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Banchini F, Capelli P, Hasnaoui A, Palmieri G, Romboli A, Giuffrida M. 3-D reconstruction in liver surgery: a systematic review. HPB (Oxford) 2024; 26:1205-1215. [PMID: 38960762 DOI: 10.1016/j.hpb.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 05/27/2024] [Accepted: 06/12/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Three-dimensional reconstruction of the liver offers several advantages to the surgeon before and during liver resection. This review discusses the factors behind the use of liver 3-D reconstruction. METHODS Systematic electronic search, according to PRISMA criteria, was performed. A literature search of scientific papers was performed until October 2023. Articles were chosen based on reference to 3-D liver reconstruction and their use in liver surgery. GRADE methodology and the modified Newcastle-Ottawa scale were used to assess the quality of the studies. RESULTS The research included 47 articles and 7724 patients were analyzed. Preoperative planning was performed with 3-D liver reconstruction in the 87.2% of the studies. Most of preoperative 3-D liver reconstructions were performed in the planning of complex or major hepatectomies. Complex hepatectomies were performed in 64.3% patients. The 55.3% of the studies reported an improved navigation and accuracy during liver resection. Four studies (8.6%) on living donor liver transplant (LDLT) concluded that 3-D liver reconstruction is useful for graft selection and vascular preservation. Nine papers (19.1%) reported an accurate measurement of future liver remnant. CONCLUSION Liver 3-D reconstruction helps surgeons in the planning of liver surgery, especially in liver graft and complex liver resections, increasing the accuracy of the surgical resection.
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Affiliation(s)
- Filippo Banchini
- Department of General Surgery, Ospedale Guglielmo da Saliceto, 29100 Piacenza, Italy
| | - Patrizio Capelli
- Department of General Surgery, Ospedale Guglielmo da Saliceto, 29100 Piacenza, Italy
| | - Anis Hasnaoui
- Department of General Surgery, Menzel Bourguiba Hospital, Tunis El Manar University, Tunis, Tunisia
| | - Gerardo Palmieri
- Department of General Surgery, Ospedale Guglielmo da Saliceto, 29100 Piacenza, Italy
| | - Andrea Romboli
- Department of General Surgery, Ospedale Guglielmo da Saliceto, 29100 Piacenza, Italy
| | - Mario Giuffrida
- Department of General Surgery, Ospedale Guglielmo da Saliceto, 29100 Piacenza, Italy.
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Kazami Y, Kaneko J, Keshwani D, Kitamura Y, Takahashi R, Mihara Y, Ichida A, Kawaguchi Y, Akamatsu N, Hasegawa K. Two-step artificial intelligence algorithm for liver segmentation automates anatomic virtual hepatectomy. JOURNAL OF HEPATO-BILIARY-PANCREATIC SCIENCES 2023; 30:1205-1217. [PMID: 37747080 DOI: 10.1002/jhbp.1357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
BACKGROUND Anatomic virtual hepatectomy with precise liver segmentation for hemilivers, sectors, or Couinaud's segments using conventional three-dimensional simulation is not automated and artificial intelligence (AI)-based algorithms have not yet been applied. METHODS Computed tomography data of 174 living-donor candidates for liver transplantation (training data) were used for developing a new two-step AI algorithm to automate liver segmentation that was validated in another 51 donors (validation data). The Pure-AI (no human intervention) and ground truth (GT, full human intervention) data groups were compared. RESULTS In the Pure-AI group, the median Dice coefficients of the right and left hemilivers were highly similar, 0.95 and 0.92, respectively; sectors, posterior to lateral: 0.86-0.92, and Couinaud's segments 1-8: 0.71-0.89. Labeling of the first-order branch as hemiliver, right or left portal vein perfectly matched; 92.8% of the second-order (sectors); 91.6% of third-order (segments) matched between the Pure-AI and GT data. CONCLUSIONS The two-step AI algorithm for liver segmentation automates anatomic virtual hepatectomy. The AI-based algorithm correctly divided all hemilivers, and more than 90% of the sectors and segments.
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Affiliation(s)
- Yusuke Kazami
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Junichi Kaneko
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Deepak Keshwani
- Imaging Technology Center, Fujifilm Corporation, Tokyo, Japan
| | | | - Ryugen Takahashi
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yuichiro Mihara
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Akihiko Ichida
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshikuni Kawaguchi
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Nobuhisa Akamatsu
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kiyoshi Hasegawa
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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High-resolution MR imaging with gadoxetate disodium for the comprehensive evaluation of potential living liver donors. Liver Transpl 2023; 29:497-507. [PMID: 36738083 DOI: 10.1097/lvt.0000000000000099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 12/21/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Several major transplantation centers have used composite multimodality evaluation for the preoperative evaluation of potential living liver donors. This approach can be time-consuming and, although rare, can cause complications. We aimed to demonstrate the clinical feasibility of our comprehensive preoperative MR protocol for the preoperative assessment of living liver donor candidates instead of composite multimodality evaluation. MATERIALS AND METHODS Thirty-five consecutive living liver donor candidates underwent multiphasic liver CT and comprehensive donor protocol MR examinations for preoperative evaluation in a single large-volume liver transplantation (LT) center. Three blinded abdominal radiologists reviewed the CT and MR images for vascular and biliary variations. The strength of agreement between CT and MR angiography was assessed using the kappa index. The detection rate of biliary anatomical variations was calculated. The sensitivity and specificity for detecting significant steatosis (>5%) were calculated. The estimated total volume and right lobe volumes measured by MR volumetry were compared with the corresponding CT volumetry measurements using the intraclass correlation coefficient (ICC). RESULTS Among the 35 patients, 26 underwent LT. The measurement of agreement showed a moderate to substantial agreement between CT and MR angiography interpretations (kappa values, 0.47-0.79; p < 0.001). Combining T2-weighted and T1-weighted MR cholangiography techniques detected all biliary anatomical variations in 9 of the 26 patients. MR-proton density fat fraction showed a sensitivity of 100% (3/3) and a specificity of 91.3% (21/23) for detecting pathologically determined steatosis (>5%). MR volumetry reached an excellent agreement with CT volumetry (reviewers 1 and 2: ICC, 0.92; 95% CI, 0.84-0.96). CONCLUSION Our one-stop comprehensive liver donor MR imaging protocol can provide complete information regarding hepatic vascular and biliary anatomies, hepatic parenchymal quality, and liver volume for living liver donor candidates and can replace composite multimodality evaluation.
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Alirr OI, Rahni AAA. Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters. J Digit Imaging 2021; 33:304-323. [PMID: 31428898 DOI: 10.1007/s10278-019-00262-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.
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Affiliation(s)
- Omar Ibrahim Alirr
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
| | - Ashrani Aizzuddin Abd Rahni
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
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Fang C, An J, Bruno A, Cai X, Fan J, Fujimoto J, Golfieri R, Hao X, Jiang H, Jiao LR, Kulkarni AV, Lang H, Lesmana CRA, Li Q, Liu L, Liu Y, Lau W, Lu Q, Man K, Maruyama H, Mosconi C, Örmeci N, Pavlides M, Rezende G, Sohn JH, Treeprasertsuk S, Vilgrain V, Wen H, Wen S, Quan X, Ximenes R, Yang Y, Zhang B, Zhang W, Zhang P, Zhang S, Qi X. Consensus recommendations of three-dimensional visualization for diagnosis and management of liver diseases. Hepatol Int 2020; 14:437-453. [PMID: 32638296 PMCID: PMC7366600 DOI: 10.1007/s12072-020-10052-y] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 05/04/2020] [Indexed: 12/14/2022]
Abstract
Three-dimensional (3D) visualization involves feature extraction and 3D reconstruction of CT images using a computer processing technology. It is a tool for displaying, describing, and interpreting 3D anatomy and morphological features of organs, thus providing intuitive, stereoscopic, and accurate methods for clinical decision-making. It has played an increasingly significant role in the diagnosis and management of liver diseases. Over the last decade, it has been proven safe and effective to use 3D simulation software for pre-hepatectomy assessment, virtual hepatectomy, and measurement of liver volumes in blood flow areas of the portal vein; meanwhile, the use of 3D models in combination with hydrodynamic analysis has become a novel non-invasive method for diagnosis and detection of portal hypertension. We herein describe the progress of research on 3D visualization, its workflow, current situation, challenges, opportunities, and its capacity to improve clinical decision-making, emphasizing its utility for patients with liver diseases. Current advances in modern imaging technologies have promised a further increase in diagnostic efficacy of liver diseases. For example, complex internal anatomy of the liver and detailed morphological features of liver lesions can be reflected from CT-based 3D models. A meta-analysis reported that the application of 3D visualization technology in the diagnosis and management of primary hepatocellular carcinoma has significant or extremely significant differences over the control group in terms of intraoperative blood loss, postoperative complications, recovery of postoperative liver function, operation time, hospitalization time, and tumor recurrence on short-term follow-up. However, the acquisition of high-quality CT images and the use of these images for 3D visualization processing lack a unified standard, quality control system, and homogeneity, which might hinder the evaluation of application efficacy in different clinical centers, causing enormous inconvenience to clinical practice and scientific research. Therefore, rigorous operating guidelines and quality control systems need to be established for 3D visualization of liver to develop it to become a mature technology. Herein, we provide recommendations for the research on diagnosis and management of 3D visualization in liver diseases to meet this urgent need in this research field.
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Affiliation(s)
- Chihua Fang
- The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510282, China.
| | - Jihyun An
- Department of Gastroenterology, Hanyang University College of Medicine and Hanyang University Guri Hospital, Guri, 11923, South Korea
| | - Antonio Bruno
- Department of Experimental, Diagnostic and Specialty Medicine-DIMES, University of Bologna, S. Orsola-Malpighi Hospital, Via Giuseppe Massarenti 9, 40138, Bologna, Italy
| | - Xiujun Cai
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, Shanghai, 200032, China.,Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Jiro Fujimoto
- Department of Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Rita Golfieri
- Department of Experimental, Diagnostic and Specialty Medicine-DIMES, University of Bologna, S. Orsola-Malpighi Hospital, Via Giuseppe Massarenti 9, 40138, Bologna, Italy
| | - Xishan Hao
- Department of Gastrointestinal Cancer Biology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Hongchi Jiang
- Department of Liver Surgery, The First Affiliated Hospital Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Long R Jiao
- HPB Surgical Unit, Department of Surgery and Cancer, Imperial College, London, W12 0HS, UK
| | - Anand V Kulkarni
- Department of Hepatology, Asian Institute of Gastroenterology, Hyderabad, India
| | - Hauke Lang
- Department of General, Visceral and Transplantation Surgery, University Medical Center of the Johannes Gutenberg-University, Langenbeckst. 1, 55131, Mainz, Germany
| | - Cosmas Rinaldi A Lesmana
- Division of Hepatobiliary, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo National General Hospital, Jakarta, 10430, Indonesia
| | - Qiang Li
- National Clinical Research Center for Cancer and Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Lianxin Liu
- Department of Hepatobillirary Surgery, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China
| | - Yingbin Liu
- Department of General Surgery, Xinhua Hospital Affiliated To Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wanyee Lau
- Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Qiping Lu
- Department of General Surgery, Central theater General Hospital of the Chinese people's Liberation Army, Wuhan, 430070, Hubei, China
| | - Kwan Man
- Department of Surgery, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Hitoshi Maruyama
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Cristina Mosconi
- Department of Experimental, Diagnostic and Specialty Medicine-DIMES, University of Bologna, S. Orsola-Malpighi Hospital, Via Giuseppe Massarenti 9, 40138, Bologna, Italy
| | - Necati Örmeci
- Department of Gastroenterology, Ankara University Medical School, Ibn'i Sina Hospital, Sihhiye, 06100, Ankara, Turkey
| | - Michael Pavlides
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Guilherme Rezende
- Internal Medicine Department, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil
| | - Joo Hyun Sohn
- Department of Gastroenterology, Hanyang University College of Medicine and Hanyang University Guri Hospital, Guri, 11923, South Korea
| | - Sombat Treeprasertsuk
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, 10700, Thailand
| | - Valérie Vilgrain
- Department of Radiology, Assistance-Publique Hôpitaux de Paris, APHP, HUPNVS, Hôpital Beaujon, 100 bd du Général Leclerc, 92110, Clichy, France
| | - Hao Wen
- Department of Hydatid & Hepatobiliary Surgery, Digestive and Vascular Surgery Centre, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China
| | - Sai Wen
- The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510282, China
| | - Xianyao Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Rafael Ximenes
- Department of Gastroenterology, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Yinmo Yang
- Department of General Surgery, Peking University First Hospital, Beijing, China
| | - Bixiang Zhang
- Department of Surgery, Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiqi Zhang
- The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510282, China
| | - Peng Zhang
- The First Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510282, China
| | - Shaoxiang Zhang
- Institute of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Xiaolong Qi
- CHESS Center, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China.
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Kavur AE, Gezer NS, Barış M, Şahin Y, Özkan S, Baydar B, Yüksel U, Kılıkçıer Ç, Olut Ş, Akar GB, Ünal G, Dicle O, Selver MA. Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors. Diagn Interv Radiol 2020; 26:11-21. [PMID: 31904568 PMCID: PMC7075579 DOI: 10.5152/dir.2019.19025] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 03/05/2019] [Accepted: 06/10/2019] [Indexed: 11/22/2022]
Abstract
PURPOSE To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging. METHODS A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results. RESULTS The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE). CONCLUSION Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.
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Affiliation(s)
- A. Emre Kavur
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Naciye Sinem Gezer
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Mustafa Barış
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Yusuf Şahin
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Savaş Özkan
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Bora Baydar
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Ulaş Yüksel
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Çağlar Kılıkçıer
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Şahin Olut
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Gözde Bozdağı Akar
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Gözde Ünal
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - Oğuz Dicle
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
| | - M. Alper Selver
- From the Graduate School of Natural and Applied Sciences (A.E.K., U.Y.), Dokuz Eylül University, İzmir, Turkey; Departments of Radiology (N.S.G., M.B., O.D.) and Electrical and Electronics Engineering (M.A.S. ), Dokuz Eylül University School of Medicine, İzmir, Turkey; Department of Computer Engineering (Y.Ş., Ş.O., G.Ü.), İstanbul Technical University, İstanbul, Turkey; Department of Electrical and Electronics Engineering (S.Ö., B.B., G.B.A.), Middle East Technical University, Ankara, Turkey; Department of Computer Engineering (Ç.K.), Uludağ University, Bursa, Turkey
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Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning. Int J Comput Assist Radiol Surg 2019; 15:239-248. [PMID: 31617057 DOI: 10.1007/s11548-019-02078-x] [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] [Received: 05/21/2019] [Accepted: 10/02/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE For the liver to remain viable, the resection during hepatectomy procedure should proceed along the major vessels; hence, the resection planes of the anatomic segments are defined, which mark the peripheries of the self-contained segments inside the liver. Liver anatomic segments identification represents an essential step in the preoperative planning for liver surgical resection treatment. METHOD The method based on constructing atlases for the portal and the hepatic veins bifurcations, the atlas is used to localize the corresponding vein in each segmented vasculature using atlas matching. Point-based registration is used to deform the mesh of atlas to the vein branch. Three-dimensional distance map of the hepatic veins is constructed; the fast marching scheme is applied to extract the centerlines. The centerlines of the labeled major veins are extracted by defining the starting and the ending points of each labeled vein. Centerline is extracted by finding the shortest path between the two points. The extracted centerline is used to define the trajectories to plot the required planes between the anatomical segments. RESULTS The proposed approach is validated on the IRCAD database. Using visual inspection, the method succeeded to extract the major veins centerlines. Based on that, the anatomic segments are defined according to Couinaud segmental anatomy. CONCLUSION Automatic liver segmental anatomy identification assists the surgeons for liver analysis in a robust and reproducible way. The anatomic segments with other liver structures construct a 3D visualization tool that is used by the surgeons to study clearly the liver anatomy and the extension of the cancer inside the liver.
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Schukfeh N, Schulze M, Holland AC, Dingemann J, Hoyer DP, Paul A, Theysohn JM. Computed tomography donor liver volumetry before liver transplantation in infants ≤10 kg: does the estimated graft diameter affect the outcome? Innov Surg Sci 2018; 3:253-259. [PMID: 31579789 PMCID: PMC6604587 DOI: 10.1515/iss-2017-0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 06/18/2018] [Indexed: 11/17/2022] Open
Abstract
Aim of the study Living donor liver transplantation (LDLT) is regularly performed in small-sized infants. Computed tomography (CT)-based donor liver volumetry is used to estimate the graft size. The aim of our study was to assess the results of CT liver volumetry and their impact on the clinical outcome after LDLT in extremely small-sized infants. Patients and methods In this study, we included all patients with a body weight of ≤10 kg who underwent living related liver transplantation at our centre between January 2004 and December 2014. In all cases of LDLT, a preoperative CT scan of the donor liver was performed, and the total liver and graft volumes were calculated. The graft shape was estimated by measuring the ventro-dorsal (thickness), cranio-caudal, and transversal (width) diameter of segment II/III. We assessed the impact of CT donor liver volumetry and other risk factors on the outcome, defined as patient and graft survival. Results In the study period, a total of 48 living related liver transplantations were performed at our centre in infants ≤10 kg [20 male (42%), 28 female (58%)]. The mean weight was 7.3 kg (range 4.4–10 kg). Among the recipients, 33 (69%) received primary abdominal closure and 15 (31%) had temporary abdominal closure. The patient and graft survival rates were 85% and 81%, respectively. In CT volumetry, the mean estimated graft volume was 255 mL (range 140–485 mL) and the actual measured mean graft weight was 307 g (range 127–463 g). The mean ventro-dorsal diameter of segment II/III was 6.9 cm (range 4.3–11.2 cm), the mean cranio-caudal diameter was 9 cm (range 5–14 cm), and the mean width was 10.5 cm (range 6–14.7 cm). The mean graft-body weight ratio (GBWR) was 4.38% (range 1.41–8.04%). A high graft weight, a GBWR >4%, and a large ventro-dorsal diameter of segment II/III were risk factors for poorer patient survival. Conclusion Preoperative assessment of the graft size is a crucial investigation before LDLT. For extremely small-sized recipients, not only the graft weight but also the graft shape seems to affect the outcome.
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Affiliation(s)
- Nagoud Schukfeh
- Department of Pediatric Surgery, Medical School Hannover, Carl-Neuberg-Str. 1, 30625 Hannover, Germany.,Department of General, Visceral and Transplant Surgery, University Hospital, University Duisburg, Essen, Germany
| | - Maren Schulze
- Department of General, Visceral and Transplant Surgery, University Hospital, University Duisburg, Essen, Germany
| | - Anna Charlotte Holland
- Department of General, Visceral and Transplant Surgery, University Hospital, University Duisburg, Essen, Germany
| | - Jens Dingemann
- Department of Pediatric Surgery, Medical School Hannover, Hannover, Germany
| | - Dieter P Hoyer
- Department of General, Visceral and Transplant Surgery, University Hospital, University Duisburg, Essen, Germany
| | - Andreas Paul
- Department of General, Visceral and Transplant Surgery, University Hospital, University Duisburg, Essen, Germany
| | - Jens M Theysohn
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital, University Duisburg-Essen, Duisburg, Germany
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Yang X, Yang JD, Yu HC, Choi Y, Yang K, Lee TB, Hwang HP, Ahn S, You H. Dr. Liver: A preoperative planning system of liver graft volumetry for living donor liver transplantation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:11-19. [PMID: 29544776 DOI: 10.1016/j.cmpb.2018.01.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 01/11/2018] [Accepted: 01/24/2018] [Indexed: 05/28/2023]
Abstract
BACKGROUND AND OBJECTIVE Manual tracing of the right and left liver lobes from computed tomography (CT) images for graft volumetry in preoperative surgery planning of living donor liver transplantation (LDLT) is common at most medical centers. This study aims to develop an automatic system with advanced image processing algorithms and user-friendly interfaces for liver graft volumetry and evaluate its accuracy and efficiency in comparison with a manual tracing method. METHODS The proposed system provides a sequential procedure consisting of (1) liver segmentation, (2) blood vessel segmentation, and (3) virtual liver resection for liver graft volumetry. Automatic segmentation algorithms using histogram analysis, hybrid level-set methods, and a customized region growing method were developed. User-friendly interfaces such as sequential and hierarchical user menus, context-sensitive on-screen hotkey menus, and real-time sound and visual feedback were implemented. Blood vessels were excluded from the liver for accurate liver graft volumetry. A large sphere-based interactive method was developed for dividing the liver into left and right lobes with a customized cutting plane. The proposed system was evaluated using 50 CT datasets in terms of graft weight estimation accuracy and task completion time through comparison to the manual tracing method. The accuracy of liver graft weight estimation was assessed by absolute difference (AD) and percentage of AD (%AD) between preoperatively estimated graft weight and intraoperatively measured graft weight. Intra- and inter-observer agreements of liver graft weight estimation were assessed by intraclass correlation coefficients (ICCs) using ten cases randomly selected. RESULTS The proposed system showed significantly higher accuracy and efficiency in liver graft weight estimation (AD = 21.0 ± 18.4 g; %AD = 3.1% ± 2.8%; percentage of %AD > 10% = none; task completion time = 7.3 ± 1.4 min) than the manual tracing method (AD = 70.5 ± 52.1 g; %AD = 10.2% ± 7.5%; percentage of %AD > 10% = 46%; task completion time = 37.9 ± 7.0 min). The proposed system showed slightly higher intra- and inter-observer agreements (ICC = 0.996 to 0.998) than the manual tracing method (ICC = 0.979 to 0.999). CONCLUSIONS The proposed system was proved accurate and efficient in liver graft volumetry for preoperative planning of LDLT.
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Affiliation(s)
- Xiaopeng Yang
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Jae Do Yang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea
| | - Hee Chul Yu
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea.
| | - Younggeun Choi
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Kwangho Yang
- Department of Surgery, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Tae Beom Lee
- Department of Surgery, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Hong Pil Hwang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea
| | - Sungwoo Ahn
- Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea
| | - Heecheon You
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
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Yoon JH, Choi JI, Jeong YY, Schenk A, Chen L, Laue H, Kim SY, Lee JM. Pre-treatment estimation of future remnant liver function using gadoxetic acid MRI in patients with HCC. J Hepatol 2016; 65:1155-1162. [PMID: 27476767 DOI: 10.1016/j.jhep.2016.07.024] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Revised: 05/25/2016] [Accepted: 07/20/2016] [Indexed: 12/14/2022]
Abstract
BACKGROUND & AIMS This study aimed to determine whether the predicted remnant liver function on dynamic hepatocyte-specific contrast media-enhanced magnetic resonance (DHCE-MR) imaging correlates with the results of the indocyanin green retention test (ICG R15) after hepatic resection or radiofrequency ablation (RFA). METHODS This prospective multicenter study was approved by the Institutional Review Boards of each hospital. Informed consents were obtained from all. DHCE-MRI and ICG R15 were performed in 57 patients scheduled to undergo hepatectomy or RFA for hepatocellular carcinoma, once before treatment and repeated on post-treatment day 3. In nine donors and three recipients, DHCE-MRI and ICG R15 were performed only preoperatively. The predicted remnant liver function (HEFml) was estimated using the hepatic extraction fraction (HEF) multiplied by the remnant liver volume, and compared with post-treatment ICG R15. Intra-individual heterogeneity of HEF was assessed using pooled coefficients of variation (CV) among hepatic segments. Finally, development of post-treatment hepatic failure was assessed according to the 50-50 criteria on post-treatment day 5. RESULTS Predicted remnant HEFml showed a negative correlation with post-treatment ICG R15 (r=-0.45, p=0.001), whereas liver volume did not (p>0.05). There were significant correlations between pre-treatment HEFml and pre-treatment ICG R15 (r=-0.33, p=0.006) and between post-treatment HEFml and post-treatment ICG R15 (r=-0.54, p<0.001). Pooled CV among segmental HEFs was 12.6%. No patients showed post-treatment liver failure on post-treatment day 5. CONCLUSIONS DHCE-MRI using Gd-EOB-DTPA was able to provide both global and segmental liver function information, and post-treatment remnant liver function predicted on pre-treatment DHCE-MRI showed a significant negative correlation with post-treatment ICG R15. LAY SUMMARY Post-treatment liver function could be predicted at pre-treatment DHCE-MRI. Liver function was heterogeneous among the liver segments. Liver anatomy, disease extent, and underlying liver function can be assessed in one DHCE-MRI examination. CLINICAL TRIAL NUMBER ClinicalTrials.gov number, NCT01490203.
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Affiliation(s)
- Jeong Hee Yoon
- Radiology, Seoul National University Hospital, Seoul, Republic of Korea; College of Medicine, Seoul, Republic of Korea
| | - Joon-Il Choi
- Radiology, Catholic Medical Center, Seoul, Republic of Korea
| | - Yong Yeon Jeong
- Chonnam National University Hwasun Hospital and Medical School, Gwang-Ju, Republic of Korea
| | | | | | | | - So Yeon Kim
- Radiology, Asan Medical Center, Seoul, Republic of Korea.
| | - Jeong Min Lee
- Radiology, Seoul National University Hospital, Seoul, Republic of Korea; College of Medicine, Seoul, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul 03087, Republic of Korea.
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Jang S, Lee JM, Lee DH, Joo I, Yoon JH, Chang W, Han JK. Value of MR elastography for the preoperative estimation of liver regeneration capacity in patients with hepatocellular carcinoma. J Magn Reson Imaging 2016; 45:1627-1636. [PMID: 27859840 DOI: 10.1002/jmri.25517] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 10/01/2016] [Indexed: 12/30/2022] Open
Abstract
PURPOSE To demonstrate the negative relationship between liver stiffness (LS) values measured at preoperative magnetic resonance elastography (MRE) and the regeneration capacity of the remnant liver after major hepatectomy, in patients with hepatocellular carcinoma (HCC). MATERIALS AND METHODS Thirty-eight patients with HCC (mean age, 57.1) who had undergone liver computed tomography (CT) and 1.5T MRE prior to right hepatectomy were included in this retrospective study. CT volumetric analysis of total functional liver (calculated by subtracting tumor volume from total liver volume), future liver remnant (FLR), and postoperative liver remnant (LR) were performed using a semiautomatic analysis program. The regeneration index was expressed as [(VLR -VFLR )/VFLR ] × 100, where VLR is the volume of the liver remnant and VFLR is the volume of the FLR. The relationship between degree of LS measured at MRE and the regeneration index was assessed using the Spearman correlation test. RESULTS Average LS value at MRE increased along with hepatic fibrosis (HF) stage (r = 0.604, P < 0.001). At MRE, a cutoff value greater than 2.46 kPa yielded 90.0% sensitivity and 100% specificity in identifying HF stage 2 or greater (area under the curve [AUC], 0.95). Mean VFLR and VLR were 477.1 ± 147.5 mL and 911.9 ± 188.8 mL, respectively. The regeneration index of the liver remnant was 102.1 ± 58.2%. LS values at MRE and calculated regeneration index after right hepatectomy showed moderate negative correlation (r = -0.361, P = 0.026). CONCLUSION LS values measured at MRE may serve as a postoperative predictor of liver regeneration in patients with liver cirrhosis and HCC. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1627-1636.
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Affiliation(s)
- Siwon Jang
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Dong Ho Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Won Chang
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
| | - Joon Koo Han
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea
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