundred forty-seven feeding arteries were found with digital subtraction angiography (DSA), cone-beam computed tomography (CT), and AFD software with the option of manual adjustment (MA). Three independent interventional radiologists analyzed the cone-beam CT images retrospectively with and without AFD and MA. Compared with the number of treated vessels, the number of true positives, false positives, false negatives, sensitivity, and interreader agreement were determined using clustered binary data analysis. RESULTS: Cone-beam CT enabled detection of 100 ± 3.5 feeding arteries (70% sensitivity) with 68.6% agreement among readers. AFD software significantly improved detection to 127±0.6 feeding arteries (86% sensitivity, P = .008) with 99.7% reader agreement and reduced the number of false negatives from an average of 47 ± 3.5 to 20 ± 0.6 (P = .008). MA of the AFD results produced similar feeding artery detection rates (127 ± 5.1, 86% sensitivity, P = .8), with lower interreader agreement (91.6%) and slightly fewer false positives (16 ± 0.0 to 14 ± 2.5, P = .4). CONCLUSIONS: AFD software significantly improved feeding artery detection rates during transarterial chemoembolization of HCC lesions with better user reproducibility compared with cone-beam CT alone. In conjunction with DSA, AFD enables maximum feeding artery detection in this setting.
PURPOSE: To evaluate the performance of automated feeder detection (AFD) software (EmboGuide; Philips Healthcare, Best, The Netherlands) on hepatocellular carcinoma (HCC) tumors during transarterial chemoembolization. MATERIALS AND METHODS: Forty-four first-time transarterial chemoembolization patients (37 men; mean age, 62 ± 11 years) were enrolled between May 2012 and July 2013. A total of 86 HCC lesions were treated (2.0 ± 1.4 lesions per patient; 27.6 ± 15.9 mm maximum diameter). One hundred forty-seven feeding arteries were found with digital subtraction angiography (DSA), cone-beam computed tomography (CT), and AFD software with the option of manual adjustment (MA). Three independent interventional radiologists analyzed the cone-beam CT images retrospectively with and without AFD and MA. Compared with the number of treated vessels, the number of true positives, false positives, false negatives, sensitivity, and interreader agreement were determined using clustered binary data analysis. RESULTS: Cone-beam CT enabled detection of 100 ± 3.5 feeding arteries (70% sensitivity) with 68.6% agreement among readers. AFD software significantly improved detection to 127±0.6 feeding arteries (86% sensitivity, P = .008) with 99.7% reader agreement and reduced the number of false negatives from an average of 47 ± 3.5 to 20 ± 0.6 (P = .008). MA of the AFD results produced similar feeding artery detection rates (127 ± 5.1, 86% sensitivity, P = .8), with lower interreader agreement (91.6%) and slightly fewer false positives (16 ± 0.0 to 14 ± 2.5, P = .4). CONCLUSIONS:AFD software significantly improved feeding artery detection rates during transarterial chemoembolization of HCC lesions with better user reproducibility compared with cone-beam CT alone. In conjunction with DSA, AFD enables maximum feeding artery detection in this setting.