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Yang TS, Gong XH, Wang L, Zhang S, Shi YP, Ren HN, Yan YQ, Zhu L, Lv L, Dai YM, Qian LJ, Xu JR, Zhou Y. Comparison of automated with manual 3D qEASL assessment based on MR imaging in hepatocellular carcinoma treated with conventional TACE. Abdom Radiol (NY) 2024:10.1007/s00261-024-04571-7. [PMID: 39297930 DOI: 10.1007/s00261-024-04571-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Revised: 09/01/2024] [Accepted: 09/04/2024] [Indexed: 09/21/2024]
Affiliation(s)
- Tian Shu Yang
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Xu Hua Gong
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Li Wang
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Shan Zhang
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Yao Ping Shi
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
- Interventional Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Hai Nan Ren
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Yun Qi Yan
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Li Zhu
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Lei Lv
- ShuKun (Beijing) Technology Co. Ltd, Beijing, China
| | | | - Li Jun Qian
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
| | - Jian Rong Xu
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
| | - Yan Zhou
- Diagnostic Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
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De B, Dogra P, Zaid M, Elganainy D, Sun K, Amer AM, Wang C, Rooney MK, Chang E, Kang HC, Wang Z, Bhosale P, Odisio BC, Newhook TE, Tzeng CWD, Cao HST, Chun YS, Vauthey JN, Lee SS, Kaseb A, Raghav K, Javle M, Minsky BD, Noticewala SS, Holliday EB, Smith GL, Koong AC, Das P, Cristini V, Ludmir EB, Koay EJ. Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.11.24313334. [PMID: 39314943 PMCID: PMC11419200 DOI: 10.1101/2024.09.11.24313334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Background Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment of RT response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly with outcomes. We hypothesized that quantitative measures of enhancement would more accurately predict clinical outcomes than size-based assessment alone and developed a model to optimize RT. Methods Pre-RT and post-RT CT scans of 154 patients with iCCA were analyzed retrospectively for measurements of tumor dimensions (for RECIST) and viable tumor volume using quantitative European Association for Study of Liver (qEASL) measurements. Binary classification and survival analyses were performed to evaluate the ability of qEASL to predict treatment outcomes, and mathematical modeling was performed to identify the mechanistic determinants of treatment outcomes and to predict optimal RT protocols. Results Multivariable analysis accounting for traditional prognostic covariates revealed that percentage change in viable volume following RT was significantly associated with OS, outperforming stratification by RECIST. Binary classification identified ≥33% decrease in viable volume to optimally correspond to response to RT. The model-derived, patient-specific tumor enhancement growth rate emerged as the dominant mechanistic determinant of treatment outcome and yielded high accuracy of patient stratification (80.5%), strongly correlating with the qEASL-based classifier. Conclusion Following RT for iCCA, changes in viable volume outperformed radiographic size-based assessment using RECIST for OS prediction. CT-derived tumor-specific mathematical parameters may help optimize RT for resistant tumors.
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Ghabili K, Windham-Herman AM, Konstantinidis M, Murali N, Borde T, Adam LC, Laage-Gaupp F, Lin M, Chapiro J, Georgiades C, Nezami N. Outcomes of repeat conventional transarterial chemoembolization in patients with liver metastases. Ann Hepatol 2024; 29:101529. [PMID: 39033928 DOI: 10.1016/j.aohep.2024.101529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 06/18/2024] [Accepted: 07/11/2024] [Indexed: 07/23/2024]
Abstract
INTRODUCTION AND OBJECTIVES Although unlimited sessions of conventional transarterial chemoembolization (cTACE) may be performed for liver metastases, there is no data indicating when treatment becomes ineffective. This study aimed to determine the optimal number of repeat cTACE sessions for nonresponding patients before abandoning cTACE in patients with liver metastases. MATERIALS AND METHODS In this retrospective, single-institutional analysis, patients with liver metastases from neuroendocrine tumors (NET), colorectal carcinoma (CRC), and lung cancer who underwent consecutive cTACE sessions from 2001 to 2015 were studied. Quantitative European Association for Study of the Liver (qEASL) criteria were utilized for response assessment. The association between the number of cTACE and 2-year, 5-year, and overall survival was evaluated to estimate the optimal number of cTACE for each survival outcome. RESULTS Eighty-five patients underwent a total of 186 cTACE sessions for 117 liver metastases, of which 30.7 % responded to the first cTACE. For the target lesions that did not respond to the first, second, and third cTACE sessions, response rates after the second, third, and fourth cTACE sessions were 33.3 %, 23 %, and 25 %, respectively. The fourth cTACE session was the optimal number for 2-year survival (HR 0.40; 95 %CI: 0.16-0.97; p = 0.04), 5-year survival (HR 0.31; 95 %CI: 0.11-0.87; p = 0.02), and overall survival (HR 0.35; 95 %CI: 0.13-0.89; p = 0.02). CONCLUSIONS Repeat cTACE in the management of liver metastases from NET, CRC, and lung cancer was associated with improved patient survival. We recommend at least four cTACE sessions before switching to another treatment for nonresponding metastatic liver lesions.
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Affiliation(s)
- Kamyar Ghabili
- Department of Radiology, Penn State Health Milton S. Hershey Medical Center, Hershey, PA, USA; Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Austin-Marley Windham-Herman
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Interventional Radiology, University of California San Diego, La Jolla, California, USA
| | - Menelaos Konstantinidis
- Institute of Health Policy, Management and Evaluation, University of Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada
| | - Nikitha Murali
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA; Section of Interventional Radiology, Department of Radiology, Northwestern Feinberg School of Medicine, Chicago, Illinois, USA
| | - Tabea Borde
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Neurology and Experimental Neurology, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt Universität zu Berlin and Berlin Institute of Health, Charité Campus Benjamin Franklin (CBF), Berlin, Germany
| | - Lucas C Adam
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Neurology and Experimental Neurology, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt Universität zu Berlin and Berlin Institute of Health, Charité Campus Benjamin Franklin (CBF), Berlin, Germany
| | - Fabian Laage-Gaupp
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - MingDe Lin
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Julius Chapiro
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Christos Georgiades
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Nariman Nezami
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical imaging, Yale University School of Medicine, New Haven, Connecticut, USA; Division of Vascular and Interventional Radiology, Department of Radiology, Medstar Georgetown Hospital, Washington, DC, USA; Georgetown University School of Medicine, Washington, DC, USA; Lombardi Comprehensive Cancer Center, Washington, DC, USA.
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4
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Linguraru MG, Bakas S, Aboian M, Chang PD, Flanders AE, Kalpathy-Cramer J, Kitamura FC, Lungren MP, Mongan J, Prevedello LM, Summers RM, Wu CC, Adewole M, Kahn CE. Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts. Radiol Artif Intell 2024; 6:e240225. [PMID: 38984986 PMCID: PMC11294958 DOI: 10.1148/ryai.240225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 04/13/2024] [Accepted: 04/25/2024] [Indexed: 07/11/2024]
Abstract
The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Keywords: Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.
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Affiliation(s)
- Marius George Linguraru
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Spyridon Bakas
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Mariam Aboian
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Peter D. Chang
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Adam E. Flanders
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Jayashree Kalpathy-Cramer
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Felipe C. Kitamura
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Matthew P. Lungren
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - John Mongan
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Luciano M. Prevedello
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Ronald M. Summers
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Carol C. Wu
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Maruf Adewole
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Charles E. Kahn
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
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5
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Liu JQ, Wang J, Huang XL, Liang TY, Zhou X, Mo ST, Xie HX, Yang KJ, Zhu GZ, Su H, Liao XW, Long LL, Peng T. A radiomics model based on magnetic resonance imaging to predict cytokeratin 7/19 expression and liver fluke infection of hepatocellular carcinoma. Sci Rep 2023; 13:17553. [PMID: 37845287 PMCID: PMC10579381 DOI: 10.1038/s41598-023-44773-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/12/2023] [Indexed: 10/18/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. HCC with liver fluke infection could harbor unique biological behaviors. This study was aimed at investigating radiomics features of HCC with liver fluke infection and establishing a model to predict the expression of cytokeratin 7 (CK7) and cytokeratin 19 (CK19) as well as prognosis at the same time. A total of 134 HCC patients were included. Gadoxetic acid-enhanced magnetic resonance imaging (MRI) images of all patients were acquired. Radiomics features of the tumor were extracted and then data dimensionality was reduced. The radiomics model was established to predict liver fluke infection and the radiomics score (Radscore) was calculated. There were 11 features in the four-phase combined model. The efficiency of the combined model increased significantly compared to each single-phase MRI model. Radscore was an independent predictor of liver fluke infection. It was also significantly different between different expression of CK7/ CK19. Meanwhile, liver fluke infection was associated with CK7/CK19 expression. A cut-off value was set up and all patients were divided into high risk and low risk groups of CK7/CK19 positive expression. Radscore was also an independent predictor of these two biomarkers. Overall survival (OS) and recurrence free survival (RFS) of negative liver fluke infection group were significantly better than the positive group. OS and RFS of negative CK7 and CK19 expression were also better, though not significantly. Positive liver fluke infection and CK19 expression prediction groups harbored significantly worse OS and RFS, survival of positive CK7 expression prediction was unsatisfying as well. A radiomics model was established to predict liver fluke infection among HCC patients. This model could also predict CK7 and CK19 expression. OS and RFS could be foreseen by this model at the same time.
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Affiliation(s)
- Jun-Qi Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Jing Wang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Xia-Ling Huang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Tian-Yi Liang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Xin Zhou
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Shu-Tian Mo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Hai-Xiang Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Ke-Jian Yang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Guang-Zhi Zhu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Hao Su
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Xi-Wen Liao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Li-Ling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Tao Peng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuang Yong Rd. 6#, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.
- Key Laboratory of Early Prevention & Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.
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Yang S, Zhang Z, Su T, Chen Q, Wang H, Jin L. Comparison of quantitative volumetric analysis and linear measurement for predicting the survival of Barcelona Clinic Liver Cancer 0- and A stage hepatocellular carcinoma after radiofrequency ablation. Diagn Interv Radiol 2023; 29:450-459. [PMID: 37154818 PMCID: PMC10679614 DOI: 10.4274/dir.2023.222055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 04/13/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE The prognostic role of the tumor volume in patients with hepatocellular carcinoma (HCC) at the Barcelona Clinic Liver Cancer (BCLC) 0 and A stages remains unclear. This study aims to compare the volumetric measurement with linear measurement in early HCC burden profile and clarify the optimal cut-off value of the tumor volume. METHODS The consecutive patients diagnosed with HCC who underwent initial and curative-intent radiofrequency ablation (RFA) were included retrospectively. The segmentation was performed semi-automatically, and enhanced tumor volume (ETV) as well as total tumor volume (TTV) were obtained. The patients were categorized into high- and low-tumor burden groups according to various cutoff values derived from commonly used diameter values, X-tile software, and decision-tree analysis. The inter- and intra-reviewer agreements were measured using the intra-class correlation coefficient. Univariate and multivariate time-to-event Cox regression analyses were performed to identify the prognostic factors of overall survival. RESULTS A total of 73 patients with 81 lesions were analyzed in the whole cohort with a median follow-up of 31.0 (interquartile range: 16.0–36.3). In tumor segmentation, excellent consistency was observed in intra- and inter-reviewer assessments. There was a strong correlation between diameter-derived spherical volume and ETV as well as ETV and TTV. As opposed to all linear candidates and 4,188 mm3 (sphere equivalent to 2 cm in diameter), ETV >14,137 mm3 (sphere equivalent to 3 cm in diameter) or 23,000 mm3 (sphere equivalent to 3.5 cm in diameter) was identified as an independent risk factor of survival. Considering the value of hazard ratio and convenience to use, when ETV was at 23,000 mm3, it was regarded as the optimal volumetric cut-off value in differentiating survival risk. CONCLUSION The volumetric measurement outperforms linear measurement on tumor burden evaluation for survival stratification in patients at BCLC 0 and A stages HCC after RFA.
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Affiliation(s)
- Siwei Yang
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhiyuan Zhang
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tianhao Su
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Qiyang Chen
- Department of Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Haochen Wang
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Long Jin
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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7
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Miszczuk M, Chapiro J, Minh DD, van Breugel JMM, Smolka S, Rexha I, Tegel B, Lin M, Savic LJ, Hong K, Georgiades C, Nezami N. Analysis of Tumor Burden as a Biomarker for Patient Survival with Neuroendocrine Tumor Liver Metastases Undergoing Intra-Arterial Therapies: A Single-Center Retrospective Analysis. Cardiovasc Intervent Radiol 2022; 45:1494-1502. [PMID: 35941241 PMCID: PMC9587516 DOI: 10.1007/s00270-022-03209-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 06/20/2022] [Indexed: 11/02/2022]
Abstract
PURPOSE To assess the value of quantitative analysis of tumor burden on baseline MRI for prediction of survival in patients with neuroendocrine tumor liver metastases (NELM) undergoing intra-arterial therapies. MATERIALS AND METHODS This retrospective single-center analysis included 122 patients with NELM who received conventional (n = 74) or drug-eluting beads, (n = 20) chemoembolization and radioembolization (n = 28) from 2000 to 2014. Overall tumor diameter (1D) and area (2D) of up to 3 largest liver lesions were measured on baseline arterially contrast enhanced MR images. Three-dimensional quantitative analysis was performed using the qEASL tool (IntelliSpace Portal Version 8, Philips) to calculate enhancing tumor burden (the ratio between enhancing tumor volume and total liver volume). Based on Q-statistics, patients were stratified into low tumor burden (TB) or high TB. RESULTS The survival curves were significantly separated between low TB and high TB groups for 1D (p < 0.001), 2D (p < 0.001) and enhancing TB (p = 0.008) measurements, with, respectively, 2.7, 2.6 and 2.2 times longer median overall survival (MOS) in the low TB group (p < 0.001, p < 0.001 and p = 0.008). Multivariate analysis showed that 1D, 2D, and enhancing TB were independent prognostic factors for MOS, with respective hazard ratios of 0.4 (95%CI: 0.2-0.6, p < 0.001), 0.4 (95%CI: 0.3-0.7, p < 0.001) and 0.5 (95%CI: 0.3-0.8, p = 0.003). CONCLUSION The overall tumor diameter, overall tumor area, and enhancing tumor burden are strong prognostic factors of overall survival in patients with neuroendocrine tumor liver metastases undergoing intra-arterial therapies.
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Affiliation(s)
- Milena Miszczuk
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Duc Do Minh
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, 13353 Berlin, Germany
| | | | - Susanne Smolka
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, 13353 Berlin, Germany
| | - Irvin Rexha
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, 13353 Berlin, Germany
| | - Bruno Tegel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, 13353 Berlin, Germany
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lynn Jeanette Savic
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, 13353 Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Kelvin Hong
- Division of Vascular and Interventional Radiology, Russel H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christos Georgiades
- Division of Vascular and Interventional Radiology, Russel H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nariman Nezami
- Division of Vascular and Interventional Radiology, Russel H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Vascular and Interventional Radiology, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 Greene St, Baltimore, MD 21201, USA
- Experimental Therapeutics Program, University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, MD, Baltimore, USA
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8
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Garg T, Shrigiriwar A, Habibollahi P, Cristescu M, Liddell RP, Chapiro J, Inglis P, Camacho JC, Nezami N. Intraarterial Therapies for the Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14143351. [PMID: 35884412 PMCID: PMC9322128 DOI: 10.3390/cancers14143351] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 12/11/2022] Open
Abstract
Image-guided locoregional therapies play a crucial role in the management of patients with hepatocellular carcinoma (HCC). Transarterial therapies consist of a group of catheter-based treatments where embolic agents are delivered directly into the tumor via their supplying arteries. Some of the transarterial therapies available include bland embolization (TAE), transarterial chemoembolization (TACE), drug-eluting beads-transarterial chemoembolization (DEB-TACE), selective internal radioembolization therapy (SIRT), and hepatic artery infusion (HAI). This article provides a review of pre-procedural, intra-procedural, and post-procedural aspects of each therapy, along with a review of the literature. Newer embolotherapy options and future directions are also briefly discussed.
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Affiliation(s)
- Tushar Garg
- Division of Vascular and Interventional Radiology, Russell H Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (T.G.); (R.P.L.)
| | - Apurva Shrigiriwar
- Division of Gastroenterology and Hepatology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA;
| | - Peiman Habibollahi
- Department of Interventional Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Mircea Cristescu
- Vascular and Interventional Radiology Division, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA;
| | - Robert P. Liddell
- Division of Vascular and Interventional Radiology, Russell H Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (T.G.); (R.P.L.)
| | - Julius Chapiro
- Section of Vascular and Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06510, USA;
| | - Peter Inglis
- Division of Vascular and Interventional Radiology, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA;
| | - Juan C. Camacho
- Department of Clinical Sciences, College of Medicine, Florida State University, Tallahassee, FL 32306, USA;
- Vascular and Interventional Radiology, Radiology Associates of Florida, Sarasota, FL 34239, USA
| | - Nariman Nezami
- Division of Vascular and Interventional Radiology, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA;
- Experimental Therapeutics Program, University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA
- Correspondence:
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9
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Borde T, Nezami N, Laage Gaupp F, Savic LJ, Taddei T, Jaffe A, Strazzabosco M, Lin M, Duran R, Georgiades C, Hong K, Chapiro J. Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI. Radiology 2022; 304:228-237. [PMID: 35412368 PMCID: PMC9270683 DOI: 10.1148/radiol.212426] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background Patients with intermediate- and advanced-stage hepatocellular carcinoma (HCC) represent a highly heterogeneous patient collective with substantial differences in overall survival. Purpose To evaluate enhancing tumor volume (ETV) and enhancing tumor burden (ETB) as new criteria within the Barcelona Clinic Liver Cancer (BCLC) staging system for optimized allocation of patients with intermediate- and advanced-stage HCC to undergo transarterial chemoembolization (TACE). Materials and Methods In this retrospective study, 682 patients with HCC who underwent conventional TACE or TACE with drug-eluting beads from January 2000 to December 2014 were evaluated. Quantitative three-dimensional analysis of contrast-enhanced MRI was performed to determine thresholds of ETV and ETB (ratio of ETV to normal liver volume). Patients with ETV below 65 cm3 or ETB below 4% were reassigned to BCLC Bn, whereas patients with ETV or ETB above the determined cutoffs were restratified or remained in BCLC Cn by means of stepwise verification of the median overall survival (mOS). Results This study included 494 patients (median age, 62 years [IQR, 56-71 years]; 401 men). Originally, 123 patients were classified as BCLC B with mOS of 24.3 months (95% CI: 21.4, 32.9) and 371 patients as BCLC C with mOS of 11.9 months (95% CI: 10.5, 14.8). The mOS of all included patients (including the BCLC B and C groups) was 15 months (95% CI: 12.3, 17.2). A total of 152 patients with BCLC C tumors were restratified into a new BCLC Bn class, in which the mOS was then 25.1 months (95% CI: 21.8, 29.7; P < .001). The mOS of the remaining patients (ie, BCLC Cn group) (n = 222; ETV ≥65 cm3 or ETB ≥4%) was 8.4 months (95% CI: 6.1, 11.2). Conclusion Substratification of patients with intermediate- and advanced-stage hepatocellular carcinoma according to three-dimensional quantitative tumor burden identified patients with a survival benefit from transarterial chemoembolization before therapy. © RSNA, 2022 Online supplemental material is available for this article.
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10
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Wang SH, Han XJ, Du J, Wang ZC, Yuan C, Chen Y, Zhu Y, Dou X, Xu XW, Xu H, Yang ZH. Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI. Insights Imaging 2021; 12:173. [PMID: 34817732 PMCID: PMC8613326 DOI: 10.1186/s13244-021-01117-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 10/26/2021] [Indexed: 12/12/2022] Open
Abstract
Background The imaging features of focal liver lesions (FLLs) are diverse and complex. Diagnosing FLLs with imaging alone remains challenging. We developed and validated an interpretable deep learning model for the classification of seven categories of FLLs on multisequence MRI and compared the differential diagnosis between the proposed model and radiologists. Methods In all, 557 lesions examined by multisequence MRI were utilised in this retrospective study and divided into training–validation (n = 444) and test (n = 113) datasets. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the model. The accuracy and confusion matrix of the model and individual radiologists were compared. Saliency maps were generated to highlight the activation region based on the model perspective. Results The AUC of the two- and seven-way classifications of the model were 0.969 (95% CI 0.944–0.994) and from 0.919 (95% CI 0.857–0.980) to 0.999 (95% CI 0.996–1.000), respectively. The model accuracy (79.6%) of the seven-way classification was higher than that of the radiology residents (66.4%, p = 0.035) and general radiologists (73.5%, p = 0.346) but lower than that of the academic radiologists (85.4%, p = 0.291). Confusion matrices showed the sources of diagnostic errors for the model and individual radiologists for each disease. Saliency maps detected the activation regions associated with each predicted class. Conclusion This interpretable deep learning model showed high diagnostic performance in the differentiation of FLLs on multisequence MRI. The analysis principle contributing to the predictions can be explained via saliency maps. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01117-z.
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Affiliation(s)
- Shu-Hui Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China.,Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong Province, People's Republic of China
| | - Xin-Jun Han
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Jing Du
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Zhen-Chang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Chunwang Yuan
- Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yinan Chen
- SenseTime Research, SenseTime, Shanghai, People's Republic of China.,WCH-SenseTime Joint Lab, SenseTime, Shanghai, Sichuan, People's Republic of China
| | - Yajing Zhu
- SenseTime Research, SenseTime, Shanghai, People's Republic of China
| | - Xin Dou
- SenseBrain Technology, SenseTime, Princeton, NJ, 08540, USA
| | - Xiao-Wei Xu
- SenseTime Research, SenseTime, Shanghai, People's Republic of China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China.
| | - Zheng-Han Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China.
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11
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Nezami N, VAN Breugel JMM, Konstantinidis M, Chapiro J, Savic LJ, Miszczuk MA, Rexha I, Lin M, Hong K, Georgiades C. Lipiodol Deposition and Washout in Primary and Metastatic Liver Tumors After Chemoembolization. In Vivo 2021; 35:3261-3270. [PMID: 34697157 DOI: 10.21873/invivo.12621] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/18/2021] [Accepted: 09/06/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND/AIM Lipiodol is the key component of conventional trans-arterial chemoembolization. Our aim was to evaluate lipiodol deposition and washout rate after conventional trans-arterial chemoembolization in intrahepatic cholangiocarcinoma and hepatic metastases originating from neuroendocrine tumors and colorectal carcinoma. PATIENTS AND METHODS This was a retrospective analysis of 44 patients with intrahepatic cholangiocarcinoma and liver metastasis from neuroendocrine tumors or colorectal carcinoma who underwent conventional trans-arterial chemoembolization. Lipiodol volume (cm3) was analyzed on non-contrast computed tomography imaging obtained within 24 h post conventional trans-arterial chemoembolization, and 40-220 days after conventional trans-arterial chemoembolization using volumetric image analysis software. Tumor response was assessed on contrast-enhanced magnetic resonance imaging 1 month after conventional trans-arterial chemoembolization. RESULTS The washout rate was longer for neuroendocrine tumors compared to colorectal carcinoma, with half-lives of 54.61 days (p<0.00001) and 19.39 days (p<0.001), respectively, with no exponential washout among intrahepatic cholangiocarcinomas (p=0.83). The half-life for lipiodol washout was longer in tumors larger than 300 cm3 compared to smaller tumors (25.43 vs. 22.71 days). Lipiodol wash out half-life was 54.76 days (p<0.01) and 29.45 days (p<0.00001) for tumors with a contrast enhancement burden of 60% or more and less than 60%, respectively. A negative exponential relationship for lipiodol washout was observed in non-responders (p<0.00001). CONCLUSION Lipiodol washout is a time-dependent process, and occurs faster in colorectal carcinoma tumors, tumors smaller than 300 cm3, tumors with baseline contrast enhancement burden of less than 60%, and non-responding target lesions.
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Affiliation(s)
- Nariman Nezami
- Section of Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, U.S.A.; .,Division of Vascular and Interventional Radiology, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, U.S.A
| | - Johanna Maria Mijntje VAN Breugel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, U.S.A.,Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands.,Medical faculty, Utrecht University, Utrecht, the Netherlands
| | - Menelaos Konstantinidis
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Julius Chapiro
- Section of Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, U.S.A.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, U.S.A
| | - Lynn Jeanette Savic
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, U.S.A.,Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Milena Anna Miszczuk
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, U.S.A
| | - Irvin Rexha
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, U.S.A.,Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Mingde Lin
- Visage Imaging, Inc., San Diego, CA, U.S.A
| | - Kelvin Hong
- Division of Vascular and Interventional Radiology, Russel H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, U.S.A
| | - Christos Georgiades
- Division of Vascular and Interventional Radiology, Russel H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, U.S.A
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12
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Letzen BS, Malpani R, Miszczuk M, de Ruiter QMB, Petty CW, Rexha I, Nezami N, Laage-Gaupp F, Lin M, Schlachter TR, Chapiro J. Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial. Clin Imaging 2021; 78:194-200. [PMID: 34022765 DOI: 10.1016/j.clinimag.2021.05.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 05/12/2021] [Accepted: 05/16/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND The use of the ethiodized oil- Lipiodol in conventional trans-arterial chemoembolization (cTACE) ensures radiopacity to visualize drug delivery in the process of providing selective drug targeting to hepatic cancers and arterial embolization. Lipiodol functions as a carrier of chemo drugs for targeted therapy, as an embolic agent, augmenting the drug effect by efflux into the portal veins as well as a predictor for the tumor response and survival. PURPOSE To prospectively evaluate the role of 3D quantitative assessment of intra-procedural Lipiodol deposition in liver tumors on CBCT immediately after cTACE as a predictive biomarker for the outcome of cTACE. MATERIALS & METHODS This was a post-hoc analysis of data from an IRB-approved prospective clinical trial. Thirty-two patients with hepatocellular carcinoma or liver metastases underwent contrast enhanced CBCT obtained immediately after cTACE, unenhanced MDCT at 24 h after cTACE, and follow-up imaging 30-, 90- and 180-days post-procedure. Lipiodol deposition was quantified on CBCT after cTACE and was characterized by 4 ordinal levels: ≤25%, >25-50%, >50-75%, >75%. Tumor response was assessed on follow-up MRI. Lipiodol deposition on imaging, correlation between Lipiodol deposition and tumor response criteria, and correlation between Lipiodol coverage and median overall survival (MOS) were evaluated. RESULTS Image analysis demonstrated a high degree of agreement between the Lipiodol deposition on CBCT and the 24 h post-TACE CT, with a Bland-Altman plot of Lipiodol deposition on imaging demonstrated a bias of 2.75, with 95%-limits-of-agreement: -16.6 to 22.1%. An inverse relationship between Lipiodol deposition in responders versus non-responders for two-dimensional EASL reached statistical significance at 30 days (p = 0.02) and 90 days (p = 0.05). Comparing the Lipiodol deposition in Modified Response Evaluation Criteria in Solid Tumors (mRECIST) responders versus non-responders showed a statistically significant higher volumetric deposition in responders for European Association for the Study of the Liver (EASL)-30d, EASL-90d, and quantitative EASL-180d. The correlation between the relative Lipiodol deposition and the change in enhancing tumor volume showed a negative association post-cTACE (30-day: p < 0.001; rho = -0.63). A Kaplan-Meier analysis for patients with high vs. low Lipiodol deposition showed a MOS of 46 vs. 33 months (p = 0.05). CONCLUSION 3D quantification of Lipiodol deposition on intra-procedural CBCT is a predictive biomarker of outcome in patients with primary or metastatic liver cancer undergoing cTACE. There are spatial and volumetric agreements between 3D quantification of Lipiodol deposition on intra-procedural CBCT and 24 h post-cTACE MDCT. The spatial and volumetric agreement between Lipiodol deposition on intra-procedural CBCT and 24 h post-cTACE MDCT could suggest that acquiring MDCT 24 h after cTACE is redundant. Importantly, the demonstrated relationship between levels of tumor coverage with Lipiodol and degree and timeline of tumor response after cTACE underline the role of Lipiodol as an intra-procedural surrogate for tumor response, with potential implications for the prediction of survival.
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Affiliation(s)
- Brian S Letzen
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Rohil Malpani
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Milena Miszczuk
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA; Department of Radiology, Charité University School of Medicine, Charitépl. 1, 10117 Berlin, Germany
| | - Quirina M B de Ruiter
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA; Philips Healthcare, Image Guided Therapy, Amstelplein 2, Amsterdam 1096 BC, Netherlands
| | - Christopher W Petty
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Irvin Rexha
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA; Department of Radiology, Charité University School of Medicine, Charitépl. 1, 10117 Berlin, Germany
| | - Nariman Nezami
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA; Division of Interventional Radiology and Image-Guided Medicine, Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Atlanta, GA 30322, USA
| | - Fabian Laage-Gaupp
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA; Visage Imaging Inc., 12625 High Bluff Drive, Suite 205, San Diego, CA 92130, USA
| | - Todd R Schlachter
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA.
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13
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Tsai YC, Shih JH, Hwang HE, Chiu NC, Lee RC, Tseng HS, Liu CA. Early prediction of 1-year tumor response of hepatocellular carcinoma with lipiodol deposition pattern through post-embolization cone-beam computed tomography during conventional transarterial chemoembolization. Eur Radiol 2021; 31:7464-7475. [PMID: 33765160 DOI: 10.1007/s00330-021-07843-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 02/13/2021] [Accepted: 02/25/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE To evaluate whether parenchyma-to-lipiodol ratio (PLR) and lesion-to-lipiodol ratio (LLR) on C-arm cone-beam computed tomography (CBCT) can predict 1-year tumor response in patients with hepatocellular carcinoma (HCC) treated with conventional transcatheter arterial chemoembolization (cTACE). METHODS This retrospective analysis included 221 HCC target lesions within up-to-seven criteria in 80 patients who underwent cTACE with arterial-phase CBCT and unenhanced CBCT after cTACE from 2015 to 2018. PLR and LLR of every tumor slice were obtained through mean density division of liver parenchyma and tumor enhancement with intratumoral lipiodol deposition. The cutoff values (COVs) of maximal PLR and LLR of every tumor were analyzed using Youden's index. The reliability of COV, correlations between the related parameters, and 1-year progression were assessed through interobserver agreement and multivariate analysis. COV validity was verified using the chi-square test and Cramer's V coefficient (V) in the validation cohort. RESULTS Standard COVs of PLR and LLR were 0.149 and 1.4872, respectively. Interobserver agreement of COV for PLR and LLR was near perfect (kappa > 0.9). Multivariate analysis suggested that COV of PLR is an independent predictor (odds ratio = 1.23532×1014, p = 4.37×10-7). COV of PLR showed strong consistency, correlation with 1-year progression in prediction model (V = 0.829-0.776; p < 0.0001), and presented as an effective predictor in the validation cohort (V = 0.766; p < 0.0001). CONCLUSION The COV of PLR (0.149) assessed through immediate post-embolization CBCT is an objective, effective, and approachable predictor of 1-year HCC progression after cTACE. KEY POINTS • The maximal PLR value indicates the least lipiodol-distributed region in an HCC tumor. The maximal LLR value indicates the least lipiodol-deposited region in the tumor due to incomplete lipiodol delivery. PLR and LLR are concepts like signal-to-noise ratio to characterize the lipiodol retention pattern objectively to predict 1-year tumor progression immediately without any quantification software for 3D image analysis immediately after cTACE treatment. • COV of PLR can facilitate the early prediction of tumor progression/recurrence and indicate the section of embolized HCC, providing the operator's good targets for sequential cTACE or combined ablation. • The validation cohort in our study verified standard COVs of PLR and LLR. The validation process was more convincing and delicate than that of previous retrospective studies.
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Affiliation(s)
- Yin-Chen Tsai
- Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan.,School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Taipei, 112, Taiwan
| | - Jou-Ho Shih
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA, 94305, USA
| | - Hsuen-En Hwang
- Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan.,School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Taipei, 112, Taiwan
| | - Nai-Chi Chiu
- Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan.,School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Taipei, 112, Taiwan
| | - Rheun-Chuan Lee
- Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan.,School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Taipei, 112, Taiwan
| | - Hsiou-Shan Tseng
- School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Taipei, 112, Taiwan.,Department of Medical Imaging, Cheng Hsin General Hospital, No. 45, Cheng Hsin St., Beitou District, Taipei, Taiwan
| | - Chien-An Liu
- Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan. .,School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Taipei, 112, Taiwan.
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14
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Tegel BR, Huber S, Savic LJ, Lin M, Gebauer B, Pollak J, Chapiro J. Quantification of contrast-uptake as imaging biomarker for disease progression of renal cell carcinoma after tumor ablation. Acta Radiol 2020; 61:1708-1716. [PMID: 32216452 DOI: 10.1177/0284185120909964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The prognosis of patients with renal cell carcinoma (RCC) depends greatly on the presence of extra-renal metastases. PURPOSE To investigate the value of total tumor volume (TTV) and enhancing tumor volume (ETV) as three-dimensional (3D) quantitative imaging biomarkers for disease aggressiveness in patients with RCC. MATERIAL AND METHODS Retrospective, HIPAA-compliant, IRB-approved study including 37 patients with RCC treated with image-guided thermal ablation during 2007-2015. TNM stage, RENAL Nephrometry Score, largest tumor diameter, TTV, and ETV were assessed on cross-sectional imaging at baseline and correlated with outcome measurements. The primary outcome was time-to-occurrence of extra-renal metastases and the secondary outcome was progression-free survival (PFS). Correlation was assessed using a Cox regression model and differences in outcomes were shown by Kaplan-Meier plots with significance and odds ratios (OR) calculated by Log-rank test/generalized Wilcoxon and continuity-corrected Woolf logit method. RESULTS Patients with a TTV or ETV > 5 cm3 were more likely to develop distant metastases compared to patients with TTV (OR 6.69, 95% confidence interval [CI] 0.33-134.4, P=0.022) or ETV (OR 8.48, 95% CI 0.42-170.1, P=0.016) < 5 cm3. Additionally, PFS was significantly worse in patients with larger ETV (P = 0.039; median PFS 51.87 months vs. 69.97 months). In contrast, stratification by median value of the established, caliper-based measurements showed no significant correlation with outcome parameters. CONCLUSION ETV, as surrogate of lesion vascularity, is a sensitive imaging biomarker for occurrence of extra-renal metastatic disease and PFS in patients with RCC.
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Affiliation(s)
- Bruno R Tegel
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität Berlin and Berlin Institute of Health, Institute of Radiology, Berlin, Germany
| | - Steffen Huber
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Lynn J Savic
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität Berlin and Berlin Institute of Health, Institute of Radiology, Berlin, Germany
| | - MingDe Lin
- U/S Imaging and Interventions, Philips Research North America, Cambridge, MA, USA
| | - Bernhard Gebauer
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität Berlin and Berlin Institute of Health, Institute of Radiology, Berlin, Germany
| | - Jeffrey Pollak
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Julius Chapiro
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
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15
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Goehler A, Harry Hsu TM, Lacson R, Gujrathi I, Hashemi R, Chlebus G, Szolovits P, Khorasani R. Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs. J Am Coll Radiol 2020; 17:1475-1484. [PMID: 32721409 DOI: 10.1016/j.jacr.2020.06.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/08/2020] [Accepted: 06/26/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Tumor response to therapy is often assessed by measuring change in liver lesion size between consecutive MRIs. However, these evaluations are both tedious and time-consuming for clinical radiologists. PURPOSE In this study, we sought to develop a convolutional neural network to detect liver metastases on MRI and applied this algorithm to assess change in tumor size on consecutive examinations. METHODS We annotated a data set of 64 patients with neuroendocrine tumors who underwent at least two consecutive liver MRIs with gadoxetic acid. We then developed a 3-D neural network using a U-Net architecture with ResNet-18 building blocks that first detected the liver and then lesions within the liver. Liver lesion labels for each examination were then matched in 3-D space using an iterative closest point algorithm followed by Kuhn-Munkres algorithm. RESULTS We developed a deep learning algorithm that detected liver metastases, co-registered the detected lesions, and then assessed the interval change in tumor burden between two multiparametric liver MRI examinations. Our deep learning algorithm was concordant in 91% with the radiologists' manual assessment about the interval change of disease burden. It had a sensitivity of 0.85 (95% confidence interval (95% CI): 0.77; 0.93) and specificity of 0.92 (95% CI: 0.87; 0.96) to classify liver segments as diseased or healthy. The mean DICE coefficient for individual lesions ranged between 0.73 and 0.81. CONCLUSIONS Our algorithm displayed high agreement with human readers for detecting change in liver lesions on MRI, offering evidence that artificial intelligence-based detectors may perform these tasks as part of routine clinical care in the future.
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Affiliation(s)
- Alexander Goehler
- Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts; Center for Evidence Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts; MIT Computer Science & Artificial Intelligence Laboratory, Cambridge, Massachusetts.
| | - Tzu-Ming Harry Hsu
- MIT Computer Science & Artificial Intelligence Laboratory, Cambridge, Massachusetts
| | - Ronilda Lacson
- Director of Education, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Director of Clinical Informatics, Harvard Medical School Library of Evidence, Boston, Massachusetts
| | - Isha Gujrathi
- Center for Evidence Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts
| | - Raein Hashemi
- Center for Evidence Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts
| | - Grzegorz Chlebus
- Fraunhofer MEVIS: Institute for Digital Medicine, Bremen, Germany
| | - Peter Szolovits
- Director of Clinical Decision Group at MIT Computer Science & Artificial Intelligence Laboratory, Cambridge, Massachusetts
| | - Ramin Khorasani
- Director of the Center for Evidence Imaging and Vice Chair of Quality/Safety, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
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16
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MRI Monitoring of Residual Vestibular Schwannomas: Modeling and Predictors of Growth. Otol Neurotol 2020; 41:1131-1139. [DOI: 10.1097/mao.0000000000002742] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Gregory J, Dioguardi Burgio M, Corrias G, Vilgrain V, Ronot M. Evaluation of liver tumour response by imaging. JHEP Rep 2020; 2:100100. [PMID: 32514496 PMCID: PMC7267412 DOI: 10.1016/j.jhepr.2020.100100] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 03/10/2020] [Accepted: 03/10/2020] [Indexed: 12/12/2022] Open
Abstract
The goal of assessing tumour response on imaging is to identify patients who are likely to benefit - or not - from anticancer treatment, especially in relation to survival. The World Health Organization was the first to develop assessment criteria. This early score, which assessed tumour burden by standardising lesion size measurements, laid the groundwork for many of the criteria that followed. This was then improved by the Response Evaluation Criteria in Solid Tumours (RECIST) which was quickly adopted by the oncology community. At the same time, many interventional oncology treatments were developed to target specific features of liver tumours that result in significant changes in tumours but have little effect on tumour size. New criteria focusing on the viable part of tumours were therefore designed to provide more appropriate feedback to guide patient management. Targeted therapy has resulted in a breakthrough that challenges conventional response criteria due to the non-linear relationship between response and tumour size, requiring the development of methods that emphasize the appearance of tumours. More recently, research into functional and quantitative imaging has created new opportunities in liver imaging. These results have suggested that certain parameters could serve as early predictors of response or could predict later tumour response at baseline. These approaches have now been extended by machine learning and deep learning. This clinical review focuses on the progress made in the evaluation of liver tumours on imaging, discussing the rationale for this approach, addressing challenges and controversies in the field, and suggesting possible future developments.
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Key Words
- (c)TACE, (conventional) transarterial chemoembolisation
- (m)RECIST, (modified) Response Evaluation Criteria in Solid Tumours
- 18F-FDG, 18F-fluorodeoxyglucose
- 90Y, yttrium-90
- ADC, apparent diffusion coefficient
- APHE, arterial phase hyperenhancement
- CEUS, contrast-enhanced ultrasound
- CRLM, colorectal liver metastases
- DWI, diffusion-weighted imaging
- EASL
- EASL, European Association for the Study of the Liver criteria
- GIST, gastrointestinal stromal tumours
- HCC, hepatocellular carcinoma
- HU, Hounsfield unit
- Imaging
- LI-RADS
- LI-RADS, Liver Imaging Reporting And Data System
- Liver
- Metastases
- PD, progressive disease
- PET, positron emission tomography
- PR, partial response
- RECIST
- SD, stable disease
- SIRT, selective internal radiotherapy
- TR, treatment response
- Tumours
- WHO, World Health Organization
- mRECIST
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Affiliation(s)
- Jules Gregory
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
| | - Marco Dioguardi Burgio
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
| | - Giuseppe Corrias
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
| | - Valérie Vilgrain
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
| | - Maxime Ronot
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
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Comparing HCC arterial tumour vascularisation on baseline imaging and after lipiodol cTACE: how do estimations of enhancing tumour volumes differ on contrast-enhanced MR and CT? Eur Radiol 2019; 30:1601-1608. [PMID: 31811428 DOI: 10.1007/s00330-019-06430-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/05/2019] [Accepted: 08/27/2019] [Indexed: 01/11/2023]
Abstract
OBJECTIVES In this study, pre-treatment target lesion vascularisation in either contrast-enhanced (CE) CT or MRI and post-treatment lipiodol deposition in native CT scans were compared in HCC patients who underwent their first cTACE treatment. We analysed the impact of stratification according to cTACE selectivity on these correlations. METHODS Seventy-eight HCC patients who underwent their first cTACE procedure were retrospectively included. Pre-treatment tumour vascularisation in arterial contrast phase and post-treatment lipiodol deposition in native CT scans were evaluated using the qEASL (quantitative tumour enhancement) method. Correlations were analysed using scatter plots, the Pearson correlation coefficient (PCC) and linear regression analysis. Subgroup analysis was performed according to lobar, segmental and subsegmental execution of cTACE. RESULTS Arterial tumour volumes in both baseline CE CT (R2 = 0.83) and CE MR (R2 = 0.82) highly correlated with lipiodol deposition after cTACE. The regression coefficient between lipiodol deposition and enhancing tumour volume was 1.39 for CT and 0.33 for MR respectively, resulting in a ratio of 4.24. After stratification according to selectivity of cTACE, the regression coefficient was 0.94 (R2 = 1) for lobar execution, 1.38 (R2 = 0.96) for segmental execution and 1.88 (R2 = 0.89) for subsegmental execution in the CE CT group. CONCLUSIONS Volumetric lipiodol deposition can be used as a reference to compare different imaging modalities in detecting vital tumour volumes. That approach proved CE MRI to be more sensitive than CE CT. Selectivity of cTACE significantly impacts the respective regression coefficients which allows for an innovative approach to the assessment of technical success after cTACE with a multitude of possible applications. KEY POINTS • Lipiodol deposition after cTACE highly correlates with pre-treatment tumour vascularisation and can be used as a reference to compare different imaging modalities in detecting vital tumour volumes. • Lipiodol deposition also correlates with the selectivity of cTACE and can therefore be used to quantify the technical success of the intervention.
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Feasibility of Yttrium-90 Radioembolization Dose Calculation Utilizing Intra-procedural Open Trajectory Cone Beam CT. Cardiovasc Intervent Radiol 2019; 43:295-301. [DOI: 10.1007/s00270-019-02198-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 03/06/2019] [Indexed: 11/30/2022]
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Luo X, Li Y, Shang Q, Liu H, Song L. Role of Diffusional Kurtosis Imaging in Evaluating the Efficacy of Transcatheter Arterial Chemoembolization in Patients with Liver Cancer. Cancer Biother Radiopharm 2019; 34:614-620. [PMID: 31560562 DOI: 10.1089/cbr.2019.2878] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Objective: To explore the role of diffusional kurtosis imaging (DKI) in evaluating the efficacy of transcatheter arterial chemoembolization (TACE) in patients with liver cancer. Materials and Methods: A total of 54 patients with primary liver cancer underwent TACE were selected as the study subjects. Magnetic resonance imaging and DKI scans were carried out before and after TACE, and the relevant parameters were analyzed. Results: Compared with those before TACE, the values of radial diffusivity (Dr), axial diffusivity (Da), and mean diffusivity (MD) of tumor tissues in the patients after TACE were significantly increased, whereas the values of axial kurtosis (Ka), fractional anisotropy of kurtosis (FAk), hepatic blood volume (HBV), hepatic blood flow (HBF), and hepatic artery perfusion (HAP) were notably decreased (p < 0.05). There were no significant changes regarding FA, radial kurtosis (Kr), mean kurtosis (MK), hepatic arterial fracture (HAF), permeability-surface area product (PS), mean transit time (MTT), and portal vein perfusion (PVP) (p > 0.05). The differences in apparent diffusion coefficients (ADCs) of different liver cancer tissues in patients under different b values after operation were statistically significant, and the ADC values of liver cancer tissues were evidently higher than those of other tumor tissues (p < 0.05). Conclusion: DKI is characterized with advantages such as fastness, simpleness, high resolution, and impregnability of the density of lipiodol. It can not only directly reflect the changes in blood perfusion at the lesion but also accurately and efficiently evaluate the remnants, necrosis, and recurrence of tumor tissues based on changes in ADC under different b values. It provides certain clinical assistance for the evaluation of the efficacy before and after TACE.
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Affiliation(s)
- Xin Luo
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Yuhua Li
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Qun Shang
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Hao Liu
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Litao Song
- Department of Radiology, Zibo Central Hospital, Zibo, China
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Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol 2019; 29:3338-3347. [PMID: 31016442 DOI: 10.1007/s00330-019-06205-9] [Citation(s) in RCA: 166] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/06/2019] [Accepted: 03/26/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI. METHODS A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set. RESULTS The DLS demonstrated a 92% accuracy, a 92% sensitivity (Sn), and a 98% specificity (Sp). Test set performance in a single run of random unseen cases showed an average 90% Sn and 98% Sp. The average Sn/Sp on these same cases for radiologists was 82.5%/96.5%. Results showed a 90% Sn for classifying hepatocellular carcinoma (HCC) compared to 60%/70% for radiologists. For HCC classification, the true positive and false positive rates were 93.5% and 1.6%, respectively, with a receiver operating characteristic area under the curve of 0.992. Computation time per lesion was 5.6 ms. CONCLUSION This preliminary deep learning study demonstrated feasibility for classifying lesions with typical imaging features from six common hepatic lesion types, motivating future studies with larger multi-institutional datasets and more complex imaging appearances. KEY POINTS • Deep learning demonstrates high performance in the classification of liver lesions on volumetric multi-phasic MRI, showing potential as an eventual decision-support tool for radiologists. • Demonstrating a classification runtime of a few milliseconds per lesion, a deep learning system could be incorporated into the clinical workflow in a time-efficient manner.
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Non-measurable infiltrative HCC: is post-contrast attenuation on CT a sign of tumor response? Eur Radiol 2018; 29:4389-4399. [PMID: 30413965 DOI: 10.1007/s00330-018-5805-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 09/07/2018] [Accepted: 09/25/2018] [Indexed: 12/23/2022]
Abstract
OBJECTIVES To evaluate the value of CT attenuation to assess the response to sorafenib in infiltrative/endovascular non-measurable advanced hepatocellular carcinoma (HCC). METHODS From 2007 to 2014, patients with infiltrative HCC ± tumor-in-vein (TIV) were retrospectively included. Attenuation of tumors and TIV were measured at baseline and follow-up on arterial and portal venous phase CT by two independent radiologists. Attenuation changes (overall and as per Choi criteria) and Child-Pugh score were correlated to overall survival. RESULTS Forty patients were included (38 men, 95%). Attenuation of both the tumors and TIV was significantly lower in follow-up CT than on baseline CT (p = 0.002 (arterial), and p = 0.001 (portal) for tumor, and p = 0.004 (arterial) and p < 0.001 (porta) for TIV). Median attenuation of TIV was significantly lower than that of the tumor in follow-up images (p = 0.010). Median OS for the entire cohort was 4 ± 1 months (95% CI: 2.1-5.9), with estimated OS rates at 6, 12, and 24 months of 43%, 29 and 12%, respectively. Baseline and follow-up CT attenuation in tumors and TVI were not correlated with survival. Survival was not significantly increased in patients with Choi criteria >15% CT HU decrease in the tumor and/or TIV during follow-up. Only Child-Pugh A (HR 4.9 (95%CI 2.3-10.7), p < 0.001) was identified as an independent factor of improved survival on multivariate analysis. CONCLUSION Despite significant changes under sorafenib, tumor attenuation of infiltrative/endovascular non-measurable HCC may be of limited value to assess survival in this subgroup of patients with very poor prognosis. KEY POINTS • Attenuation of both tumors and tumor-in-vein decreases after sorafenib. • Attenuation decrease is more marked in the tumor-in-vein than in the tumor. • Attenuation decrease is not associated with longer overall survival.
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Bargellini I, Crocetti L, Turini FM, Lorenzoni G, Boni G, Traino AC, Caramella D, Cioni R. Response Assessment by Volumetric Iodine Uptake Measurement: Preliminary Experience in Patients with Intermediate-Advanced Hepatocellular Carcinoma Treated with Yttrium-90 Radioembolization. Cardiovasc Intervent Radiol 2018; 41:1373-1383. [PMID: 29654507 DOI: 10.1007/s00270-018-1962-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 04/05/2018] [Indexed: 02/08/2023]
Abstract
PURPOSE To retrospectively compare early response to yttrium-90 radioembolization (Y90) according to volumetric iodine uptake (VIU) changes, Response Evaluation Criteria In Solid Tumor 1.1 (RECIST 1.1) and modified RECIST (mRECIST) in patients with intermediate-advanced hepatocellular carcinoma (HCC) and to explore their association with survival. MATERIALS AND METHODS Twenty-four patients treated with Y90 and evaluated with dual-energy computed tomography before and 6 weeks after treatment were included. VIU was measured on late arterial phase spectral images; 6-week VIU response was defined as: complete response (CR, absence of enhancing tumor), partial response (PR, ≥ 15% VIU reduction), progressive disease (PD, ≥ 10% VIU increase) and stable disease (criteria of CR/PR/PD not met). RECIST 1.1 and mRECIST were evaluated at 6 weeks and 6 months. Responders included CR and PR. Overall survival (OS) was evaluated by Kaplan-Meier analysis and compared by Cox regression analysis. RESULTS High intraobserver and interobserver agreements were observed in VIU measurements (k > 0.98). VIU identified a higher number of responders (18 patients, 75%), compared to RECIST 1.1 (12.5% at 6 weeks and 23.8% at 6 months) and mRECIST (29.2% at 6 weeks and 61.9% at 6 months). There was no significant correlation between OS and RECIST 1.1 (P = 0.45 at 6 weeks; P = 0.21 at 6 months) or mRECIST (P = 0.38 at 6 weeks; P = 0.79 at 6 months); median OS was significantly higher in VIU responders (17.2 months) compared to non-responders (7.4 months) (P = 0.0022; HR 8.85; 95% CI 1.29-88.1). CONCLUSION VIU is highly reproducible; as opposite to mRECIST and RECIST 1.1, early VIU response correlates with OS after Y90 in intermediate-advanced HCC patients.
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Affiliation(s)
- Irene Bargellini
- Department of Diagnostic and Interventional Radiology, Pisa University Hospital, Via Paradisa 2, 56126, Pisa, Italy.
| | - Laura Crocetti
- Department of Diagnostic and Interventional Radiology, Pisa University Hospital, Via Paradisa 2, 56126, Pisa, Italy
| | - Francesca Maria Turini
- Department of Diagnostic and Interventional Radiology, Pisa University Hospital, Via Paradisa 2, 56126, Pisa, Italy
| | - Giulia Lorenzoni
- Department of Diagnostic and Interventional Radiology, Pisa University Hospital, Via Paradisa 2, 56126, Pisa, Italy
| | - Giuseppe Boni
- Department of Nuclear Medicine, Pisa University Hospital, Via Roma 55, 56126, Pisa, Italy
| | | | - Davide Caramella
- Department of Diagnostic and Interventional Radiology, Pisa University Hospital, Via Paradisa 2, 56126, Pisa, Italy
| | - Roberto Cioni
- Department of Diagnostic and Interventional Radiology, Pisa University Hospital, Via Paradisa 2, 56126, Pisa, Italy
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Abstract
Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related deaths worldwide with rapidly growing incidence rates in the USA and Europe. Despite improving surveillance programs, most patients are diagnosed at intermediate to advanced stages and are no longer amenable to curative therapies, such as ablation, surgical resection and liver transplantation. For such patients, catheter-based image-guided embolotherapies such as transarterial chemoembolization (TACE) represent the standard of care and mainstay therapy, as recommended and endorsed by a variety of national guidelines and staging systems. The main benefit of these therapies is explained by the preferentially arterial blood supply of liver tumors, which allows to deliver the anticancer therapy directly to the tumor-feeding artery while sparing the healthy hepatic tissue mainly supplied by the portal vein. The tool box of an interventional oncologist contains several different variants of transarterial treatment modalities. Ever since the first TACE more than 30 years ago, these techniques have been progressively refined, both with respect to drug delivery materials and with respect to angiographic micro-catheter and image-guidance technology, thus substantially improving therapeutic outcomes of HCC. This review will summarize the fundamental principles, technical and clinical data on the application of different embolotherapies, such as bland transarterial embolization, Lipiodol-based conventional transarterial chemoembolization as well as TACE with drug-eluting beads (DEB-TACE). Clinical data on 90Yttrium radioembolization as an emerging alternative, mostly applied for niche indications such as HCC with portal vein invasion, will be discussed. Furthermore, we will summarize the principle of HCC staging, patient allocation and response assessment in the setting of HCC embolotherapy. In addition, we will evaluate the role of cone-beam computed tomography as a novel intra-procedural image-guidance technology. Finally, this review will touch on new technical developments such as radiopaque, imageable DEBs and the rationale and role of combined systemic and locoregional therapies, mostly in combination with Sorafenib.
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Sahu S, Schernthaner R, Ardon R, Chapiro J, Zhao Y, Sohn JH, Fleckenstein F, Lin M, Geschwind JF, Duran R. Imaging Biomarkers of Tumor Response in Neuroendocrine Liver Metastases Treated with Transarterial Chemoembolization: Can Enhancing Tumor Burden of the Whole Liver Help Predict Patient Survival? Radiology 2016; 283:883-894. [PMID: 27831830 DOI: 10.1148/radiol.2016160838] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Purpose To investigate whether whole-liver enhancing tumor burden [ETB] can serve as an imaging biomarker and help predict survival better than World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), modified RECIST (mRECIST), and European Association for the Study of the Liver (EASL) methods in patients with multifocal, bilobar neuroendocrine liver metastases (NELM) after the first transarterial chemoembolization (TACE) procedure. Materials and Methods This HIPAA-compliant, institutional review board-approved retrospective study included 51 patients (mean age, 57.8 years ± 13.2; range, 13.5-85.8 years) with multifocal, bilobar NELM treated with TACE. The largest area (WHO), longest diameter (RECIST), longest enhancing diameter (mRECIST), largest enhancing area (EASL), and largest enhancing volume (ETB) were measured at baseline and after the first TACE on contrast material-enhanced magnetic resonance images. With three-dimensional software, ETB was measured as more than 2 standard deviations the signal intensity of a region of interest in normal liver. Response was assessed with WHO, RECIST, mRECIST, and EASL methods according to their respective criteria. For ETB response, a decrease in enhancement of at least 30%, 50%, and 65% was analyzed by using the Akaike information criterion. Survival analysis included Kaplan-Meier curves and Cox regressions. Results Treatment response occurred in 5.9% (WHO criteria), 2.0% (RECIST), 25.5% (mRECIST), and 23.5% (EASL criteria) of patients. With 30%, 50%, and 65% cutoffs, ETB response was seen in 60.8%, 39.2%, and 21.6% of patients, respectively, and was the only biomarker associated with a survival difference between responders and nonresponders (45.0 months vs 10.0 months, 84.3 months vs 16.7 months, and 85.2 months vs 21.2 months, respectively; P < .01 for all). The 50% cutoff provided the best survival model (hazard ratio [HR]: 0.2; 95% confidence interval [CI]: 0.1, 0.4). At multivariate analysis, ETB response was an independent predictor of survival (HR: 0.2; 95% CI: 0.1, 0.6). Conclusion Volumetric ETB is an early treatment response biomarker and surrogate for survival in patients with multifocal, bilobar NELM after the first TACE procedure. © RSNA, 2016.
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Affiliation(s)
- Sonia Sahu
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - Ruediger Schernthaner
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - Roberto Ardon
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - Julius Chapiro
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - Yan Zhao
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - Jae Ho Sohn
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - Florian Fleckenstein
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - MingDe Lin
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - Jean-François Geschwind
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
| | - Rafael Duran
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, Johns Hopkins Hospital, Baltimore, Md (S.S., R.S., Y.Z., J.H.S., F.F., J.F.G., R.D.); Department of Radiology, Yale University School of Medicine, 330 Cedar St, TE 2-230, New Haven, CT 06520 (S.S., R.S., J.C., Y.Z., J.H.S., F.F., J.F.G., R.D.); Medisys, Philips Research, Suresnes, France (R.A.); and U/S Imaging and Interventions (UII), Philips Research North America, Cambridge, Mass (M.L.)
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Abstract
Cancer therapy is mainly based on different combinations of surgery, radiotherapy, and chemotherapy. Additionally, targeted therapies (designed to disrupt specific tumor hallmarks, such as angiogenesis, metabolism, proliferation, invasiveness, and immune evasion), hormonotherapy, immunotherapy, and interventional techniques have emerged as alternative oncologic treatments. Conventional imaging techniques and current response criteria do not always provide the necessary information regarding therapy success particularly to targeted therapies. In this setting, MR imaging offers an attractive combination of anatomic, physiologic, and molecular information, which may surpass these limitations, and is being increasingly used for therapy response assessment.
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Fleckenstein FN, Schernthaner RE, Duran R, Sohn JH, Sahu S, Zhao Y, Hamm B, Gebauer B, Lin M, Geschwind JF, Chapiro J. 3D Quantitative tumour burden analysis in patients with hepatocellular carcinoma before TACE: comparing single-lesion vs. multi-lesion imaging biomarkers as predictors of patient survival. Eur Radiol 2016; 26:3243-52. [PMID: 26762942 PMCID: PMC4942412 DOI: 10.1007/s00330-015-4168-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Revised: 10/25/2015] [Accepted: 12/14/2015] [Indexed: 01/01/2023]
Abstract
OBJECTIVES To compare the ability of single- vs. multi-lesion assessment on baseline MRI using 1D- and 3D-based measurements to predict overall survival (OS) in patients with hepatocellular carcinoma (HCC) before transarterial chemoembolization (TACE). METHODS This retrospective analysis included 122 patients. A quantitative 3D analysis was performed on baseline MRI to calculate enhancing tumour volume (ETV [cm(3)]) and enhancing tumour burden (ETB [%]) (ratio between ETV [cm(3)] and liver volume). Furthermore, enhancing and overall tumour diameters were measured. Patients were stratified into two groups using thresholds derived from the BCLC staging system. Statistical analysis included Kaplan-Meier plots, uni- and multivariate cox proportional hazard ratios (HR) and concordances. RESULTS All methods achieved good separation of the survival curves (p < 0.05). Multivariate analysis showed an HR of 5.2 (95 % CI 3.1-8.8, p < 0.001) for ETV [cm(3)] and HR 6.6 (95 % CI 3.7-11.5, p < 0.001) for ETB [%] vs. HR 2.6 (95 % CI 1.2-5.6, p = 0.012) for overall diameter and HR 3.0 (95 % CI 1.5-6.3, p = 0.003) for enhancing diameter. Concordances were highest for ETB [%], with no added predictive power for multi-lesion assessment (difference between concordances not significant). CONCLUSION 3D quantitative assessment is a stronger predictor of survival as compared to diameter-based measurements. Assessing multiple lesions provides no substantial improvement in predicting OS than evaluating the dominant lesion alone. KEY POINTS • 3D quantitative tumour assessment on baseline MRI predicts survival in HCC patients. • 3D quantitative tumour assessment predicts survival better than any current radiological method. • Multiple lesion assessment provides no improvement than evaluating the dominant lesion alone. • Measuring enhancing tumour volume in proportion to liver volume reflects tumour burden.
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Affiliation(s)
- Florian N Fleckenstein
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, MD, USA
- Department of Diagnostic and Interventional Radiology, Charité Universitätsmedizin, Campus Virchow Klinikum, Berlin, Germany
| | - Rüdiger E Schernthaner
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, MD, USA
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rafael Duran
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, MD, USA
- Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Jae Ho Sohn
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Sonia Sahu
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Yan Zhao
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Bernd Hamm
- Department of Diagnostic and Interventional Radiology, Charité Universitätsmedizin, Campus Virchow Klinikum, Berlin, Germany
| | - Bernhard Gebauer
- Department of Diagnostic and Interventional Radiology, Charité Universitätsmedizin, Campus Virchow Klinikum, Berlin, Germany
| | - MingDe Lin
- U/S Imaging and Interventions, Philips Research North America, Cambridge, MA, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA
| | - Jean-François Geschwind
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
| | - Julius Chapiro
- Department of Diagnostic and Interventional Radiology, Charité Universitätsmedizin, Campus Virchow Klinikum, Berlin, Germany
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA
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Favelier S, Estivalet L, Pottecher P, Loffroy R. Novel imaging biomarkers of response to transcatheter arterial chemoembolization in hepatocellular carcinoma patients. Chin J Cancer Res 2015; 27:611-26. [PMID: 26752936 PMCID: PMC4697100 DOI: 10.3978/j.issn.1000-9604.2015.07.02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Affiliation(s)
- Sylvain Favelier
- Department of Vascular, Oncologic and Interventional Radiology, University of Dijon School of Medicine, Bocage Teaching Hospital, Dijon Cedex, France
| | - Louis Estivalet
- Department of Vascular, Oncologic and Interventional Radiology, University of Dijon School of Medicine, Bocage Teaching Hospital, Dijon Cedex, France
| | - Pierre Pottecher
- Department of Vascular, Oncologic and Interventional Radiology, University of Dijon School of Medicine, Bocage Teaching Hospital, Dijon Cedex, France
| | - Romaric Loffroy
- Department of Vascular, Oncologic and Interventional Radiology, University of Dijon School of Medicine, Bocage Teaching Hospital, Dijon Cedex, France
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3D quantitative assessment of response to fractionated stereotactic radiotherapy and single-session stereotactic radiosurgery of vestibular schwannoma. Eur Radiol 2015; 26:849-57. [PMID: 26139318 DOI: 10.1007/s00330-015-3895-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Revised: 05/19/2015] [Accepted: 06/16/2015] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To determine clinical outcome of patients with vestibular schwannoma (VS) after treatment with fractionated stereotactic radiotherapy (FSRT) and single-session stereotactic radiosurgery (SRS) by using 3D quantitative response assessment on MRI. MATERIALS This retrospective analysis included 162 patients who underwent radiation therapy for sporadic VS. Measurements on T1-weighted contrast-enhanced MRI (in 2-year post-therapy intervals: 0-2, 2-4, 4-6, 6-8, 8-10, 10-12 years) were taken for total tumour volume (TTV) and enhancing tumour volume (ETV) based on a semi-automated technique. Patients were considered non-responders (NRs) if they required subsequent microsurgical resection or developed radiological progression and tumour-related symptoms. RESULTS Median follow-up was 4.1 years (range: 0.4-12.0). TTV and ETV decreased for both the FSRT and SRS groups. However, only the FSRT group achieved significant tumour shrinkage (p < 0.015 for TTV, p < 0.005 for ETV over time). The 11 NRs showed proportionally greater TTV (median TTV pre-treatment: 0.61 cm(3), 8-10 years after: 1.77 cm(3)) and ETV despite radiation therapy compared to responders (median TTV pre-treatment: 1.06 cm(3); 10-12 years after: 0.81 cm(3); p = 0.001). CONCLUSION 3D quantification of VS showed a significant decrease in TTV and ETV on FSRT-treated patients only. NR had significantly greater TTV and ETV over time. KEY POINTS Only FSRT not GK-treated patients showed significant tumour shrinkage over time. Clinical non-responders showed significantly less tumour shrinkage when compared to responders. 3D volumetric assessment of vestibular schwannoma shows advantages over unidimensional techniques.
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Wang Z, Chapiro J, Schernthaner R, Duran R, Chen R, Geschwind JF, Lin M. Multimodality 3D Tumor Segmentation in HCC Patients Treated with TACE. Acad Radiol 2015; 22:840-5. [PMID: 25863795 DOI: 10.1016/j.acra.2015.03.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Revised: 02/12/2015] [Accepted: 03/08/2015] [Indexed: 02/07/2023]
Abstract
RATIONALE AND OBJECTIVES To validate the concordance of a semiautomated multimodality lesion segmentation technique between contrast-enhanced magnetic resonance imaging (CE-MRI), cone-beam computed tomography (CBCT), and multidetector CT (MDCT) in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). MATERIALS AND METHODS This retrospective analysis included 45 patients with unresectable HCC who underwent baseline CE-MRI within 1 month before the treatment, intraprocedural CBCT during conventional TACE, and MDCT within 24 hours after TACE. Fourteen patients were excluded because of atypical lesion morphology, portal vein invasion, or small lesion size which precluded sufficient lesion visualization. Thirty-one patients with a total of 40 target lesions were included into the analysis. A tumor segmentation software, based on non-Euclidean geometry and theory of radial basis functions, was used to allow for the segmentation of target lesions in 3D on all three modalities. The algorithm created image-based masks located in a 3D region whose center and size were defined by the user, yielding the nomenclature "semiautomatic". On the basis of that, tumor volumes on all three modalities were calculated and compared using a linear regression model (R(2) values). Residual plots were used to analyze drift and variance of the values. RESULTS The mean value of tumor volumes was 18.72 ± 19.13 cm(3) (range, 0.41-59.16 cm(3)) on CE-MRI, 21.26 ± 21.99 cm(3) (range, 0.62-86.82 cm(3)) on CBCT, and 19.88 ± 20.88 cm(3) (range, 0.45-75.24 cm(3)) on MDCT. The average volumes of the tumor were not significantly different between CE-MRI and DP-CBCT, DP-CBCT and MDCT, MDCT and CE-MRI (P = .577, .770, and .794, respectively). A strong correlation between volumes on CE-MRI and CBCT, CBCT and MDCT, MDCT and CE-MRI was observed (R(2) = 0.974, 0.992 and 0.983, respectively). When plotting the residuals, no drift was observed for all methods showing deviations of no >10% of absolute volumes (in cm(3)). CONCLUSIONS A semiautomated 3D segmentation of HCC lesions treated with TACE provides high volumetric concordance across all tested imaging modalities.
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Tacher V, Lin M, Duran R, Yarmohammadi H, Lee H, Chapiro J, Chao M, Wang Z, Frangakis C, Sohn JH, Maltenfort MG, Pawlik T, Geschwind JF. Comparison of Existing Response Criteria in Patients with Hepatocellular Carcinoma Treated with Transarterial Chemoembolization Using a 3D Quantitative Approach. Radiology 2015; 278:275-84. [PMID: 26131913 DOI: 10.1148/radiol.2015142951] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PURPOSE To compare currently available non-three-dimensional methods (Response Evaluation Criteria in Solid Tumors [RECIST], European Association for Study of the Liver [EASL], modified RECIST [mRECIST[) with three-dimensional (3D) quantitative methods of the index tumor as early response markers in predicting patient survival after initial transcatheter arterial chemoembolization (TACE). MATERIALS AND METHODS This was a retrospective single-institution HIPAA-compliant and institutional review board-approved study. From November 2001 to November 2008, 491 consecutive patients underwent intraarterial therapy for liver cancer with either conventional TACE or TACE with drug-eluting beads. A diagnosis of hepatocellular carcinoma (HCC) was made in 290 of these patients. The response of the index tumor on pre- and post-TACE magnetic resonance images was assessed retrospectively in 78 treatment-naïve patients with HCC (63 male; mean age, 63 years ± 11 [standard deviation]). Each response assessment method (RECIST, mRECIST, EASL, and 3D methods of volumetric RECIST [vRECIST] and quantitative EASL [qEASL]) was used to classify patients as responders or nonresponders by following standard guidelines for the uni- and bidimensional measurements and by using the formula for a sphere for the 3D measurements. The Kaplan-Meier method with the log-rank test was performed for each method to evaluate its ability to help predict survival of responders and nonresponders. Uni- and multivariate Cox proportional hazard ratio models were used to identify covariates that had significant association with survival. RESULTS The uni- and bidimensional measurements of RECIST (hazard ratio, 0.6; 95% confidence interval [CI]: 0.3, 1.0; P = .09), mRECIST (hazard ratio, 0.6; 95% CI: 0.6, 1.0; P = .05), and EASL (hazard ratio, 1.1; 95% CI: 0.6, 2.2; P = .75) did not show a significant difference in survival between responders and nonresponders, whereas vRECIST (hazard ratio, 0.6; 95% CI: 0.3, 1.0; P = .04), qEASL (Vol) (hazard ratio, 0.5; 95% CI: 0.3, 0.9; P = .02), and qEASL (%) (hazard ratio, 0.3; 95% CI: 0.15, 0.60; P < .001) did show a significant difference between these groups. CONCLUSION The 3D-based imaging biomarkers qEASL and vRECIST were tumor response criteria that could be used to predict patient survival early after initial TACE and enabled clear identification of nonresponders.
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Affiliation(s)
- Vania Tacher
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - MingDe Lin
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Rafael Duran
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Hooman Yarmohammadi
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Howard Lee
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Julius Chapiro
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Michael Chao
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Zhijun Wang
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Constantine Frangakis
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Jae Ho Sohn
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Mitchell Gil Maltenfort
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Timothy Pawlik
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
| | - Jean-François Geschwind
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology (V.T., R.D., H.Y., H.L., J.C., M.C., Z.W., J.H.S., J.F.G.), and Department of Surgery (T.P.), Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287; Department of U/S Imaging and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.); Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (C.F.); and The Rothman Institute, Philadelphia, Pa (M.G.M.)
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Intraprocedural 3D Quantification of Lipiodol Deposition on Cone-Beam CT Predicts Tumor Response After Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma. Cardiovasc Intervent Radiol 2015; 38:1548-56. [PMID: 26001366 DOI: 10.1007/s00270-015-1129-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 04/26/2015] [Indexed: 12/23/2022]
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.
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Radiologic-pathologic analysis of quantitative 3D tumour enhancement on contrast-enhanced MR imaging: a study of ROI placement. Eur Radiol 2015; 26:103-13. [PMID: 25994198 DOI: 10.1007/s00330-015-3812-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 04/13/2015] [Accepted: 04/21/2015] [Indexed: 12/21/2022]
Abstract
OBJECTIVES To investigate the influence of region-of-interest (ROI) placement on 3D tumour enhancement [Quantitative European Association for the Study of the Liver (qEASL)] in hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE). METHODS Phase 1: 40 HCC patients had nine ROIs placed by one reader using systematic techniques (3 ipsilateral to the lesion, 3 contralateral to the lesion, and 3 dispersed throughout the liver) and qEASL variance was measured. Intra-class correlations were computed. Phase 2: 15 HCC patients with histosegmentation were selected. Six ROIs were systematically placed by AC (3 ROIs ipsilateral and 3 ROIs contralateral to the lesion). Three ROIs were placed by 2 radiologists. qEASL values were compared to histopathology by Pearson's correlation, linear regression, and median difference. RESULTS Phase 1: The dispersed method (abandoned in phase 2) had low consistency and high variance. Phase 2: qEASL correlated strongly with pathology in systematic methods [Pearson's correlation coefficient = 0.886 (ipsilateral) and 0.727 (contralateral)] and in clinical methods (0.625 and 0.879). However, ipsilateral placement matched best with pathology (median difference: 5.4 %; correlation: 0.89; regression CI: [0.904, 0.1409]). CONCLUSIONS qEASL is a robust method with comparable values among tested placements. Ipsilateral placement showed high consistency and better pathological correlation. KEY POINTS Ipsilateral and contralateral ROI placement produces high consistency and low variance. Both ROI placement methods produce qEASL values that correlate well with histopathology. Ipsilateral ROI placement produces best correlation to pathology along with high consistency.
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Schernthaner RE, Chapiro J, Sahu S, Withagen P, Duran R, Sohn JH, Radaelli A, van der Bom IM, Geschwind JFH, Lin M. Feasibility of a Modified Cone-Beam CT Rotation Trajectory to Improve Liver Periphery Visualization during Transarterial Chemoembolization. Radiology 2015; 277:833-41. [PMID: 26000642 DOI: 10.1148/radiol.2015142821] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
PURPOSE To compare liver coverage and tumor detectability by using preprocedural magnetic resonance (MR) images as a reference, as well as radiation exposure of cone-beam computed tomography (CT) with different rotational trajectories. MATERIALS AND METHODS Fifteen patients (nine men and six women; mean age ± standard deviation, 65 years ± 5) with primary or secondary liver cancer were retrospectively included in this institutional review board-approved study. A modified cone-beam CT protocol was used in which the C-arm rotates from +55° to -185° (open arc cone-beam CT) instead of -120° to +120° (closed arc cone-beam CT). Each patient underwent two sessions of transarterial chemoembolization between February 2013 and March 2014 with closed arc and open arc cone-beam CT (during the first and second transarterial chemoembolization sessions, respectively, as part of the institutional transarterial chemoembolization protocol). For each cone-beam CT examination, liver volume and tumor detectability were assessed by using MR images as the reference. Radiation exposure was compared by means of a phantom study. For statistical analysis, paired t tests and a Wilcoxon signed rank test were performed. RESULTS Mean liver volume imaged was 1695 cm(3) ± 542 and 1857 cm(3) ± 571 at closed arc and open arc cone-beam CT, respectively. The coverage of open arc cone-beam CT was significantly higher compared with closed arc cone-beam CT (97% vs 86% of the MR imaging liver volume, P = .002). In eight patients (53%), tumors were partially or completely outside the closed arc cone-beam CT field of view. All tumors were within the open arc cone-beam CT field of view. The open arc cone-beam CT radiation exposure by means of weighted CT index was slightly lower compared with that of closed arc cone-beam CT (-5.1%). CONCLUSION Open arc cone-beam CT allowed for a significantly improved intraprocedural depiction of peripheral hepatic tumors while achieving a slight radiation exposure reduction.
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Affiliation(s)
- Rüdiger E Schernthaner
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - Julius Chapiro
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - Sonia Sahu
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - Paul Withagen
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - Rafael Duran
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - Jae Ho Sohn
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - Alessandro Radaelli
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - Imramsjah Martin van der Bom
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - Jean-François H Geschwind
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
| | - MingDe Lin
- From the Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional Radiology, The Johns Hopkins Hospital, 1800 Orleans St, Sheikh Zayed Tower, Suite 7203, Baltimore, MD 21287 (R.E.S., J.C., S.S., R.D., J.H.S., J.F.H.G.); Philips Healthcare, Best, the Netherlands (P.W., A.R., I.M.v.d.B.); and Department of Ultrasound and Interventions, Philips Research North America, Briarcliff Manor, NY (M.L.)
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