Zhijun Wang1,2, Rongxin Chen3, Rafael Duran4, Yan Zhao5, Gayane Yenokyan6, Julius Chapiro7, Rüdiger Schernthaner8, Alessandro Radaelli9, MingDe Lin10, Jean-François Geschwind11. 1. Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD, 21287, USA. wangzj301hospital@163.com. 2. Department of Interventional Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China. wangzj301hospital@163.com. 3. Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD, 21287, USA. chenrongxin@zs-hospital.sh.cn. 4. Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD, 21287, USA. rafaelduran.md@gmail.com. 5. Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD, 21287, USA. yanzhao211@163.com. 6. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21287, USA. Gyenokya@jhsph.edu. 7. Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD, 21287, USA. j.chapiro@googlemail.com. 8. Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD, 21287, USA. rschern1@jhmi.edu. 9. iXR, Philips Healthcare, Best, The Netherlands. alessandro.radaelli@philips.com. 10. Ultrasound Imaging and Interventions (UII), Philips Research North America, Briarcliff Manor, NY, USA. ming.lin@philips.com. 11. Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD, 21287, USA. jfg@jhmi.edu.
Abstract
PURPOSE: To evaluate whether intraprocedural 3D quantification of Lipiodol deposition on cone-beam computed tomography (CBCT) can predict tumor response on follow-up contrast-enhanced magnetic resonance imaging (CE-MRI) in patients with hepatocellular carcinoma (HCC) treated with conventional transarterial chemoembolization (cTACE). MATERIALS AND METHODS: This IRB approved, retrospective analysis included 36 patients with 51 HCC target lesions, who underwent cTACE with CBCT. CE-MRI was acquired at baseline and 1 month after cTACE. Overall tumor volumes as well as intratumoral Lipiodol volumes on CBCT were measured and compared with the overall and necrotic (non-enhancing) tumor volumes on CE-MRI using the paired student's t test. Tumor response on CE-MRI was assessed using modified response evaluation criteria in solid tumors (mRECIST). A linear regression model was used to correlate tumor volumes, Lipiodol volumes, and the percentage of Lipiodol deposition on CBCT with the corresponding parameters on CE-MRI. Nonparametric spearman rank-order correlation and trend test were used to correlate the percentage of Lipiodol deposition in the tumor with tumor response. RESULT: A strong correlation between overall tumor volumes on CBCT and CE-MRI was observed (R(2) = 0.986). In addition, a strong correlation was obtained between the volume of Lipiodol deposition on CBCT and tumor necrosis (in cm(3)) on CE-MRI (R(2) = 0.960), and between the percentage of Lipiodol deposition and tumor necrosis (R(2) = 0.979). Importantly, the extent of Lipiodol deposition (in percentage of total tumor volume) correlated strongly with tumor response on CE-MRI (Spearman rho = 0.84, p < 0.001). CONCLUSIONS: Intraprocedural 3D quantification of Lipiodol deposition on CBCT can be used to predict tumor response on follow-up CE-MRI.
PURPOSE: To evaluate whether intraprocedural 3D quantification of Lipiodol deposition on cone-beam computed tomography (CBCT) can predict tumor response on follow-up contrast-enhanced magnetic resonance imaging (CE-MRI) in patients with hepatocellular carcinoma (HCC) treated with conventional transarterial chemoembolization (cTACE). MATERIALS AND METHODS: This IRB approved, retrospective analysis included 36 patients with 51 HCC target lesions, who underwent cTACE with CBCT. CE-MRI was acquired at baseline and 1 month after cTACE. Overall tumor volumes as well as intratumoral Lipiodol volumes on CBCT were measured and compared with the overall and necrotic (non-enhancing) tumor volumes on CE-MRI using the paired student's t test. Tumor response on CE-MRI was assessed using modified response evaluation criteria in solid tumors (mRECIST). A linear regression model was used to correlate tumor volumes, Lipiodol volumes, and the percentage of Lipiodol deposition on CBCT with the corresponding parameters on CE-MRI. Nonparametric spearman rank-order correlation and trend test were used to correlate the percentage of Lipiodol deposition in the tumor with tumor response. RESULT: A strong correlation between overall tumor volumes on CBCT and CE-MRI was observed (R(2) = 0.986). In addition, a strong correlation was obtained between the volume of Lipiodol deposition on CBCT and tumor necrosis (in cm(3)) on CE-MRI (R(2) = 0.960), and between the percentage of Lipiodol deposition and tumor necrosis (R(2) = 0.979). Importantly, the extent of Lipiodol deposition (in percentage of total tumor volume) correlated strongly with tumor response on CE-MRI (Spearman rho = 0.84, p < 0.001). CONCLUSIONS: Intraprocedural 3D quantification of Lipiodol deposition on CBCT can be used to predict tumor response on follow-up CE-MRI.
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