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Zheng T, Qu Y, Chen J, Yang J, Yan H, Jiang H, Song B. Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers. Abdom Radiol (NY) 2024:10.1007/s00261-024-04225-8. [PMID: 38372765 DOI: 10.1007/s00261-024-04225-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/20/2024]
Abstract
A diagnosis of cirrhosis initiates a shift in the management of chronic liver disease and affects the diagnostic workflow and treatment decision of primary liver cancer. Liver biopsy remains the gold standard for cirrhosis diagnosis, but it is invasive and susceptible to sampling bias and observer variability. Various qualitative and quantitative imaging biomarkers based on ultrasound, CT and MRI have been proposed for noninvasive diagnosis of cirrhosis. Qualitative imaging features are easy to apply but have moderate diagnostic sensitivity. Elastography techniques allow quantitative assessment of liver stiffness and are highly accurate for cirrhosis diagnosis. Ultrasound elastography are widely used in clinical practice, while MR elastography has narrower availability. Although not applicable in clinical practice yet, other quantitative imaging features, including liver surface nodularity, linear and volumetric measurement, extracellular volume fraction, liver enhancement on hepatobiliary phase, and parameters derived from diffusion-weighted imaging, can provide additional information of liver morphology, perfusion, and function, thus may increase diagnosis performance. The introduction of radiomics and deep learning has further improved diagnostic accuracy while reducing subjectivity. Several imaging features may also help to assess liver function and outcomes in patients with cirrhosis. In this review, we summarize the qualitative and quantitative imaging biomarkers for noninvasive cirrhosis diagnosis, and the assessment of liver function and outcomes, and discuss the challenges and future directions in this field.
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Affiliation(s)
- Tianying Zheng
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China
| | - Yali Qu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China
| | - Jie Yang
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hualin Yan
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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Singh S, Mohajer B, Wells SA, Garg T, Hanneman K, Takahashi T, AlDandan O, McBee MP, Jawahar A. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research. Acad Radiol 2024:S1076-6332(24)00024-2. [PMID: 38286723 DOI: 10.1016/j.acra.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024]
Abstract
Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.
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Affiliation(s)
- Shiva Singh
- Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland (S.S.)
| | - Bahram Mohajer
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland (B.M., T.G.)
| | - Shane A Wells
- Radiology, University of Michigan, Ann Arbor, Michigan (S.W.)
| | - Tushar Garg
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland (B.M., T.G.)
| | - Kate Hanneman
- Medical Imaging, University of Toronto, Toronto, ON, Canada (K.H.)
| | | | - Omran AlDandan
- Radiology, King Fahd Hospital of the University, Al Khobar, Saudi Arabia (O.A.)
| | - Morgan P McBee
- Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina (M.P.M.)
| | - Anugayathri Jawahar
- Radiology, Northwestern University-Feinberg School of Medicine, 800, Arkes Pavilion, 676 N St. Clair St, Chicago, IL 60611 (A.J.).
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Qazi Arisar FA, Salinas-Miranda E, Ale Ali H, Lajkosz K, Chen C, Azhie A, Healy GM, Deniffel D, Haider MA, Bhat M. Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study. Transpl Int 2023; 36:11149. [PMID: 37720416 PMCID: PMC10503435 DOI: 10.3389/ti.2023.11149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 08/09/2023] [Indexed: 09/19/2023]
Abstract
Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag patients at risk of developing future graft fibrosis. CT scans of 254 patients at 3-6 months post-liver transplant were retrospectively analyzed. Volumetric radiomic features were extracted from the portal phase using an Artificial Intelligence-based tool (PyRadiomics). The primary endpoint was clinically significant (≥F2) graft fibrosis. A 10-fold cross-validated LASSO model using clinical and radiomic features was developed. In total, 75 patients (29.5%) developed ≥F2 fibrosis by a median of 19 (4.3-121.8) months. The maximum liver attenuation at the venous phase (a radiomic feature reflecting venous perfusion), primary etiology, donor/recipient age, recurrence of disease, brain-dead donor, tacrolimus use at 3 months, and APRI score at 3 months were predictive of ≥F2 fibrosis. The combination of radiomics and the clinical features increased the AUC to 0.811 from 0.793 for the clinical-only model (p = 0.008) and from 0.664 for the radiomics-only model (p < 0.001) to predict future ≥F2 fibrosis. This pilot study exploring the role of radiomics demonstrates that the addition of radiomic features in a clinical model increased the model's performance. Further studies are required to investigate the generalizability of this experimental tool.
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Affiliation(s)
- Fakhar Ali Qazi Arisar
- Ajmera Transplant Program, Toronto General Hospital, University Health Network, Toronto, ON, Canada
- Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- National Institute of Liver and GI Diseases, Dow University of Health Sciences, Karachi, Pakistan
| | - Emmanuel Salinas-Miranda
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Hamideh Ale Ali
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Katherine Lajkosz
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Catherine Chen
- Ajmera Transplant Program, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Amirhossein Azhie
- Ajmera Transplant Program, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Gerard M. Healy
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Dominik Deniffel
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Masoom A. Haider
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, Toronto General Hospital, University Health Network, Toronto, ON, Canada
- Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute and Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
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Monti S, Truppa ME, Albanese S, Mancini M. Radiomics and Radiogenomics in Preclinical Imaging on Murine Models: A Narrative Review. J Pers Med 2023; 13:1204. [PMID: 37623455 PMCID: PMC10455673 DOI: 10.3390/jpm13081204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/18/2023] [Accepted: 07/27/2023] [Indexed: 08/26/2023] Open
Abstract
Over the past decade, medical imaging technologies have become increasingly significant in both clinical and preclinical research, leading to a better understanding of disease processes and the development of new diagnostic and theranostic methods. Radiomic and radiogenomic approaches have furthered this progress by exploring the relationship between imaging characteristics, genomic information, and outcomes that qualitative interpretations may have overlooked, offering valuable insights for personalized medicine. Preclinical research allows for a controlled environment where various aspects of a pathology can be replicated in animal models, providing radiomic and radiogenomic approaches with the unique opportunity to investigate the causal connection between imaging and molecular factors. The aim of this review is to present the current state of the art in the application of radiomics and radiogenomics on murine models. This review will provide a brief description of relevant articles found in the literature with a discussion on the implications and potential translational relevance of these findings.
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Affiliation(s)
| | | | - Sandra Albanese
- National Research Council, Institute of Biostructures and Bioimaging, 80145 Naples, Italy; (S.M.); (M.E.T.); (M.M.)
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Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study. Eur Radiol 2023; 33:1653-1667. [PMID: 36149481 DOI: 10.1007/s00330-022-09137-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/05/2022] [Accepted: 09/01/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To investigate the value of R2* mapping-based radiomics nomograms in staging liver fibrosis in patients with chronic hepatitis B. METHODS Between January 2020 and December 2020, 151 patients with chronic hepatitis B were randomly divided into training (n = 103) and validation (n = 48) cohorts. From January to February 2021, 58 patients were included in a test cohort. Radiomics features were selected using the interclass correlation coefficient and least absolute shrinkage and selection operator method. Three radiomics nomograms, combining the radiomics score (Radscore) derived from R2* mapping and clinical variables, were used for staging significant and advanced fibrosis, and cirrhosis. Performance of the model was evaluated using the AUC. The utility and clinical benefits were evaluated using the continuous net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS The Radscore calculated by 12 radiomics features and independent factors (laminin and platelet) of advanced fibrosis were used to construct the radiomics nomograms. In the test cohort, the AUCs of the radiomics nomograms for staging significant fibrosis, advanced fibrosis, and cirrhosis were 0.738 (95% confidence interval [CI]: 0.604-0.872), 0.879 (95% CI: 0.779-0.98), and 0.952 (95% CI: 0.878-1), respectively. NRI, IDI, and DCA confirmed that radiomics nomograms demonstrated varying degrees of clinical benefit and improvement for advanced fibrosis and cirrhosis, but not for significant fibrosis. CONCLUSIONS Radiomics nomograms combined with R2* mapping-based Radscore, laminin, and platelet have value in staging advanced fibrosis and cirrhosis but limited value for staging significant fibrosis. KEY POINTS • Laminin and platelets were independent predictors of advanced fibrosis. • Radiomics analysis based on R2* mapping was beneficial for evaluating advanced fibrosis and cirrhosis. • It was difficult to distinguish significant fibrosis using a radiomics nomogram, which is possibly due to the complex pathological microenvironment of chronic liver diseases.
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Radiomics analysis of contrast-enhanced T1W MRI: predicting the recurrence of acute pancreatitis. Sci Rep 2023; 13:2762. [PMID: 36797285 PMCID: PMC9935887 DOI: 10.1038/s41598-022-13650-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/26/2022] [Indexed: 02/18/2023] Open
Abstract
To investigate the predictive value of radiomics based on T1-weighted contrast-enhanced MRI (CE-MRI) in forecasting the recurrence of acute pancreatitis (AP). A total of 201 patients with first-episode of acute pancreatitis were enrolled retrospectively (140 in the training cohort and 61 in the testing cohort), with 69 and 30 patients who experienced recurrence in each cohort, respectively. Quantitative image feature extraction was obtained from MR contrast-enhanced late arterial-phase images. The optimal radiomics features retained after dimensionality reduction were used to construct the radiomics model through logistic regression analysis, and the clinical characteristics were collected to construct the clinical model. The nomogram model was established by linearly integrating the clinically independent risk factor with the optimal radiomics signature. The five best radiomics features were determined by dimensionality reduction. The radiomics model had a higher area under the receiver operating characteristic curve (AUC) than the clinical model for estimating the recurrence of acute pancreatitis for both the training cohort (0.915 vs. 0.811, p = 0.020) and testing cohort (0.917 vs. 0.681, p = 0.002). The nomogram model showed good performance, with an AUC of 0.943 in the training cohort and 0.906 in the testing cohort. The radiomics model based on CE-MRI showed good performance for optimizing the individualized prediction of recurrent acute pancreatitis, which provides a reference for the prevention and treatment of recurrent pancreatitis.
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Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1147111. [PMID: 36619303 PMCID: PMC9812615 DOI: 10.1155/2022/1147111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 12/29/2022]
Abstract
Diffuse liver diseases are highly prevalent conditions around the world, including pathological liver changes that occur when hepatocytes are damaged and liver function declines, often leading to a chronic condition. In the last years, Magnetic Resonance Imaging (MRI) is reaching an important role in the study of diffuse liver diseases moving from qualitative to quantitative assessment of liver parenchyma. In fact, this can allow noninvasive accurate and standardized assessment of diffuse liver diseases and can represent a concrete alternative to biopsy which represents the current reference standard. MRI approach already tested for other pathologies include diffusion-weighted imaging (DWI) and radiomics, able to quantify different aspects of diffuse liver disease. New emerging MRI quantitative methods include MR elastography (MRE) for the quantification of the hepatic stiffness in cirrhotic patients, dedicated gradient multiecho sequences for the assessment of hepatic fat storage, and iron overload. Thus, the aim of this review is to give an overview of the technical principles and clinical application of new quantitative MRI techniques for the evaluation of diffuse liver disease.
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Gabryś HS, Gote-Schniering J, Brunner M, Bogowicz M, Blüthgen C, Frauenfelder T, Guckenberger M, Maurer B, Tanadini-Lang S. Transferability of radiomic signatures from experimental to human interstitial lung disease. Front Med (Lausanne) 2022; 9:988927. [DOI: 10.3389/fmed.2022.988927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022] Open
Abstract
BackgroundInterstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD.MethodsBleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores.ResultsPredictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model’s performance to AUC = 0.912 ± 0.058.ConclusionRadiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts.
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Zou L, Zhang H, Wang Q, Zhong W, Du Y, Liu H, Xing W. Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model. Magn Reson Imaging 2022; 94:36-42. [PMID: 35988836 DOI: 10.1016/j.mri.2022.08.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/08/2022] [Accepted: 08/14/2022] [Indexed: 11/17/2022]
Abstract
PURPOSE To evaluate the feasibility of simultaneous quantification of liver fibrosis, liver steatosis and abnormal iron deposition using mDixon Quant based on radiomics analysis, and to eliminate the interference among different histopathologic features. METHODS One hundred and twenty rabbits that were administered CCl4 for 4-16 weeks and a cholesterol rich diet for the initial 4 weeks in the experimental group and 20 rabbits in the control group were examined using mDixon. Radiomics features of the whole liver were extracted from PDFF and R2* and radiomics models for discriminating steatosis: S0-S1 vs. S2-S4, fibrosis: F0-F2 vs. F3-F4 and iron deposition: normal vs. abnormal were constructed respectively and evaluated using receiver operating characteristic (ROC) curves with the histopathological results as reference standard. Combined corrected models merging the radscore and the other two histopathologic features were evaluated using multiple logistic regression analyses and compared with radiomics models. RESULTS The area under the ROC curve (AUC) of the radiomics model with PDFF features was 0.886 and 0.843 in the training and the test set, respectively, for the diagnosis of liver steatosis grade S0-1 and S2-S4. The radiomics model based on R2* features were 0.815 and 0.801 for distinguishing F0-F2 and F3-F4 and 0.831 and 0.738 for discriminating abnormal iron deposition in the training and test set, respectively. The corrected model for liver steatosis and fibrosis (0.944 and 0.912 in the test set) outperformed the radiomics models by eliminating the interference of histopathologic features(P < 0.05), but had comparable diagnostic performance for abnormal iron deposition(P > 0.05). CONCLUSIONS It is feasible for mDixon to simultaneously quantify whole liver steatosis, fibrosis and iron deposition based on radiomics analysis. It is valuable to minimize the interference of different pathological features for the assessment of liver steatosis and fibrosis.
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Affiliation(s)
- LiQiu Zou
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - Hao Zhang
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - WenXin Zhong
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - YaNan Du
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - HaiFeng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China.
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Duan YY, Qin J, Qiu WQ, Li SY, Li C, Liu AS, Chen X, Zhang CX. Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram. Clin Radiol 2022; 77:e723-e731. [PMID: 35811157 DOI: 10.1016/j.crad.2022.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 12/18/2022]
Abstract
AIM To investigate the performance of a generative adversarial network (GAN) model for staging liver fibrosis and its radiomics-based nomogram for predicting cirrhosis. MATERIALS AND METHODS This two-centre retrospective study included 434 patients for whom input data of ultrasound images and histopathological data (obtained within 1 month of ultrasound examinations) were assigned to the training cohort (249 patients), the internal cohort (92 patients), and the external (93 patients) cohort. A data augmentation method based on a GAN model was used. The discriminative performance was evaluated for classifying fibrosis of S4 and ≥S3. Deep-learning radiomics features were extracted for the prediction of cirrhosis (S4). To perform feature reduction and selection, the least absolute shrinkage and selection operator (LASSO) algorithm was applied. Radiomics scores, along with clinical factors, were incorporated into a nomogram using multivariable logistic regression analysis. The performance of the models was estimated with respect to discrimination power, calibration, and clinical benefits. RESULTS The areas under the receiver operating characteristic curve (AUCs) values of the GAN were 0.832/0.762 (≥S3), and 0.867/0.835 (S4) for internal/external test sets, respectively. The radiomics nomogram that intergrated radiomics scores and clinical factors showed good calibration and discrimination ability of 0.922 (AUC) in the training dataset, 0.896 in the internal dataset, and 0.861 in the external dataset. Decision curve analysis (DCA) demonstrated that the nomogram outperformed radiologist and haematological indices in terms of the most clinical benefits. CONCLUSIONS The GAN model could be applied to discriminate fibrosis stages, and a favourable predictive accuracy for diagnosing cirrhosis was achieved using a deep-learning radiomics nomogram.
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Affiliation(s)
- Y-Y Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China
| | - J Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China
| | - W-Q Qiu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China
| | - S-Y Li
- Department of Ultrasound, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, No. 20 Yuhuangdingdong Road, Zhifu District, Yantai 264099, Shandong Province, China
| | - C Li
- Department of Biomedical Engineering, Hefei University of Technology, No. 193 Tunxi Road, Baohe District, Hefei 230009, Anhui Province, China
| | - A-S Liu
- Department of Ultrasound, The First Affiliated Hospital of Anhui University of Chinese Medicine, No. 117 Meishan Road, Shushan District, Hefei 230022, Anhui Province, China
| | - X Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, No. 93 Jinzhai Road, Baohe District, Hefei 230026, Anhui Province, China
| | - C-X Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China.
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Lee YS, Lee JE, Yi HS, Jung YK, Jun DW, Kim JH, Seo YS, Yim HJ, Kim BH, Kim JW, Lee CH, Yeon JE, Lee J, Um SH, Byun KS. MRE-based NASH score for diagnosis of nonalcoholic steatohepatitis in patients with nonalcoholic fatty liver disease. Hepatol Int 2022; 16:316-324. [PMID: 35254642 DOI: 10.1007/s12072-022-10300-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 01/07/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND AND AIMS As the prevalence of nonalcoholic fatty liver disease (NAFLD) is approximately 30% in the general population, it is important to develop a non-invasive biomarker for the diagnosis of nonalcoholic steatohepatitis (NASH). This prospective cross-sectional study aimed to develop a scoring system for NASH diagnosis through multiparametric magnetic resonance (MR) and clinical indicators. METHODS Medical history, laboratory tests, and MR parameters of patients with NAFLD were assessed. A scoring system was developed using a logistic regression model. In total, 127 patients (58 with nonalcoholic fatty liver [NAFL] and 69 with NASH) were enrolled. After evaluating 23 clinical characteristics of the patients (4 categorical and 19 numeric variables) for the NASH diagnostic model, an equation for MR elastography (MRE)-based NASH score was obtained using 3 demographic factors, 2 laboratory variables, and MRE. RESULTS The MRE-based NASH score showed a satisfactory accuracy for NASH diagnosis (c-statistics, 0.841; 95% CI 0.772-0.910). At a cut-off MRE-based NASH score of 0.68 for NASH diagnosis, its sensitivity was 0.68 and specificity was 0.91. When an MRE-based NASH score of 0.37 was used as a cut-off for NASH exclusion, the sensitivity was 0.91 and specificity was 0.55. Overall, 35% (44/127) of patients were in the gray zone (between 0.37 and 0.68). Internal validation via bootstrapping also indicated the satisfactory accuracy of NASH diagnosis (optimism-corrected statistics, 0.811). CONCLUSION MRE-based NASH score is a useful and accurate non-invasive biomarker for diagnosis of NASH in patients with NAFLD.
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Affiliation(s)
- Young-Sun Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Ji Eun Lee
- Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hyon-Seung Yi
- Department of Internal Medicine, School of Medicine, Chungnam National University, Daejeon, 35015, Republic of Korea
| | - Young Kul Jung
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Dae Won Jun
- Department of Internal Medicine, Hanyang University School of Medicine, Seoul, Republic of Korea
| | - Ji Hoon Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Yeon Seok Seo
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Hyung Joon Yim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Baek-Hui Kim
- Department of Pathology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jeong Woo Kim
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Chang Hee Lee
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jong Eun Yeon
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea.
| | - Juneyoung Lee
- Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.
| | - Soon Ho Um
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Kwan Soo Byun
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
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Wan T, Wu C, Meng M, Liu T, Li C, Ma J, Qin Z. Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings. J Magn Reson Imaging 2021; 55:1491-1503. [PMID: 34549842 DOI: 10.1002/jmri.27930] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/05/2021] [Accepted: 09/10/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Preoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning. PURPOSE To investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA. STUDY TYPE Retrospective. POPULATION One hundred and fifty-six PMA patients (soft consistency, N = 104 vs. hard consistency, N = 52), divided into training (N = 108) and test (N = 48) cohorts. The tumor consistency was determined on surgical findings. FIELD STRENGTH/SEQUENCE T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1CE), and T2-weighted imaging (T2WI) using spin-echo sequences with a 3.0-T scanner. ASSESSMENT An automated three-dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top-discriminative features were identified using the minimum-redundancy maximum-relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency. STATISTICAL TESTS Mann-Whitney U-test and Chi-square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with P-value<0.05 was considered statistically significant. RESULTS Eleven mpMRI-based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI/T1CE/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87-0.92), ACC of 0.87 (CI: 0.84-0.89), SEN of 0.83 (CI: 0.81-0.85), and SPE of 0.87 (CI: 0.85-0.99) in the test cohort. DATA CONCLUSION Radiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tao Wan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Chunxue Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ming Meng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Chuzhong Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zengchang Qin
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
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