Maxime Ronot1, Mohamed Abdel-Rehim2, Antoine Hakimé3, Viseth Kuoch3, Marion Roux4, Mélanie Chiaradia5, Valérie Vilgrain3, Thierry de Baere5, Frédéric Deschamps3. 1. Department of Radiology, University Hospitals Paris Nord Val de Seine, Hôpital Beaujon, Clichy; Université Paris Diderot, Sorbonne Paris Cité, Paris; Institut National de la Santé et de la Recherche Médicale U1149, Centre de Recherche Biomédicale Bichat-Beaujon, Paris. Electronic address: maxime.ronot@bjn.aphp.fr. 2. Department of Radiology, University Hospitals Paris Nord Val de Seine, Hôpital Beaujon, Clichy; Université Paris Diderot, Sorbonne Paris Cité, Paris. 3. Department of Radiology, University Hospitals Paris Nord Val de Seine, Hôpital Beaujon, Clichy; Université Paris Diderot, Sorbonne Paris Cité, Paris; Institut National de la Santé et de la Recherche Médicale U1149, Centre de Recherche Biomédicale Bichat-Beaujon, Paris. 4. Department of Radiology, University Hospitals Paris Nord Val de Seine, Hôpital Beaujon, Clichy. 5. Department of Interventional Radiology, Gustave Roussy, Villejuif, France.
Abstract
PURPOSE: To compare the ability of dedicated software and conventional cone-beam computed tomography (CT) analysis to identify tumor-feeding vessels in hypervascular liver tumors treated with chemoembolization. MATERIAL AND METHODS: Between January 2012 and January 2013, 45 patients (32 men, mean age of 61 y; range, 27-85 y) were enrolled, and 66 tumors were treated (mean, 32 mm ± 18; range, 10-81 mm) with conventional chemoembolization with arterial cone-beam CT. Data were independently analyzed by six interventional radiologists with standard postprocessing software, a computer-aided analysis with FlightPlan for liver (FPFL; ie, "raw FPFL"), and a review of this computer-aided FPFL analysis ("reviewed FPFL"). Analyses were compared with a reference reading established by two study supervisors in consensus who had access to all imaging data. Sensitivities, positive predictive values (PPVs), and false-positive (FP) ratios were compared by McNemar, χ(2), and Fisher exact tests. Analysis durations were compared by Mann-Whitney test, and interreader agreement was assessed. RESULTS: Reference reading identified 179 feeder vessels. The sensitivity of raw FPFL was significantly higher than those of reviewed FPFL and conventional analyses (90.9% vs 83.2% and 82.1%; P < .0001), with lower PPV (82.9% vs 91.2% and 90.6%, respectively; P < .0001), higher FP ratio (17.1% vs 9.4% and 8.8%, respectively; P < .0001), and greater interreader agreement (92% vs 80% and 79%, respectively; P < .0001). Reviewed FPFL analysis took significantly longer than both other analyses (P < .0001). CONCLUSIONS: The FPFL analysis software enabled a fast, accurate, and sensitive detection of tumor feeder vessels.
PURPOSE: To compare the ability of dedicated software and conventional cone-beam computed tomography (CT) analysis to identify tumor-feeding vessels in hypervascular liver tumors treated with chemoembolization. MATERIAL AND METHODS: Between January 2012 and January 2013, 45 patients (32 men, mean age of 61 y; range, 27-85 y) were enrolled, and 66 tumors were treated (mean, 32 mm ± 18; range, 10-81 mm) with conventional chemoembolization with arterial cone-beam CT. Data were independently analyzed by six interventional radiologists with standard postprocessing software, a computer-aided analysis with FlightPlan for liver (FPFL; ie, "raw FPFL"), and a review of this computer-aided FPFL analysis ("reviewed FPFL"). Analyses were compared with a reference reading established by two study supervisors in consensus who had access to all imaging data. Sensitivities, positive predictive values (PPVs), and false-positive (FP) ratios were compared by McNemar, χ(2), and Fisher exact tests. Analysis durations were compared by Mann-Whitney test, and interreader agreement was assessed. RESULTS: Reference reading identified 179 feeder vessels. The sensitivity of raw FPFL was significantly higher than those of reviewed FPFL and conventional analyses (90.9% vs 83.2% and 82.1%; P < .0001), with lower PPV (82.9% vs 91.2% and 90.6%, respectively; P < .0001), higher FP ratio (17.1% vs 9.4% and 8.8%, respectively; P < .0001), and greater interreader agreement (92% vs 80% and 79%, respectively; P < .0001). Reviewed FPFL analysis took significantly longer than both other analyses (P < .0001). CONCLUSIONS: The FPFL analysis software enabled a fast, accurate, and sensitive detection of tumor feeder vessels.
Authors: F H Cornelis; A Borgheresi; E N Petre; E Santos; S B Solomon; K Brown Journal: Cardiovasc Intervent Radiol Date: 2017-08-02 Impact factor: 2.740
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