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Oh J, Wu D, Hong B, Lee D, Kang M, Li Q, Kim K. Texture-preserving low dose CT image denoising using Pearson divergence. Phys Med Biol 2024; 69:115021. [PMID: 38688292 DOI: 10.1088/1361-6560/ad45a4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/30/2024] [Indexed: 05/02/2024]
Abstract
Objective.The mean squared error (MSE), also known asL2loss, has been widely used as a loss function to optimize image denoising models due to its strong performance as a mean estimator of the Gaussian noise model. Recently, various low-dose computed tomography (LDCT) image denoising methods using deep learning combined with the MSE loss have been developed; however, this approach has been observed to suffer from the regression-to-the-mean problem, leading to over-smoothed edges and degradation of texture in the image.Approach.To overcome this issue, we propose a stochastic function in the loss function to improve the texture of the denoised CT images, rather than relying on complicated networks or feature space losses. The proposed loss function includes the MSE loss to learn the mean distribution and the Pearson divergence loss to learn feature textures. Specifically, the Pearson divergence loss is computed in an image space to measure the distance between two intensity measures of denoised low-dose and normal-dose CT images. The evaluation of the proposed model employs a novel approach of multi-metric quantitative analysis utilizing relative texture feature distance.Results.Our experimental results show that the proposed Pearson divergence loss leads to a significant improvement in texture compared to the conventional MSE loss and generative adversarial network (GAN), both qualitatively and quantitatively.Significance.Achieving consistent texture preservation in LDCT is a challenge in conventional GAN-type methods due to adversarial aspects aimed at minimizing noise while preserving texture. By incorporating the Pearson regularizer in the loss function, we can easily achieve a balance between two conflicting properties. Consistent high-quality CT images can significantly help clinicians in diagnoses and supporting researchers in the development of AI-diagnostic models.
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Affiliation(s)
- Jieun Oh
- Center for Advanced Medical Computing and Analysis (CAMCA), Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
- Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
| | - Dufan Wu
- Center for Advanced Medical Computing and Analysis (CAMCA), Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Boohwi Hong
- Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
| | - Dongheon Lee
- Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
| | - Minwoong Kang
- Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis (CAMCA), Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Kyungsang Kim
- Center for Advanced Medical Computing and Analysis (CAMCA), Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
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Liu H, Fu Y, Guo D, Li S, Jin Y, Zhang A, Wu C. TMM: A comprehensive CAD system for hepatic fibrosis 5-grade METAVIR staging based on liver MRI. Med Phys 2024; 51:2032-2043. [PMID: 37734071 DOI: 10.1002/mp.16700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/16/2023] [Accepted: 05/26/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Precise staging of hepatic fibrosis with MRI is necessary as it can assist precision medicine. Computer aided diagnosis (CAD) system with distinguishing radiomics features and radiologists domain knowledge is expected to obtain 5-grade meta-analysis of histological data in viral hepatitis (METAVIR) staging. PURPOSE This study aims to obtain the precise staging of hepatic fibrosis based on MRI as it predicts the risk of future liver-related morbidity and the need for treatment, monitoring and surveillance. Based on METAVIR score, fibrosis can be divided into five stages. Most previous researches focus on binary classification, such as cirrhosis versus non-cirrhosis, early versus advanced fibrosis, and substantial fibrosis or not. In this paper, a comprehensive CAD system TMM is proposed to precisely class hepatic fibrosis into five stages for precision medicine instead of the common binary classification. METHODS We propose a novel hepatic fibrosis staging CAD system TMM which includes three modules, Two-level Image Statistical Radiomics Feature (TISRF), Monotonic Error Correcting Output Codes (MECOC) and Monotone Multiclassification with Deep Forest (MMDF). TISRF extracts radiomics features for distinguishing different hepatic fibrosis stages. MECOC is proposed to encode monotonic multiclass by making full use of the progressive severity of hepatic fibrosis and increase the fault tolerance and error correction ability. MMDF combines multiple Deep Forest network to ensure the final five-class classification, which can achieve more precise classification than the common binary classification. The performance of the proposed hepatic fibrosis CAD system is tested on the hepatic data collected from our rabbits models of fibrosis. RESULTS A total of 140 regions of interest (ROI) are selected from MRI T1W of liver fibrosis models in 35 rabbits with F0(n = 16), F1(n = 28), F2(n = 29), F3(n = 44) and F4(n = 23). The performance is evaluated by five-fold cross-validation. TMM can achieve the highest total accuracy of 72.14% for five fibrosis stages compared with other popular classifications. To make a comprehensive comparison, a binary classification experiment have been carried out. CONCLUSIONS T1WI can obtain precise staging of hepatic fibrosis with the help of comprehensive CAD including radiomics features extraction inspired by radiologists, monotonic multiclass according to the severity of hepatic fibrosis, and deep learning classification.
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Affiliation(s)
- Hui Liu
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
| | - Yaqing Fu
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
| | - Dongmei Guo
- Department of Radiology Second Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Shuo Li
- Department of Radiology Second Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Yilin Jin
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
| | - Aoran Zhang
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
| | - Chengjun Wu
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
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Liu Y, Wu J, Zhou J, Guo J, Liang C, Xing Y, Wang Z, Chen L, Ding Y, Ren D, Bai Y, Hu D. Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:108006. [PMID: 38215580 DOI: 10.1016/j.cmpb.2024.108006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/14/2024]
Abstract
OBJECTION The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features. METHODS A retrospective study was conducted, including 600 dust-exposed workers and 300 confirmed pneumoconiosis patients. Chest computed tomography (CT) images were divided into a training set and a test set in a 2:1 ratio. Whole-lung segmentation was performed using deep learning models for feature extraction of radiomics. Two feature selection algorithms and five classification models were used. The optimal model was selected using a 10-fold cross-validation strategy, and the calibration curve and decision curve were evaluated. To verify the applicability of the model, the diagnostic efficiency and accuracy between the model and human interpretation were compared. Additionally, the risk probabilities for different risk groups defined by the model were compared at different time intervals. RESULTS Four radiomics features were ultimately used to construct the predictive model. The logistic regression model was the most stable in both the training set and testing set, with an area under curve (AUC) of 0.964 (95 % confidence interval [CI], 0.950-0.976) and 0.947 (95 %CI, 0.925-0.964). In the training and testing sets, the Brier scores were 0.092 and 0.14, respectively, with threshold probability ranges of 2 %-99 % and 2 %-85 %. These results indicate that the model exhibits good calibration and clinical benefit. The comparison between the model and human interpretation showed that the model was not inferior in terms of diagnostic efficiency and accuracy. Additionally, the high-risk population identified by the model was diagnosed as pneumoconiosis two years later. CONCLUSION This study provides a meticulous and quantifiable method for detecting and assessing the risk of pneumoconiosis, building upon accurate diagnosis. Employing risk scoring and probability estimation, not only enhances the efficiency of diagnostic physicians but also provides a valuable reference for controlling the occurrence of pneumoconiosis.
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Affiliation(s)
- Yafeng Liu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Jing Wu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China.
| | - Jiawei Zhou
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Jianqiang Guo
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Chao Liang
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Yingru Xing
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, PR China
| | - Zhongyu Wang
- Ziwei King Star Digital Technology Co., Ltd., Hefei, PR China
| | - Lijuan Chen
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China
| | - Yan Ding
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China
| | - Dingfei Ren
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China.
| | - Ying Bai
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Dong Hu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China; Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, PR China.
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Hu N, Yan G, Tang M, Wu Y, Song F, Xia X, Chan LWC, Lei P. CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease. Eur Radiol Exp 2023; 7:72. [PMID: 37985560 PMCID: PMC10661153 DOI: 10.1186/s41747-023-00387-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/12/2023] [Indexed: 11/22/2023] Open
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.
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Affiliation(s)
- Na Hu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Gang Yan
- Department of Nuclear Medicine, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Maowen Tang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Yuhui Wu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Fasong Song
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xing Xia
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
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Gong X, Guo Y, Zhu T, Xing D, Shi Q, Zhang M. Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis. Jpn J Radiol 2023; 41:983-993. [PMID: 37071251 DOI: 10.1007/s11604-023-01423-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/29/2023] [Indexed: 04/19/2023]
Abstract
PURPOSE Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis. MATERIALS AND METHODS Thirty-three rabbits were randomly divided into 27 carbon tetrachloride-induced liver fibrosis group and 6 control group. Spectral CT contrast-enhanced scan was performed in batches, and the liver fibrosis was staged according to the histopathological results. The portal venous phase spectral CT parameters [70 keV CT value, normalized iodine concentration (NIC), spectral HU curve slope (λHU)] were measured, and MaZda texture analysis was performed on 70 keV monochrome images. Three dimensionality reduction methods and four statistical methods in B11 module were used to perform discriminant analysis and calculate misclassified rate (MCR), and ten texture features under the lowest combination of MCR were statistically analyzed. Receiver operating characteristic curve (ROC) was used to calculate the diagnostic performance of spectral parameters and texture features for significant liver fibrosis. Finally, the binary logistic regression was used to further screen independent predictors and establish model. RESULTS A total of 23 experimental rabbits and 6 control rabbits were included, of which 16 had significant liver fibrosis. Three spectral CT parameters with significant liver fibrosis were significantly lower than those of non-significant liver fibrosis (p < 0.05), and the AUC ranged from 0.846 to 0.913. The combination analysis of mutual information (MI) and nonlinear discriminant analysis (NDA) had the lowest MCR, which with 0%. In the filtered texture features, four were statistically significant and AUC > 0.5, ranges from 0.764 to 0.875. The logistic regression model showed that Perc.90% and NIC could be used as independent predictors, the overall prediction accuracy of the model was 89.7% and the AUC was 0.976. CONCLUSION Spectral CT parameters and texture features have high diagnostic value for predicting significant liver fibrosis in rabbits, and the combination of the two can improve its diagnostic efficiency.
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Affiliation(s)
- Xiuru Gong
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China
| | - Yaxin Guo
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China
| | - Tingting Zhu
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China
| | - Dongwei Xing
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China
| | - Qi Shi
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China.
| | - Minguang Zhang
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China.
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Dichtel LE, Tabari A, Mercaldo ND, Corey KE, Husseini J, Osganian SA, Chicote ML, Rao EM, Miller KK, Bredella MA. CT Texture Analysis in Nonalcoholic Fatty Liver Disease (NAFLD). J Clin Exp Hepatol 2023; 13:760-766. [PMID: 37693260 PMCID: PMC10483004 DOI: 10.1016/j.jceh.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/04/2023] [Indexed: 09/12/2023] Open
Abstract
Background Nonalcoholic fatty liver disease (NAFLD) is the most common form of liver disease worldwide. There are limited biomarkers that can detect progression from simple steatosis to nonalcoholic steatohepatitis (NASH). The purpose of our study was to utilize CT texture analysis to distinguish steatosis from NASH. Methods 16 patients with NAFLD (38% male, median (interquartile range): age 57 (48-64) years, BMI 37.5 (35.0-46.8) kg/m2) underwent liver biopsy and abdominal non-contrast CT. CT texture analysis was performed to quantify gray-level tissue summaries (e.g., entropy, kurtosis, skewness, and attenuation) using commercially available software (TexRad, Cambridge England). Logistic regression analyses were performed to quantify the association between steatosis/NASH status and CT texture. ROC curve analysis was performed to determine sensitivity, specificity, AUC, 95% CIs, and cutoff values of texture parameters to differentiate steatosis from NASH. Results By histology, 6/16 (37%) of patients had simple steatosis and 10/16 (63%) had NASH. Patients with NASH had lower entropy (median, interquartile range (IQR): 4.3 (4.1, 4.8) vs. 5.0 (4.9, 5.2), P = 0.013) and lower mean value of positive pixels (MPP) (34.4 (21.8, 52.2) vs. 66.5 (57.0, 70.7), P = 0.009) than those with simple steatosis. Entropy values below 4.73 predict NASH with 100% (95%CI: 67-100%) specificity and 80% (50-100%) sensitivity, AUC: 0.88. MPP values below 54.0 predict NASH with 100% (67-100%) specificity and 100% (50-100%) sensitivity, AUC 0.90. Conclusion Our study provides preliminary evidence that CT texture analysis may serve as a novel imaging biomarker for disease activity in NAFLD and the discrimination of steatosis and NASH.
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Affiliation(s)
- Laura E. Dichtel
- Harvard Medical School, Boston, MA, USA
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Nathaniel D. Mercaldo
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Kathleen E. Corey
- Harvard Medical School, Boston, MA, USA
- Department of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - Jad Husseini
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Mark L. Chicote
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Elizabeth M. Rao
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Karen K. Miller
- Harvard Medical School, Boston, MA, USA
- Neuroendocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Miriam A. Bredella
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
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Zheng S, He K, Zhang L, Li M, Zhang H, Gao P. Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging. Eur J Radiol 2023; 165:110912. [PMID: 37290363 DOI: 10.1016/j.ejrad.2023.110912] [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: 03/13/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/10/2023]
Abstract
Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.
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Affiliation(s)
- Shuang Zheng
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Kan He
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Lei Zhang
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Mingyang Li
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Huimao Zhang
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Pujun Gao
- Department of Hepatology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
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Zhao D, Wang W, Tang T, Zhang YY, Yu C. Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review. Comput Struct Biotechnol J 2023; 21:3315-3326. [PMID: 37333860 PMCID: PMC10275698 DOI: 10.1016/j.csbj.2023.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023] Open
Abstract
Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.
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Affiliation(s)
- Dan Zhao
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Wei Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Tian Tang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Ying-Ying Zhang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Chen Yu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
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Herrmann J, Petit P, Grabhorn E, Lenz A, Jürgens J, Franchi-Albella S. Liver cirrhosis in children - the role of imaging in the diagnostic pathway. Pediatr Radiol 2023; 53:714-726. [PMID: 36040526 PMCID: PMC10027649 DOI: 10.1007/s00247-022-05480-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/23/2022] [Accepted: 07/31/2022] [Indexed: 10/14/2022]
Abstract
Liver cirrhosis in children is a rare disease with multifactorial causes that are distinct from those in adults. Underlying reasons include cholestatic, viral, autoimmune, hereditary, metabolic and cardiac disorders. Early detection of fibrosis is important as clinical stabilization or even reversal of fibrosis can be achieved in some disorders with adequate treatment. This article focuses on the longitudinal evaluation of children with chronic liver disease with noninvasive imaging tools, which play an important role in detecting cirrhosis, defining underlying causes, grading fibrosis and monitoring patients during follow-up. Ultrasound is the primary imaging modality and it is used in a multiparametric fashion. Magnetic resonance imaging and computed tomography are usually applied second line for refined tissue characterization, clarification of nodular lesions and full delineation of abdominal vessels, including portosystemic communications.
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Affiliation(s)
- Jochen Herrmann
- Section of Pediatric Radiology, Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20251, Hamburg, Germany.
| | - Philippe Petit
- Aix Marseille Université, Hopital Timone-Enfants, Marseille, France
| | - Enke Grabhorn
- Department of Pediatric Gastroenterology and Hepatology, University Medical Center Hamburg, Hamburg, Germany
| | - Alexander Lenz
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center, Hamburg, Germany
| | - Julian Jürgens
- Section of Pediatric Radiology, Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20251, Hamburg, Germany
| | - Stéphanie Franchi-Albella
- Department of Pediatric Radiology, Hôpital Bicêtre, National Reference Centre for Rare Pediatric Liver Diseases, Paris, France
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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11
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Development of a Computer System for Automatically Generating a Laser Photocoagulation Plan to Improve the Retinal Coagulation Quality in the Treatment of Diabetic Retinopathy. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
In this article, the development of a computer system for high-tech medical uses in ophthalmology is proposed. An overview of the main methods and algorithms that formed the basis of the coagulation plan planning system is presented. The system provides the formation of a more effective plan for laser coagulation in comparison with the use of existing coagulation techniques. An analysis of monopulse- and pattern-based laser coagulation techniques in the treatment of diabetic retinopathy has shown that modern treatment methods do not provide the required efficacy of medical laser coagulation procedures, as the laser energy is nonuniformly distributed across the pigment epithelium and may exert an excessive effect on parts of the retina and anatomical elements. The analysis has shown that the efficacy of retinal laser coagulation for the treatment of diabetic retinopathy is determined by the relative position of coagulates and parameters of laser exposure. In the course of the development of the computer system proposed herein, main stages of processing diagnostic data were identified. They are as follows: the allocation of the laser exposure zone, the evaluation of laser pulse parameters that would be safe for the fundus, mapping a coagulation plan in the laser exposure zone, followed by the analysis of the generated plan for predicting the therapeutic effect. In the course of the study, it was found that the developed algorithms for placing coagulates in the area of laser exposure provide a more uniform distribution of laser energy across the pigment epithelium when compared to monopulse- and pattern-based laser coagulation techniques.
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Hirano R, Rogalla P, Farrell C, Hoppel B, Fujisawa Y, Ohyu S, Hattori C, Sakaguchi T. Development of a classification method for mild liver fibrosis using non-contrast CT image. Int J Comput Assist Radiol Surg 2022; 17:2041-2049. [PMID: 35930131 DOI: 10.1007/s11548-022-02724-x] [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: 01/07/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE Detection of early-stage liver fibrosis has direct clinical implications on patient management and treatment. The aim of this paper is to develop a non-invasive, cost-effective method for classifying liver disease between "non-fibrosis" (F0) and "fibrosis" (F1-F4), and to evaluate the classification performance quantitatively. METHODS Image data from 75 patients who underwent a simultaneous liver biopsy and non-contrast CT examination were used for this study. Non-contrast CT image texture features such as wavelet-based features, standard deviation of variance filter, and mean CT number were calculated in volumes of interest (VOIs) positioned within the liver parenchyma. In addition, a combined feature was calculated using logistic regression with L2-norm regularization to further improve fibrosis detection. Based on the final pathology from the liver biopsy, the patients were labelled either as "non-fibrosis" or "fibrosis". Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUROC), specificity, sensitivity, and accuracy were determined for the algorithm to differentiate between "non-fibrosis" and "fibrosis". RESULTS The combined feature showed the highest classification performance with an AUROC of 0.86, compared to the wavelet-based feature (AUROC, 0.76), the standard deviation of variance filter (AUROC, 0.65), and mean CT number (AUROC, 0.84). The combined feature's specificity, sensitivity, and accuracy were 0.66, 0.88, and 0.76, respectively, showing the most promising results. CONCLUSION A new non-invasive and cost-effective method was developed to classify liver diseases between "non-fibrosis" (F0) and "fibrosis" (F1-F4). The proposed method makes it possible to detect liver fibrosis in asymptomatic patients using non-contrast CT images for better patient management and treatment.
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Affiliation(s)
- Ryo Hirano
- Research and Development Center, Canon Medical Systems Corporation, Otawara, Japan.
| | - Patrik Rogalla
- Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
| | | | | | - Yasuko Fujisawa
- Research and Development Center, Canon Medical Systems Corporation, Otawara, Japan
| | - Shigeharu Ohyu
- Research and Development Center, Canon Medical Systems Corporation, Otawara, Japan
| | - Chihiro Hattori
- Research and Development Center, Canon Medical Systems Corporation, Otawara, Japan
| | - Takuya Sakaguchi
- Research and Development Center, Canon Medical Systems Corporation, Otawara, Japan
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13
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Wang J, Tang S, Mao Y, Wu J, Xu S, Yue Q, Chen J, He J, Yin Y. Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker. Hepatol Int 2022; 16:627-639. [PMID: 35347597 PMCID: PMC9174317 DOI: 10.1007/s12072-022-10326-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/03/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images. MATERIALS AND METHODS This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men). RESULTS In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70-87%, specificity range of 71-97%, and accuracy range of 82-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis. CONCLUSION Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.
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Affiliation(s)
- Jincheng Wang
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Preparatory School for Chinese Students To Japan, The Training Center of Ministry of Education for Studying Overseas, Changchun, China
| | - Shengnan Tang
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Yingfan Mao
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Jin Wu
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Shanshan Xu
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Qi Yue
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Jun Chen
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jian He
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China.
| | - Yin Yin
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China.
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
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Cioni D, Gabelloni M, Sanguinetti A, De Rosa L, Aringhieri G, Tintori R, Candita G, Febi M, Faita F, Lencioni R, Neri E. A New SteatoScore in the Evaluation of Non-Alcoholic Liver Disease in Oncologic Patients. Front Oncol 2022; 12:873524. [PMID: 35574336 PMCID: PMC9093140 DOI: 10.3389/fonc.2022.873524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The aims of this study were to evaluate the reproducibility of a new multi-parametric steatoscore (new SteatoScore) in oncologic patients with non-alcoholic fatty liver disease (NAFLD) and to compare it with computed tomography (CT). Materials and Methods Fifty-one (31 men, 20 women) oncologic patients, with a mean age and weight of 63.9 years and 78.33 kg, respectively, were retrospectively enrolled in the study. Patients underwent ultrasound (US) and computed tomography (CT) examinations as part of their oncologic follow-up protocol. US examinations were performed by using a 3.5-MHz convex probe. During the US examination, three standardized clips were obtained in each patient. Two operators performed all measurements, one of whom repeated the processing twice in 1 year. Hepatic/renal ratio (HR), attenuation rate (AR), diaphragm visualization (DV), hepatic/portal vein ratio (HPV), and portal vein wall visualization (PVW) were acquired and calculated by using Matlab and inserted in a multi-parametric algorithm called new SteatoScore. On unenhanced CT scan, hepatic attenuation (HA), liver-spleen difference (L-S), and liver/spleen ratio (L/S) were measured by placement of a region of interest (ROI) within liver and spleen parenchyma, avoiding areas with vessels and biliary ducts. Results The intra-observer variability was greater than the inter-observer one, with intraclass correlation coefficient (ICC) values of 0.94 and 0.97, respectively. Correlation between single US and CT parameters provided an agreement in no case exceeding 50%. New SteatoScore showed high reproducibility, and high coefficient of correlation with L-S (R = −0.64; p < 0.0001) and L/S (R = −0.62; p < 0.0001) at CT. Conclusion New SteatoScore has a high reproducibility and shows a good correlation with unenhanced CT in evaluation of oncologic patients with NAFLD.
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Affiliation(s)
- Dania Cioni
- Department of Surgical, Medical, Molecular Pathology and Emergency Medicine, University of Pisa, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Andrea Sanguinetti
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Laura De Rosa
- Hepatology Unit, Pisa University Hospital, Pisa, Italy
| | - Giacomo Aringhieri
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Rachele Tintori
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Gianvito Candita
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Maria Febi
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Francesco Faita
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Riccardo Lencioni
- Department of Surgical, Medical, Molecular Pathology and Emergency Medicine, University of Pisa, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
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15
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Non-invasive quantitative diagnosis of liver fibrosis with an artificial neural network. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06257-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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16
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Chen ZW, Xiao HM, Ye X, Liu K, Rios RS, Zheng KI, Jin Y, Targher G, Byrne CD, Shi J, Yan Z, Chi XL, Zheng MH. A novel radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with biopsy-proven nonalcoholic fatty liver disease: a derivation and independent validation study. Hepatobiliary Surg Nutr 2022; 11:212-226. [PMID: 35464279 DOI: 10.21037/hbsn-21-23] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/08/2021] [Indexed: 12/12/2022]
Abstract
Background Currently, there are no effective methods for assessing hepatic inflammation without resorting to histological examination of liver tissue obtained by biopsy. T2-weighted images (T2WI) are routinely obtained from liver magnetic resonance imaging (MRI) scan sequences. We aimed to establish a radiomics signature based on T2WI (T2-RS) for assessment of hepatic inflammation in people with nonalcoholic fatty liver disease (NAFLD). Methods A total of 203 individuals with biopsy-confirmed NAFLD from two independent Chinese cohorts with liver MRI examination were enrolled in this study. The hepatic inflammatory activity score (IAS) was calculated by the unweighted sum of the histologic scores for lobular inflammation and ballooning. One thousand and thirty-two radiomics features were extracted from the localized region of interest (ROI) in the right liver lobe of T2WI and, subsequently, selected by minimum redundancy maximum relevance and least absolute shrinkage and selection operator (LASSO) methods. The T2-RS was calculated by adding the selected features weighted by their coefficients. Results Eighteen radiomics features from Laplacian of Gaussian, wavelet, and original images were selected for establishing T2-RS. The T2-RS value differed significantly between groups with increasing grades of hepatic inflammation (P<0.01). The T2-RS yielded an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.80 [95% confidence interval (CI): 0.71-0.89] for predicting hepatic inflammation in the training cohort with excellent calibration. The AUROCs of T2-RS in the internal cohort and external validation cohorts were 0.77 (0.61-0.93) and 0.75 (0.63-0.84), respectively. Conclusions The T2-RS derived from radiomics analysis of T2WI shows promising utility for predicting hepatic inflammation in individuals with NAFLD.
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Affiliation(s)
- Zhong-Wei Chen
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huan-Ming Xiao
- Department of Hepatology, Guangdong Provincial Hospital of Chinese Medicine, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xinjian Ye
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Liu
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Rafael S Rios
- NAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kenneth I Zheng
- NAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yi Jin
- Department of Pathology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Giovanni Targher
- Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Christopher D Byrne
- Southampton National Institute for Health Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, Southampton, UK
| | - Junping Shi
- Department of Hepatology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiao-Ling Chi
- Department of Hepatology, Guangdong Provincial Hospital of Chinese Medicine, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ming-Hua Zheng
- NAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment for The Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
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Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging. Diagnostics (Basel) 2022; 12:diagnostics12020550. [PMID: 35204639 PMCID: PMC8870954 DOI: 10.3390/diagnostics12020550] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/09/2022] [Accepted: 02/17/2022] [Indexed: 02/05/2023] Open
Abstract
Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.
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Computed Tomography-Based Radiomic Analysis for Preoperatively Predicting the Macrovesicular Steatosis Grade in Cadaveric Donor Liver Transplantation. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2491023. [PMID: 35103236 PMCID: PMC8800621 DOI: 10.1155/2022/2491023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 12/09/2021] [Accepted: 12/31/2021] [Indexed: 11/30/2022]
Abstract
This study is aimed at determining the ability of computed tomography- (CT-) based radiomic analysis to distinguish between grade 0/1 and grade 2/3 macrovesicular steatosis (MaS) in cadaveric donor liver transplantation cases. Preoperative noncontrast-enhanced CT images of 150 patients with biopsy-confirmed MaS were analyzed retrospectively; these patients were classified into the low-grade MaS (n = 100, grade 0 or 1) and high-grade MaS (n = 50, grade 2 or 3) groups. Three-dimensional spherical regions of interest of 40 pixel (2.5 cm) in diameter were placed in the right anterior and left lateral segments of the liver. Thereafter, 300 regions of interest (ROIs) were segmented and randomly assigned to the training and testing groups at a ratio of 7 : 3. A total of 402 radiomic features were extracted from each ROI. For MaS classification, a radiomic model was established using multivariate logistic regression analysis. Clinical data, including age, sex, and liver function, were collected to establish the clinical model at the patient level. The performances of the radiomic and clinical models, i.e., the diagnostic discrimination, calibration, and clinical utilities, were evaluated. The radiomic model, with seven selected features, depicted a good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.907 (95% confidence interval (CI): 0.869–0.940) in the training cohort and 0.906 (95% CI: 0.843–0.959) in the testing cohort. The calibration curve revealed good agreement between the predicted and observed probabilities in the training and testing cohorts (both P > 0.05 in the H-L test). Decision curve analysis revealed that the radiomic model was more beneficial than the treat-all or treat-none schemes for predicting the MaS grade. Alanine transaminase and gamma-glutamyl transferase were used for building the clinical model, and the AUC was 0.784 in the total cohort. The CT-based radiomic model outperforming the conventional clinical model could provide an important reference for MaS grading in cadaveric liver donors.
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TURAL İÇ, YURTTUTAN N, BAYKARA M, KIZILDAĞ B. Investigation of the computerized tomography histogram analysis in distinction of distal ureteral stone and pelvic phlebolith. EGE TIP DERGISI 2021. [DOI: 10.19161/etd.1037332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging "what a clinical radiologist needs to know". Eur J Radiol 2021; 146:110055. [PMID: 34902669 DOI: 10.1016/j.ejrad.2021.110055] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 04/09/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023]
Abstract
Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.
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Affiliation(s)
| | | | | | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, USA and Knowledge Engineering Center, Global Biomedical Technologies, Inc, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy.
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A Combination Model of Radiomics Features and Clinical Biomarkers as a Nomogram to Differentiate Nonadvanced From Advanced Liver Fibrosis: A Retrospective Study. Acad Radiol 2021; 28 Suppl 1:S45-S54. [PMID: 34023199 DOI: 10.1016/j.acra.2020.08.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/20/2020] [Accepted: 08/20/2020] [Indexed: 02/07/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a combination model of radiomics features and clinical biomarkers to differentiate nonadvanced from advanced liver fibrosis. MATERIALS AND METHODS One hundred and eight consecutive patients with pathologically diagnosed liver fibrosis were randomly placed in a training or a test cohort at a ratio of 2:1. For each patient, 1674 radiomics features extracted from portal venous phase CT images were reduced by using minimum redundancy and maximum relevant. The optimal features identified were incorporated into the radiomics model. Eight clinical markers were evaluated. Integrated with clinical independent risk factors, a combination model was built. A nomogram was also established from the model. The performance of the models was assessed. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomogram. RESULTS The radiomics model established using five features achieved a promising level of discrimination between nonadvanced and advanced liver fibrosis. The combination model incorporated the radiomics signature with two clinical biomarkers and showed good calibration and discrimination. The training and testing cohort results of the radiomics model were area under curve values 0.864 and 0.772, accuracy 77.8% and 77.8%, sensitivity 86.7% and 73.1%, and specificity 71.4% and 90.0%, respectively. For the combination model, the training and testing cohort results were area under curve values 0.915 and 0.897, accuracy 83.3% and 86.1%, sensitivity 86% and 80.6%, and specificity 82.6% and 92.3%, respectively. The decision curve indicated the nomogram has potential in clinical application. CONCLUSION This combination model provides a promising approach for differentiating non-advanced from advanced liver fibrosis.
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Ghalati MK, Nunes A, Ferreira H, Serranho P, Bernardes R. Texture Analysis and its Applications in Biomedical Imaging: A Survey. IEEE Rev Biomed Eng 2021; 15:222-246. [PMID: 34570709 DOI: 10.1109/rbme.2021.3115703] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.
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Ilyasova NY, Shirokanev AS, Demin NS. Development of High-Performance Algorithms for the Segmentation of Fundus Images Using a Graphics Processing Unit. PATTERN RECOGNITION AND IMAGE ANALYSIS 2021. [DOI: 10.1134/s1054661821030135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Budai BK, Frank V, Shariati S, Fejér B, Tóth A, Orbán V, Bérczi V, Kaposi PN. CT texture analysis of abdominal lesions – Part I.: Liver lesions. IMAGING 2021. [DOI: 10.1556/1647.2021.00007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
AbstractArtificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.
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Affiliation(s)
- Bettina Katalin Budai
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Veronica Frank
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Sonaz Shariati
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Bence Fejér
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Ambrus Tóth
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Vince Orbán
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Viktor Bérczi
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Pál Novák Kaposi
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
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Pancreatic Ductal Adenocarcinoma: Relating Biomechanics and Prognosis. J Clin Med 2021; 10:jcm10122711. [PMID: 34205335 PMCID: PMC8234178 DOI: 10.3390/jcm10122711] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/12/2021] [Accepted: 06/16/2021] [Indexed: 02/07/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and carries a dismal prognosis. Resectable patients are treated predominantly with surgery while borderline resectable patients may receive neoadjuvant treatment (NAT) to downstage their disease prior to possible resection. PDAC tissue is stiffer than healthy pancreas, and tissue stiffness is associated with cancer progression. Another feature of PDAC is increased tissue heterogeneity. We postulate that tumour stiffness and heterogeneity may be used alongside currently employed diagnostics to better predict prognosis and response to treatment. In this review we summarise the biomechanical changes observed in PDAC, explore the factors behind these changes and describe the clinical consequences. We identify methods available for assessing PDAC biomechanics ex vivo and in vivo, outlining the relative merits of each. Finally, we discuss the potential use of radiological imaging for prognostic use.
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Liver segmental volume and attenuation ratio (LSVAR) on portal venous CT scans improves the detection of clinically significant liver fibrosis compared to liver segmental volume ratio (LSVR). Abdom Radiol (NY) 2021; 46:1912-1921. [PMID: 33156949 PMCID: PMC8131336 DOI: 10.1007/s00261-020-02834-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 09/21/2020] [Accepted: 10/17/2020] [Indexed: 12/14/2022]
Abstract
Background The aim of this proof-of-concept study was to show that the liver segmental volume and attenuation ratio (LSVAR) improves the detection of significant liver fibrosis on portal venous CT scans by adding the liver vein to cava attenuation (LVCA) to the liver segmental volume ratio (LSVR). Material and methods Patients who underwent portal venous phase abdominal CT scans and MR elastography (reference standard) within 3 months between 02/2016 and 05/2017 were included. The LSVAR was calculated on portal venous CT scans as LSVR*LVCA, while the LSVR represented the volume ratio between Couinaud segments I-III and IV-VIII, and the LVCA represented the density of the liver veins compared to the density in the vena cava. The LSVAR and LSVR were compared between patients with and without significantly elevated liver stiffness (based on a cutoff value of 3.5 kPa) using the Mann–Whitney U test and ROC curve analysis. Results The LSVR and LSVAR allowed significant differentiation between patients with (n = 19) and without (n = 122) significantly elevated liver stiffness (p < 0.001). However, the LSVAR showed a higher area under the curve (AUC = 0.96) than the LSVR (AUC = 0.74). The optimal cutoff value was 0.34 for the LSVR, which detected clinically increased liver stiffness with a sensitivity of 53% and a specificity of 88%. With a cutoff value of 0.67 for the LSVAR, the sensitivity increased to 95% while maintaining a specificity of 89%. Conclusion The LSVAR improves the detection of significant liver fibrosis on portal venous CT scans compared to the LSVR.
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Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma. Sci Rep 2021; 11:6933. [PMID: 33767315 PMCID: PMC7994625 DOI: 10.1038/s41598-021-86497-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 03/16/2021] [Indexed: 12/31/2022] Open
Abstract
To explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.
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He D, Zhang C, Qiu W, Xie Q. Diagnosis of liver fibrosis in patients with hepatitis B-related liver disease using ultrasound with wave-number domain attenuation coefficient. TURKISH JOURNAL OF GASTROENTEROLOGY 2021; 31:923-929. [PMID: 33626006 DOI: 10.5152/tjg.2020.20139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
BACKGROUND/AIMS The importance of identifying the stage of liver fibrosis has motivated the development of non-invasive methods. This study aimed to evaluate the applicability of ultrasound analysis involving the wave-number domain attenuation coefficient (W-Ac) in the non-invasive quantitative differentiation of liver fibrosis. MATERIALS AND METHODS This was a prospective study of inpatients with hepatitis B-related liver disease treated between October 2016 and January 2018. In ultrasound, the echo from the near-field liver tissue was selected as the reference signal. The W-Ac of liver tissues was based on the fast Fourier transform of the acquired post-beamforming radio frequency signals. These values were compared with fibrosis from biopsy METAVIR score results. A receiver operating characteristic (ROC) curve tested the W-Ac method. RESULTS A total of 46 patients were enrolled, including 27 males and 19 females. Fibrosis was stage F0 in 12 patients, F1 in 13 patients, F2 in 10 patients, F3 in 7 patients, and F4 in 4 patients. W-Ac increased with the progression of liver fibrosis up to stage F3. There were differences between F0 and F4 stages (p<0.001) and between any 2 stages of fibrosis (p<0.05), except for stages F3 and F4. There was a significant correlation between W-Ac and METAVIR score (r=0.795, p<0.001). W-Ac differed between non-fibrosis (F0) and fibrosis (F1-F4) groups (p<0.001) and in the normal (F0), early fibrosis (F1-2), and late fibrosis groups (F3-4) (p<0.001). ROC area under the curve was 0.890, and at a cut-off of 0.12153, sensitivity was 0.706 and specificity was 0.830. CONCLUSIONS W-Ac allowed assessment of liver fibrosis in clinical practice.
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Affiliation(s)
- Danqing He
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, China
| | - Wenqian Qiu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, China
| | - Qinxiu Xie
- Department of Infectious Disease, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, China
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Abstract
OBJECTIVE. The purpose of this study was to evaluate the utility of laboratory and CT metrics in identifying patients with high-risk nonalcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS. Patients with biopsy-proven NAFLD who underwent CT within 1 year of biopsy were included. Histopathologic review was performed by an experienced gastrointestinal pathologist to determine steatosis, inflammation, and fibrosis. The presence of any lobular inflammation and hepatocyte ballooning was categorized as nonalcoholic steatohepatitis (NASH). Patients with NAFLD and advanced fibrosis (stage F3 or higher) were categorized as having high-risk NAFLD. Aspartate transaminase to platelet ratio index and Fibrosis-4 (FIB-4) laboratory scores were calculated. CT metrics included hepatic attenuation, liver segmental volume ratio (LSVR), splenic volume, liver surface nodularity score, and selected texture features. In addition, two readers subjectively assessed the presence of NASH (present or not present) and fibrosis (stages F0-F4). RESULTS. A total of 186 patients with NAFLD (mean age, 49 years; 74 men and 112 women) were included. Of these, 87 (47%) had NASH and 112 (60%) had moderate to severe steatosis. A total of 51 patients were classified as fibrosis stage F0, 42 as F1, 23 as F2, 37 as F3, and 33 as F4. Additionally, 70 (38%) had advanced fibrosis (stage F3 or F4) and were considered to have high-risk NAFLD. FIB-4 score correlated with fibrosis (ROC AUC of 0.75 for identifying high-risk NAFLD). Of the individual CT parameters, LSVR and splenic volume performed best (AUC of 0.69 for both for detecting high-risk NAFLD). Subjective reader assessment performed best among all parameters (AUCs of 0.78 for reader 1 and 0.79 for reader 2 for detecting high-risk NAFLD). FIB-4 and subjective scores were complementary (combined AUC of 0.82 for detecting high-risk NAFLD). For NASH assessment, FIB-4 performed best (AUC of 0.68), whereas the AUCs were less than 0.60 for all individual CT features and subjective assessments. CONCLUSION. FIB-4 and multiple CT findings can identify patients with high-risk NAFLD (advanced fibrosis or cirrhosis). However, the presence of NASH is elusive on CT.
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Using qualitative descriptors of chronic liver disease on MRI: A practice prone to error. Clin Imaging 2021; 74:89-92. [PMID: 33461018 DOI: 10.1016/j.clinimag.2020.12.023] [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: 09/02/2020] [Revised: 11/18/2020] [Accepted: 12/26/2020] [Indexed: 11/20/2022]
Abstract
PURPOSE Assess accuracy of qualitative descriptors for chronic liver disease (CLD) in radiology reports compared to histopathological staging. METHODS Database search for patients with hepatitis B/C (HBV/HCV) CLD, abdominal MRI during 2009-2016, and liver biopsy within 6 months of MRI or prior biopsy showing cirrhosis. Reports reviewed for mention of CLD and associated descriptors. Findings stratified into categories: normal/no mention of CLD; changes of CLD without qualitative descriptor; mild/early; moderate; severe/advanced and cirrhosis. Descriptive ranges categorized to the lesser degree. Percent concordance/discordance of descriptors and Scheuer stage (F0-F4), false positive (FP), false negative (FN) and sensitivity/specificity calculated. RESULTS 309 patients, median age 54 (24-74). 91% had HCV (282/309), 7% HBV and 2% both HBV/HCV. Biopsy showed 19% without CLD/F0; 8% F1, 15% F2, 15% F3 and 43% F4. 188 MRI reports (61%) stated CLD was present; however, 16 had no fibrosis on histopathology (9% FP). 39% (121/309) did not mention or stated no CLD; however, 78 had CLD on histopathology (64% FN). 59% of FN were early fibrosis (F1 or F2), 27% F3 and 11% F4. Overall sensitivity and specificity was 69% and 73%, respectively. 77% (145/188) of MRI reports used a descriptive qualifier when describing CLD. 10% were concordant with exact histopathology staging. Of discordant reports, 90% identified CLD but under-called severity. CONCLUSION Abdominal radiologists can detect CLD on MRI but degree of CLD is often under-called compared to histopathology suggesting radiologists should refrain from qualitative descriptors in assessing CLD on MRI and reaffirms the need for quantitative imaging.
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Alnazer I, Bourdon P, Urruty T, Falou O, Khalil M, Shahin A, Fernandez-Maloigne C. Recent advances in medical image processing for the evaluation of chronic kidney disease. Med Image Anal 2021; 69:101960. [PMID: 33517241 DOI: 10.1016/j.media.2021.101960] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 11/18/2020] [Accepted: 12/31/2020] [Indexed: 12/31/2022]
Abstract
Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.
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Affiliation(s)
- Israa Alnazer
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France; AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon.
| | - Pascal Bourdon
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Thierry Urruty
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Omar Falou
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon; American University of Culture and Education, Koura, Lebanon; Lebanese University, Faculty of Science, Tripoli, Lebanon
| | - Mohamad Khalil
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Ahmad Shahin
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Christine Fernandez-Maloigne
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
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Hectors SJ, Kennedy P, Huang KH, Stocker D, Carbonell G, Greenspan H, Friedman S, Taouli B. Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI. Eur Radiol 2020; 31:3805-3814. [PMID: 33201285 DOI: 10.1007/s00330-020-07475-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/26/2020] [Accepted: 11/05/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES To (1) develop a fully automated deep learning (DL) algorithm based on gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and (2) compare the diagnostic performance of DL vs. MR elastography (MRE) for noninvasive staging of liver fibrosis. METHODS This single-center retrospective study included 355 patients (M/F 238/117, mean age 60 years; training, n = 178; validation, n = 123; test, n = 54) who underwent gadoxetic acid-enhanced abdominal MRI, including HBP and MRE, and pathological evaluation of the liver within 1 year of MRI. Cropped liver HBP images from a custom-written fully automated liver segmentation were used as input for DL. A transfer learning approach based on the ImageNet VGG16 model was used. Different DL models were built for the prediction of fibrosis stages F1-4, F2-4, F3-4, and F4. ROC analysis was performed to evaluate the performance of DL in training, validation, and test sets and of MRE liver stiffness in the test set. RESULTS AUC values of DL were 0.99/0.70/0.77 (F1-4), 0.92/0.71/0.91 (F2-4), 0.91/0.78/0.90 (F3-4), and 0.98/0.83/0.85 (F4) for training/validation/test sets, respectively. The AUCs of MRE liver stiffness in the test set were 0.86 (F1-4), 0.87 (F2-4), 0.92 (F3-4), and 0.86 (F4). AUCs of MRE and DL were not significantly different for any of the fibrosis stages (p > 0.134). CONCLUSIONS The fully automated DL models based on HBP gadoxetic acid MRI showed good-to-excellent diagnostic performance for staging of liver fibrosis, with similar diagnostic performance to MRE. After validation in independent sets, the DL algorithm may allow for noninvasive liver fibrosis assessment without the need for additional MRI hardware. KEY POINTS • The developed deep learning algorithm, based on routine standard-of-care gadoxetic acid-enhanced MRI data, showed good-to-excellent diagnostic performance for noninvasive staging of liver fibrosis. • The diagnostic performance of the deep learning algorithm was equivalent to that of MR elastography in a separate test set.
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Affiliation(s)
- Stefanie J Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Paul Kennedy
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| | - Kuang-Han Huang
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Prealize Health, Palo Alto, CA, USA
| | - Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,Institute of Interventional and Diagnostic Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,Department of Radiology, Virgen de la Arrixaca University Clinical Hospital, University of Murcia, Murcia, Spain
| | - Hayit Greenspan
- Medical Imaging Processing Lab, Faculty of Engineering, Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Scott Friedman
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.
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Park J, Kim JH, Kim JE, Park SJ, Yi NJ, Han JK. Prediction of liver regeneration in recipients after living-donor liver transplantation in using preoperative CT texture analysis and clinical features. Abdom Radiol (NY) 2020; 45:3763-3774. [PMID: 32296898 DOI: 10.1007/s00261-020-02518-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
PURPOSE The aim of the study is to predict the rate of liver regeneration in recipients after living-donor liver transplantation using preoperative CT texture and shape analysis of the future graft. METHODS 102 donor-recipient pairs who underwent living-donor liver transplantation using right lobe grafts were retrospectively included. We semi-automatically segmented the future graft from preoperative CT. The volume of the future graft (LVpre) was measured, and texture and shape analyses were performed. The graft liver was segmented from postoperative follow-up CT and the volume of the graft (LVpost) was measured. The regeneration index was defined by the following equation: [(LVpost-LVpre)/LVpre] × 100(%). We performed a stepwise, multivariate linear regression analysis to investigate the association between clinical, texture and shape parameters and the RI and to make the best-fit predictive model. RESULTS The mean regeneration index was 47.5 ± 38.6%. In univariate analysis, the volume of the future graft, energy, effective diameter, surface area, sphericity, roundnessm, compactness1, and grey-level co-occurrence matrix contrast as well as several clinical parameters were significantly associated with the regeneration index (p < 0.05). The best-fit predictive model for the regeneration index made by multivariate analysis was as follows: Regeneration index (%) = 127.020-0.367 × effective diameter - 1.827 × roundnessm + 47.371 × recipient body surface area (m2) + 12.041 × log(recipient white blood cell count) (× 103/μL)+ 18.034 (if the donor was female). CONCLUSION The effective diameter and roundnessm of the future graft were associated with liver regeneration. Preoperative CT texture analysis of future grafts can be useful for predicting liver regeneration in recipients after living-donor liver transplantation.
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Budai BK, Tóth A, Borsos P, Frank VG, Shariati S, Fejér B, Folhoffer A, Szalay F, Bérczi V, Kaposi PN. Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis. BMC Med Imaging 2020; 20:108. [PMID: 32957949 PMCID: PMC7507285 DOI: 10.1186/s12880-020-00508-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 09/10/2020] [Indexed: 12/13/2022] Open
Abstract
Background CT texture analysis (CTTA) has been successfully used to assess tissue heterogeneity in multiple diseases. The purpose of this work is to demonstrate the value of three-dimensional CTTA in the evaluation of diffuse liver disease. We aimed to develop CTTA based prediction models, which can be used for staging of fibrosis in different anatomic liver segments irrespective of variations in scanning parameters. Methods We retrospectively collected CT scans of thirty-two chronic hepatitis patients with liver fibrosis. The CT examinations were performed on either a 16- or a 64-slice scanner. Altogether 354 anatomic liver segments were manually highlighted on portal venous phase images, and 1117 three-dimensional texture parameters were calculated from each segment. The segments were divided between groups of low-grade and high-grade fibrosis using shear-wave elastography. The highly-correlated features (Pearson r > 0.95) were filtered out, and the remaining 453 features were normalized and used in a classification with k-means and hierarchical cluster analysis. The segments were split between the train and test sets in equal proportion (analysis I) or based on the scanner type (analysis II) into 64-slice train 16-slice validation cohorts for machine learning classification, and a subset of highly prognostic features was selected with recursive feature elimination. Results A classification with k-means and hierarchical cluster analysis divided segments into four main clusters. The average CT density was significantly higher in cluster-4 (110 HU ± SD = 10.1HU) compared to the other clusters (c1: 96.1 HU ± SD = 11.3HU; p < 0.0001; c2: 90.8 HU ± SD = 16.8HU; p < 0.0001; c3: 93.1 HU ± SD = 17.5HU; p < 0.0001); but there was no difference in liver stiffness or scanner type among the clusters. The optimized random forest classifier was able to distinguish between low-grade and high-grade fibrosis with excellent cross-validated accuracy in both the first and second analysis (AUC = 0.90, CI = 0.85–0.95 vs. AUC = 0.88, CI = 0.84–0.91). The final support vector machine model achieved an excellent prediction rate in the second analysis (AUC = 0.91, CI = 0.88–0.94) and an acceptable prediction rate in the first analysis (AUC = 0.76, CI = 0.67–0.84). Conclusions In conclusion, CTTA-based models can be successfully applied to differentiate high-grade from low-grade fibrosis irrespective of the imaging platform. Thus, CTTA may be useful in the non-invasive prognostication of patients with chronic liver disease.
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Affiliation(s)
- Bettina Katalin Budai
- Department of Radiology, Medical Imaging Centre, Semmelweis University Faculty of Medicine, Korányi Sándor street 2., Budapest, H-1083, Hungary.
| | - Ambrus Tóth
- Department of Radiology, Medical Imaging Centre, Semmelweis University Faculty of Medicine, Korányi Sándor street 2., Budapest, H-1083, Hungary
| | - Petra Borsos
- Department of Radiology, Medical Imaging Centre, Semmelweis University Faculty of Medicine, Korányi Sándor street 2., Budapest, H-1083, Hungary
| | - Veronica Grace Frank
- Department of Radiology, Medical Imaging Centre, Semmelweis University Faculty of Medicine, Korányi Sándor street 2., Budapest, H-1083, Hungary
| | - Sonaz Shariati
- Department of Radiology, Medical Imaging Centre, Semmelweis University Faculty of Medicine, Korányi Sándor street 2., Budapest, H-1083, Hungary
| | - Bence Fejér
- Department of Radiology, Medical Imaging Centre, Semmelweis University Faculty of Medicine, Korányi Sándor street 2., Budapest, H-1083, Hungary
| | - Anikó Folhoffer
- 1st Department of Internal Medicine, Semmelweis University Faculty of Medicine, Korányi Sándor street 2/a, Budapest, H-1083, Hungary
| | - Ferenc Szalay
- 1st Department of Internal Medicine, Semmelweis University Faculty of Medicine, Korányi Sándor street 2/a, Budapest, H-1083, Hungary
| | - Viktor Bérczi
- Department of Radiology, Medical Imaging Centre, Semmelweis University Faculty of Medicine, Korányi Sándor street 2., Budapest, H-1083, Hungary
| | - Pál Novák Kaposi
- Department of Radiology, Medical Imaging Centre, Semmelweis University Faculty of Medicine, Korányi Sándor street 2., Budapest, H-1083, Hungary
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Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1:37-50. [DOI: 10.35712/aig.v1.i2.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/22/2020] [Accepted: 08/27/2020] [Indexed: 02/06/2023] Open
Abstract
Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.
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Affiliation(s)
- Pei Feng
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Zhen-Dong Wang
- Department of Ultrasound, Beijing Sihui Hospital of Traditional Chinese Medicine, Beijing 100022, China
| | - Wei Fan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Heng Liu
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Jing-Jing Pan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
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Choi B, Choi IY, Cha SH, Yeom SK, Chung HH, Lee SH, Cha J, Lee JH. Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography. Jpn J Radiol 2020; 38:1179-1189. [PMID: 32666182 DOI: 10.1007/s11604-020-01020-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 07/06/2020] [Indexed: 12/01/2022]
Abstract
PURPOSE To evaluate feasibility of computer tomography texture analysis (CTTA) at different energy level using dual-energy spectral detector CT for liver fibrosis. MATERIALS AND METHODS Eighty-seven patients who underwent a spectral CT examination and had a reference standard of liver fibrosis (histopathologic findings, n = 61, or clinical findings for normal, n = 26) were included. Mean gray-level intensity, mean number of positive pixels (MPP), entropy, skewness, and kurtosis using commercially available software (TexRAD) were compared at different energy levels. Optimal CTTA parameter cutoffs to diagnose liver fibrosis were evaluated. CTTA parameters at different energy levels correlated with liver fibrosis. The association of CTTA parameters with energy level was evaluated. RESULTS Mean gray-level intensity, skewness, kurtosis, and entropy showed significant differences between patients with and without clinically significant hepatic fibrosis (P < 0.05). Mean gray-level intensity at 50 keV was significantly positively correlated with liver fibrosis (ρ = 0.502, P < 0.001). To diagnose stages F2-F4, entropy and mean gray-level intensity at low keV level showed the largest area under the curve (AUC; 0.79 and 0.79). Estimated marginal means (EMMs) of mean gray-level intensity showed prominent differences at low energy levels. CONCLUSION CTTA parameters from different keV levels demonstrated meaningful accuracy for diagnosis of liver fibrosis or clinically significant hepatic fibrosis.
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Affiliation(s)
- ByukGyung Choi
- Department of Radiology, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - In Young Choi
- Department of Radiology, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea.
| | - Sang Hoon Cha
- Department of Radiology, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Suk Keu Yeom
- Department of Radiology, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Hwan Hoon Chung
- Department of Radiology, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Seung Hwa Lee
- Department of Radiology, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Jaehyung Cha
- Department of Biostatistics, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Ju-Han Lee
- Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
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Homayounieh F, Saini S, Mostafavi L, Doda Khera R, Sühling M, Schmidt B, Singh R, Flohr T, Kalra MK. Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT. Int J Comput Assist Radiol Surg 2020; 15:1727-1736. [DOI: 10.1007/s11548-020-02212-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/02/2020] [Indexed: 12/16/2022]
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Li Q, Yu B, Tian X, Cui X, Zhang R, Guo Q. Deep residual nets model for staging liver fibrosis on plain CT images. Int J Comput Assist Radiol Surg 2020; 15:1399-1406. [PMID: 32556922 DOI: 10.1007/s11548-020-02206-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/27/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE The early diagnosis of liver fibrosis is crucial for the prevention of liver cirrhosis and liver cancer. As gold standard for staging liver fibrosis, liver biopsy is an invasive procedure that carries the risk of serious complications. The aim of this study was to evaluate the performance of the residual neural network (ResNet), a non-invasive methods, for staging liver fibrosis using plain CT images. METHODS This retrospective study involved 347 patients subjected to liver CT scanning and liver biopsy. For each patient, we selected three axial images adjacent to the puncture location in the eighth or ninth inter-space on the right side. After processing and enhancement (rotation, translation, and amplification), these images were used as input data for the ResNet model. The model used a fivefold cross-validation method. In each fold, the images of approximately 80% of the total sample size (278 patients) were used for training the ResNet model, the other 20% (69 patients) were used for testing the trained network, with the liver biopsy pathology results as gold standard. The proportion of patients in each fibrosis stage was equal for training and test groups. The final result was the mean of the fivefold cross-validation in the test group. The performance of the ResNet model was evaluated for the test group by receiver operating characteristic (ROC) analysis. RESULTS For the ResNet model, the area under the ROC curve (AUC) for assessing cirrhosis (F4), advanced fibrosis (F3 or higher), significant fibrosis (F2 or higher), and mild fibrosis (F1 or higher) was 0.97, 0.94, 0.90, and 0.91, respectively. CONCLUSIONS The ResNet model analysis of plain CT images exhibited high diagnostic efficiency for liver fibrosis staging. As a convenient, fast, and economical non-invasive diagnostic method, the ResNet model can be used to assist radiologists and clinicians in liver fibrosis evaluations.
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Affiliation(s)
- Qiuju Li
- Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, Liaoning, China
| | - Bing Yu
- Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, Liaoning, China
| | - Xi Tian
- Institute of Advanced Research, Infervision, Beijing, China
| | - Xing Cui
- Institute of Advanced Research, Infervision, Beijing, China
| | - Rongguo Zhang
- Institute of Advanced Research, Infervision, Beijing, China
| | - Qiyong Guo
- Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, Liaoning, China.
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Equilibrium CT Texture Analysis for the Evaluation of Hepatic Fibrosis: Preliminary Evaluation against Histopathology and Extracellular Volume Fraction. J Pers Med 2020; 10:jpm10020046. [PMID: 32485820 PMCID: PMC7354541 DOI: 10.3390/jpm10020046] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/19/2020] [Accepted: 05/28/2020] [Indexed: 12/20/2022] Open
Abstract
Background: Evaluate equilibrium contrast-enhanced CT (EQ-CT) texture analysis (EQ-CTTA) against histologically-quantified fibrosis, serum-based enhanced liver fibrosis panel (ELF) and imaging-based extracellular volume fraction (ECV) in chronic hepatitis. Methods: This study was a re-analysis of image data from a previous prospective study. Pre- and equilibrium-phase post-IV contrast CT datasets were collected from patients with chronic hepatitis with contemporaneous liver biopsy and serum ELF measurement between April 2011 and July 2013. Biopsy samples were analysed to derive collagen proportionate area (CPA). EQ-CTTA was performed with a filtration histogram technique using texture analysis software, with texture quantification using statistical and histogram-based metrics (mean, skewness, standard deviation, entropy, etc.). Association between pre-contrast and EQ-CTTA against CPA, ECV and ELF was evaluated using Spearman’s rank correlation coefficient (rs). Results: Complete datasets collected in 29 patients (16 male; 13 female), mean age (range): 49 (22–66 years). Liver ECV, CPA and ELF had a median (interquartile range) of 0.26 (0.24–0.29); 5.0 (3.0–13.7) and 9.71 (8.39–10.92). Difference in segment VII hepatic CTTA (medium texture scale) between EQ-CT and pre-contrast images was significantly and positively associated with ELF score (mean: rs = 0.69, p < 0.001; skewness: rs = 0.57, p = 0.007). Significant negative associations were observed between pre-contrast and EQ-CT whole hepatic CTTA (coarse texture scale) with CPA (pre-contrast, SD: rs = −0.66, p < 0.001) and ECV (EQ-CT, entropy: rs = −0.58, p = 0.006). Conclusions: Hepatic EQ-CTTA demonstrates significant association with validated markers of liver fibrosis, suggesting a role in non-invasive quantification of severity in diffuse fibrosis.
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Schawkat K, Ciritsis A, von Ulmenstein S, Honcharova-Biletska H, Jüngst C, Weber A, Gubler C, Mertens J, Reiner CS. Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology. Eur Radiol 2020; 30:4675-4685. [PMID: 32270315 DOI: 10.1007/s00330-020-06831-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/11/2020] [Accepted: 03/24/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES To compare the diagnostic accuracy of texture analysis (TA)-derived parameters combined with machine learning (ML) of non-contrast-enhanced T1w and T2w fat-saturated (fs) images with MR elastography (MRE) for liver fibrosis quantification. METHODS In this IRB-approved prospective study, liver MRIs of participants with suspected chronic liver disease who underwent liver biopsy between August 2015 and May 2018 were analyzed. Two readers blinded to clinical and histopathological findings performed TA. The participants were categorized into no or low-stage (0-2) and high-stage (3-4) fibrosis groups. Confusion matrices were calculated using a support vector machine combined with principal component analysis. The diagnostic accuracy of ML-based TA of liver fibrosis and MRE was assessed by area under the receiver operating characteristic curves (AUC). Histopathology served as reference standard. RESULTS A total of 62 consecutive participants (40 men; mean age ± standard deviation, 48 ± 13 years) were included. The accuracy of TA and ML on T1w was 85.7% (95% confidence interval [CI] 63.7-97.0) and 61.9% (95% CI 38.4-81.9) on T2w fs for classification of liver fibrosis into low-stage and high-stage fibrosis. The AUC for TA on T1w was similar to MRE (0.82 [95% CI 0.59-0.95] vs. 0.92 [95% CI 0.71-0.99], p = 0.41), while the AUC for T2w fs was significantly lower compared to MRE (0.57 [95% CI 0.34-0.78] vs. 0.92 [95% CI 0.71-0.99], p = 0.008). CONCLUSION Our results suggest that liver fibrosis can be quantified with TA-derived parameters of T1w when combined with a ML algorithm with similar accuracy compared to MRE. KEY POINTS • Liver fibrosis can be categorized into low-stage fibrosis (0-2) and high-stage fibrosis (3-4) using texture analysis-derived parameters of T1-weighted images with a machine learning approach. • For the differentiation of low-stage fibrosis and high-stage fibrosis, the diagnostic accuracy of texture analysis on T1-weighted images combined with a machine learning algorithm is similar compared to MR elastography.
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Affiliation(s)
- Khoschy Schawkat
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.,Division of Abdominal Imaging, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,University of Zurich, Zurich, Switzerland
| | - Alexander Ciritsis
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Sophie von Ulmenstein
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Hanna Honcharova-Biletska
- University of Zurich, Zurich, Switzerland.,Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Christoph Jüngst
- University of Zurich, Zurich, Switzerland.,Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Achim Weber
- University of Zurich, Zurich, Switzerland.,Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Christoph Gubler
- University of Zurich, Zurich, Switzerland.,Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Joachim Mertens
- University of Zurich, Zurich, Switzerland.,Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Caecilia S Reiner
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland. .,University of Zurich, Zurich, Switzerland.
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Hu W, Yang H, Xu H, Mao Y. Radiomics based on artificial intelligence in liver diseases: where we are? Gastroenterol Rep (Oxf) 2020; 8:90-97. [PMID: 32280468 PMCID: PMC7136719 DOI: 10.1093/gastro/goaa011] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 09/22/2019] [Accepted: 10/27/2019] [Indexed: 12/12/2022] Open
Abstract
Radiomics uses computers to extract a large amount of information from different types of images, form various quantifiable features, and select relevant features using artificial-intelligence algorithms to build models, in order to predict the outcomes of clinical problems (such as diagnosis, treatment, prognosis, etc.). The study of liver diseases by radiomics will contribute to early diagnosis and treatment of liver diseases and improve survival and cure rates of liver diseases. This field is currently in the ascendant and may have great development in the future. Therefore, we summarize the progress of current research in this article and then point out the related deficiencies and the direction of future research.
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Affiliation(s)
- Wenmo Hu
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Huayu Yang
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Haifeng Xu
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Yilei Mao
- Department of Liver Surgery, Peking Union Medical College Hospital, PUMC, Chinese Academy of Medical Sciences, Beijing, P. R. China
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Alessandrino F, Qin L, Cruz G, Sahu S, Rosenthal MH, Meyerhardt JA, Shinagare AB. 5-Fluorouracil induced liver toxicity in patients with colorectal cancer: role of computed tomography texture analysis as a potential biomarker. Abdom Radiol (NY) 2019; 44:3099-3106. [PMID: 31250179 DOI: 10.1007/s00261-019-02110-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE To assess if CT texture analysis (TA) can serve as a biomarker of liver toxicity in patients with colorectal cancer treated with 5-fluorouracil (5-FU)-based chemotherapy. METHODS In this IRB-approved, HIPAA-compliant retrospective study, patients with colorectal cancer treated with 5-FU-based regimens during 2008-2010 were identified from institutional electronic database. Total 43 patients (23 women; mean age 56 years) with normal baseline liver function tests (LFTs), availability of baseline (pre-chemotherapy) and first follow-up CT (median 1.7 months, interquartile range (IQR) 1.5-2.5) performed during chemotherapy were included. Two single-slice ROI of right and left liver lobe were obtained on baseline and first follow-up CT for TA. Texture features [mean, entropy, kurtosis, skewness, mean of positive pixel, standard deviation (SD)] were extracted using a commercially available software (TexRAD; Feedback Medical Ltd, Cambridge, UK). Changes in texture parameters between baseline and follow-up CT were evaluated with Wilcoxon signed-rank test for patients with and without LFT elevation during chemotherapy. RESULTS Patients with LFT elevation (n = 34; 79%) showed significantly different mean, entropy, skewness, and SD (p values range 0.007-0.047) between baseline and first follow-up CT. No significant changes in features were observed in patients without LFT elevation (n = 9; 21%). In 19 patients (56%), first follow-up CT was performed before elevation of LFTs was observed. CONCLUSIONS This proof-of-concept study shows that there are early changes in liver texture on first follow-up CT in patients with LFT elevation during 5-FU-based chemotherapy for colorectal cancer. In more than 50% of cases, these changes occur before LFT elevation becomes evident on blood tests.
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Affiliation(s)
- Francesco Alessandrino
- Department of Imaging, Dana Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
| | - Lei Qin
- Department of Imaging, Dana Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Gisele Cruz
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Sonia Sahu
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Michael H Rosenthal
- Department of Imaging, Dana Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Jeffrey A Meyerhardt
- Gastrointestinal Cancer Treatment Center, Dana Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Atul B Shinagare
- Department of Imaging, Dana Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
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Shin HJ, Kwak JY, Lee E, Lee MJ, Yoon H, Han K, Kim MJ. Texture Analysis to Differentiate Malignant Renal Tumors in Children Using Gray-Scale Ultrasonography Images. ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:2205-2212. [PMID: 31076232 DOI: 10.1016/j.ultrasmedbio.2019.03.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 03/18/2019] [Accepted: 03/26/2019] [Indexed: 06/09/2023]
Abstract
We assessed the feasibility of texture analysis to differentiate Wilms tumor, clear cell sarcoma and rhabdoid tumor of the kidney in children using gray-scale ultrasonography images. Children who had pre-operative renal ultrasonography images of the three tumors from January 2002 to February 2017 were retrospectively included as the test set, and children with the same criteria from March 2017 to December 2018 were included as the validation set. From histogram and second-order statistics, features were compared between the tumors, and diagnostic performances were assessed. Among a total of 32 children (24 children with Wilms tumors, five children with clear cell sarcomas and three children with rhabdoid tumors) from the test set, features from the second-order statistics showed an area under the curve greater than 0.89 for differentiating Wilms tumor from the others. These features aided in the differentiation of tumor type in the two children with Wilms tumors in the validation set. Therefore, texture analysis from gray-scale ultrasonography images can be used to differentiate Wilms tumors from clear cell sarcomas and rhabdoid tumors in children.
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Affiliation(s)
- Hyun Joo Shin
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Young Kwak
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Eunjung Lee
- Department of Computational Science and Engineering, Yonsei University, Seoul, South Korea
| | - Mi-Jung Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Haesung Yoon
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Myung-Joon Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea.
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Quantification of Degree of Liver Fibrosis Using Fibrosis Area Fraction Based on Statistical Chi-Square Analysis of Heterogeneity of Liver Tissue Texture on Routine Ultrasound Images. Acad Radiol 2019; 26:1001-1007. [PMID: 30393055 DOI: 10.1016/j.acra.2018.10.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 10/12/2018] [Accepted: 10/12/2018] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES We present a novel method to quantify the degree of liver fibrosis using fibrosis area fraction based on statistical chi-square analysis of heterogeneity of echo texture within liver on routine ultrasound images. We demonstrate, in a clinical study, that fibrosis area fraction derived this way has the potential to become a noninvasive, quantitative radiometric discriminator of normal or low-grade liver fibrosis (Ishak fibrosis score range = F0-3) and advanced liver fibrosis or cirrhosis (Ishak fibrosis score range = F4-6) on routine ultrasound images. MATERIALS AND METHODS This retrospective patient study was institutional review board approved. Ultrasound images of 100 patients (61 males, 39 females; 18-81 years) who underwent nontargeted ultrasound-guided biopsy were randomly divided into two groups: a training group consisted of 31 cases, and a validation group that contained the rest cases. An investigator manually selected a primary region of interest (ROI; approximately 4-6 cm2) in the liver tissue while avoiding nonhepatic parenchyma. The primary ROI contained a large number of secondary ROIs (25 × 25 pixels) to maintain the precision of statistical analysis. Sample variance σ2 of image gradient (a texture feature related to the amount of edge structures) was calculated in secondary ROIs in a roster scan fashion. A theoretical derivation was presented to estimate population variance [Formula: see text] of image gradient across the primary ROI from mean gradient µ of secondary ROIs. The χ2 (χ2 = σ2/ [Formula: see text] ) was computed at each secondary ROI, forming a χ2 map of liver tissue heterogeneity. A cut-off value was optimized from datasets in the training group by comparing to the fibrosis grades determined by biopsy. This cut-off value was then applied to the datasets in the validation group to convert the χ2 maps into binary images, from which fibrosis area fractions (fraction of fibrosis area to the total area of the primary ROI) were calculated and entered in a statistical analysis. RESULTS In the training group, the optimal setting was found to be [Formula: see text] = 6.0, which resulted a maximum discrimination of F0-3 vs F4-6: p < 0.0001, area under curve = 0.985, sensitivity = 93.7%, specificity = 93.3%. When this setting was applied to the datasets in the validation group, a distinct separation was seen between the two classes (p < 0.0001). F0-3 class had an average fibrosis area fraction of 4.7% (1.7%-11.4%), whereas the F4-6 class had an average fibrosis area fraction of 17.3% (9.8%-29.6%). A strong correlation was demonstrated between the fibrosis area fraction and histological fibrosis grade determined by biopsy (area under curve = 0.89, sensitivity = 87.9%, specificity = 90.3%). CONCLUSION The presented method is a promising noninvasive tool for distinguishing normal or low-grade liver fibrosis (F0-3) and advanced liver fibrosis or cirrhosis (F4-6) from routine ultrasound images. These findings support the further development of texture heterogeneity analysis, particularly fibrosis area fraction, as a quantitative biomarker for distinguishing various liver fibrosis grades.
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Cannella R, Borhani AA, Tublin M, Behari J, Furlan A. Diagnostic value of MR-based texture analysis for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). Abdom Radiol (NY) 2019; 44:1816-1824. [PMID: 30788556 DOI: 10.1007/s00261-019-01931-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the performance of MR-based texture analysis (TA) for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). METHODS Fifty-four adult patients (33 females, 21 males, mean age 49.8 ± 13.5 years) with biopsy-proven NAFLD were enrolled and underwent MR imaging on a 1.5 T system. TA parameters were extracted on axial noncontrast 3D-GRE T1W images (slice thickness = 4.6 mm) using a commercially available research software (TexRAD). Receiver operating curves (ROC), areas under the ROC (AUROC) and 95% confidence intervals (CI) were calculated to assess the accuracy of each TA parameter for the diagnosis of significant (F ≥ 2) and advanced fibrosis (F ≥ 3). The correlation between TA and histopathological features of nonalcoholic steatohepatitis (NASH) was tested calculating the Spearman's rank correlation coefficient (ρ). RESULTS Thirty-seven (68%) subjects had significant fibrosis and 20 (37%) had advanced fibrosis. The TA parameters with the best performance were standard deviation (SD) and entropy, respectively, with AUROC 0.755 (95% CI 0.619-0.862, p ≤ 0.0002) and 0.769 (95% CI 0.634-0.873, p < 0.0001) for significant fibrosis and AUROC 0.746 (95% CI 0.609-0.854, p ≤ 0.0004) and 0.754 (95% CI 0.618-0.861, p ≤ 0.0002) for advanced fibrosis. SD and entropy demonstrated a moderate correlation with the degree of fibrosis (ρ = 0.457 and 0.480; p < 0.01). No significant correlation was found between TA parameters and other histopathological features of NASH. CONCLUSIONS Entropy and SD extracted on T1-weighted MR images have fair accuracy for the diagnosis of significant and advanced hepatic fibrosis in patients with NAFLD.
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Affiliation(s)
- Roberto Cannella
- Department of Radiology, University of Pittsburgh School of Medicine, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Section of Radiology - Di.Bi.Med, University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy
| | - Amir A Borhani
- Department of Radiology, University of Pittsburgh School of Medicine, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
| | - Mitchell Tublin
- Department of Radiology, University of Pittsburgh School of Medicine, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
| | - Jaideep Behari
- Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
| | - Alessandro Furlan
- Department of Radiology, University of Pittsburgh School of Medicine, 200 Lothrop Street, Pittsburgh, PA, 15213, USA.
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Sung P, Lee JM, Joo I, Lee S, Kim TH, Ganeshan B. Evaluation of the Impact of Iterative Reconstruction Algorithms on Computed Tomography Texture Features of the Liver Parenchyma Using the Filtration-Histogram Method. Korean J Radiol 2019; 20:558-568. [PMID: 30887738 PMCID: PMC6424830 DOI: 10.3348/kjr.2018.0368] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Accepted: 10/05/2018] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE To evaluate whether computed tomography (CT) reconstruction algorithms affect the CT texture features of the liver parenchyma. MATERIALS AND METHODS This retrospective study comprised 58 patients (normal liver, n = 34; chronic liver disease [CLD], n = 24) who underwent liver CT scans using a single CT scanner. All CT images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR) (iDOSE⁴), and model-based IR (IMR). On arterial phase (AP) and portal venous phase (PVP) CT imaging, quantitative texture analysis of the liver parenchyma using a single-slice region of interest was performed at the level of the hepatic hilum using a filtration-histogram statistic-based method with different filter values. Texture features were compared among the three reconstruction methods and between normal livers and those from CLD patients. Additionally, we evaluated the inter- and intra-observer reliability of the CT texture analysis by calculating intraclass correlation coefficients (ICCs). RESULTS IR techniques affect various CT texture features of the liver parenchyma. In particular, model-based IR frequently showed significant differences compared to FBP or hybrid IR on both AP and PVP CT imaging. Significant variation in entropy was observed between the three reconstruction algorithms on PVP imaging (p < 0.05). Comparison between normal livers and those from CLD patients revealed that AP images depend more strongly on the reconstruction method used than PVP images. For both inter- and intra-observer reliability, ICCs were acceptable (> 0.75) for CT imaging without filtration. CONCLUSION CT texture features of the liver parenchyma evaluated using the filtration-histogram method were significantly affected by the CT reconstruction algorithm used.
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Affiliation(s)
- Pamela Sung
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Sanghyup Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Tae Hyung Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Balaji Ganeshan
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, England, UK
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Esposito A, Palmisano A, Antunes S, Colantoni C, Rancoita PMV, Vignale D, Baratto F, Della Bella P, Del Maschio A, De Cobelli F. Assessment of Remote Myocardium Heterogeneity in Patients with Ventricular Tachycardia Using Texture Analysis of Late Iodine Enhancement (LIE) Cardiac Computed Tomography (cCT) Images. Mol Imaging Biol 2019. [PMID: 29536321 PMCID: PMC6153681 DOI: 10.1007/s11307-018-1175-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Purpose Diffuse remodeling of myocardial extra-cellular matrix is largely responsible for left ventricle (LV) dysfunction and arrhythmias. Our hypothesis is that the texture analysis of late iodine enhancement (LIE) cardiac computed tomography (cCT) images may improve characterization of the diffuse extra-cellular matrix changes. Our aim was to extract volumetric extracellular volume (ECV) and LIE texture features of non-scarred (remote) myocardium from cCT of patients with recurrent ventricular tachycardia (rVT), and to compare these radiomic features with LV-function, LV-remodeling, and underlying cardiac disease. Procedures Forty-eight patients suffering from rVT were prospectively enrolled: 5/48 with idiopathic VT (IVT), 23/48 with post-ischemic dilated cardiomyopathy (ICM), 9/48 with idiopathic dilated cardiomyopathy (IDCM), and 11/48 with scars from a previous healed myocarditis (MYO). All patients underwent echocardiography to assess LV systolic and diastolic function and cCT with pre-contrast, angiographic, and LIE scan to obtain end-diastolic volume (EDV), ECV, and first-order texture parameters of Hounsfield Unit (HU) of remote myocardium in LIE [energy, entropy, HU-mean, HU-median, standard deviation (SD), and mean absolute deviation (MAD)]. Results Energy, HU mean, and HU median by cCT texture analysis correlated with ECV (rho = 0.5650, rho = 0.5741, rho = 0.5068; p < 0.0005). cCT-derived ECV, HU-mean, HU-median, SD, and MAD correlated directly to EDV by cCT and inversely to ejection fraction by echocardiography (p < 0.05). SD and MAD correlated with diastolic function by echocardiography (rho = 0.3837, p = 0.0071; rho = 0.3330, p = 0.0208). MYO and IVT patients were characterized by significantly lower values of SD and MAD when compared with ICM and IDCM patients, independently of LV-volume systolic and diastolic function. Conclusions Texture analysis of LIE may expand cCT capability of myocardial characterization. Myocardial heterogeneity (SD and MAD) was associated with LV dilatation, systolic and diastolic function, and is able to potentially identify the different patterns of structural remodeling characterizing patients with rVT of different etiology.
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Affiliation(s)
- Antonio Esposito
- Clinical and Experimental Radiology Unit, Experimental Imaging Center, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy. .,Vita-Salute San Raffaele University, Milan, Italy.
| | - Anna Palmisano
- Clinical and Experimental Radiology Unit, Experimental Imaging Center, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Sofia Antunes
- Images Post-Processing and Analysis Unit, Experimental Imaging Center, San Raffaele Scientific Institute, Milan, Italy
| | - Caterina Colantoni
- Clinical and Experimental Radiology Unit, Experimental Imaging Center, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Maria Vittoria Rancoita
- University Centre for Statistics in the Biomedical Sciences (CUSSB), Vita-Salute San Raffaele University, Milan, Italy
| | - Davide Vignale
- Clinical and Experimental Radiology Unit, Experimental Imaging Center, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Francesca Baratto
- Arrhythmia Unit and Electrophysiology Laboratories, San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Della Bella
- Arrhythmia Unit and Electrophysiology Laboratories, San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Del Maschio
- Clinical and Experimental Radiology Unit, Experimental Imaging Center, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco De Cobelli
- Clinical and Experimental Radiology Unit, Experimental Imaging Center, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
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Ding J, Xing Z, Jiang Z, Zhou H, Di J, Chen J, Qiu J, Yu S, Zou L, Xing W. Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging. Eur Radiol 2018; 29:2293-2301. [PMID: 30560361 DOI: 10.1007/s00330-018-5911-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 10/24/2018] [Accepted: 11/23/2018] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To explore the value of texture analysis based on diffusion-weighted imaging (DWI), blood oxygen level-dependent MRI (BOLD), and susceptibility-weighted imaging (SWI) in evaluating renal dysfunction. METHODS Seventy-two patients (mean age 53.72 ± 13.46 years) underwent MRI consisting of DWI, BOLD, and SWI. According to their estimated glomerular filtration rate (eGFR), the patients were classified into either severe renal function impairment (sRI, eGFR < 30 mL/min/1.73 m2), non-severe renal function impairment (non-sRI, eGFR ≥ 30 mL/min/1.73 m2, and < 80 mL/min/1.73 m2), or control (CG, eGFR ≥ 80 mL/min/1.73 m2) groups. Thirteen texture features were extracted and then were analyzed to select the most valuable for discerning the three groups with each imaging method. A ROC curve was performed to compare the capacities of the features to differentiate non-sRI from sRI or CG. RESULTS Six features proved to be the most valuable for assessing renal dysfunction: 0.25QuantileDWI, 0.5QuantileDWI, HomogeneityDWI, EntropyBOLD, SkewnessSWI, and CorrelationSWI. Three features derived from DWI (0.25QuantileDWI, 0.5QuantileDWI, and HomogeneityDWI) were smaller in sRI than in non-sRI; EntropyBOLD and CorrelationSWI were smaller in non-sRI than in CG (p < 0.05). 0.25QuantileDWI, 0.5QuantileDWI, and HomogeneityDWI showed similar capacities for differentiating sRI from non-sRI. Similarly, EntropyBOLD and CorrelationSWI showed equal capacities for differentiating non-sRI from CG. CONCLUSION Texture analysis based on DWI, BOLD, and SWI can assist in assessing renal dysfunction, and texture features based on BOLD and SWI may be suitable for assessing renal dysfunction during early stages. KEY POINTS • Texture analysis based on MRI techniques allowed for assessing renal dysfunction. • Texture features based on BOLD and SWI, but not DWI, may be suitable for assessing renal function impairment during early stages. • SWI exhibited a similar capacity to BOLD for assessing renal dysfunction.
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Affiliation(s)
- Jiule Ding
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China
| | - Zhaoyu Xing
- Department of Urology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China
| | - Zhenxing Jiang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China
| | - Hua Zhou
- Department of Nephrology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China
| | - Jia Di
- Department of Nephrology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China
| | - Jie Chen
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China
| | - Jianguo Qiu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China
| | - Shengnan Yu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China
| | - Liqiu Zou
- Department of Radiology, Shenzhen nanshan People's Hospital, Shenzhen University Health Science Center, Shenzhen, 518000, Guangdong, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China.
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Zheng BH, Liu LZ, Zhang ZZ, Shi JY, Dong LQ, Tian LY, Ding ZB, Ji Y, Rao SX, Zhou J, Fan J, Wang XY, Gao Q. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer 2018; 18:1148. [PMID: 30463529 PMCID: PMC6249916 DOI: 10.1186/s12885-018-5024-z] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 10/31/2018] [Indexed: 12/18/2022] Open
Abstract
Background Radiomics is an emerging field in oncological research. In this study, we aimed at developing a radiomics score (rad-score) to estimate postoperative recurrence and survival in patients with solitary hepatocellular carcinoma (HCC). Methods A total of 319 solitary HCC patients (training cohort: n = 212; validation cohort: n = 107) were enrolled. Radiomics features were extracted from the artery phase of preoperatively acquired computed tomography (CT) in all patients. A rad-score was generated by using the least absolute shrinkage and selection operator (lasso) logistic model. Kaplan-Meier and Cox’s hazard regression analyses were used to evaluate the prognostic significance of the rad-score. Final nomograms predicting recurrence and survival of solitary HCC patients were established based on the rad-score and clinicopathological factors. C-index and calibration statistics were used to assess the performance of nomograms. Results Six potential radiomics features were selected out of 110 texture features to formulate the rad-score. Low rad-score positively correlated with aggressive tumor phenotypes, like larger tumor size and vascular invasion. Meanwhile, low rad-score was significantly associated with increased recurrence and reduced survival. In addition, multivariate analysis identified the rad-score as an independent prognostic factor (recurrence: Hazard ratio (HR): 2.472, 95% confident interval (CI): 1.339–4.564, p = 0.004;survival: HR: 1.558, 95%CI: 1.022–2.375, p = 0.039). Notably, the nomogram integrating rad-score had a better prognostic performance as compared with traditional staging systems. These results were further confirmed in the validation cohort. Conclusions The preoperative CT image based rad-score was an independent prognostic factor for the postoperative outcome of solitary HCC patients. This score may be complementary to the current staging system and help to stratify individualized treatments for solitary HCC patients. Electronic supplementary material The online version of this article (10.1186/s12885-018-5024-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bo-Hao Zheng
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Long-Zi Liu
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Zhi-Zhi Zhang
- Department of Hematology, Shanghai Jiao Tong University School of Medicine Affiliated Tongren Hospital, Shanghai, China
| | - Jie-Yi Shi
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Liang-Qing Dong
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ling-Yu Tian
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Zhen-Bin Ding
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yuan Ji
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Sheng-Xiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jian Zhou
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.,Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Xiao-Ying Wang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. .,State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, People's Republic of China.
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Accuracy of liver surface nodularity quantification on MDCT for staging hepatic fibrosis in patients with hepatitis C virus. Abdom Radiol (NY) 2018; 43:2980-2986. [PMID: 29572714 DOI: 10.1007/s00261-018-1572-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE To evaluate semi-automated measurement of liver surface nodularity (LSN) on MDCT in a cause-specific cohort of patients with chronic hepatitis C virus infection (HCV) for identification of hepatic fibrosis (stages F0-4). METHODS MDCT scans in patients with known HCV were evaluated with an independently validated, semi-automated LSN measurement tool. Consecutive LSN measurements along the anterior liver surface were performed to derive mean LSN scores. Scores were compared with METAVIR fibrosis stage (F0-4). Fibrosis stages F0-3 were based on biopsy results within 1 year of CT. Most patients with cirrhosis (F4) also had biopsy within 1 year; the remaining cases had unequivocal clinical/imaging evidence of cirrhosis and biopsy was not indicated. RESULTS 288 patients (79F/209M; mean age, 49.7 years) with known HCV were stratified based on METAVIR fibrosis stage: F0 (n = 43), F1 (n = 29), F2 (n = 53), F3 (n = 37), and F4 (n = 126). LSN scores increased with increasing fibrosis (mean: F0 = 2.3 ± 0.2, F1 = 2.4 ± 0.3, F2 = 2.6 ± 0.5, F3 = 2.9 ± 0.6, F4 = 3.8 ± 1.0; p < 0.001). For identification of significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (≥ F4), the ROC AUCs were 0.88, 0.89, and 0.90, respectively. The sensitivity and specificity for significant fibrosis (≥ F2) using LSN threshold of 2.80 were 0.68 and 0.97; for advanced fibrosis (≥ F3; threshold = 2.77) were 0.83 and 0.85; and for cirrhosis (≥ F4, LSN threshold = 2.9) were 0.90 and 0.80. CONCLUSION Liver surface nodularity assessment at MDCT allows for accurate discrimination of intermediate stages of hepatic fibrosis in a cause-specific cohort of patients with HCV, particularly at more advanced levels.
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