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Ye Z, Yao S, Yang T, Li Q, Li Z, Song B. Abdominal Diffusion-Weighted MRI With Simultaneous Multi-Slice Acquisition: Agreement and Reproducibility of Apparent Diffusion Coefficients Measurements. J Magn Reson Imaging 2024; 59:1170-1178. [PMID: 37334872 DOI: 10.1002/jmri.28876] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 06/07/2023] [Accepted: 06/07/2023] [Indexed: 06/21/2023] [Imported: 08/15/2023] Open
Abstract
BACKGROUND Simultaneous multi-slice diffusion-weighted imaging (SMS-DWI) can shorten acquisition time in abdominal imaging. PURPOSE To investigate the agreement and reproducibility of apparent diffusion coefficient (ADC) from abdominal SMS-DWI acquired with different vendors and different breathing schemes. STUDY TYPE Prospective. SUBJECTS Twenty volunteers and 10 patients. FIELD STRENGTH/SEQUENCE 3.0 T, SMS-DWI with a diffusion-weighted echo-planar imaging sequence. ASSESSMENT SMS-DWI was acquired using breath-hold and free-breathing techniques in scanners from two vendors, yielding four scans in each participant. Average ADC values were measured in the liver, pancreas, spleen, and both kidneys. Non-normalized ADC and ADCs normalized to the spleen were compared between vendors and breathing schemes. STATISTICAL TESTS Paired t-test or Wilcoxon signed rank test; intraclass correlation coefficient (ICC); Bland-Altman method; coefficient of variation (CV) analysis; significance level: P < 0.05. RESULTS Non-normalized ADCs from the four SMS-DWI scans did not differ significantly in the spleen (P = 0.262, 0.330, 0.166, 0.122), right kidney (P = 0.167, 0.538, 0.957, 0.086), and left kidney (P = 0.182, 0.281, 0.504, 0.405), but there were significant differences in the liver and pancreas. For normalized ADCs, there were no significant differences in the liver (P = 0.315, 0.915, 0.198, 0.799), spleen (P = 0.815, 0.689, 0.347, 0.423), pancreas (P = 0.165, 0.336, 0.304, 0.584), right kidney (P = 0.165, 0.336, 0.304, 0.584), and left kidney (P = 0.496, 0.304, 0.443, 0.371). Inter-reader agreements of non-normalized ADCs were good to excellent (ICCs ranged from 0.861 to 0.983), and agreement and reproducibility were good to excellent depending on anatomic location (CVs ranged from 3.55% to 13.98%). Overall CVs for abdominal ADCs from the four scans were 6.25%, 7.62%, 7.08, and 7.60%. DATA CONCLUSION The normalized ADCs from abdominal SMS-DWI may be comparable between different vendors and breathing schemes, showing good agreement and reproducibility. ADC changes above approximately 8% may potentially be considered as a reliable quantitative biomarker to assess disease or treatment-related changes. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Li
- MR Collaborations, Siemens Healthineers, Shanghai, China
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, Sanya People's Hospital, Sanya, China
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Taouli B, Ba-Ssalamah A, Chapiro J, Chhatwal J, Fowler K, Kang TW, Knobloch G, Koh DM, Kudo M, Lee JM, Murakami T, Pinato DJ, Ringe KI, Song B, Tabrizian P, Wang J, Yoon JH, Zeng M, Zhou J, Vilgrain V. Consensus report from the 10th global forum for liver magnetic resonance imaging: multidisciplinary team discussion. Eur Radiol 2023; 33:9167-9181. [PMID: 37439935 PMCID: PMC10667403 DOI: 10.1007/s00330-023-09919-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/15/2023] [Accepted: 05/23/2023] [Indexed: 07/14/2023] [Imported: 08/15/2023]
Abstract
The 10th Global Forum for Liver Magnetic Resonance Imaging was held in October 2021. The themes of the presentations and discussions at this Forum are described in detail in the review by Taouli et al (2023). The focus of this second manuscript developed from the Forum is on multidisciplinary tumor board perspectives in hepatocellular carcinoma (HCC) management: how to approach early-, mid-, and late-stage management from the perspectives of a liver surgeon, an interventional radiologist, and an oncologist. The manuscript also includes a panel discussion by multidisciplinary experts on three selected cases that explore challenging aspects of HCC management. CLINICAL RELEVANCE STATEMENT: This review highlights the importance of a multidisciplinary team approach in liver cancer patients and includes the perspectives of a liver surgeon, an interventional radiologist, and an oncologist, including illustrative case studies. KEY POINTS: • A liver surgeon, interventional radiologist, and oncologist presented their perspectives on the treatment of early-, mid-, and late-stage HCC. • Different perspectives on HCC management between specialties emphasize the importance of multidisciplinary tumor boards. • A multidisciplinary faculty discussed challenging aspects of HCC management, as highlighted by three case studies.
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Affiliation(s)
- Bachir Taouli
- Department of Diagnostic, Molecular, and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Ahmed Ba-Ssalamah
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jagpreet Chhatwal
- Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kathryn Fowler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Tae Wook Kang
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Gesine Knobloch
- Global Medical and Clinical Affairs and Digital Development, Radiology, Bayer Pharmaceuticals, Berlin, Germany
| | - Dow-Mu Koh
- Department of Diagnostic Radiology, Royal Marsden Hospital, Sutton, UK
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - David J Pinato
- Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London, UK; Division of Oncology, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Kristina I Ringe
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Parissa Tabrizian
- Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Wang
- Department of Radiology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou; Liver Disease Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jian Zhou
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Valérie Vilgrain
- Université Paris Cité and Department of Radiology, Assistance-Publique Hôpitaux de Paris, APHP Nord, Hôpital Beaujon, Clichy, France
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Taouli B, Ba-Ssalamah A, Chapiro J, Chhatwal J, Fowler K, Kang TW, Knobloch G, Koh DM, Kudo M, Lee JM, Murakami T, Pinato DJ, Ringe KI, Song B, Tabrizian P, Wang J, Yoon JH, Zeng M, Zhou J, Vilgrain V. Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management. Eur Radiol 2023; 33:9152-9166. [PMID: 37500964 PMCID: PMC10730664 DOI: 10.1007/s00330-023-09928-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/15/2023] [Accepted: 05/23/2023] [Indexed: 07/29/2023] [Imported: 08/15/2023]
Abstract
The 10th Global Forum for Liver Magnetic Resonance Imaging (MRI) was held as a virtual 2-day meeting in October 2021, attended by delegates from North and South America, Asia, Australia, and Europe. Most delegates were radiologists with experience in liver MRI, with representation also from specialists in liver surgery, oncology, and hepatology. Presentations, discussions, and working groups at the Forum focused on the following themes: • Gadoxetic acid in clinical practice: Eastern and Western perspectives on current uses and challenges in hepatocellular carcinoma (HCC) screening/surveillance, diagnosis, and management • Economics and outcomes of HCC imaging • Radiomics, artificial intelligence (AI) and deep learning (DL) applications of MRI in HCC. These themes are the subject of the current manuscript. A second manuscript discusses multidisciplinary tumor board perspectives: how to approach early-, mid-, and late-stage HCC management from the perspectives of a liver surgeon, interventional radiologist, and oncologist (Taouli et al, 2023). Delegates voted on consensus statements that were developed by working groups on these meeting themes. A consensus was considered to be reached if at least 80% of the voting delegates agreed on the statements. CLINICAL RELEVANCE STATEMENT: This review highlights the clinical applications of gadoxetic acid-enhanced MRI for liver cancer screening and diagnosis, as well as its cost-effectiveness and the applications of radiomics and AI in patients with liver cancer. KEY POINTS: • Interpretation of gadoxetic acid-enhanced MRI differs slightly between Eastern and Western guidelines, reflecting different regional requirements for sensitivity vs specificity. • Emerging data are encouraging for the cost-effectiveness of gadoxetic acid-enhanced MRI in HCC screening and diagnosis, but more studies are required. • Radiomics and artificial intelligence are likely, in the future, to contribute to the detection, staging, assessment of treatment response and prediction of prognosis of HCC-reducing the burden on radiologists and other specialists and supporting timely and targeted treatment for patients.
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Affiliation(s)
- Bachir Taouli
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Ahmed Ba-Ssalamah
- Department of Biomedical Imaging and Image-guided therapy, Medical University of Vienna, Vienna, Austria
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jagpreet Chhatwal
- Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kathryn Fowler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Tae Wook Kang
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Gesine Knobloch
- Global Medical and Clinical Affairs and Digital Development, Radiology, Bayer Pharmaceuticals, Berlin, Germany
| | - Dow-Mu Koh
- Department of Diagnostic Radiology, Royal Marsden Hospital, Sutton, UK
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - David J Pinato
- Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London, UK
- Division of Oncology, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Kristina I Ringe
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Parissa Tabrizian
- Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Wang
- Department of Radiology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
- Liver Disease Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jian Zhou
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Valérie Vilgrain
- Université Paris Cité and Department of Radiology, Assistance-Publique Hôpitaux de Paris, APHP Nord, Hôpital Beaujon, Clichy, France
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Wu Y, Ye Z, Chen J, Deng L, Song B. Photon Counting CT: Technical Principles, Clinical Applications, and Future Prospects. Acad Radiol 2023; 30:2362-2382. [PMID: 37369618 DOI: 10.1016/j.acra.2023.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 05/27/2023] [Accepted: 05/28/2023] [Indexed: 06/29/2023] [Imported: 08/15/2023]
Abstract
Photon-counting computed tomography (PCCT) is a new technique that utilizes photon-counting detectors to convert individual X-ray photons directly into an electrical signal, which can achieve higher spatial resolution, improved iodine signal, radiation dose reduction, artifact reduction, and multienergy imaging. This review introduces the technical principles of PCCT, and summarizes its first-in-human experience and current applications in clinical settings, and discusses the future prospects of PCCT.
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Affiliation(s)
- Yingyi Wu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China (Y.Y.W., Z.Y., J.C., L.P.D., B.S.)
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China (Y.Y.W., Z.Y., J.C., L.P.D., B.S.)
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China (Y.Y.W., Z.Y., J.C., L.P.D., B.S.)
| | - Liping Deng
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China (Y.Y.W., Z.Y., J.C., L.P.D., B.S.)
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China (Y.Y.W., Z.Y., J.C., L.P.D., B.S.); Department of Radiology, Sanya People' s Hospital, Sanya, Hainan, China (B.S.).
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Zhang L, Zheng T, Wu Y, Wei H, Yang T, Zhu X, Yang J, Chen Y, Wang Y, Qu Y, Chen J, Zhang Y, Jiang H, Song B. Preoperative MRI-based multiparametric model for survival prediction in hepatocellular carcinoma patients with portal vein tumor thrombus following hepatectomy. Eur J Radiol 2023; 165:110895. [PMID: 37276744 DOI: 10.1016/j.ejrad.2023.110895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 05/25/2023] [Indexed: 06/07/2023] [Imported: 08/15/2023]
Abstract
PURPOSE To develop a predictive model integrating clinical and MRI features for postoperative survival in patients with hepatocellular carcinoma (HCC) and portal vein tumor thrombus (PVTT). METHOD Between January 2008 and May 2021, consecutive HCC patients with PVTT who underwent preoperative contrast-enhanced MRI and surgical resection at a tertiary hospital were retrospectively enrolled. The MR images were independently reviewed by two blinded radiologists. Univariate and multivariate Cox regression analyses were performed to construct a prognostic score for overall survival (OS). RESULTS Ninety-four patients were included (mean age, 50.1 years; 84 men). During a median follow-up period of 15.3 months, 72 (76.6%) patients died (median OS, 15.4 months; median disease-free survival [DFS], 4.6 months). The sum size of the two largest tumors (hazard ratio [HR], 3.050; p < 0.001) and tumor growth subtype (HR, 1.928; p = 0.006) on MRI, serum albumin (HR, 0.948; p = 0.02), and age (HR, 0.978; p = 0.04) were associated with OS and incorporated in the prognostic score. Accordingly, patients were stratified into a high-risk or low-risk group, and the OS in the high-risk group was shorter than that in the low-risk group for the entire cohort (11.7 vs. 25.0 months, p < 0.001) and for patients with Cheng's type I (12.1 vs. 25.9 months, p = 0.002) and type II PVTT (11.7 vs. 25.0 months, p = 0.004). The DFS in the high-risk group was shorter than that in the low-risk group for the entire cohort (4.5 vs. 6.1 months, p = 0.001). CONCLUSIONS Based on the sum size of the two largest tumors, tumor growth subtype, albumin, and age, the prognostic score allowed accurate preoperative risk stratification in HCC patients with PVTT, independent of Cheng's PVTT classification.
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Affiliation(s)
- Lin Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Tianying Zheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuanan Wu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaomei Zhu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jie Yang
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yanshu Wang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yali Qu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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Yuan Y, Liao K, Huang Z, Deng L, Tang H, Wang Y, Ye Z, Chen X, Song B, Li Z. Feasibility of using software-aided selection of virtual monoenergetic level for optimal image quality of acute necrotising pancreatitis based on dual-energy computed tomography: a preliminary study. BMC Med Imaging 2023; 23:95. [PMID: 37464338 PMCID: PMC10355045 DOI: 10.1186/s12880-023-01032-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/23/2023] [Indexed: 07/20/2023] [Imported: 08/15/2023] Open
Abstract
OBJECTIVE This study aimed to assess the feasibility of software-aided selection of monoenergetic level for acute necrotising pancreatitis (ANP) depiction compared to other automatic image series generated using dual-energy computed tomography (CT). METHODS The contrast-enhanced dual-source dual-energy CT images in the portal venous phase of 48 patients with ANP were retrospectively analysed. Contrast-to-noise ratio (CNR) of pancreatic parenchyma-to-necrosis, signal-to-noise ratio (SNR) of the pancreas, image noise, and score of subjective diagnosis were measured, calculated, and compared among the CT images of 100 kV, Sn140 kV, weighted-average 120 kV, and optimal single-energy level for CNR. RESULTS CNR of pancreatic parenchyma-to-necrosis in the images of 100 kV, Sn140 kV, weighted-average 120 kV, and the optimal single-energy level for CNR was 5.18 ± 2.39, 3.13 ± 1.35, 5.69 ± 2.35, and 9.99 ± 5.86, respectively; SNR of the pancreas in each group was 6.31 ± 2.77, 4.27 ± 1.56, 7.21 ± 2.69, and 11.83 ± 6.30, respectively; image noise in each group was 18.78 ± 5.20, 17.79 ± 4.63, 13.28 ± 3.13, and 9.31 ± 2.96, respectively; and score of subjective diagnosis in each group was 3.56 ± 0.50, 3.00 ± 0.55, 3.48 ± 0.55, and 3.88 ± 0.33, respectively. The four measurements of the optimal single-energy level for CNR images were significantly different from those of images in the other three groups (P < 0.05). CNR of pancreatic parenchyma-to-necrosis, SNR of the pancreas, and score of subjective diagnosis in the images of the optimal single-energy level for CNR were significantly higher, while the image noise was lower than those in the other three groups (all P = 0.000). CONCLUSION Optimal single-energy level imaging for CNR of dual-source CT could improve quality of CT images in patients with ANP, enhancing the display of necrosis in the pancreas.
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Affiliation(s)
- Yuan Yuan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China
| | - Kai Liao
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China
| | - Liping Deng
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China
| | - Hehan Tang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China
| | - Yi Wang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China
| | - Xinyue Chen
- CT collaboration, Siemens-healthineers, Chengdu, 610041, Sichuan, P.R. China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China.
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan, P.R. China.
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Yang L, Li XM, Zhang MN, Yao J, Song B. Nomogram Models for Distinguishing Intraductal Carcinoma of the Prostate From Prostatic Acinar Adenocarcinoma Based on Multiparametric Magnetic Resonance Imaging. Korean J Radiol 2023; 24:668-680. [PMID: 37404109 DOI: 10.3348/kjr.2022.1022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 04/29/2023] [Accepted: 05/16/2023] [Indexed: 07/06/2023] [Imported: 08/15/2023] Open
Abstract
OBJECTIVE To compare multiparametric magnetic resonance imaging (MRI) features of intraductal carcinoma of the prostate (IDC-P) with those of prostatic acinar adenocarcinoma (PAC) and develop prediction models to distinguish IDC-P from PAC and IDC-P with a high proportion (IDC ≥ 10%, hpIDC-P) from IDC-P with a low proportion (IDC < 10%, lpIDC-P) and PAC. MATERIALS AND METHODS One hundred and six patients with hpIDC-P, 105 with lpIDC-P and 168 with PAC, who underwent pretreatment multiparametric MRI between January 2015 and December 2020 were included in this study. Imaging parameters, including invasiveness and metastasis, were evaluated and compared between the PAC and IDC-P groups as well as between the hpIDC-P and lpIDC-P subgroups. Nomograms for distinguishing IDC-P from PAC, and hpIDC-P from lpIDC-P and PAC, were made using multivariable logistic regression analysis. The discrimination performance of the models was assessed using the receiver operating characteristic area under the curve (ROC-AUC) in the sample, where the models were derived from without an independent validation sample. RESULTS The tumor diameter was larger and invasive and metastatic features were more common in the IDC-P than in the PAC group (P < 0.001). The distribution of extraprostatic extension (EPE) and pelvic lymphadenopathy was even greater, and the apparent diffusion coefficient (ADC) ratio was lower in the hpIDC-P than in the lpIDC-P group (P < 0.05). The ROC-AUCs of the stepwise models based solely on imaging features for distinguishing IDC-P from PAC and hpIDC-P from lpIDC-P and PAC were 0.797 (95% confidence interval, 0.750-0.843) and 0.777 (0.727-0.827), respectively. CONCLUSION IDC-P was more likely to be larger, more invasive, and more metastatic, with obviously restricted diffusion. EPE, pelvic lymphadenopathy, and a lower ADC ratio were more likely to occur in hpIDC-P, and were also the most useful variables in both nomograms for predicting IDC-P and hpIDC-P.
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Affiliation(s)
- Ling Yang
- Department of Radiology, West China Hospital, Sichuan University, Sichuan, China
| | - Xue-Ming Li
- Department of Radiology, West China Hospital, Sichuan University, Sichuan, China
| | - Meng-Ni Zhang
- Department of Pathology, West China Hospital, Sichuan University, Sichuan, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Sichuan, China.
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Xie Y, Liu S, Lin H, Wu M, Shi F, Pan F, Zhang L, Song B. Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis. Front Neurol 2023; 14:1126949. [PMID: 37456640 PMCID: PMC10345199 DOI: 10.3389/fneur.2023.1126949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 05/30/2023] [Indexed: 07/18/2023] [Imported: 08/15/2023] Open
Abstract
Background Intracranial aneurysm (IA) is a nodular protrusion of the arterial wall caused by the localized abnormal enlargement of the lumen of a brain artery, which is the primary cause of subarachnoid hemorrhage. Accurate rupture risk prediction can effectively aid treatment planning, but conventional rupture risk estimation based on clinical information is subjective and time-consuming. Methods We propose a novel classification method based on the CTA images for differentiating aneurysms that are prone to rupture. The main contribution of this study is that the learning-based method proposed in this study leverages deep learning and radiomics features and integrates clinical information for a more accurate prediction of the risk of rupture. Specifically, we first extracted the provided aneurysm regions from the CTA images as 3D patches with the lesions located at their centers. Then, we employed an encoder using a 3D convolutional neural network (CNN) to extract complex latent features automatically. These features were then combined with radiomics features and clinical information. We further applied the LASSO regression method to find optimal features that are highly relevant to the rupture risk information, which is fed into a support vector machine (SVM) for final rupture risk prediction. Results The experimental results demonstrate that our classification method can achieve accuracy and AUC scores of 89.78% and 89.09%, respectively, outperforming all the alternative methods. Discussion Our study indicates that the incorporation of CNN and radiomics analysis can improve the prediction performance, and the selected optimal feature set can provide essential biomarkers for the determination of rupture risk, which is also of great clinical importance for individualized treatment planning and patient care of IA.
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Affiliation(s)
- Yuan Xie
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shuyu Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hen Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Min Wu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Pan
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Lichi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Chen K, Cai Z, Cao Y, Jiang L, Jiang Y, Gu H, Fu S, Xia C, Lui S, Gong Q, Song B, Ai H. Kinetically inert manganese (II)-based hybrid micellar complexes for magnetic resonance imaging of lymph node metastasis. Regen Biomater 2023; 10:rbad053. [PMID: 37293571 PMCID: PMC10244211 DOI: 10.1093/rb/rbad053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/06/2023] [Accepted: 05/11/2023] [Indexed: 06/10/2023] [Imported: 08/15/2023] Open
Abstract
The localization and differential diagnosis of the sentinel lymph nodes (SLNs) are particularly important for tumor staging, surgical planning and prognosis. In this work, kinetically inert manganese (II)-based hybrid micellar complexes (MnCs) for magnetic resonance imaging (MRI) were developed using an amphiphilic manganese-based chelate (C18-PhDTA-Mn) with reliable kinetic stability and self-assembled with a series of amphiphilic PEG-C18 polymers of different molecular weights (C18En, n = 10, 20, 50). Among them, the probes composed by 1:10 mass ratio of manganese chelate/C18En had slightly different hydrodynamic particle sizes with similar surface charges as well as considerable relaxivities (∼13 mM-1 s-1 at 1.5 T). In vivo lymph node imaging in mice revealed that the MnC MnC-20 formed by C18E20 with C18-PhDTA-Mn at a hydrodynamic particle size of 5.5 nm had significant signal intensity brightening effect and shortened T1 relaxation time. At an imaging probe dosage of 125 μg Mn/kg, lymph nodes still had significant signal enhancement in 2 h, while there is no obvious signal intensity alteration in non-lymphoid regions. In 4T1 tumor metastatic mice model, SLNs showed less signal enhancement and smaller T1 relaxation time variation at 30 min post-injection, when compared with normal lymph nodes. This was favorable to differentiate normal lymph nodes from SLN under a 3.0-T clinical MRI scanner. In conclusion, the strategy of developing manganese-based MR nanoprobes was useful in lymph node imaging.
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Affiliation(s)
| | | | - Yingzi Cao
- National Engineering Research Center for Biomaterials, Sichuan University, Chengdu 610064, China
| | - Lingling Jiang
- National Engineering Research Center for Biomaterials, Sichuan University, Chengdu 610064, China
| | - Yuting Jiang
- National Engineering Research Center for Biomaterials, Sichuan University, Chengdu 610064, China
| | - Haojie Gu
- National Engineering Research Center for Biomaterials, Sichuan University, Chengdu 610064, China
| | - Shengxiang Fu
- National Engineering Research Center for Biomaterials, Sichuan University, Chengdu 610064, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Su Lui
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Key Laboratory of Transplant Engineering and Immunology, NHC, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Fujian, Xiamen 361000, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
| | - Hua Ai
- Correspondence address. Tel: +86 28 85413991, E-mail:
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Zhang Y, Wei H, Song B. Magnetic resonance imaging for treatment response evaluation and prognostication of hepatocellular carcinoma after thermal ablation. Insights Imaging 2023; 14:87. [PMID: 37188987 DOI: 10.1186/s13244-023-01440-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 04/24/2023] [Indexed: 05/17/2023] [Imported: 08/15/2023] Open
Abstract
Hepatocellular carcinoma (HCC) accounts for the vast majority of primary liver cancer and constitutes a major global health challenge. Tumor ablation with either radiofrequency ablation (RFA) or microwave ablation (MWA) is recommended as a curative-intent treatment for early-stage HCC. Given the widespread use of thermal ablation in routine clinical practice, accurate evaluation of treatment response and patient outcomes has become crucial in optimizing individualized management strategies. Noninvasive imaging occupies the central role in the routine management of patients with HCC. Magnetic resonance imaging (MRI) could provide full wealth of information with respect to tumor morphology, hemodynamics, function and metabolism. With accumulation of liver MR imaging data, radiomics analysis has been increasingly applied to capture tumor heterogeneity and provide prognostication by extracting high-throughput quantitative imaging features from digital medical images. Emerging evidence suggests the potential role of several qualitative, quantitative and radiomic MRI features in prediction of treatment response and patient prognosis after ablation of HCC. Understanding the advancements of MRI in the evaluation of ablated HCCs may facilitate optimal patient care and improved outcomes. This review provides an overview of the emerging role of MRI in treatment response evaluation and prognostication of HCC patients undergoing ablation. CLINICAL RELEVANCE STATEMENT: MRI-based parameters can help predict treatment response and patient prognosis after HCC ablation and thus guide treatment planning. KEY POINTS: 1. ECA-MRI provides morphological and hemodynamic assessment of ablated HCC. 2. EOB-MRI provides more information for tumor response prediction after ablation. 3. DWI improve the characterization of HCC and optimize treatment decision. 4. Radiomics analysis enables characterization of tumor heterogeneity guidance of clinical decision-making. 5. Further studies with multiple radiologists and sufficient follow-up period are needed.
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Affiliation(s)
- Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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Chen Z, Wang Y, Zhang H, Yin H, Hu C, Huang Z, Tan Q, Song B, Deng L, Xia Q. Deep Learning Models for Severity Prediction of Acute Pancreatitis in the Early Phase From Abdominal Nonenhanced Computed Tomography Images. Pancreas 2023; 52:e45-e53. [PMID: 37378899 DOI: 10.1097/mpa.0000000000002216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/29/2023] [Imported: 08/15/2023]
Abstract
OBJECTIVES To develop and validate deep learning (DL) models for predicting the severity of acute pancreatitis (AP) by using abdominal nonenhanced computed tomography (CT) images. METHODS The study included 978 AP patients admitted within 72 hours after onset and performed abdominal CT on admission. The image DL model was built by the convolutional neural networks. The combined model was developed by integrating CT images and clinical markers. The performance of the models was evaluated by using the area under the receiver operating characteristic curve. RESULTS The clinical, Image DL, and the combined DL models were developed in 783 AP patients and validated in 195 AP patients. The combined models possessed the predictive accuracy of 90.0%, 32.4%, and 74.2% for mild, moderately severe, and severe AP. The combined DL model outperformed clinical and image DL models with 0.820 (95% confidence interval, 0.759-0.871), the sensitivity of 84.76% and the specificity of 66.67% for predicting mild AP and the area under the receiver operating characteristic curve of 0.920 (95% confidence interval, 0.873-0.954), the sensitivity of 90.32%, and the specificity of 82.93% for predicting severe AP. CONCLUSIONS The DL technology allows nonenhanced CT images as a novel tool for predicting the severity of AP.
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Affiliation(s)
- Zhiyao Chen
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Huiling Zhang
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Cheng Hu
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qingyuan Tan
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | | | - Lihui Deng
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Xia
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
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12
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Wei H, Yang T, Chen J, Duan T, Jiang H, Song B. Prognostic implications of CT/MRI LI-RADS in hepatocellular carcinoma: State of the art and future directions. Liver Int 2022; 42:2131-2144. [PMID: 35808845 DOI: 10.1111/liv.15362] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 06/13/2022] [Accepted: 07/05/2022] [Indexed: 02/05/2023] [Imported: 08/15/2023]
Abstract
Hepatocellular carcinoma (HCC) is the fourth most lethal malignancy with an increasing incidence worldwide. Management of HCC has followed several clinical staging systems that rely on tumour morphologic characteristics and clinical variables. However, these algorithms are unlikely to profile the full landscape of tumour aggressiveness and allow accurate prognosis stratification. Noninvasive imaging biomarkers on computed tomography (CT) or magnetic resonance imaging (MRI) exhibit a promising prospect to refine the prognostication of HCC. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, techniques, interpretation, reporting and data collection of liver imaging. At present, it has been widely accepted as an effective diagnostic system for HCC in at-risk patients. Emerging data have provided new insights into the potential of CT/MRI LI-RADS in HCC prognostication, which may help refine the prognostic paradigm of HCC that promises to direct individualized management and improve patient outcomes. Therefore, this review aims to summarize several prognostic imaging features at CT/MRI for patients with HCC; the available evidence regarding the use of LI-RDAS for evaluation of tumour biology and clinical outcomes, pitfalls of current literature, and future directions for LI-RADS in the management of HCC.
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Affiliation(s)
- Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, Sanya People's Hospital, Sanya, China
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Jiang H, Song B, Qin Y, Konanur M, Wu Y, McInnes MDF, Lafata KJ, Bashir MR. Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study. J Magn Reson Imaging 2022; 56:399-412. [PMID: 34994029 DOI: 10.1002/jmri.28056] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The Liver Imaging Reporting and Data System (LI-RADS) is widely used for diagnosing hepatocellular carcinoma (HCC), however, with unsatisfactory sensitivity, complex ancillary features, and inadequate integration with gadoxetate disodium (EOB)-enhanced MRI. PURPOSE To modify LI-RADS (mLI-RADS) on EOB-MRI. STUDY TYPE Secondary analysis of a prospective observational study. POPULATION Between July 2015 and September 2018, 224 consecutive high-risk patients (median age, 51 years; range, 26-83; 180 men; training/testing sets: 169/55 patients) with 742 (median size, 13 mm; interquartile range, 7-27; 498 HCCs) LR-3/4/5 observations. FIELD STRENGTH/SEQUENCE 3.0 T T2 -weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar, and 3D T1 -weighted gradient echo sequences. ASSESSMENT Three radiologists (with 5, 5, and 10 years of experience in liver MR imaging, respectively) blinded to the reference standard (histopathology or imaging follow-up) reviewed all MR images independently. In the training set, the optimal LI-RADS version 2018 (v2018) features selected by Random Forest analysis were used to develop mLI-RADS via decision tree analysis. STATISTICAL TESTS In an independent testing set, diagnostic performances of mLI-RADS, LI-RADS v2018, and the Korean Liver Cancer Association (KLCA) guidelines were computed using a generalized estimating equation model and compared with McNemar's test. A two-tailed P < 0.05 was statistically significant. RESULTS Five features (nonperipheral "washout," restricted diffusion, nonrim arterial phase hyperenhancement [APHE], mild-moderate T2 hyperintensity, and transitional phase hypointensity) constituted mLI-RADS, and mLR-5 was nonperipheral washout coupled with either nonrim APHE or restricted diffusion. In the testing set, mLI-RADS was significantly more sensitive (72%) and accurate (80%) than LI-RADS v2018 (sensitivity, 61%; accuracy 74%; both P < 0.001) and the KLCA guidelines (sensitivity, 64%; accuracy 74%; both P < 0.001), without sacrificing positive predictive value (mLI-RADS, 94%; LI-RADS v2018, 94%; KLCA guidelines, 92%). DATA CONCLUSION In high-risk patients, the EOB-MRI-based mLI-RADS was simpler and more sensitive for HCC than LI-RADS v2018 while maintaining high positive predictive value. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Meghana Konanur
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Yuanan Wu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Matthew D F McInnes
- Departments of Radiology and Epidemiology, University of Ottawa, Ottawa, Ontario, Canada
- Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Kyle J Lafata
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Center for Advanced Magnetic Resonance in Medicine, Duke University Medical Center, Durham, North Carolina, USA
- Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
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Ye Z, Song B, Lee PM, Ohliger MA, Laustsen C. Hyperpolarized carbon 13 MRI in liver diseases: Recent advances and future opportunities. Liver Int 2022; 42:973-983. [PMID: 35230742 PMCID: PMC9313895 DOI: 10.1111/liv.15222] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 01/20/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023]
Abstract
Hyperpolarized carbon-13 magnetic resonance imaging (HP 13 C MRI) is a recently translated metabolic imaging technique. With dissolution dynamic nuclear polarization (d-DNP), more than 10 000-fold signal enhancement can be readily reached, making it possible to visualize real-time metabolism and specific substrate-to-metabolite conversions in the liver after injecting carbon-13 labelled probes. Increasing evidence suggests that HP 13 C MRI is a potential tool in detecting liver abnormalities, predicting disease progression and monitoring response treatment. In this review, we will introduce the recent progresses of HP 13 C MRI in diffuse liver diseases and liver malignancies and discuss its future opportunities from a clinical perspective, hoping to provide a comprehensive overview of this novel technique in liver diseases and highlight its scientific and clinical potential in the field of hepatology.
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Affiliation(s)
- Zheng Ye
- Department of RadiologyWest China Hospital, Sichuan UniversityChengduSichuanChina
- The MR Research Center, Department of Clinical MedicineAarhus UniversityAarhusDenmark
| | - Bin Song
- Department of RadiologyWest China Hospital, Sichuan UniversityChengduSichuanChina
| | - Philip M. Lee
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Michael A. Ohliger
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Christoffer Laustsen
- The MR Research Center, Department of Clinical MedicineAarhus UniversityAarhusDenmark
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Wan S, He Y, Zhang X, Wei Y, Song B. Quantitative measurements of esophageal varices using computed tomography for prediction of severe varices and the risk of bleeding: a preliminary study. Insights Imaging 2022; 13:47. [PMID: 35286491 PMCID: PMC8921428 DOI: 10.1186/s13244-022-01189-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/10/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND We aimed to assess whether the quantitative parameters of esophageal varices (EV) based on computed tomography (CT) can noninvasively predict severe EV and the risk of esophageal variceal bleeding (EVB). METHODS A total of 136 endoscopically confirmed EV patients were included in this retrospective study and were divided into a non-conspicuous (mild-to-moderate EV, n = 30) and a conspicuous EV group (severe EV, n = 106), a bleeding (n = 89) and a non-bleeding group (n = 47). EV grade (EVG), EV diameter (EVD), cross-sectional surface area (CSA), EV volume (EVV), spleen volume (SV), splenic vein (SNV), portal vein (PV), diameter of left gastric vein (DLGV), and the opening type of LGV were measured independently using 3D-slicer. Univariate and multivariate logistic analysis were used to determine the independent factors and the receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic performance. RESULTS The difference of EVG, EVD, CSA, EVV, DLGV, SNV between the conspicuous and non-conspicuous EV group were statistically significant (p < 0.05), area under the curves (AUCs) of them for predicting severe EV were 0.72, 0.772, 0.704, 0.768, 0.707, 0.65, with corresponding sensitivities of 70.3%, 63.5%, 50%, 74.3%, 52.7%, 48.6%, specificities of 71.4%, 85.7%, 100%, 71.4%, 81%, 81%, respectively. EVG, CSA (odds ratio 3.258, 95% CI 1.597-6.647; 1.029, 95% CI 1.008-1.050) were found to be independent predictive factors. However, there was no significant difference of the included indices between the bleeding and non-bleeding group (p > 0.05). CONCLUSIONS CT can be used as a noninvasive method to predict the severity of EV, which may reduce the invasive screening of endoscopy.
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Affiliation(s)
- Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, 610041, People's Republic of China
| | - Yuhao He
- Department of Neurosurgery, Third People's Hospital of Chengdu, Chengdu, 610031, People's Republic of China
| | - Xin Zhang
- Pharmaceutical Diagnostic Team, GE Healthcare, Life Sciences, Beijing, 100176, People's Republic of China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, 610041, People's Republic of China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, 610041, People's Republic of China.
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Zhao J, Zhang W, Zhu YY, Zheng HY, Xu L, Zhang J, Liu SY, Li FY, Song B. Development and Validation of Noninvasive MRI-Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma. J Magn Reson Imaging 2022; 55:787-802. [PMID: 34296802 DOI: 10.1002/jmri.27846] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/08/2021] [Accepted: 07/09/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Cholangiocarcinoma is a type of hepatobiliary tumor. For perihilar cholangiocarcinoma (pCCA), patients who experience early recurrence (ER) have a poor prognosis. Preoperative accurate prediction of postoperative ER can avoid unnecessary operation; however, prediction is challenging. PURPOSE To develop a novel signature based on clinical and/or MRI radiomics features of pCCA to preoperatively predict ER. STUDY TYPE Retrospective. POPULATION One hundred eighty-four patients (median age, 61.0 years; interquartile range: 53.0-66.8 years) including 115 men and 69 women. FIELD STRENGTH/SEQUENCE A 1.5 T; volumetric interpolated breath-hold examination (VIBE) sequence. ASSESSMENT The models were developed from the training set (128 patients) and validated in a separate testing set (56 patients). The contrast-enhanced arterial and portal vein phase MR images of hepatobiliary system were used for extracting radiomics features. The correlation analysis, least absolute shrinkage and selection operator (LASSO) logistic regression (LR), backward stepwise LR were mainly used for radiomics feature selection and modeling (Modelradiomic ). The univariate and multivariate backward stepwise LR were used for preoperative clinical predictors selection and modeling (Modelclinic ). The radiomics and preoperative clinical predictors were combined by multivariate LR method to construct clinic-radiomics nomogram (Modelcombine ). STATISTICAL TESTS Chi-squared (χ2 ) test or Fisher's exact test, Mann-Whitney U-test or t-test, Delong test. Two tailed P < 0.05 was considered statistically significant. RESULTS Based on the comparison of area under the curves (AUC) using Delong test, Modelclinic and Modelcombine had significantly better performance than Modelradiomic and tumor-node-metastasis (TNM) system in training set. In the testing set, both Modelclinic and Modelcombine had significantly better performance than TNM system, whereas only Modelcombine was significantly superior to Modelradiomic . However, the AUC values were not significantly different between Modelclinic and Modelcombine (P = 0.156 for training set and P = 0.439 for testing set). DATA CONCLUSION A noninvasive model combining the MRI-based radiomics signature and clinical variables is potential to preoperatively predict ER for pCCA. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 4.
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Affiliation(s)
- Jian Zhao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Wei Zhang
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Yuan-Yi Zhu
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Hao-Yu Zheng
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Li Xu
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Jun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Si-Yun Liu
- GE Healthcare (China), Beijing, 100176, China
| | - Fu-Yu Li
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
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Jiang H, Song B, Qin Y, Wei Y, Konanur M, Wu Y, Zaki IH, McInnes MDF, Lafata KJ, Bashir MR. Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity. J Magn Reson Imaging 2022; 55:493-506. [PMID: 34236120 DOI: 10.1002/jmri.27824] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/23/2021] [Accepted: 06/23/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The Liver Imaging Reporting and Data System (LI-RADS) is widely accepted as a reliable diagnostic scheme for hepatocellular carcinoma (HCC) in at-risk patients. However, its application is hampered by substantial complexity and suboptimal diagnostic sensitivity. PURPOSE To propose data-driven modifications to the LI-RADS version 2018 (v2018) major feature system (rLI-RADS) on gadoxetate disodium (EOB)-enhanced magnetic resonance imaging (MRI) to improve sensitivity and simplicity while maintaining high positive predictive value (PPV) for detecting HCC. STUDY TYPE Retrospective. POPULATION Two hundred and twenty-four consecutive at-risk patients (training dataset: 169, independent testing dataset: 55) with 742 LR-3 to LR-5 liver observations (HCC: N = 498 [67%]) were analyzed from a prospective observational registry collected between July 2015 and September 2018. FIELD STRENGTH/SEQUENCE 3.0 T/T2-weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar and three-dimensional (3D) T1-weighted gradient echo sequences. ASSESSMENT All images were evaluated by three independent abdominal radiologists who were blinded to all clinical, pathological, and follow-up information. Composite reference standards of either histopathology or imaging follow-up were used. STATISTICAL TESTS In the training dataset, LI-RADS v2018 major features were used to develop rLI-RADS based on their associated PPV for HCC. In an independent testing set, diagnostic performances of LI-RADS v2018 and rLI-RADS were computed using a generalized estimating equation model and compared with McNemar's test. A P value <0.05 was considered statistically significant. RESULTS The median (interquartile range) size of liver observations was 13 mm (7-27 mm). The diagnostic table for rLI-RADS encompassed 9 cells, as opposed to 16 cells for LI-RADS v2018. In the testing set, compared to LI-RADS v2018, rLI-RADS category 5 demonstrated a significantly superior sensitivity (76% vs. 61%) while maintaining comparably high PPV (92.5% vs. 94.1%, P = 0.126). DATA CONCLUSION Compared with LI-RADS v2018, rLI-RADS demonstrated improved simplicity and significantly superior diagnostic sensitivity for HCC in at-risk patients. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Meghana Konanur
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Yuanan Wu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Islam H Zaki
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Matthew D F McInnes
- Departments of Radiology and Epidemiology, University of Ottawa, Ottawa, Ontario, Canada
- Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Kyle J Lafata
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Center for Advanced Magnetic Resonance in Medicine, Duke University Medical Center, Durham, North Carolina, USA
- Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
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Zeng N, Wang Y, Cheng Y, Huang Z, Song B. Imaging evaluation of the pancreas in diabetic patients. Abdom Radiol (NY) 2022; 47:715-726. [PMID: 34786594 DOI: 10.1007/s00261-021-03340-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 02/05/2023]
Abstract
Diabetes mellitus (DM) is becoming a global epidemic and its diagnosis and monitoring are based on laboratory testing which sometimes have limitations. The pancreas plays a key role in metabolism and is involved in the pathogenesis of DM. It has long been known through cadaver biopsies that pancreas volume is decreased in patients with DM. With the development of different imaging modalities over the last two decades, many studies have attempted to determine whether there other changes occurred in the pancreas of diabetic patients. This review summarizes current knowledge about the use of different imaging approaches (such as CT, MR, and US) and radiomics for exploring pancreatic changes in diabetic patients. Imaging studies are expected to produce reliable information regarding DM, and radiomics could provide increasingly valuable information to identify some undetectable features and help diagnose and predict the occurrence of diabetes through pancreas imaging.
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Affiliation(s)
- Ni Zeng
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Yi Wang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Yue Cheng
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
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Yang L, Li XM, Hu YJ, Zhang MN, Yao J, Song B. Multidetector CT Characteristics of Fumarate Hydratase-Deficient Renal Cell Carcinoma and Papillary Type II Renal Cell Carcinoma. Korean J Radiol 2021; 22:1996-2005. [PMID: 34668351 PMCID: PMC8628156 DOI: 10.3348/kjr.2021.0212] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 06/08/2021] [Accepted: 07/20/2021] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE To investigate the multidetector computed tomography (MDCT) features of fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) with germline or somatic mutations, and compare them with those of papillary type II RCC (pRCC type II). MATERIALS AND METHODS A total of 24 patients (mean ± standard deviation, 40.4 ± 14.7 years) with pathologically confirmed FH-deficient RCC (15 with germline and 9 with somatic mutations) and 54 patients (58.6 ± 12.6 years) with pRCC type II were enrolled. The MDCT features were retrospectively reviewed and compared between the two entities and mutation subgroups, and were correlated with the clinicopathological findings. RESULTS All the lesions were unilateral and single. Compared with pRCC type II, FH-deficient RCC was more prevalent among younger patients (40.4 ± 14.7 vs. 58.6 ± 12.6, p < 0.001) and tended to be larger (8.1 ± 4.1 vs. 5.4 ± 3.2, p = 0.002). Cystic solid patterns were more common in FH-deficient RCC (20/24 vs. 16/54, p < 0.001), with 16 of the 20 (80.0%) cystic solid tumors having showed typical polycystic and thin smooth walls and/or septa, with an eccentric solid component. Lymph node (16/24 vs. 16/54, p = 0.003) and distant (11/24 vs. 3/54, p < 0.001) metastases were more frequent in FH-deficient RCC. FH-deficient RCC and pRCC type II showed similar attenuation in the unenhanced phase. The attenuation in the corticomedullary phase (CMP) (76.3% ± 25.0% vs. 60.2 ± 23.6, p = 0.008) and nephrographic phase (NP) (87.7 ± 20.5, vs. 71.2 ± 23.9, p = 0.004), absolute enhancement in CMP (39.0 ± 24.8 vs. 27.1 ± 22.7, p = 0.001) and NP (50.5 ± 20.5 vs. 38.2 ± 21.9, p = 0.001), and relative enhancement ratio to the renal cortex in CMP (0.35 ± 0.26 vs. 0.24 ± 0.19, p = 0.001) and NP (0.43 ± 0.24 vs. 0.29 ± 0.19, p < 0.001) were significantly higher in FH-deficient RCC. No significant difference was found between the FH germline and somatic mutation subgroups in any of the parameters. CONCLUSION The MDCT features of FH-deficient RCC were different from those of pRCC type II, whereas there was no statistical difference between the germline and somatic mutation subgroups. A kidney mass with a cystic solid pattern and metastatic tendency, especially in young patients, should be considered for FH-deficient RCC.
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Affiliation(s)
- Ling Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xue-Ming Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ya-Jun Hu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Meng-Ni Zhang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
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Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13:1599-1615. [PMID: 34853638 PMCID: PMC8603458 DOI: 10.4251/wjgo.v13.i11.1599] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/02/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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21
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Yang CW, Che F, Liu XJ, Yin Y, Zhang B, Song B. Insight into gastrointestinal heterotopic pancreas: imaging evaluation and differential diagnosis. Insights Imaging 2021; 12:144. [PMID: 34674040 PMCID: PMC8531187 DOI: 10.1186/s13244-021-01089-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/29/2021] [Indexed: 02/08/2023] Open
Abstract
Heterotopic pancreas (HP) is an uncommon congenital abnormality in the developmental process of the pancreas, with gastrointestinal heterotopic pancreas (GHP) being the most common HP. The clinical manifestations of GHP may have variable patterns of presentation, dictated by both the anatomic location and the functional ability of the lesion. The most common imaging modality in detecting GHP is computed tomography (CT), while gastrointestinal barium fluoroscopy, endoscopic ultrasonography, and magnetic resonance imaging (MRI) are also applied. The density and enhancement patterns of GHP are consistent with histological classifications. GHP with a predominantly acinar tissue component manifests homogeneous and marked enhancement on CT images, whereas a predominantly ductal GHP presents heterogeneous and mild enhancement. On MRI, the appearance and signal intensity of GHP were paralleled to the normal pancreas on all sequences and were characterized by T1-weighted high signal and early marked enhancement. This article provides a comprehensive review of the histopathology, clinical manifestations, imaging features of various modalities, and differential diagnosis of GHP. It is hoped that this review will improve clinicians' knowledge of GHP and aid in accurate preoperative diagnosis, thereby reducing the misdiagnosis rate.
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Affiliation(s)
- Cai-Wei Yang
- West China School of Medicine, Sichuan University, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Feng Che
- West China School of Medicine, Sichuan University, Chengdu, 610041, China
| | - Xi-Jiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Yuan Yin
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Bo Zhang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China.
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22
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Xiong X, Ye Z, Tang H, Wei Y, Nie L, Wei X, Liu Y, Song B. MRI of Temporomandibular Joint Disorders: Recent Advances and Future Directions. J Magn Reson Imaging 2021; 54:1039-1052. [PMID: 32869470 DOI: 10.1002/jmri.27338] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 08/07/2020] [Accepted: 08/07/2020] [Indexed: 02/05/2023] Open
Abstract
Temporomandibular joint disorders (TMDs) are a prevalent disease covering pain and dysfunction of temporomandibular joints and masticatory muscles, which can be detrimental to quality of life. Magnetic resonance imaging (MRI) is a powerful and noninvasive tool for the imaging and understanding of TMD. With the recent technical development of dynamic and quantitative MRI techniques, including diffusion-weighted imaging, T2 mapping, and ultrashort/zero echo time, it is now feasible in TMD imaging and has been preliminarily investigated with promising results. In this review we will discuss the recent advances of MRI techniques in TMD and its future directions, and hope to highlight the scientific potential and clinical value of novel MRI techniques in diagnosing and treating TMD. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
- Xin Xiong
- State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hehan Tang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | | | | | - Yang Liu
- State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Abstract
Online supplemental material is available for this article.
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Affiliation(s)
- Lei Tang
- From the Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu 610041, China (L.T., B.S.); and Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China (L.T.)
| | - Bin Song
- From the Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu 610041, China (L.T., B.S.); and Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China (L.T.)
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Zhang Y, Huang ZX, Song B. Role of imaging in evaluating the response after neoadjuvant treatment for pancreatic ductal adenocarcinoma. World J Gastroenterol 2021; 27:3037-3049. [PMID: 34168406 PMCID: PMC8192284 DOI: 10.3748/wjg.v27.i22.3037] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/08/2021] [Accepted: 04/26/2021] [Indexed: 02/06/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Despite the development of multimodality treatments, including surgical resection, radiotherapy, and chemotherapy, the long-term prognosis of patients with PDAC remains poor. Recently, the introduction of neoadjuvant treatment (NAT) has made more patients amenable to surgery, increasing the possibility of R0 resection, treatment of occult micro-metastasis, and prolongation of overall survival. Imaging plays a vital role in tumor response evaluation after NAT. However, conventional imaging modalities such as multidetector computed tomography have limited roles in the assessment of tumor resectability after NAT for PDAC because of the similar appearance of tissue fibrosis and tumor infiltration. Perfusion computed tomography, using blood perfusion as a biomarker, provides added value in predicting the histopathologic response of PDAC to NAT by reflecting the changes in tumor matrix and fibrosis content. Other imaging technologies, including diffusion-weighted imaging of magnetic resonance imaging and positron emission tomography, can reveal the tumor response by monitoring the structural changes in tumor cells and functional metabolic changes in tumors after NAT. In addition, with the renewed interest in data acquisition and analysis, texture analysis and radiomics have shown potential for the early evaluation of the response to NAT, thus improving patient stratification to achieve accurate and intensive treatment. In this review, we briefly introduce the application and value of NAT in resectable and unresectable PDAC. We also summarize the role of imaging in evaluating the response to NAT for PDAC, as well as the advantages, limitations, and future development directions of current imaging techniques.
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Affiliation(s)
- Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zi-Xing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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Yao S, Wei Y, Ye Z, Chen J, Duan T, Zhang Z, Song B. Hepatic Steatosis Has No Effect in Diagnosis Accuracy of LI-RADS v2018 Categorization of Hepatocellular Carcinoma in MR Imaging. J Magn Reson Imaging 2021. [PMID: 34121266 DOI: 10.1002/jmri.27783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND In clinical practice, hepatocellular carcinoma (HCC) is widely diagnosed by using MRI, however, whether the imaging features are affected by hepatic steatosis (HS) is still unknown. PURPOSE To investigate and compare the differences in HCC related imaging features between with- and without-HS groups, and to further determine whether HS affects the diagnosis accuracy of Liver Imaging Reporting and Data System (LI-RADS) v2018 of HCC in MRI. STUDY TYPE Prospective. SUBJECTS One hundred and seventy-one patients (mean age, 52 ± 11 years; range, 26-83 years) including 137 men and 34 women. FIELD STRENGTH/SEQUENCE 3.0 T, gradient echo (GRE). ASSESSMENT Subjects were classified as HS and non-HS groups according to MRI-proton density fat-fraction (PDFF). HS was defined as MRI-PDFF >5.6%. Three radiologists accessed HCC features and assigned LI-RADS categories in MRI independently based on LI-RADS v2018. Frequencies of HCC major features and LR categorization assignment between the two groups as well as interobserver agreement between the two radiologists were assessed. STATISTICAL TESTS Unpaired t-test, Chi-square test, Fisher's exact test, kappa statistic, intraclass correlation coefficient (ICC). A two-sided P value <0.05 was considered as statistically significant. RESULTS Major features including arterial hyperenhancement (APHE), enhancing "capsule" and nonperipheral "washout" observed between HS and non-HS groups were not significantly different (78.95% vs.78.62%, P = 0.866; 57.89% vs.52.98%, P = 0.483; and 75% vs.81.46%, P = 0.257, respectively), and the assessment of observation size showed a borderline difference (P = 0.059). No significant difference in LR-5 assignment between the two groups (69.74% vs. 72.85% for reader 1, P = 0.641; 71.05% vs. 72.19% for reader 2, P = 0.877). Interobserver agreement between the two radiologists showed almost perfect in LR-5 assignment (κ = 0.869) and size observation (ICC = 0.997). DATA CONCLUSION The diagnosis of HCC based on LI-RADS v2018 in MRI is of comparable performance regardless of HS, in which there is no significant difference in either the major imaging features or LR categorization. LEVEL OF EVIDENCE 2 Technical Efficacy Stage: 2.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhen Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Zhang W, Yin H, Huang Z, Zhao J, Zheng H, He D, Li M, Tan W, Tian S, Song B. Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer. Cancer Med 2021; 10:4164-4173. [PMID: 33963688 PMCID: PMC8209621 DOI: 10.1002/cam4.3957] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning model that could preoperatively predict the MSI status of rectal cancer based on magnetic resonance images. METHODS This single-center retrospective study included 491 rectal cancer patients with pathologically proven microsatellite status. Patients were randomly divided into the training/validation cohort (n = 395) and the testing cohort (n = 96). A clinical model using logistic regression was constructed to discriminate MSI status using only clinical factors. Based on a modified MobileNetV2 architecture, deep learning models were tested for the predictive ability of MSI status from magnetic resonance images, with or without integrating clinical factors. RESULTS The clinical model correctly classified 37.5% of MSI status in the testing cohort, with an AUC value of 0.573 (95% confidence interval [CI], 0.468 ~ 0.674). The pure imaging-based model and the combined model correctly classified 75.0% and 85.4% of MSI status in the testing cohort, with AUC values of 0.820 (95% CI, 0.718 ~ 0.884) and 0.868 (95% CI, 0.784 ~ 0.929), respectively. Both deep learning models performed better than the clinical model (p < 0.05). There was no statistically significant difference between the deep learning models with or without integrating clinical factors. CONCLUSIONS Deep learning based on high-resolution T2-weighted magnetic resonance images showed a good predictive performance for MSI status in rectal cancer patients. The proposed model may help to identify patients who would benefit from chemotherapy or immunotherapy and determine individualized therapeutic strategies for these patients.
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Affiliation(s)
- Wei Zhang
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
- Department of RadiologySichuan Provincial Corps HospitalChinese People's Armed Police ForcesLeshanChina
| | - Hongkun Yin
- Institute of Advanced ResearchInferVisionBeijingChina
| | - Zixing Huang
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Jian Zhao
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
- Department of RadiologySichuan Provincial Corps HospitalChinese People's Armed Police ForcesLeshanChina
| | - Haoyu Zheng
- Department of RadiologySichuan Provincial Corps HospitalChinese People's Armed Police ForcesLeshanChina
| | - Du He
- Department of PathologyWest China HospitalSichuan UniversityChengduChina
| | - Mou Li
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Weixiong Tan
- Institute of Advanced ResearchInferVisionBeijingChina
| | - Song Tian
- Institute of Advanced ResearchInferVisionBeijingChina
| | - Bin Song
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
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Zhang J, Wu Z, Zhao J, Liu S, Zhang X, Yuan F, Shi Y, Song B. Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival. Eur Radiol 2021; 31:3661-3672. [PMID: 33245493 DOI: 10.1007/s00330-020-07524-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 09/22/2020] [Accepted: 11/16/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE Clinical evidence suggests that the response to immune checkpoint blockade depends on the immune status in the tumor microenvironment. This study aims to predict the immunophenotyping (IP) and overall survival (OS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative magnetic resonance imaging (MRI) texture analysis. METHODS A total of 78 ICC patients were included and divided into inflamed (n = 26) or non-inflamed (n = 52) immunophenotyping based on the density of CD8+ T cells. The enhanced T1-weighted MRI in the arterial phase was employed with texture analysis. The logistic regression analysis was applied to select the significant features related to IP. The OS-related feature was determined by Cox proportional-hazards model and Kaplan-Meier analysis. IP and OS predictive models were developed using the selected features, respectively. RESULTS Three wavelets and one 3D feature have favorable ability to discriminate IP, a combination of which performed best with an AUC of 0.919. The inflamed immunophenotyping had a better prognosis than the non-inflamed one. The 5-year survival rates of the two groups were 48.5% and 25.3%, respectively (p < 0.05). The only wavelet-HLH_firstorder_Median feature was associated with OS and used to build the OS predictive model with a C-index of 0.70 (95% CI, 0.57, 0.82), which could well stratify ICC patients into high- and low-risk groups. The 1-, 3-, and 5-year survival probabilities of the stratified groups were 62.5%, 30.0%, and 24.2%, and 89.5%, 62.2%, and 42.1%, respectively (p < 0.05). CONCLUSION The MRI texture signature could serve as a potential predictive biomarker for the IP and OS of ICC patients. KEY POINTS • The MRI texture signature, including three wavelets and one 3D feature, showed significant associations with immunophenotyping of ICC, and all have favorable ability to discriminate immunophenotyping; a combination of the above features performed best with an AUC of 0.919. • The only wavelet-HLH_firstorder_Median feature was associated with the OS of ICC and used to build the OS predictive model, which could well stratify ICC patients into high- and low-risk groups.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Zhenru Wu
- Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jian Zhao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Siyun Liu
- Pharmaceutical Diagnostic Team, GE Healthcare, Life Sciences, Beijing, 100176, China
| | - Xin Zhang
- Pharmaceutical Diagnostic Team, GE Healthcare, Life Sciences, Beijing, 100176, China
| | - Fang Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yujun Shi
- Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
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Jiang H, Song B, Qin Y, Chen J, Xiao D, Ha HI, Liu X, Oloruntoba-Sanders O, Erkanli A, Muir AJ, Bashir MR. Diagnosis of LI-RADS M lesions on gadoxetate-enhanced MRI: identifying cholangiocarcinoma-containing tumor with serum markers and imaging features. Eur Radiol 2021; 31:3638-3648. [PMID: 33245494 DOI: 10.1007/s00330-020-07488-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/14/2020] [Accepted: 11/06/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The LI-RADS M (LR-M) category describes hepatic lesions probably or definitely malignant, but not specific for hepatocellular carcinoma in at-risk patients. Differentiation among LR-M entities, particularly detecting cholangiocarcinoma-containing tumors (M-CCs), is essential for treatment and prognosis. Thus, we aimed to develop diagnostic models on gadoxetate disodium-enhanced MRI comprising serum tumor markers and LI-RADS imaging features for M-CC. METHODS Consecutive at-risk patients with LR-M lesions exclusively (no co-existing LR-4 and/or LR-5 lesions) were retrieved retrospectively from a prospectively collected database spanning 3 years. Intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (c-HCC-CCA) were classified together as M-CC. LI-RADS features determined by three independent radiologists and clinically relevant serum tumor markers were used to generate M-CC diagnostic models through logistic regression analysis against histology. Per-patient performance was evaluated using area under the receiver operating curve (AUC), sensitivity, and specificity. RESULTS Forty-five patients were included, 42.2% (19/45) with hepatocellular carcinoma, 33.3% (15/45) with ICC, 13.3% (6/45) with c-HCC-CCA, and 11.1% (5/45) with other hepatic lesions. Carbohydrate antigen (CA)19-9 > 38 U/mL, α-fetoprotein (AFP) > 4.8 ng/mL, and absence of the LI-RADS feature "blood products in mass" were significant predictors of M-CC. Combining three predictors demonstrated AUC of 0.862, sensitivity of 76%, and specificity of 88%. The risk of M-CC with all three criteria fulfilled was 98% (AUC, 0.690; sensitivity, 38%; specificity, 100%). CONCLUSIONS In at-risk patients with LR-M lesions, integrating CA19-9, AFP, and the LI-RADS feature "blood products in mass" achieved high diagnostic performance for M-CC. When all three criteria were fulfilled, the specificity for M-CC was 100%. KEY POINTS • In at-risk patients who had LR-M lesions exclusively (no concomitant LR-4/5 lesions), a model with carbohydrate antigen > 38 U/mL, α-fetoprotein > 4.8 ng/mL, and absence of the LI-RADS feature "blood products in mass" achieved high accuracy for diagnosing cholangiocarcinoma-containing tumors. • In patients of whom all three criteria were fulfilled, the specificity for M-CC was 100%, which might reduce or eliminate the need for biopsy confirmation.
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Affiliation(s)
- Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Radiology and Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Box 3808, Durham, NC, 27710, USA
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Dong Xiao
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Hong Ii Ha
- Department of Radiology and Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Box 3808, Durham, NC, 27710, USA
| | - Xijiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | | | - Alaattin Erkanli
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Andrew J Muir
- Department of Medicine (Gastroenterology), Duke University Medical Center, Durham, NC, 27710, USA
| | - Mustafa R Bashir
- Department of Radiology and Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Box 3808, Durham, NC, 27710, USA.
- Department of Medicine (Gastroenterology), Duke University Medical Center, Durham, NC, 27710, USA.
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Wei H, Jiang H, Zheng T, Zhang Z, Yang C, Ye Z, Duan T, Song B. LI-RADS category 5 hepatocellular carcinoma: preoperative gadoxetic acid-enhanced MRI for early recurrence risk stratification after curative resection. Eur Radiol 2021; 31:2289-2302. [PMID: 33001306 PMCID: PMC7979599 DOI: 10.1007/s00330-020-07303-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/01/2020] [Accepted: 09/15/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVES To explore the role of preoperative gadoxetic acid-enhanced MRI in stratifying the risk of early recurrence in patients with LR-5 hepatocellular carcinoma (HCC) by LI-RADS v2018 after curative resection. METHODS Between July 2015 and August 2018, this study evaluated consecutive treatment-naïve at-risk LR-5 HCC patients who underwent gadoxetic acid-enhanced MRI examination within 2 weeks before curative resection. The Cox regression analysis was performed to identify potential predictors of early recurrence. Disease-free survival (DFS) rates were analyzed and compared by using the Kaplan-Meier method and log-rank tests. RESULTS Fifty-three of 103 (51.5%) patients experienced early recurrence. Three MRI findings were significantly associated with early recurrence: corona enhancement (hazard ratio [HR]: 2.116; p = 0.013), peritumoral hypointensity on hepatobiliary phase (HBP) (HR: 2.262; p = 0.007), and satellite nodule (HR: 2.777; p = 0.005). An additional risk factor was AFP level > 400 ng/mL (HR: 1.975; p = 0.016). Based on the number of MRI predictors, LR-5 HCC patients were stratified into three subgroups: LR-5a (60/103; no predictor), LR-5b (26/103; one predictor), and LR-5c (17/103; two or three predictors), with low, medium, and high risk of early recurrence, respectively. The 2-year DFS rate of LR-5a, LR-5b, and LR-5c patients was 65.0%, 38.5%, and 5.9%, respectively, while the corresponding median DFS was undefined, 17.1 months, and 5.1 months, respectively (p < 0.001). CONCLUSIONS In at-risk LR-5 HCC patients, corona enhancement, peritumoral hypointensity on HBP, and satellite nodule could be used to preoperatively stratify the risk of early recurrence after hepatectomy. KEY POINTS • Corona enhancement, peritumoral hypointensity on HBP, satellite nodule, and serum AFP level > 400 ng/mL were significant predictors of early recurrence in patients with LR-5 HCC after hepatectomy. • Based on the number of predictive MRI findings, LR-5 HCC patients could be preoperatively stratified into three subgroups: LR-5a, LR-5b, and LR-5c, with significantly different risk of early recurrence and disease-free survival. • Preoperative risk stratification is essential for the identification of patients at increased risk of postoperative early recurrence, which may contribute to risk-based personalized management for LR-5 HCC patients.
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Affiliation(s)
- Hong Wei
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, Sichuan, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, Sichuan, China
| | - Tianying Zheng
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, Sichuan, China
| | - Zhen Zhang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, Sichuan, China
| | - Caiwei Yang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, Sichuan, China
| | - Zheng Ye
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, Sichuan, China
| | - Ting Duan
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, Sichuan, China.
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Abstract
Purpose: To determine the anatomy of the cystic artery by dual-source CT, and correlate imaging findings with those patients who had laparoscopic cholecystectomy (LC). Materials and Methods: Following institutional review board approval, a total of 289 consecutive patients (204 men and 85 women) were evaluated with CT for abdominal pain, including 55 patients subsequently underwent LC. Location of the cystic artery termination, distance between the cystic artery origin and the gallbladder, and angle between the cystic artery and its parent artery were evaluated by two radiologists. The laparoscopic surgical video record (gold standard) was similarly evaluated by a surgeon. Results: A total of 256 cystic arteries in the 247 patients were included. Cystic artery terminations are predominately found in ventral Calot triangle plane (50.8%, type II). Cystic artery origin immediately adjacent to the gallbladder surface was seen in 11/256 (4.3%). Zero angle between the cystic artery and its parent artery was found in 17 of 256 cystic arteries (6.6%). The cystic arteries and the Calot triangle were depicted in 49 patients (95% confidence interval: 85%, 97%). For all 49 patients, CT imaging findings were consistent with surgical video records. No case involved vascular and biliary injury occurred. Conclusions: Given the large number of LC performed each year, better knowledge of anatomic variation of the cystic artery could potentially prevent arterial injury and bile duct injury, particularly for patients with unusual anatomy.
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Affiliation(s)
- Li Li
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, People's Republic of China
| | - Qiang Li
- College of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, People's Republic of China
| | - Mingguo Xie
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, People's Republic of China
| | - Wenwei Zuo
- Department of General Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, People's Republic of China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, People's Republic of China
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Tang J, Liu J, Gu Z, Song B. Outcomes of Augmentation in Osteoporotic Vertebral Compression Fractures Showing a Cleft Sign on MRI. Cardiovasc Intervent Radiol 2021; 44:428-435. [PMID: 33388869 DOI: 10.1007/s00270-020-02753-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/18/2020] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Intravertebral clefts (IVCs) typically occur in association with osteoporotic vertebral compression fractures (OVCFs) and can be characterized based on magnetic resonance imaging (MRI). This study aimed to identify the clinical characteristics of IVCs with different MRI signals and assess their influence on outcomes of vertebral augmentation. MATERIALS AND METHODS We retrospectively recruited patients with OVCFs and associated IVCs who underwent vertebral augmentation. Patients were stratified into two groups based on whether the IVCs were full of liquid or gas, as determined by MRI signals. Patients were also stratified based on whether vertebral augmentation involved percutaneous kyphoplasty (PKP) or vertebroplasty (PVP). Pre- and postprocedural parameters were compared between groups. RESULTS A total of 194 fractured vertebrae (86 liquid-filled, 108 gas-filled) were examined. Scores for bone cement distribution were significantly higher in the gas group than in the liquid group, indicating broader cement distribution in the gas group. In both groups, intervention significantly improved pain and mobility scores. Among patients with gas-filled IVCs, the incidence of bone cement leakage and recollapse of treated vertebrae were significantly higher after PKP than after PVP. In the liquid group, incidence of bone cement leakage and recollapse of treated vertebrae did not differ significantly between patients who received PKP or PVP. CONCLUSION Vertebral augmentation is effective for treating OVCFs with gas- or liquid-filled IVCs. However, in patients with gas-filled IVCs, PKP may be associated with higher incidence of cement leakage and recollapse of treated vertebrae than PVP. Liquid-filled IVCs may not promote bone cement distribution.
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Affiliation(s)
- Jing Tang
- Department of Radiology, Sichuan University West China Hospital, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Jin Liu
- Department of Orthopedics, Chengdu First People's Hospital, Wanxiang North Road, No.18, Chengdu, 610000, China.
| | - Zuchao Gu
- Department of Orthopedics, Chengdu First People's Hospital, Wanxiang North Road, No.18, Chengdu, 610000, China
| | - Bin Song
- Department of Radiology, Sichuan University West China Hospital, Guoxue Xiang, No. 37, Chengdu, 610041, China.
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Dick J, Darras KE, Lexa FJ, Denton E, Ehara S, Galloway H, Jankharia B, Kassing P, Kumamaru KK, Mildenberger P, Morozov S, Pyatigorskaya N, Song B, Sosna J, van Buchem M, Forster BB. An International Survey of Quality and Safety Programs in Radiology. Can Assoc Radiol J 2021; 72:135-141. [PMID: 32066249 DOI: 10.1177/0846537119899195] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE The aim of this study was to determine the status of radiology quality improvement programs in a variety of selected nations worldwide. METHODS A survey was developed by select members of the International Economics Committee of the American College of Radiology on quality programs and was distributed to committee members. Members responded on behalf of their country. The 51-question survey asked about 12 different quality initiatives which were grouped into 4 themes: departments, users, equipment, and outcomes. Respondents reported whether a designated type of quality initiative was used in their country and answered subsequent questions further characterizing it. RESULTS The response rate was 100% and represented Australia, Canada, China, England, France, Germany, India, Israel, Japan, the Netherlands, Russia, and the United States. The most frequently reported quality initiatives were imaging appropriateness (91.7%) and disease registries (91.7%), followed by key performance indicators (83.3%) and morbidity and mortality rounds (83.3%). Peer review, equipment accreditation, radiation dose monitoring, and structured reporting were reported by 75.0% of respondents, followed by 58.3% of respondents for quality audits and critical incident reporting. The least frequently reported initiatives included Lean/Kaizen exercises and physician performance assessments, implemented by 25.0% of respondents. CONCLUSION There is considerable diversity in the quality programs used throughout the world, despite some influence by national and international organizations, from whom further guidance could increase uniformity and optimize patient care in radiology.
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Affiliation(s)
- Jeremy Dick
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Kathryn E Darras
- University of British Columbia, Vancouver, British Columbia, Canada
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Frank J Lexa
- Department of Medical Imaging, 12216University of Arizona College of Medicine, Tucson, AZ, USA
- The Radiology Leadership Institute and Commission on Leadership and Practice Development, 72672American College of Radiology, Tucson, AZ, USA
| | - Erika Denton
- Norfolk & Norwich University Hospital, Norwich, Norfolk, United Kingdom
| | - Shigeru Ehara
- Department of Radiology, Tohoku Medical and Pharmaceutical University, Sendai, Tohoku, Japan
| | | | | | - Pam Kassing
- 72672American College of Radiology, Reston, VA, USA
| | | | - Peter Mildenberger
- Department of Radiology, 9182University Medical Center Mainz, Mainz, Germany
| | | | - Nadya Pyatigorskaya
- Department of Neuroradiology, 27063Sorbonne University, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Bin Song
- West China Hospital, 12530Sichuan University, Chengdu, Sichuan, China
| | - Jacob Sosna
- Department of Radiology, 58884Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Marcus van Buchem
- Department of Radiology, 4501Leiden University Medical Center, Leiden, the Netherlands
| | - Bruce B Forster
- University of British Columbia, Vancouver, British Columbia, Canada
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Di D, Shi F, Yan F, Xia L, Mo Z, Ding Z, Shan F, Song B, Li S, Wei Y, Shao Y, Han M, Gao Y, Sui H, Gao Y, Shen D. Hypergraph learning for identification of COVID-19 with CT imaging. Med Image Anal 2021; 68:101910. [PMID: 33285483 PMCID: PMC7690277 DOI: 10.1016/j.media.2020.101910] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 10/25/2020] [Accepted: 11/13/2020] [Indexed: 02/08/2023]
Abstract
The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.
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Affiliation(s)
- Donglin Di
- BNRist, THUIBCS, KLISS, School of Software, Tsinghua University, Beijing, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Zhongxiang Ding
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Zhejiang, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Bin Song
- Department of Radiology, Sichuan University West China Hospital, Chengdu, Sichuan Province, China
| | - Shengrui Li
- BNRist, THUIBCS, KLISS, School of Software, Tsinghua University, Beijing, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Ying Shao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Miaofei Han
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yaozong Gao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - He Sui
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yue Gao
- BNRist, THUIBCS, KLISS, School of Software, Tsinghua University, Beijing, China.
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.
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Yang CW, Liu XJ, Wei Y, Wan S, Ye Z, Yao S, Zeng N, Cheng Y, Song B. Use of computed tomography for distinguishing heterotopic pancreas from gastrointestinal stromal tumor and leiomyoma. Abdom Radiol (NY) 2021; 46:168-178. [PMID: 32613400 DOI: 10.1007/s00261-020-02631-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 06/15/2020] [Accepted: 06/23/2020] [Indexed: 02/05/2023]
Abstract
PURPOSE To determine whether morphologic features and semiquantitative parameters of computed tomography (CT) could be used to distinguish heterotopic pancreas from gastrointestinal stromal tumor (GIST) and leiomyoma. METHODS This retrospective study evaluated CT images of heterotopic pancreases (n = 28), GISTs (n = 57), and leiomyomas (n = 26) located in the upper gastrointestinal tract. Morphologic imaging features of lesions were analyzed, including location, contour, margin, attenuation, growth pattern, enhancement type, enhancement degree, enlarged vessels feeding or draining the mass, hyperenhancement of the overlying mucosa, low intralesional attenuation, calcification, and a duct-like structure. Semiquantitative parameters included long diameter (LD), short diameter (SD), LD/SD ratio, and lesion and aorta CT values during plain CT (Lp and Ap), arterial phase (La and Aa), and venous phase (Lv and Av). Diagnostic performance of these findings and parameters were evaluated by receiver operating characteristic (ROC) analysis. RESULTS Morphologic CT findings (including lesion contour, margin, attenuation, growth pattern, enhancement type, and enhancement degree) and semiquantitative parameters except for LD/SD were demonstrated to be significant for differentiating heterotopic pancreas from GIST and leiomyoma (all P < 0.01). Of these, location, low intralesional attenuation, duct-like structure and LD, SD, Lv, and Sv values showed good diagnostic performance with the areas under curve (AUC) higher than 0.70. The presence of a duct-like structure demonstrated the best diagnostic ability with AUC of 0.929 [95% confidence interval (CI) 0.864-0.969], sensitivity of 5.7% (95% CI 67.3-96.0), and specificity of 100% (95% CI 95.7-100), respectively. When the three morphologic features (location, low intralesional attenuation, duct-like structure) were used in combination, the AUC was improved to 0.980 (95% CI 0.952-1). CONCLUSION CT features, especially the morphologic features, could be used to differentiate heterotopic pancreas from GIST and leiomyoma in the upper gastrointestinal tract and, thus, provide a more accurate method for non-invasive preoperative diagnosis. Additionally, the presence of a duct-like structure demonstrated to be a reliable indicator for heterotopic pancreas among the morphologic and semiquantitative CT features.
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Affiliation(s)
- Cai-Wei Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Xi-Jiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Ni Zeng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Yue Cheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China.
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Tang A, Abukasm K, Moura Cunha G, Song B, Wang J, Wagner M, Dietrich CF, Brancatelli G, Ueda K, Choi JY, Aguirre D, Sirlin CB. Imaging of hepatocellular carcinoma: a pilot international survey. Abdom Radiol (NY) 2021; 46:205-215. [PMID: 32488557 DOI: 10.1007/s00261-020-02598-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
PURPOSE To perform an international survey on current practices in imaging-based surveillance, diagnosis, staging, and assessment of treatment response for HCC. MATERIALS AND METHODS Three themes were covered in this international survey: demographics of respondents and liver imaging expertise; imaging practices for screening, surveillance, diagnosis, staging, and assessment of treatment response for HCC; and diagnostic imaging systems used. Descriptive summaries were created. RESULTS Of 151 respondents, 22.5% were from Asia, 6.0% from Europe, 19.9% from North America, 26.5% from South America, and 25.2% from Australasia; 57.0% respondents worked in academic and 34.4% in private or mixed settings. Non-contrast ultrasound was most commonly used for screening and surveillance of HCC (90.7%), and multiphase computed tomography was used for diagnosis (96.0%). Extracellular contrast agents (69.5%) were the most commonly used MRI contrast agents and Lumason/SonoVue (31.1%) is the most commonly used contrast-enhanced ultrasound contrast agent. A majority (94.0%) of respondents use ancillary imaging features for assessment of liver lesions in at-risk patients. Usage of diagnostic imaging systems for HCC varied by region. RECIST or mRECIST criteria were most commonly used for assessing HCC treatment response (48.3%). Most respondents agreed that a standardized classification for the diagnosis of HCC is needed (68.9%) and that an atlas and lexicon would help improve inter-reader agreement (71.5%). CONCLUSION Practices and recommendations for imaging of HCC vary between geographical regions. Future efforts to develop a unified system should address regional differences and potential barriers for adoption of a standardized diagnostic system for HCC.
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Affiliation(s)
- An Tang
- Department of Radiology, Radio-Oncology and Nuclear Medicine, Centre Hospitalier de L'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, Québec, H2X 3J4, Canada.
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montréal, Québec, Canada.
| | - Karma Abukasm
- Department of Radiology, Radio-Oncology and Nuclear Medicine, Centre Hospitalier de L'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, Québec, H2X 3J4, Canada
| | - Guilherme Moura Cunha
- Clínica de Diagnóstico Por Imagem (CDPI) - DASA, Rio de Janeiro, Brazil
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jin Wang
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Mathilde Wagner
- Department of Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France
| | | | - Giuseppe Brancatelli
- Dipartimento Di Biomedicina, Neuroscienze E Diagnostica Avanzata (BIND), University Hospital of Palermo, Palermo, Italy
| | - Kazuhiko Ueda
- Department of Diagnostic Radiology, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | | | | | - Claude B Sirlin
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
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Zhu X, Song B, Shi F, Chen Y, Hu R, Gan J, Zhang W, Li M, Wang L, Gao Y, Shan F, Shen D. Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan. Med Image Anal 2021; 67:101824. [PMID: 33091741 PMCID: PMC7547024 DOI: 10.1016/j.media.2020.101824] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 08/23/2020] [Accepted: 09/25/2020] [Indexed: 02/08/2023]
Abstract
With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.
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Affiliation(s)
- Xiaofeng Zhu
- Center for Future Media and school of computer science and technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Bin Song
- Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, China.
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yanbo Chen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Rongyao Hu
- Center for Future Media and school of computer science and technology, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Natural and Computational Sciences, Massey University Auckland, Auckland 0745, New Zealand
| | - Jiangzhang Gan
- Center for Future Media and school of computer science and technology, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Natural and Computational Sciences, Massey University Auckland, Auckland 0745, New Zealand
| | - Wenhai Zhang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Man Li
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Liye Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yaozong Gao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China.
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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Wang Y, Liu K, Xie X, Song B. Contrast-associated acute kidney injury: An update of risk factors, risk factor scores, and preventive measures. Clin Imaging 2021; 69:354-362. [PMID: 33069061 DOI: 10.1016/j.clinimag.2020.10.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 09/03/2020] [Accepted: 10/01/2020] [Indexed: 02/05/2023]
Abstract
As lifespans lengthen, age-related diseases such as cardiovascular disease and diabetes are becoming more prevalent. Correspondingly, the use of contrast agents for medical imaging is also becoming more common, and there is increasing awareness of contrast-associated acute kidney injury (CA-AKI). There is no specific treatment for CA-AKI, and clinicians currently focus on prevention, interventions that alter its pathogenesis, and identification of risk factors. Although the incidence of CA-AKI is low in the general population, the risk of CA-AKI can reach 20% to 30% in patients with multiple risk factors. Many models have been applied in the clinic to assess the risk factors for CA-AKI, enable identification of high-risk groups, and improve clinical management. Hypotonic or isotonic contrast media are recommended to prevent CA-AKI in high-risk patients. Patients with risk factors should avoid using contrast media multiple times within a short period of time. All nephrotoxic drugs should be stopped at least 24 h before the administration of contrast media in high-risk populations, and adequate hydration is recommended for all patients. This review summarizes the pathophysiology of CA-AKI and the progress in diagnosis and differential diagnosis; updates the risk factors and risk factor scoring systems; reviews the latest advances related to prevention and treatment; discusses current problems in epidemiological studies; and highlights the importance of identifying high-risk subjects to control modifiable risk factors and use of a rating scale to estimate the risk and implement appropriate prevention strategies.
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Affiliation(s)
- Yi Wang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Kaixiang Liu
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Scienceand Technology of China, Chengdu, China; Department of Nephrology, The Second Clinical Medical Institution of North Sichuan Medical College (Nanchong Central Hospital), Nanchong, China
| | - Xisheng Xie
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Scienceand Technology of China, Chengdu, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
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Zhang T, Wei Y, He X, Yuan Y, Yuan F, Ye Z, Li X, Tang H, Song B. Prediction of Remnant Liver Regeneration after Right Hepatectomy in Patients with Hepatocellular Carcinoma Using Preoperative CT Texture Analysis and Clinical Features. Contrast Media Mol Imaging 2021; 2021:5572470. [PMID: 34220379 PMCID: PMC8213498 DOI: 10.1155/2021/5572470] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 05/28/2021] [Accepted: 06/02/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To predict the regenerative rate of liver in patients with HCCs after right hepatectomy using texture analysis on preoperative CT combined with clinical features. MATERIALS AND METHODS 88 patients with 90 HCCs who underwent right hepatectomy were retrospectively included. The future remnant liver was semiautomatically segmented, and the volume of future remnant liver on preoperative CT (LVpre) and the volume of remnant liver on following-up CT (LVfu) were measured. We calculated the regeneration index (RI) by the following equation: (LVfu - LVpre)/LVpre) × 100 (%). The support vector machine recursive method was used for the feature selection. The Naive Bayes classifier was used to predict liver RI, and 5-fold cross-validation was performed to adjust the parameters. Sensitivity, specificity, and accuracy were calculated to evaluate the diagnostic efficiency of the model. RESULTS The mean RI was 142.99 ± 92.17%. Of all clinical parameters and texture features, the AST, ALB, PT-INR, Perc.10%, and S(5, -5)Correlat were found to be statistically significant with RI. The diagnostic sensitivity, specificity, and accuracy of the model in the training group were 0.902, 0.634, and 0.768, and the AUC value of the obtained model was 0.841. In the test group, the sensitivity, specificity, and accuracy of the model were 1.0, 0.429, and 0.778, respectively, and the AUC value was 0.844. CONCLUSION The use of texture analysis on preoperative CT combined with clinical features can be helpful in predicting the liver regeneration rate in patients with HCCs after right hepatectomy.
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Affiliation(s)
- Tong Zhang
- 1Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- 1Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
| | - Xiaopeng He
- 2Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China
| | - Yuan Yuan
- 1Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
| | - Fang Yuan
- 1Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- 1Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
| | - Xin Li
- 3GE Healthcare Research, Nanjing 210000, China
| | - Hehan Tang
- 1Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- 1Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
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Wei H, Song B. Elastography for Longitudinal Assessment of Liver Fibrosis after Antiviral Therapy: A Review. J Clin Transl Hepatol 2020; 8:445-453. [PMID: 33447528 PMCID: PMC7782123 DOI: 10.14218/jcth.2020.00033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 07/23/2020] [Accepted: 08/10/2020] [Indexed: 02/05/2023] Open
Abstract
Chronic hepatitis B or C viral infection is a common cause of liver cirrhosis and hepatocellular carcinoma. Fibrosis regression can be achieved after long-term antiviral therapy (AVT). Monitoring of dynamic changes in liver fibrosis after treatment is essential for establishing prognosis and formulation of a follow-up surveillance program. Routine surveillance of fibrosis after AVT by liver biopsy, the gold standard for fibrosis assessment, is hindered by its invasive nature, sampling error and observer variability. Elastography is a noninvasive quantitative alternative that has been widely used and validated for the staging of liver fibrosis prior to treatment. Recently, increasing research interest has been focused on the role of elastography in longitudinal assessment of liver fibrosis after AVT. In this review, the basic principles, acquisition techniques, diagnostic performances, and strengths and limitations of ultrasound elastography and magnetic resonance elastography are presented. Emerging evidence regarding the use of elastography techniques for the monitoring of liver fibrosis after AVT is summarized. Current challenges and future directions are also discussed, designed to optimize the application of these techniques in clinical practice.
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Affiliation(s)
- Hong Wei
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Correspondence to: Bin Song, Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, Sichuan 610041, China. Tel: +86-28-85423680, Fax: +86-28-85582499, E-mail:
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Zhang J, Wu Z, Zhang X, Liu S, Zhao J, Yuan F, Shi Y, Song B. Machine learning: an approach to preoperatively predict PD-1/PD-L1 expression and outcome in intrahepatic cholangiocarcinoma using MRI biomarkers. ESMO Open 2020; 5:e000910. [PMID: 33239315 PMCID: PMC7689588 DOI: 10.1136/esmoopen-2020-000910] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/29/2020] [Accepted: 10/13/2020] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE To investigate the preoperative predictive value of non-invasive imaging biomarkers for programmed cell death protein 1/programmed cell death protein ligand 1 (PD-1/PD-L1) expression and outcome in intrahepatic cholangiocarcinoma (ICC) using machine learning. METHODS PD-1/PD-L1 expression in 98 ICC patients was assessed by immunohistochemistry, and their prognostic effects were analysed using Cox regression and Kaplan-Meier analysis. Radiomic features were extracted from MRI in the arterial and portal vein phases, and three sets of Radiomics score (Radscore) with good performance were derived respectively as biomarkers for predicting PD-1, PD-L1 expression and overall survival (OS). PD-1 and PD-L1 expression models were developed using the Radscore (arterial phase), clinico-radiological factors and clinical factors, individually and in combination. The imaging-based OS predictive model was constructed by combining independent predictors among clinico-radiological, clinical factors and OS Radscore. Pathology-based OS model using pathological and clinical factors was also constructed and compared with imaging-based OS model. RESULTS The highest area under the curves of the models predicting PD-1 and PD-L1 expression was 0.897 and 0.890, respectively. PD-1+ and PD-L1+ cases had worse outcomes than negative cases. The 5-year survival rates of PD-1+ and PD-1- cases were 12.5% and 48.3%, respectively (p<0.05), whereas the 5-year survival was 21.9% and 39.4% for PD-L1+ and PD-L1- cases, respectively (p<0.05). The imaging-based OS model involved predictors of clinico-radiological 'imaging classification', radiomics 'Radscore' from arterial phase and carcinoembryonic antigen (CEA) level (C-index:0.721). It performed better than pathology-based model (C-index: 0.698) constructed by PD-1/PD-L1 expression status and CEA level. The imaging-based OS model is potential for practice when the pathology assay is unavailable and could divide ICC patients into high-risk and low-risk groups, with 1-year, 3-year and 5-year survival rates of 57.1%, 14.3% and 12.4%, and 87.8%, 63.3% and 55.3%, respectively (p<0.001). CONCLUSIONS MRI radiomics could derive promising and non-invasive biomarker in evaluating PD-1/PD-L1 expression and prognosis of ICC patients.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Zhenru Wu
- Department of Pathology, Sichuan University West China Hospital, Chengdu, China
| | - Xin Zhang
- Pharmaceutical Diagnostic Team, GE Healthcare, Life Sciences, Chengdu, China
| | - Siyun Liu
- Pharmaceutical Diagnostic Team, GE Healthcare, Life Sciences, Chengdu, China
| | - Jian Zhao
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Fang Yuan
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Yujun Shi
- Department of Pathology, Sichuan University West China Hospital, Chengdu, China.
| | - Bin Song
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China.
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Wei H, Jiang H, Liu X, Qin Y, Zheng T, Liu S, Zhang X, Song B. Can LI-RADS imaging features at gadoxetic acid-enhanced MRI predict aggressive features on pathology of single hepatocellular carcinoma? Eur J Radiol 2020; 132:109312. [PMID: 33022551 DOI: 10.1016/j.ejrad.2020.109312] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/16/2020] [Accepted: 09/24/2020] [Indexed: 02/05/2023]
Abstract
PURPOSE To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features at preoperative gadoxetic acid-enhanced MRI can predict microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) and to evaluate their associations with recurrence after curative resection of single HCC. MATERIALS AND METHODS From July 2015 to September 2018, 111 consecutive patients with pathologically confirmed HCC who underwent gadoxetic acid-enhanced MRI within 1 month before surgery were included in this retrospective study. Significant MRI findings and clinical parameters for predicting MVI, high-grade HCCs and postoperative recurrence were identified by logistic regression model and Cox proportional hazards model. RESULTS Twenty-six of 111 (23.4 %) patients had MVI and 36 of 111 (32.4 %) patients had high-grade HCCs, whereas 44 of 95 (46.3 %) patients experienced recurrence. Tumor size > 5 cm (OR = 9.852; p < 0.001) and absence of nodule-in-nodule architecture (OR = 8.302; p = 0.001) were independent predictors of MVI. Enhancing capsule (OR = 4.396; p = 0.004) and corona enhancement (OR = 3.765; p = 0.021) were independent predictors of high-grade HCCs. Blood products in mass (HR = 2.275; p = 0.009), corona enhancement (HR = 4.332; p < 0.001), and serum AFP level > 400 ng/mL (HR = 2.071; p = 0.023) were independent predictors of recurrence. CONCLUSION LI-RADS imaging features can be used as potential biomarkers for predicting aggressive pathologic features and recurrence of HCC. The identification of prognostic LI-RADS imaging features may facilitate the selection of surgical candidates and optimize the management of HCC patients.
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Affiliation(s)
- Hong Wei
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Xijiao Liu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Yun Qin
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Tianying Zheng
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | | | | | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
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Affiliation(s)
- Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Abstract
RATIONALE Mycobacterium tuberculosis (TB) remains a serious threat in developing countries. Primary isolated hepatic tuberculosis is extremely rare. Because of its non-specific imaging features, noninvasive preoperative imaging diagnosis of isolated hepatic tuberculoma remains challenging. PATIENT CONCERNS A 48-year-old man was admitted to our hospital due for suspected liver neoplasm during health examination. DIAGNOSES The tests for blood, liver function, and tumor markers were within normal range. Preoperative ultrasonography (US) showed a hypoechoic lesion with a longitudinal diameter of 2.5 cm in segment six of liver. It exhibited early arterial phase hyperenhancement and late arterial phase rapid washout in contrast-enhanced US. It demonstrated hyperintensity in T2-weighted magnetic resonance imaging and partly restricted diffusion in diffusion-weighted imaging. For this nodule, the preoperative diagnosis was small hepatocellular carcinoma (HCC). INTERVENTIONS Laparoscopic hepatectomy was performed. Intraoperative extensive adhesion in the abdominal cavity and liver was found. The lesion had undergone expansive growth. OUTCOMES Microscopically, a granuloma with some necrosis was detected. With both acid-fast staining and TB fragment polymerase chain reaction showing positive results, TB was the final histology diagnosis. After surgery, the patient declined any anti-TB medication. During the follow-up, he had no symptoms. In the sixth month after surgery, he underwent an upper abdominal US. It showed no lesions in the liver. LESSONS Because of non-specific imaging findings and non-specific symptoms, a diagnosis of isolated hepatic TB is difficult to make, especially for small lesions. A diagnosis of HCC should be made cautiously when small isolated lesions in the liver are encountered, especially in patients without a history of hepatitis and with negative tumor markers.
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Affiliation(s)
| | | | | | | | - Fei Liu
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Sun L, Mo Z, Yan F, Xia L, Shan F, Ding Z, Song B, Gao W, Shao W, Shi F, Yuan H, Jiang H, Wu D, Wei Y, Gao Y, Sui H, Zhang D, Shen D. Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT. IEEE J Biomed Health Inform 2020; 24:2798-2805. [PMID: 32845849 PMCID: PMC8545164 DOI: 10.1109/jbhi.2020.3019505] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/13/2020] [Accepted: 08/22/2020] [Indexed: 02/05/2023]
Abstract
Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.
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Affiliation(s)
- Liang Sun
- College of Computer Science and Technology, MIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing University of Aeronautics and AstronauticsNanjing211106China
| | - Zhanhao Mo
- Department of RadiologyChina-Japan Union Hospital of Jilin UniversityChangchun130021China
| | - Fuhua Yan
- Department of RadiologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghai200240China
| | - Liming Xia
- Department of RadiologyTongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Fei Shan
- Department of RadiologyShanghai Public Health Clinical CenterFudan UniversityShanghai200433China
| | - Zhongxiang Ding
- Department of RadiologyHangzhou First Peoples HospitalZhejiang University School of MedicineHangzhou310027China
| | - Bin Song
- Department of RadiologySichuan University West China HospitalChengdu610041China
| | - Wanchun Gao
- Department of RadiologyQianjiang Central HospitalJishou University School of MedicineChongqing409000China
| | - Wei Shao
- College of Computer Science and Technology, MIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing University of Aeronautics and AstronauticsNanjing211106China
| | - Feng Shi
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai201399China
| | - Huan Yuan
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai201399China
| | - Huiting Jiang
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai201399China
| | - Dijia Wu
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai201399China
| | - Ying Wei
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai201399China
| | - Yaozong Gao
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai201399China
| | - He Sui
- Department of RadiologyChina-Japan Union Hospital of Jilin UniversityChangchun130021China
| | - Daoqiang Zhang
- College of Computer Science and Technology, MIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing University of Aeronautics and AstronauticsNanjing211106China
| | - Dinggang Shen
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai201399China
- Department of Brain and Cognitive EngineeringKorea UniversitySeoul02841Republic of Korea
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Yue Y, Li M, Zhang X, Yu H, Song B. Prediction of clinically relevant pancreatic fistula after pancreatic surgery using preoperative CT scan: A systematic review and meta-analysis. Pancreatology 2020; 20:1558-1565. [PMID: 32972835 DOI: 10.1016/j.pan.2020.09.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 08/05/2020] [Accepted: 09/09/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND Body composition analysis has emerged as a practical tool for predicting outcomes following pancreatic surgery. However, the impact of body composition disorders on clinically relevant postoperative pancreatic fistula (CR-POPF) remains inconclusive. The aim of this study was to review and analyse whether radiographically assessed body composition is predictive of CR-POPF. METHODS PubMed, MEDLINE, Web of Science, and the Cochrane Library databases were searched up to January 2020 to identify relevant studies. CR-POPF was defined according to the definition and grading system proposed by the International Study Group on Pancreatic Surgery (ISGPS). Pooled odds ratios (OR) for CR-POPF were calculated to evaluate the predictive values of radiographically assessed body composition. RESULTS Fifteen studies published between 2008 and 2019 with a total of 3136 patients were included. There was a significant increase in the incidence of CR-POPF in patients with visceral obesity (OR 2.97, 95% CI 2.05-4.29, P < 0.00001) and sarcopenic obesity (OR 2.88, 95% CI 1.31-6.34, P = 0.009). Conversely, the impact of sarcopenia (OR 0.91, 95% CI 0.65-1.28, P = 0.59) and low muscle attenuation (MA) on CR-POPF did not reach statistical significance. CONCLUSION Preoperative visceral obesity and sarcopenic obesity are more effective at predicting CR-POPF than decreased muscle quantity and quality. This finding may lead to appropriate management and early intervention of patients at risk of CR-POPF.
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Affiliation(s)
- Yufeng Yue
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Mou Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Xubing Zhang
- Department of General Surgery, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Haopeng Yu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
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Li M, Zhu YZ, Zhang YC, Yue YF, Yu HP, Song B. Radiomics of rectal cancer for predicting distant metastasis and overall survival. World J Gastroenterol 2020; 26:5008-5021. [PMID: 32952346 PMCID: PMC7476170 DOI: 10.3748/wjg.v26.i33.5008] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/16/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Rectal cancer (RC) patient stratification by different factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification, personalized treatment, and prognostication of RC patients.
AIM To build a novel model for predicting the presence of distant metastases and 3-year overall survival (OS) in RC patients.
METHODS This was a retrospective analysis of 148 patients (76 males and 72 females) with RC treated with curative resection, without neoadjuvant or postoperative chemoradiotherapy, between October 2012 and December 2015. These patients were allocated to a training or validation set, with a ratio of 7:3. Radiomic features were extracted from portal venous phase computed tomography (CT) images of RC. The least absolute shrinkage and selection operator regression analysis was used for feature selection. Multivariate logistic regression analysis was used to develop the radiomics signature (Rad-score) and the clinicoradiologic risk model (the combined model). Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of the models for predicting distant metastasis of RC. The association of the combined model with 3-year OS was investigated by Kaplan-Meier survival analysis.
RESULTS A total of 51 (34.5%) patients had distant metastases, while 26 (17.6%) patients died, and 122 (82.4%) patients lived at least 3 years post-surgery. The values of both the Rad-score (consisted of three selected features) and the combined model were significantly different between the distant metastasis group and the non-metastasis group (0.46 ± 0.21 vs 0.32 ± 0.24 for the Rad-score, and 0.60 ± 0.23 vs 0.28 ± 0.26 for the combined model; P < 0.001 for both models). Predictors contained in the combined model included the Rad-score, pathological N-stage, and T-stage. The addition of histologic grade to the model failed to show incremental prognostic value. The combined model showed good discrimination, with areas under the curve of 0.842 and 0.802 for the training set and validation set, respectively. For the survival analysis, the combined model was associated with an improved OS in the whole cohort and the respective subgroups.
CONCLUSION This study presents a clinicoradiologic risk model, visualized in a nomogram, that can be used to facilitate individualized prediction of distant metastasis and 3-year OS in patients with RC.
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Affiliation(s)
- Mou Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yu-Zhou Zhu
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yong-Chang Zhang
- Department of Radiology, Chengdu Seventh People’s Hospital, Chengdu 610213, Sichuan Province, China
| | - Yu-Feng Yue
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hao-Peng Yu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
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Zhao J, Zhang W, Zhang J, Zhang Y, Ma WJ, Liu SY, Li FY, Song B. Survival analysis of patients with stage T2a and T2b perihilar cholangiocarcinoma treated with radical resection. BMC Cancer 2020; 20:849. [PMID: 32883228 PMCID: PMC7650292 DOI: 10.1186/s12885-020-07357-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 08/27/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Both the 7th and 8th editions of the American Joint Committee on Cancer (AJCC) staging system for perihilar cholangiocarcinoma (pCCA) had the same definition for T2a and T2b. But the value of this classification as prognostic factor remains unclear. METHODS 178 patients with stage T2a or T2b who underwent curative intent resection for pCCA between Jan 2010 and Dec 2018 were enrolled. Relationships between survival and clinicopathological factors including patient demographics and tumor characteristics were evaluated using univariate and multivariate Cox regression analysis. The overall survival (OS) were calculated by Kaplan-Meier method. RESULTS There was no significant difference in OS between T2a and T2b groups, and the median OS duration were 37 and 31 months (P = 0.354). Both the 7th and 8th edition of the AJCC TNM staging demonstrated a poor prognostic predictive performance. High level of preoperative AST (≥85.0 IU/L) and CA19-9 (≥1000 U/mL), vascular resection and lower pathological differentiation of the tumor were the independent predictors for poor survival after resection. CONCLUSION The newly released 8th edition of AJCC staging system demonstrated a poor ability to discriminate the prognosis of patients with stage T2a and T2b pCCA after resection.
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Affiliation(s)
- Jian Zhao
- Department of Radiology, West China Hospital, Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, 610041, Sichuan Province, P.R. China
- Department of Radiology, Armed Police Force Hospital of Sichuan, 614000, Leshan, Sichuan, P.R. China
| | - Wei Zhang
- Department of Radiology, West China Hospital, Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, 610041, Sichuan Province, P.R. China
- Department of Radiology, Armed Police Force Hospital of Sichuan, 614000, Leshan, Sichuan, P.R. China
| | - Jun Zhang
- Department of Radiology, West China Hospital, Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, 610041, Sichuan Province, P.R. China
| | - Yi Zhang
- Department of Radiology, Armed Police Force Hospital of Sichuan, 614000, Leshan, Sichuan, P.R. China
| | - Wen-Jie Ma
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, P.R. China
| | - Si-Yun Liu
- GE healthcare (China), Beijing, 100176, P.R. China
| | - Fu-Yu Li
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, P.R. China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, 610041, Sichuan Province, P.R. China.
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Wei J, Jiang H, Gu D, Niu M, Fu F, Han Y, Song B, Tian J. Radiomics in liver diseases: Current progress and future opportunities. Liver Int 2020; 40:2050-2063. [PMID: 32515148 PMCID: PMC7496410 DOI: 10.1111/liv.14555] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/28/2020] [Accepted: 05/29/2020] [Indexed: 02/05/2023]
Abstract
Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Hanyu Jiang
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Dongsheng Gu
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Meng Niu
- Department of Interventional RadiologyThe First Affiliated Hospital of China Medical UniversityShenyangChina
| | - Fangfang Fu
- Department of Medical ImagingHenan Provincial People’s HospitalZhengzhouHenanChina
- Department of Medical ImagingPeople’s Hospital of Zhengzhou University. ZhengzhouHenanChina
| | - Yuqi Han
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Bin Song
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Jie Tian
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
- Beijing Advanced Innovation Center for Big Data‐Based Precision MedicineSchool of MedicineBeihang UniversityBeijingChina
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of EducationSchool of Life Science and TechnologyXidian UniversityXi’anShaanxiChina
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Affiliation(s)
- Zheng Ye
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Bin Song
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
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Ouyang X, Huo J, Xia L, Shan F, Liu J, Mo Z, Yan F, Ding Z, Yang Q, Song B, Shi F, Yuan H, Wei Y, Cao X, Gao Y, Wu D, Wang Q, Shen D. Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia. IEEE Trans Med Imaging 2020; 39:2595-2605. [PMID: 32730212 DOI: 10.1109/tmi.2020.2995508] [Citation(s) in RCA: 175] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.
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