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Qi H, Jiang S, Nan J, Guo H, Cheng C, He X, Jin H, Zhang R, Lei J. Application and research progress of magnetic resonance proton density fat fraction in metabolic dysfunction-associated steatotic liver disease: a comprehensive review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04448-9. [PMID: 39048719 DOI: 10.1007/s00261-024-04448-9] [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: 04/29/2024] [Revised: 06/06/2024] [Accepted: 06/07/2024] [Indexed: 07/27/2024]
Abstract
Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD), is a chronic liver disorder associated with disturbances in lipid metabolism. The disease is prevalent worldwide, particularly closely linked with metabolic syndromes such as obesity and diabetes. Magnetic Resonance Proton Density Fat Fraction (MRI-PDFF), serving as a non-invasive and highly quantitative imaging assessment tool, holds promising applications in the diagnosis and research of MASLD. This paper aims to comprehensively review and summarize the applications and research progress of MRI-PDFF technology in MASLD, analyze its strengths and challenges, and anticipate its future developments in clinical practice.
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Affiliation(s)
- Hongyan Qi
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Chengguan District, Lanzhou City, 730000, Gansu Province, China
| | | | - Jiang Nan
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Hang Guo
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Cai Cheng
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Xin He
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Hongyang Jin
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Rongfan Zhang
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Chengguan District, Lanzhou City, 730000, Gansu Province, China.
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
- Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, Gansu, China.
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Jeon SK, Joo I, Park J, Yoo J. Automated hepatic steatosis assessment on dual-energy CT-derived virtual non-contrast images through fully-automated 3D organ segmentation. LA RADIOLOGIA MEDICA 2024; 129:967-976. [PMID: 38869829 PMCID: PMC11252222 DOI: 10.1007/s11547-024-01833-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/30/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE To evaluate the efficacy of volumetric CT attenuation-based parameters obtained through automated 3D organ segmentation on virtual non-contrast (VNC) images from dual-energy CT (DECT) for assessing hepatic steatosis. MATERIALS AND METHODS This retrospective study included living liver donor candidates having liver DECT and MRI-determined proton density fat fraction (PDFF) assessments. Employing a 3D deep learning algorithm, the liver and spleen were automatically segmented from VNC images (derived from contrast-enhanced DECT scans) and true non-contrast (TNC) images, respectively. Mean volumetric CT attenuation values of each segmented liver (L) and spleen (S) were measured, allowing for liver attenuation index (LAI) calculation, defined as L minus S. Agreements of VNC and TNC parameters for hepatic steatosis, i.e., L and LAI, were assessed using intraclass correlation coefficients (ICC). Correlations between VNC parameters and MRI-PDFF values were assessed using the Pearson's correlation coefficient. Their performance to identify MRI-PDFF ≥ 5% and ≥ 10% was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS Of 252 participants, 56 (22.2%) and 16 (6.3%) had hepatic steatosis with MRI-PDFF ≥ 5% and ≥ 10%, respectively. LVNC and LAIVNC showed excellent agreement with LTNC and LAITNC (ICC = 0.957 and 0.968) and significant correlations with MRI-PDFF values (r = - 0.585 and - 0.588, Ps < 0.001). LVNC and LAIVNC exhibited areas under the ROC curve of 0.795 and 0.806 for MRI-PDFF ≥ 5%; and 0.916 and 0.932, for MRI-PDFF ≥ 10%, respectively. CONCLUSION Volumetric CT attenuation-based parameters from VNC images generated by DECT, via automated 3D segmentation of the liver and spleen, have potential for opportunistic hepatic steatosis screening, as an alternative to TNC images.
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Affiliation(s)
- Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center Seoul National University Hospital, Seoul, Korea.
| | - Junghoan Park
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea
| | - Jeongin Yoo
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea
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Horbal SR, Belancourt PX, Zhang P, Holcombe SA, Saini S, Wang SC, Sales AE, Su GL. Independent Associations of Aortic Calcification with Cirrhosis and Liver Related Mortality in Veterans with Chronic Liver Disease. Dig Dis Sci 2024; 69:2681-2690. [PMID: 38653948 PMCID: PMC11258161 DOI: 10.1007/s10620-024-08450-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Abdominal aortic calcifications (AAC) are incidentally found on medical imaging and useful cardiovascular burden approximations. The Morphomic Aortic Calcification Score (MAC) leverages automated deep learning methods to quantify and score AACs. While associations of AAC and non-alcoholic fatty liver disease (NAFLD) have been described, relationships of AAC with other liver diseases and clinical outcome are sparse. This study's purpose was to evaluate AAC and liver-related death in a cohort of Veterans with chronic liver disease (CLD). METHODS We utilized the VISN 10 CLD cohort, a regional cohort of Veterans with the three forms of CLD: NAFLD, hepatitis C (HCV), alcohol-associated (ETOH), seen between 2008 and 2014, with abdominal CT scans (n = 3604). Associations between MAC and cirrhosis development, liver decompensation, liver-related death, and overall death were evaluated with Cox proportional hazard models. RESULTS The full cohort demonstrated strong associations of MAC and cirrhosis after adjustment: HR 2.13 (95% CI 1.63, 2.78), decompensation HR 2.19 (95% CI 1.60, 3.02), liver-related death HR 2.13 (95% CI 1.46, 3.11), and overall death HR 1.47 (95% CI 1.27, 1.71). These associations seemed to be driven by the non-NAFLD groups for decompensation and liver-related death [HR 2.80 (95% CI 1.52, 5.17; HR 2.34 (95% CI 1.14, 4.83), respectively]. DISCUSSION MAC was strongly and independently associated with cirrhosis, liver decompensation, liver-related death, and overall death. Surprisingly, stratification results demonstrated comparable or stronger associations among those with non-NAFLD etiology. These findings suggest abdominal aortic calcification may predict liver disease severity and clinical outcomes in patients with CLD.
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Affiliation(s)
- Steven R Horbal
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA.
- Morphomics Analysis Group, University of Michigan, Ann Arbor, MI, USA.
| | | | - Peng Zhang
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
- Morphomics Analysis Group, University of Michigan, Ann Arbor, MI, USA
| | - Sven A Holcombe
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
- Morphomics Analysis Group, University of Michigan, Ann Arbor, MI, USA
| | - Sameer Saini
- Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI, USA
| | - Stewart C Wang
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Anne E Sales
- VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Sinclair School of Nursing and Department of Family and Community Medicine, University of Missouri, Colombia, MO, USA
| | - Grace L Su
- Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI, USA
- Gastroenterology Section, Ann Arbor VA Healthcare System, Ann Arbor, MI, USA
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Martín-Saladich Q, Pericàs JM, Ciudin A, Ramirez-Serra C, Escobar M, Rivera-Esteban J, Aguadé-Bruix S, González Ballester MA, Herance JR. Metabolic-associated fatty liver voxel-based quantification on CT images using a contrast adapted automatic tool. Med Image Anal 2024; 95:103185. [PMID: 38718716 DOI: 10.1016/j.media.2024.103185] [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: 11/14/2022] [Revised: 12/22/2023] [Accepted: 04/19/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND & AIMS Metabolic-dysfunction associated fatty liver disease (MAFLD) is highly prevalent and can lead to liver complications and comorbidities, with non-invasive tests such as vibration-controlled transient elastography (VCTE) and invasive liver biopsies being used for diagnosis The aim of the present study was to develop a new fully automatized method for quantifying the percentage of fat in the liver based on a voxel analysis on computed tomography (CT) images to solve previously unconcluded diagnostic deficiencies either in contrast (CE) or non-contrast enhanced (NCE) assessments. METHODS Liver and spleen were segmented using nn-UNet on CE- and NCE-CT images. Radiodensity values were obtained for both organs for defining the key benchmarks for fatty liver assessment: liver mean, liver-to-spleen ratio, liver-spleen difference, and their average. VCTE was used for validation. A classification task method was developed for detection of suitable patients to fulfill maximum reproducibility across cohorts and highlight subjects with other potential radiodensity-related diseases. RESULTS Best accuracy was attained using the average of all proposed benchmarks being the liver-to-spleen ratio highly useful for CE and the liver-to-spleen difference for NCE. The proposed whole-organ automatic segmentation displayed superior potential when compared to the typically used manual region-of-interest drawing as it allows to accurately obtain the percent of fat in liver, among other improvements. Atypical patients were successfully stratified through a function based on biochemical data. CONCLUSIONS The developed method tackles the current drawbacks including biopsy invasiveness, and CT-related weaknesses such as lack of automaticity, dependency on contrast agent, no quantification of the percentage of fat in liver, and limited information on region-to-organ affectation. We propose this tool as an alternative for individualized MAFLD evaluation by an early detection of abnormal CT patterns based in radiodensity whilst abording detection of non-suitable patients to avoid unnecessary exposure to CT radiation. Furthermore, this work presents a surrogate aid for assessing fatty liver at a primary assessment of MAFLD using elastography data.
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Affiliation(s)
- Queralt Martín-Saladich
- Nuclear Medicine, Radiology and Cardiology Departments, Medical Molecular Imaging Research Group, Vall d'Hebron Research Institute (VHIR), Vall d'Hebron University Hospital, Autonomous University Barcelona, Barcelona 08035, Spain; Department of Information and Communication Technologies, BCN MedTech, Universitat Pompeu Fabra, Barcelona 08018, Spain
| | - Juan M Pericàs
- Vall d'Hebron Institute for Research, Liver Unit, Vall d'Hebron University Hospital, Barcelona 08035, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Andreea Ciudin
- Endocrinology Department, Diabetes and Metabolism Research Group, VHIR, Vall d'Hebron University Hospital, Autonomous University Barcelona, Barcelona 08035, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Clara Ramirez-Serra
- Clinical Biochemistry Research Group, Vall d'Hebron Research Institute (VHIR), Biochemical Core Facilities, Vall d'Hebron University Hospital, Autonomous University Barcelona, Barcelona 08035, Spain
| | - Manuel Escobar
- Nuclear Medicine, Radiology and Cardiology Departments, Medical Molecular Imaging Research Group, Vall d'Hebron Research Institute (VHIR), Vall d'Hebron University Hospital, Autonomous University Barcelona, Barcelona 08035, Spain
| | - Jesús Rivera-Esteban
- Vall d'Hebron Institute for Research, Liver Unit, Vall d'Hebron University Hospital, Barcelona 08035, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid 28029, Spain
| | - Santiago Aguadé-Bruix
- Nuclear Medicine, Radiology and Cardiology Departments, Medical Molecular Imaging Research Group, Vall d'Hebron Research Institute (VHIR), Vall d'Hebron University Hospital, Autonomous University Barcelona, Barcelona 08035, Spain
| | - Miguel A González Ballester
- Department of Information and Communication Technologies, BCN MedTech, Universitat Pompeu Fabra, Barcelona 08018, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona 08010, Spain
| | - José Raul Herance
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid 28029, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid 28029, Spain.
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Pickhardt PJ, Blake GM, Moeller A, Garrett JW, Summers RM. Post-contrast CT liver attenuation alone is superior to the liver-spleen difference for identifying moderate hepatic steatosis. Eur Radiol 2024:10.1007/s00330-024-10816-2. [PMID: 38834787 DOI: 10.1007/s00330-024-10816-2] [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: 01/06/2024] [Revised: 04/05/2024] [Accepted: 04/20/2024] [Indexed: 06/06/2024]
Abstract
OBJECTIVE To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values. MATERIALS AND METHODS A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis. RESULTS The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001). CONCLUSION Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less. CLINICAL RELEVANCE STATEMENT Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection. KEY POINTS The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone.
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Affiliation(s)
- Perry J Pickhardt
- The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
| | - Glen M Blake
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Alex Moeller
- The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - John W Garrett
- The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
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Zamanian H, Shalbaf A. Estimation of non-alcoholic steatohepatitis (NASH) disease using clinical information based on the optimal combination of intelligent algorithms for feature selection and classification. Comput Methods Biomech Biomed Engin 2024; 27:964-979. [PMID: 37254745 DOI: 10.1080/10255842.2023.2217978] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 05/12/2023] [Indexed: 06/01/2023]
Abstract
The early diagnosis of NASH disease can decrease the risk of proceeding elements and treatment costs for patients. This study aims to present an optimal combination of intelligent algorithms using advanced machine learning methods, including different feature selections and classifications based on clinical data and blood factors. In this work, collected data were from 176 patients to investigate NASH disease, and 19 features were extracted. We then sought to find the best combination of features based on different feature selection algorithms such as Feature Forward Selection (FFS), Minimum Redundancy Maximum Relevance (MRMR), and Mutual Information (MI). Finally, we used nine classifier frameworks with different mathematical mechanisms, including random forest (RF), logistic regression (LR), Linear Discriminant Analysis (LDA), AdaBoost, K nearest neighbors (KNN), multilayer perceptron model (MLP), support vector machine (SVM), and decision tree (DT) to estimate NASH disease. Our investigation revealed that the combination of dominant features, namely body mass index (BMI), glutamic pyruvic transaminase (GPT), total cholesterol (TC), high-density lipoprotein (HDL), Ezetimibe, lipoprotein level Lp(a), Loge(Lp(a)), total triglyceride (TG), Creatinine (Cre), HbA1c, Fibrate, and Sex, selected by the MRMR algorithm and classified by the RF method can provide the most appropriate performance based on less computation effort and maximum performance with accuracy, AUC, precision, and recall indices, which are 81.51 ± 9.35 , 82.53 ± 11.24 , 85.28 ± 9.68 , and 89.49 ± 7.92 , respectively. This study investigated the configuration of feature selection and classifier that is most suitable for classifying NASH disease based on clinical data and blood factors. The proposed intelligent algorithm based on MRMR and RF classifier can automatically diagnose NASH disease with appropriate performance and present an initial report without any further invasive methods. It also clarifies the diagnostic process and, as a result, the continuation of their prevention and treatment cycle.
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Affiliation(s)
- Hamed Zamanian
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Zhang Z, Li G, Wang Z, Xia F, Zhao N, Nie H, Ye Z, Lin JS, Hui Y, Liu X. Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT. Sci Rep 2024; 14:11987. [PMID: 38796521 PMCID: PMC11127985 DOI: 10.1038/s41598-024-62887-2] [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: 10/10/2023] [Accepted: 05/22/2024] [Indexed: 05/28/2024] Open
Abstract
Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.
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Affiliation(s)
- Zhongyi Zhang
- Department of Nephrology, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Shandong University, Jinan, 250033, Shandong, China
| | - Guixia Li
- Department of Nephrology, Shenzhen Third People's Hospital, the Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518112, Guangdong, China
| | - Ziqiang Wang
- Department of Nephrology, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102, Hainan, China
| | - Feng Xia
- Department of Cardiovascular Surgery, Wuhan Asia General Hospital, Wuhan, 430000, Hubei, China
| | - Ning Zhao
- The First Clinical Medical School, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Huibin Nie
- Department of Nephrology, Chengdu First People's Hospital, Chengdu, 610021, Sichuan, China
| | - Zezhong Ye
- Independent Researcher, Boston, MA, 02115, USA
| | - Joshua S Lin
- Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Yiyi Hui
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
| | - Xiangchun Liu
- Department of Nephrology, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Shandong University, Jinan, 250033, Shandong, China.
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Moeller AR, Garrett JW, Summers RM, Pickhardt PJ. Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase. Abdom Radiol (NY) 2024:10.1007/s00261-024-04376-8. [PMID: 38744704 DOI: 10.1007/s00261-024-04376-8] [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: 02/09/2024] [Revised: 05/04/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVE Fully-automated CT-based algorithms for quantifying numerous biomarkers have been validated for unenhanced abdominal scans. There is great interest in optimizing the documentation and reporting of biophysical measures present on all CT scans for the purposes of opportunistic screening and risk profiling. The purpose of this study was to determine and adjust the effect of intravenous (IV) contrast on these automated body composition measures at routine portal venous phase post-contrast imaging. METHODS Final study cohort consisted of 1,612 older adults (mean age, 68.0 years; 594 women) all imaged utilizing a uniform CT urothelial protocol consisting of pre-contrast, portal venous, and delayed excretory phases. Fully-automated CT-based algorithms for quantifying numerous biomarkers, including muscle and fat area and density, bone mineral density, and solid organ volume were applied to pre-contrast and portal venous phases. The effect of IV contrast upon these body composition measures was analyzed. Regression analyses, including square of the Pearson correlation coefficient (r2), were performed for each comparison. RESULTS We found that simple, linear relationships can be derived to determine non-contrast equivalent values from the post-contrast CT biomeasures. Excellent positive linear correlation (r2 = 0.91-0.99) between pre- and post-contrast values was observed for all automated soft tissue measures, whereas moderate positive linear correlation was observed for bone attenuation (r2 = 0.58-0.76). In general, the area- and volume-based measurement require less adjustment than attenuation-based measures, as expected. CONCLUSION Fully-automated quantitative CT-biomarker measures at portal venous phase abdominal CT can be adjusted to a non-contrast equivalent using simple, linear relationships.
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Affiliation(s)
- Alexander R Moeller
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center 600 Highland Ave., Madison, WI, 53792-3252, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center 600 Highland Ave., Madison, WI, 53792-3252, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center 600 Highland Ave., Madison, WI, 53792-3252, USA.
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Lee MH, Zea R, Garrett JW, Summers RM, Pickhardt PJ. AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals. Abdom Radiol (NY) 2024; 49:1330-1340. [PMID: 38280049 DOI: 10.1007/s00261-023-04161-z] [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: 09/26/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/29/2024]
Abstract
PURPOSE To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition measures associated with increased risk for all-cause mortality and adverse cardiovascular events. METHODS Fully automated AI body composition tools quantifying abdominal aortic calcium, abdominal fat (visceral [VAT], visceral-to-subcutaneous ratio [VSR]), and muscle attenuation (muscle HU) were applied to non-contrast CT examinations in adults undergoing screening CT colonography (CTC). Patients were partitioned into 5 socioeconomic groups based on the national ADI rank at the census block group level. Pearson correlation analysis was performed to determine the association between national ADI and body composition measures. One-way analysis of variance was used to compare means across groups. Odds ratios (ORs) were generated using high-risk, high specificity (90% specificity) body composition thresholds with the most disadvantaged groups being compared to the least disadvantaged group (ADI < 20). RESULTS 7785 asymptomatic adults (mean age, 57 years; 4361:3424 F:M) underwent screening CTC from April 2004-December 2016. ADI rank data were available in 7644 patients. Median ADI was 31 (IQR 22-43). Aortic calcium, VAT, and VSR had positive correlation with ADI and muscle attenuation had a negative correlation with ADI (all p < .001). Compared with the least disadvantaged group, mean differences for the most disadvantaged group (ADI > 80) were: Aortic calcium (Agatston) = 567, VAT = 27 cm2, VSR = 0.1, and muscle HU = -6 HU (all p < .05). Compared with the least disadvantaged group, the most disadvantaged group had significantly higher odds of having high-risk body composition measures: Aortic calcium OR = 3.8, VAT OR = 2.5, VSR OR = 2.0, and muscle HU OR = 3.1(all p < .001). CONCLUSION Fully automated CT body composition tools show that socioeconomic disadvantage is associated with high-risk body composition measures and can be used to identify individuals at increased risk for all-cause mortality and adverse cardiovascular events.
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Affiliation(s)
- Matthew H Lee
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA
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Liu D, Garrett JW, Perez AA, Zea R, Binkley NC, Summers RM, Pickhardt PJ. Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures. Br J Radiol 2024; 97:770-778. [PMID: 38379423 PMCID: PMC11027263 DOI: 10.1093/bjr/tqae041] [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: 04/05/2023] [Revised: 09/27/2023] [Accepted: 02/19/2024] [Indexed: 02/22/2024] Open
Abstract
OBJECTIVE Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls. METHODS In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived. RESULTS Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686. CONCLUSION Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging. ADVANCES IN KNOWLEDGE CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.
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Affiliation(s)
- Daniel Liu
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Alberto A Perez
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Neil C Binkley
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Potomac, MD, 20892, United States
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
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11
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Catania R, Jia L, Haghshomar M, Miller FH, Borhani AA. Detection of moderate hepatic steatosis on contrast-enhanced dual-source dual-energy CT: Role and accuracy of virtual non-contrast CT. Eur J Radiol 2024; 172:111328. [PMID: 38325187 DOI: 10.1016/j.ejrad.2024.111328] [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: 10/20/2023] [Revised: 12/20/2023] [Accepted: 01/18/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE To investigate diagnostic accuracy of virtual non contrast (VNC) images, based on dual-source dual-energy CT (dsDECT), for detection of at least moderate steatosis and to define a threshold value to make this diagnosis on VNC. METHODS This single-institution retrospective study included patients who had multi-phasic protocol dsDECT. Regions of interests were placed in different segments of the liver and spleen on true non-contrast (TNC), VNC, and portal-venous phase (PVP) images. At least moderate steatosis was defined as liver attenuation (LHU) < 40 HU on TNC. Diagnostic performance of VNC to detect steatosis was determined and the new threshold was tested in a validation cohort. RESULTS 236 patients were included in training cohort. Mean liver attenuation values were 51.3 ± 10.8 HU and 58.1 ± 11.5 HU for TNC and VNC (p < 0.001), with a mean difference (VNC - TNC) of 6.8 ± 6.9 HU. Correlation between TNC and VNC was strong (r = 0.81, p < 0.001). The AUCs of LHU on VNC for detection of hepatic steatosis were 0.92 (95 % Cl: 0.86-0.98), 0.92 (95 % Cl: 0.87-0.97), 0.92 (95 % Cl: 0.86-0.99), 0.91 (95 % Cl: 0.84-0.97), and 0.87 (95 % Cl: 0.80-0.95) for entire liver, left lateral, left medial, right anterior, and right posterior segments, respectively. VNC had sensitivity/specificity of 100 % /42 % when using a threshold of 40 HU; they were 69 % and 95 %, respectively, when using optimized threshold of 46 HU. This threshold showed similar performance in validation cohort (n = 80). CONCLUSIONS Hepatic attenuation on VNC has promising performance for detection of at least moderate steatosis. Proposed threshold of 46 HU provides high specificity and moderate sensitivity to detect steatosis.
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Affiliation(s)
- Roberta Catania
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Arkes Family Pavilion, Suite 800, Chicago, IL 60611, United States.
| | - Leo Jia
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Arkes Family Pavilion, Suite 800, Chicago, IL 60611, United States.
| | - Maryam Haghshomar
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Arkes Family Pavilion, Suite 800, Chicago, IL 60611, United States.
| | - Frank H Miller
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Arkes Family Pavilion, Suite 800, Chicago, IL 60611, United States.
| | - Amir A Borhani
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Arkes Family Pavilion, Suite 800, Chicago, IL 60611, United States.
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12
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Lee MH, Liu D, Garrett JW, Perez A, Zea R, Summers RM, Pickhardt PJ. Comparing fully automated AI body composition measures derived from thin and thick slice CT image data. Abdom Radiol (NY) 2024; 49:985-996. [PMID: 38158424 DOI: 10.1007/s00261-023-04135-1] [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: 07/20/2023] [Revised: 11/17/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data. METHODS In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis. RESULTS A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland-Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (- 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (- 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400. CONCLUSION Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.
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Affiliation(s)
- Matthew H Lee
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
| | - Daniel Liu
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Alberto Perez
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
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13
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Jimenez Ramos M, Kendall TJ, Drozdov I, Fallowfield JA. A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease. Ann Hepatol 2024; 29:101278. [PMID: 38135251 PMCID: PMC10907333 DOI: 10.1016/j.aohep.2023.101278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.
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Affiliation(s)
- Maria Jimenez Ramos
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK
| | - Timothy J Kendall
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK; Edinburgh Pathology, University of Edinburgh, 51 Little France Crescent, Old Dalkeith Rd, Edinburgh EH16 4SA, UK
| | - Ignat Drozdov
- Bering Limited, 54 Portland Place, London, W1B 1DY, UK
| | - Jonathan A Fallowfield
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK.
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14
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Yoo J, Joo I, Jeon SK, Park J, Yoon SH. Utilizing fully-automated 3D organ segmentation for hepatic steatosis assessment with CT attenuation-based parameters. Eur Radiol 2024:10.1007/s00330-024-10660-4. [PMID: 38393403 DOI: 10.1007/s00330-024-10660-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/22/2023] [Accepted: 01/26/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES To investigate the clinical utility of fully-automated 3D organ segmentation in assessing hepatic steatosis on pre-contrast and post-contrast CT images using magnetic resonance spectroscopy (MRS)-proton density fat fraction (PDFF) as reference standard. MATERIALS AND METHODS This retrospective study analyzed 362 adult potential living liver donors with abdominal CT scans and MRS-PDFF. Using a deep learning-based tool, mean volumetric CT attenuation of the liver and spleen were measured on pre-contrast (liver(L)_pre and spleen(S)_pre) and post-contrast (L_post and S_post) images. Agreements between volumetric and manual region-of-interest (ROI)-based measurements were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. Diagnostic performances of volumetric parameters (L_pre, liver-minus-spleen (L-S)_pre, L_post, and L-S_post) were evaluated for detecting MRS-PDFF ≥ 5% and ≥ 10% using receiver operating characteristic (ROC) curve analysis and compared with those of ROI-based parameters. RESULTS Among the 362 subjects, 105 and 35 had hepatic steatosis with MRS-PDFF ≥ 5% and ≥ 10%, respectively. Volumetric and ROI-based measurements revealed ICCs of 0.974, 0.825, 0.992, and 0.962, with mean differences of -4.2 HU, -3.4 HU, -1.2 HU, and -7.7 HU for L_pre, S_pre, L_post, and S_post, respectively. Volumetric L_pre, L-S_pre, L_post, and L-S_post yielded areas under the ROC curve of 0.813, 0.813, 0.734, and 0.817 for MRS-PDFF ≥ 5%; and 0.901, 0.915, 0.818, and 0.868 for MRS-PDFF ≥ 10%, comparable with those of ROI-based parameters (0.735-0.818; and 0.816-0.895, Ps = 0.228-0.911). CONCLUSION Automated 3D segmentation of the liver and spleen in CT scans can provide volumetric CT attenuation-based parameters to detect and grade hepatic steatosis, applicable to pre-contrast and post-contrast images. CLINICAL RELEVANCE STATEMENT Volumetric CT attenuation-based parameters of the liver and spleen, obtained through automated segmentation tools from pre-contrast or post-contrast CT scans, can efficiently detect and grade hepatic steatosis, making them applicable for large population data collection. KEY POINTS • Automated organ segmentation enables the extraction of CT attenuation-based parameters for the target organ. • Volumetric liver and spleen CT attenuation-based parameters are highly accurate in hepatic steatosis assessment. • Automated CT measurements from pre- or post-contrast imaging show promise for hepatic steatosis screening in large cohorts.
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Affiliation(s)
- Jeongin Yoo
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
- Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
| | - Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Junghoan Park
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- MEDICALIP. Co. Ltd., Seoul, Korea
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15
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Song I, Thompson EW, Verma A, MacLean MT, Duda J, Elahi A, Tran R, Raghupathy P, Swago S, Hazim M, Bhattaru A, Schneider C, Vujkovic M, Torigian DA, Kahn CE, Gee JC, Borthakur A, Kripke CM, Carson CC, Carr R, Jehangir Q, Ko YA, Litt H, Rosen M, Mankoff DA, Schnall MD, Shou H, Chirinos J, Damrauer SM, Serper M, Chen J, Rader DJ, Witschey WRT, Sagreiya H. Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis. Sci Rep 2024; 14:53. [PMID: 38167550 PMCID: PMC10761858 DOI: 10.1038/s41598-023-49470-x] [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: 05/24/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥ - 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients-lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.
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Affiliation(s)
- Isabel Song
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Elizabeth W Thompson
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Anurag Verma
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew T MacLean
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey Duda
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Ameena Elahi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Richard Tran
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Pavan Raghupathy
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Sophia Swago
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mohamad Hazim
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Abhijit Bhattaru
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Carolin Schneider
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marijana Vujkovic
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Drew A Torigian
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Charles E Kahn
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - James C Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Arijitt Borthakur
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Colleen M Kripke
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher C Carson
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rotonya Carr
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, School of Medicine, University of Washington, Seattle, WA, USA
| | - Qasim Jehangir
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yi-An Ko
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harold Litt
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mark Rosen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - David A Mankoff
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mitchell D Schnall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Julio Chirinos
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marina Serper
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Walter R T Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Hersh Sagreiya
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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Michel LJ, Rospleszcz S, Reisert M, Rau A, Nattenmueller J, Rathmann W, Schlett CL, Peters A, Bamberg F, Weiss J. Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach. PLOS DIGITAL HEALTH 2024; 3:e0000429. [PMID: 38227569 PMCID: PMC10791001 DOI: 10.1371/journal.pdig.0000429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 12/07/2023] [Indexed: 01/18/2024]
Abstract
AIM Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting. METHODS In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status. RESULTS The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001). CONCLUSIONS Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.
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Affiliation(s)
- Lea J. Michel
- Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
| | - Susanne Rospleszcz
- Department of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Germany
| | - Marco Reisert
- Medical Physics, Department of Radiology, Medical Center—University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
| | - Johanna Nattenmueller
- Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; German Center for Diabetes Research, München-Neuherberg, Germany
| | - Christopher. L. Schlett
- Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
| | - Annette Peters
- Department of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Germany
- German Center for Diabetes Research (DZD), partner site Neuherberg, Neuherberg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
| | - Jakob Weiss
- Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
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17
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Yee J, Dachman A, Kim DH, Kobi M, Laghi A, McFarland E, Moreno C, Park SH, Pickhardt PJ, Plumb A, Pooler BD, Zalis M, Chang KJ. CT Colonography Reporting and Data System (C-RADS): Version 2023 Update. Radiology 2024; 310:e232007. [PMID: 38289209 DOI: 10.1148/radiol.232007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The CT Colonography Reporting and Data System (C-RADS) has withstood the test of time and proven to be a robust classification scheme for CT colonography (CTC) findings. C-RADS version 2023 represents an update on the scheme used for colorectal and extracolonic findings at CTC. The update provides useful insights gained since the implementation of the original system in 2005. Increased experience has demonstrated confusion on how to classify the mass-like appearance of the colon consisting of soft tissue attenuation that occurs in segments with acute or chronic diverticulitis. Therefore, the update introduces a new subcategory, C2b, specifically for mass-like diverticular strictures, which are likely benign. Additionally, the update simplifies extracolonic classification by combining E1 and E2 categories into an updated extracolonic category of E1/E2 since, irrespective of whether a finding is considered a normal variant (category E1) or an otherwise clinically unimportant finding (category E2), no additional follow-up is required. This simplifies and streamlines the classification into one category, which results in the same management recommendation.
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Affiliation(s)
- Judy Yee
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Abraham Dachman
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - David H. Kim
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Mariya Kobi
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Andrea Laghi
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Elizabeth McFarland
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Courtney Moreno
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Seong Ho Park
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Perry J. Pickhardt
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Andrew Plumb
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - B Dustin Pooler
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Michael Zalis
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
| | - Kevin J Chang
- From the Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, 111 E 210th St, Bronx, NY 10467 (J.Y.); Department of Radiology, University of Chicago, Chicago, Ill (A.D.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.K., P.P., B.D.P.); Department of Radiology, Columbia University Irving Medical Center, New York, NY (M.K.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy (A.L.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (E.M.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Ga (C.M.); Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.H.P.); Department of Imaging, University College London, London, United Kingdom (A.P.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.Z.); and Department of Radiology, Boston University Medical Center, Boston, Mass (K.J.C.)
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Pickhardt PJ, Blake GM, Kimmel Y, Weinstock E, Shaanan K, Hassid S, Abbas A, Fox MA. Detection of Moderate Hepatic Steatosis on Portal Venous Phase Contrast-Enhanced CT: Evaluation Using an Automated Artificial Intelligence Tool. AJR Am J Roentgenol 2023; 221:748-758. [PMID: 37466185 DOI: 10.2214/ajr.23.29651] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
BACKGROUND. Precontrast CT is an established means of evaluating for hepatic steatosis; postcontrast CT has historically been limited for this purpose. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of portal venous phase postcontrast CT in detecting at least moderate hepatic steatosis using liver and spleen attenuation measurements determined by an automated artificial intelligence (AI) tool. METHODS. This retrospective study included 2917 patients (1381 men, 1536 women; mean age, 56.8 years) who underwent a CT examination that included at least two series through the liver. Examinations were obtained from an AI vendor's data lake of data from 24 centers in one U.S. health care network and 29 centers in one Israeli health care network. An automated deep learning tool extracted liver and spleen attenuation measurements. The reference for at least moderate steatosis was precontrast liver attenuation of less than 40 HU (i.e., estimated liver fat > 15%). A radiologist manually reviewed examinations with outlier AI results to confirm portal venous timing and identify issues impacting attenuation measurements. RESULTS. After outlier review, analysis included 2777 patients with portal venous phase images. Prevalence of at least moderate steatosis was 13.9% (387/2777). Patients without and with at least moderate steatosis, respectively, had mean postcontrast liver attenuation of 104.5 ± 18.1 (SD) HU and 67.1 ± 18.6 HU (p < .001); a mean difference in postcontrast attenuation between the liver and the spleen (hereafter, postcontrast liver-spleen attenuation difference) of -7.6 ± 16.4 (SD) HU and -31.8 ± 20.3 HU (p < .001); and mean liver enhancement of 49.3 ± 15.9 (SD) HU versus 38.6 ± 13.6 HU (p < .001). Diagnostic performance for the detection of at least moderate steatosis was higher for postcontrast liver attenuation (AUC = 0.938) than for the postcontrast liver-spleen attenuation difference (AUC = 0.832) (p < .001). For detection of at least moderate steatosis, postcontrast liver attenuation had sensitivity and specificity of 77.8% and 93.2%, respectively, at less than 80 HU and 90.5% and 78.4%, respectively, at less than 90 HU; the postcontrast liver-spleen attenuation difference had sensitivity and specificity of 71.4% and 79.3%, respectively, at less than -20 HU and 87.0% and 62.1%, respectively, at less than -10 HU. CONCLUSION. Postcontrast liver attenuation outperformed the postcontrast liver-spleen attenuation difference for detecting at least moderate steatosis in a heterogeneous patient sample, as evaluated using an automated AI tool. Splenic attenuation likely is not needed to assess for at least moderate steatosis on postcontrast images. CLINICAL IMPACT. The technique could promote early detection of clinically significant nonalcoholic fatty liver disease through individualized or large-scale opportunistic evaluation.
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Affiliation(s)
- Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252
| | - Glen M Blake
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom
| | | | | | | | | | - Ahmad Abbas
- Department of Radiology, Barzilai University Medical Center, Ashkelon, Israel
| | - Matthew A Fox
- Nanox-AI, Ltd., Neve Ilan, Israel
- Department of Radiology, Samson Assuta Ashdod University Hospital, Ashdod, Israel
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19
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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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20
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Wang H, Solomon J, Reza SMS, Yang HJ, Chu WT, Crozier I, Sayre PJ, Lee BY, Mani V, Friedrich TC, O’Connor DH, Worwa G, Kuhn JH, Calcagno C, Castro MA. Repeatability of computed tomography liver radiomic features in a nonhuman primate model of diet-induced steatosis. J Med Imaging (Bellingham) 2023; 10:066004. [PMID: 38090646 PMCID: PMC10711681 DOI: 10.1117/1.jmi.10.6.066004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 02/01/2024] Open
Abstract
Purpose We describe a method to identify repeatable liver computed tomography (CT) radiomic features, suitable for detection of steatosis, in nonhuman primates. Criteria used for feature selection exclude nonrepeatable features and may be useful to improve the performance and robustness of radiomics-based predictive models. Approach Six crab-eating macaques were equally assigned to two experimental groups, fed regular chow or an atherogenic diet. High-resolution CT images were acquired over several days for each macaque. First-order and second-order radiomic features were extracted from six regions in the liver parenchyma, either with or without liver-to-spleen intensity normalization from images reconstructed using either a standard (B-filter) or a bone-enhanced (D-filter) kernel. Intrasubject repeatability of each feature was assessed using a paired t -test for all scans and the minimum p -value was identified for each macaque. Repeatable features were defined as having a minimum p -value among all macaques above the significance level after Bonferroni's correction. Features showing a significant difference with respect to diet group were identified using a two-sample t -test. Results A list of repeatable features was generated for each type of image. The largest number of repeatable features was achieved from spleen-normalized D-filtered images, which also produced the largest number of second-order radiomic features that were repeatable and different between diet groups. Conclusions Repeatability depends on reconstruction kernel and normalization. Features were quantified and ranked based on their repeatability. Features to be excluded for more robust models were identified. Features that were repeatable but different between diet groups were also identified.
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Affiliation(s)
- Hui Wang
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
| | - Jeffrey Solomon
- Frederick National Laboratory for Cancer Research, Clinical Monitoring Research Program Directorate, Frederick, Maryland, United States
| | - Syed M. S. Reza
- National Institutes of Health, Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, Bethesda, Maryland, United States
| | - Hee-Jeong Yang
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
| | - Winston T. Chu
- National Institutes of Health, Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, Bethesda, Maryland, United States
| | - Ian Crozier
- Frederick National Laboratory for Cancer Research, Clinical Monitoring Research Program Directorate, Frederick, Maryland, United States
| | - Philip J. Sayre
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
| | - Byeong Y. Lee
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
| | - Venkatesh Mani
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
| | - Thomas C. Friedrich
- University of Wisconsin–Madison, Department of Pathobiological Sciences, School of Veterinary Medicine, Madison, Wisconsin, United States
| | - David H. O’Connor
- University of Wisconsin–Madison, Department of Pathology and Laboratory Medicine, Madison, Wisconsin, United States
| | - Gabriella Worwa
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
| | - Jens H. Kuhn
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
| | - Claudia Calcagno
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
| | - Marcelo A. Castro
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Fort Detrick, Frederick, Maryland, United States
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21
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Torgersen J, Akers S, Huo Y, Terry JG, Carr JJ, Ruutiainen AT, Skanderson M, Levin W, Lim JK, Taddei TH, So-Armah K, Bhattacharya D, Rentsch CT, Shen L, Carr R, Shinohara RT, McClain M, Freiberg M, Justice AC, Re VL. Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV. Pharmacoepidemiol Drug Saf 2023; 32:1121-1130. [PMID: 37276449 PMCID: PMC10527049 DOI: 10.1002/pds.5648] [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: 11/10/2022] [Revised: 05/06/2023] [Accepted: 05/31/2023] [Indexed: 06/07/2023]
Abstract
PURPOSE Hepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic studies to identify medications associated with steatosis have not been conducted because methods to evaluate liver fat within digitized images have not been developed. We determined the accuracy of a deep learning algorithm (automatic liver attenuation region-of-interest-based measurement [ALARM]) to identify steatosis within clinically obtained noncontrast abdominal CT images compared to manual radiologist review and evaluated its performance by HIV status. METHODS We performed a cross-sectional study to evaluate the performance of ALARM within noncontrast abdominal CT images from a sample of patients with and without HIV in the US Veterans Health Administration. We evaluated the ability of ALARM to identify moderate-to-severe hepatic steatosis, defined by mean absolute liver attenuation <40 Hounsfield units (HU), compared to manual radiologist assessment. RESULTS Among 120 patients (51 PWH) who underwent noncontrast abdominal CT, moderate-to-severe hepatic steatosis was identified in 15 (12.5%) persons via ALARM and 12 (10%) by radiologist assessment. Percent agreement between ALARM and radiologist assessment of absolute liver attenuation <40 HU was 95.8%. Sensitivity, specificity, positive predictive value, and negative predictive value of ALARM were 91.7% (95%CI, 51.5%-99.8%), 96.3% (95%CI, 90.8%-99.0%), 73.3% (95%CI, 44.9%-92.2%), and 99.0% (95%CI, 94.8%-100%), respectively. No differences in performance were observed by HIV status. CONCLUSIONS ALARM demonstrated excellent accuracy for moderate-to-severe hepatic steatosis regardless of HIV status. Application of ALARM to radiographic repositories could facilitate real-world studies to evaluate medications associated with steatosis and assess differences by HIV status.
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Affiliation(s)
- Jessie Torgersen
- Department of Medicine, Penn Center for AIDS Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real World Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Scott Akers
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - James G. Terry
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - J. Jeffrey Carr
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - Melissa Skanderson
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Woody Levin
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Joseph K. Lim
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Tamar H. Taddei
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Kaku So-Armah
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Debika Bhattacharya
- VA Greater Los Angeles Healthcare System and David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Christopher T. Rentsch
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real World Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rotonya Carr
- Department of Medicine, Division of Gastroenterology, University of Washington, Seattle, WA, USA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real World Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, 19104
| | | | - Matthew Freiberg
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Amy C. Justice
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
- Division of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Vincent Lo Re
- Department of Medicine, Penn Center for AIDS Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real World Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Mervak BM, Fried JG, Wasnik AP. A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging. Diagnostics (Basel) 2023; 13:2889. [PMID: 37761253 PMCID: PMC10529018 DOI: 10.3390/diagnostics13182889] [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: 05/25/2023] [Revised: 08/23/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.
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Affiliation(s)
| | | | - Ashish P. Wasnik
- Department of Radiology, University of Michigan—Michigan Medicine, 1500 E. Medical Center Dr., Ann Arbor, MI 48109, USA; (B.M.M.); (J.G.F.)
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Wang D, Guo Y, Yin Q, Cao H, Chen X, Qian H, Ji M, Zhang J. Analgesia quality index improves the quality of postoperative pain management: a retrospective observational study of 14,747 patients between 2014 and 2021. BMC Anesthesiol 2023; 23:281. [PMID: 37598151 PMCID: PMC10439647 DOI: 10.1186/s12871-023-02240-8] [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: 05/11/2023] [Accepted: 08/10/2023] [Indexed: 08/21/2023] Open
Abstract
BACKGROUND The application of artificial intelligence patient-controlled analgesia (AI-PCA) facilitates the remote monitoring of analgesia management, the implementation of mobile ward rounds, and the automatic recording of all types of key data in the clinical setting. However, it cannot quantify the quality of postoperative analgesia management. This study aimed to establish an index (analgesia quality index (AQI)) to re-monitor and re-evaluate the system, equipment, medical staff and degree of patient matching to quantify the quality of postoperative pain management through machine learning. METHODS Utilizing the wireless analgesic pump system database of the Cancer Hospital Affiliated with Nantong University, this retrospective observational study recruited consecutive patients who underwent postoperative analgesia using AI-PCA from June 1, 2014, to August 31, 2021. All patients were grouped according to whether or not the AQI was used to guide the management of postoperative analgesia: The control group did not receive the AQI guidance for postoperative analgesia and the experimental group received the AQI guidance for postoperative analgesia. The primary outcome was the incidence of moderate-to-severe pain (numeric rating scale (NRS) score ≥ 4) and the second outcome was the incidence of total adverse reactions. Furthermore, indicators of AQI were recorded. RESULTS A total of 14,747 patients were included in this current study. The incidence of moderate-to-severe pain was 26.3% in the control group and 21.7% in the experimental group. The estimated ratio difference was 4.6% between the two groups (95% confidence interval [CI], 3.2% to 6.0%; P < 0.001). There were significant differences between groups. Otherwise, the differences in the incidence of total adverse reactions between the two groups were nonsignificant. CONCLUSIONS Compared to the traditional management of postoperative analgesia, application of the AQI decreased the incidence of moderate-to-severe pain. Clinical application of the AQI contributes to improving the quality of postoperative analgesia management and may provide guidance for optimum pain management in the postoperative setting.
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Affiliation(s)
- Di Wang
- Department of Anesthesiology, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yihui Guo
- Department of Anesthesiology, The People's Hospital of Pizhou, Pizhou Hospital affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Qian Yin
- Department of Anesthesiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Hanzhong Cao
- Department of Anesthesiology, Tumor Hospital Affiliated to NanTong University, Nantong, Jiangsu, China
| | - Xiaohong Chen
- Department of Anesthesiology, Tumor Hospital Affiliated to NanTong University, Nantong, Jiangsu, China
| | - Hua Qian
- Department of Anesthesiology, Tumor Hospital Affiliated to NanTong University, Nantong, Jiangsu, China
| | - Muhuo Ji
- Department of Anesthesiology, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Jianfeng Zhang
- Department of Anesthesiology, Tumor Hospital Affiliated to NanTong University, Nantong, Jiangsu, China.
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Han JM, Cho JH, Kim HI, Suh S, Lee YJ, Lee JW, Kim KM, Bae JC. Greater Severity of Steatosis Is Associated with a Higher Risk of Incident Diabetes: A Retrospective Longitudinal Study. Endocrinol Metab (Seoul) 2023; 38:418-425. [PMID: 37435662 PMCID: PMC10475961 DOI: 10.3803/enm.2023.1729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGRUOUND Fatty liver is associated with increased risk of developing type 2 diabetes. We aimed to evaluate whether the severity of hepatic steatosis is associated with incident diabetes. METHODS We conducted a longitudinal analysis using data from 1,798 participants who underwent a comprehensive health checkup and abdominal computed tomography (CT). We assessed the association between baseline liver attenuation value on non-contrast CT images and risk of incident diabetes. All the participants were categorized into three groups based on the baseline liver attenuation value on non-contrast CT images: without hepatic steatosis (>57 Hounsfield unit [HU]), mild hepatic steatosis (41-57 HU), and moderate to severe hepatic steatosis (≤40 HU). RESULTS During a median follow-up period of 5 years, 6.0% of the study participants progressed to diabetes. The incidence of diabetes was 17.3% in the moderate to severe hepatic steatosis group, 9.0% in the mild steatosis group, and 2.9% in those without hepatic steatosis. In a multivariate adjustment model, as compared with participants without hepatic steatosis, those with moderate to severe steatosis had a hazard ratio (HR) of 3.24 (95% confidence interval [CI], 1.64 to 4.2) for the development of diabetes, and those in the mild steatosis group had a HR of 2.33 (95% CI, 1.42 to 3.80). One standard deviation decrease in mean CT attenuation values of the liver was associated with a 40% increase in the development of diabetes (multivariate adjusted HR, 1.40; 95% CI, 1.2 to 1.63). CONCLUSION We found a positive association between severity of hepatic steatosis and risk of incident diabetes. Greater severity of steatosis was associated with a higher risk of incident diabetes.
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Affiliation(s)
- Ji Min Han
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Jung Hwan Cho
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Hye In Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Sunghwan Suh
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Yu-Ji Lee
- Division of Nephrology, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Jung Won Lee
- Division of Gastroenterology, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Kwang Min Kim
- Division of Gastroenterology, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Ji Cheol Bae
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
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25
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Nachit M, Horsmans Y, Summers RM, Leclercq IA, Pickhardt PJ. AI-based CT Body Composition Identifies Myosteatosis as Key Mortality Predictor in Asymptomatic Adults. Radiology 2023; 307:e222008. [PMID: 37191484 PMCID: PMC10315523 DOI: 10.1148/radiol.222008] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 03/19/2023] [Accepted: 03/29/2023] [Indexed: 05/17/2023]
Abstract
Background Body composition data have been limited to adults with disease or older age. The prognostic impact in otherwise asymptomatic adults is unclear. Purpose To use artificial intelligence-based body composition metrics from routine abdominal CT scans in asymptomatic adults to clarify the association between obesity, liver steatosis, myopenia, and myosteatosis and the risk of mortality. Materials and Methods In this retrospective single-center study, consecutive adult outpatients undergoing routine colorectal cancer screening from April 2004 to December 2016 were included. Using a U-Net algorithm, the following body composition metrics were extracted from low-dose, noncontrast, supine multidetector abdominal CT scans: total muscle area, muscle density, subcutaneous and visceral fat area, and volumetric liver density. Abnormal body composition was defined by the presence of liver steatosis, obesity, muscle fatty infiltration (myosteatosis), and/or low muscle mass (myopenia). The incidence of death and major adverse cardiovascular events were recorded during a median follow-up of 8.8 years. Multivariable analyses were performed accounting for age, sex, smoking status, myosteatosis, liver steatosis, myopenia, type 2 diabetes, obesity, visceral fat, and history of cardiovascular events. Results Overall, 8982 consecutive outpatients (mean age, 57 years ± 8 [SD]; 5008 female, 3974 male) were included. Abnormal body composition was found in 86% (434 of 507) of patients who died during follow-up. Myosteatosis was found in 278 of 507 patients (55%) who died (15.5% absolute risk at 10 years). Myosteatosis, obesity, liver steatosis, and myopenia were associated with increased mortality risk (hazard ratio [HR]: 4.33 [95% CI: 3.63, 5.16], 1.27 [95% CI: 1.06, 1.53], 1.86 [95% CI: 1.56, 2.21], and 1.75 [95% CI: 1.43, 2.14], respectively). In 8303 patients (excluding 679 patients without complete data), after multivariable adjustment, myosteatosis remained associated with increased mortality risk (HR, 1.89 [95% CI: 1.52, 2.35]; P < .001). Conclusion Artificial intelligence-based profiling of body composition from routine abdominal CT scans identified myosteatosis as a key predictor of mortality risk in asymptomatic adults. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Tong and Magudia in this issue.
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Affiliation(s)
- Maxime Nachit
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
| | - Yves Horsmans
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
| | - Ronald M. Summers
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
| | - Isabelle A. Leclercq
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
| | - Perry J. Pickhardt
- From the Laboratory of Hepato-Gastroenterology, Institut de Recherche
Expérimentale et Clinique, UCLouvain, Brussels, Belgium (M.N., I.A.L.);
Service d'Hépato-Gastro-Entérologie, Cliniques
Universitaires Saint-Luc, Brussels, Belgium (Y.H.); Imaging Biomarkers and
Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National
Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of
Radiology, University of Wisconsin School of Medicine & Public Health,
Madison, Wis (P.J.P.)
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Pickhardt PJ, Summers RM, Garrett JW, Krishnaraj A, Agarwal S, Dreyer KJ, Nicola GN. Opportunistic Screening: Radiology Scientific Expert Panel. Radiology 2023; 307:e222044. [PMID: 37219444 PMCID: PMC10315516 DOI: 10.1148/radiol.222044] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/03/2022] [Accepted: 12/01/2022] [Indexed: 05/24/2023]
Abstract
Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods. Body CT represents an ideal high-volume modality whereby a quantitative assessment of tissue composition (eg, bone, muscle, fat, and vascular calcium) can provide valuable risk stratification and help detect unsuspected presymptomatic disease. The emergence of "explainable" AI algorithms that fully automate these measurements could eventually lead to their routine clinical use. Potential barriers to widespread implementation of opportunistic CT screening include the need for buy-in from radiologists, referring providers, and patients. Standardization of acquiring and reporting measures is needed, in addition to expanded normative data according to age, sex, and race and ethnicity. Regulatory and reimbursement hurdles are not insurmountable but pose substantial challenges to commercialization and clinical use. Through demonstration of improved population health outcomes and cost-effectiveness, these opportunistic CT-based measures should be attractive to both payers and health care systems as value-based reimbursement models mature. If highly successful, opportunistic screening could eventually justify a practice of standalone "intended" CT screening.
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Affiliation(s)
- Perry J. Pickhardt
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Ronald M. Summers
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - John W. Garrett
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Arun Krishnaraj
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Sheela Agarwal
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Keith J. Dreyer
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
| | - Gregory N. Nicola
- From the Departments of Radiology (P.J.P., J.W.G.) and Medical
Physics (J.W.G.), University of Wisconsin School of Medicine & Public
Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI
53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,
Radiology and Imaging Sciences, National Institutes of Health Clinical Center,
Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University
of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill
Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham,
Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ
(G.N.N.)
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27
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Jang W, Song JS. Non-Invasive Imaging Methods to Evaluate Non-Alcoholic Fatty Liver Disease with Fat Quantification: A Review. Diagnostics (Basel) 2023; 13:diagnostics13111852. [PMID: 37296703 DOI: 10.3390/diagnostics13111852] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/17/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Hepatic steatosis without specific causes (e.g., viral infection, alcohol abuse, etc.) is called non-alcoholic fatty liver disease (NAFLD), which ranges from non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH), fibrosis, and NASH-related cirrhosis. Despite the usefulness of the standard grading system, liver biopsy has several limitations. In addition, patient acceptability and intra- and inter-observer reproducibility are also concerns. Due to the prevalence of NAFLD and limitations of liver biopsies, non-invasive imaging methods such as ultrasonography (US), computed tomography (CT), and magnetic resonance imaging (MRI) that can reliably diagnose hepatic steatosis have developed rapidly. US is widely available and radiation-free but cannot examine the entire liver. CT is readily available and helpful for detection and risk classification, significantly when analyzed using artificial intelligence; however, it exposes users to radiation. Although expensive and time-consuming, MRI can measure liver fat percentage with magnetic resonance imaging proton density fat fraction (MRI-PDFF). Specifically, chemical shift-encoded (CSE)-MRI is the best imaging indicator for early liver fat detection. The purpose of this review is to provide an overview of each imaging modality with an emphasis on the recent progress and current status of liver fat quantification.
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Affiliation(s)
- Weon Jang
- Department of Radiology, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju 54907, Jeonbuk, Republic of Korea
- Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju 54907, Jeonbuk, Republic of Korea
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Jeonbuk, Republic of Korea
| | - Ji Soo Song
- Department of Radiology, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju 54907, Jeonbuk, Republic of Korea
- Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju 54907, Jeonbuk, Republic of Korea
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Jeonbuk, Republic of Korea
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Liu D, Binkley NC, Perez A, Garrett JW, Zea R, Summers RM, Pickhardt PJ. CT image-based biomarkers acquired by AI-based algorithms for the opportunistic prediction of falls. BJR Open 2023; 5:20230014. [PMID: 37953870 PMCID: PMC10636337 DOI: 10.1259/bjro.20230014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/15/2023] [Accepted: 04/11/2023] [Indexed: 11/14/2023] Open
Abstract
Objective Evaluate whether biomarkers measured by automated artificial intelligence (AI)-based algorithms are suggestive of future fall risk. Methods In this retrospective age- and sex-matched case-control study, 9029 total patients underwent initial abdominal CT for a variety of indications over a 20-year interval at one institution. 3535 case patients (mean age at initial CT, 66.5 ± 9.6 years; 63.4% female) who went on to fall (mean interval to fall, 6.5 years) and 5494 controls (mean age at initial CT, 66.7 ± 9.8 years; 63.4% females; mean follow-up interval, 6.6 years) were included. Falls were identified by electronic health record review. Validated and fully automated quantitative CT algorithms for skeletal muscle, adipose tissue, and trabecular bone attenuation at the level of L1 were applied to all scans. Uni- and multivariate assessment included hazard ratios (HRs) and area under the receiver operating characteristic (AUROC) curve. Results Fall HRs (with 95% CI) for low muscle Hounsfield unit, high total adipose area, and low bone Hounsfield unit were 1.82 (1.65-2.00), 1.31 (1.19-1.44) and 1.91 (1.74-2.11), respectively, and the 10-year AUROC values for predicting falls were 0.619, 0.556, and 0.639, respectively. Combining all these CT biomarkers further improved the predictive value, including 10-year AUROC of 0.657. Conclusion Automated abdominal CT-based opportunistic measures of muscle, fat, and bone offer a novel approach to risk stratification for future falls, potentially by identifying patients with osteosarcopenic obesity. Advances in knowledge There are few well-established clinical tools to predict falls. We use novel AI-based body composition algorithms to leverage incidental CT data to help determine a patient's future fall risk.
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Affiliation(s)
- Daniel Liu
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Neil C Binkley
- Osteoporosis Clinical Research Program, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Alberto Perez
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
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Yamada A, Kamagata K, Hirata K, Ito R, Nakaura T, Ueda D, Fujita S, Fushimi Y, Fujima N, Matsui Y, Tatsugami F, Nozaki T, Fujioka T, Yanagawa M, Tsuboyama T, Kawamura M, Naganawa S. Clinical applications of artificial intelligence in liver imaging. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01638-1. [PMID: 37165151 DOI: 10.1007/s11547-023-01638-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/12/2023]
Abstract
This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.
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Affiliation(s)
- Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Kenji Hirata
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-Ku, Kumamoto, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-Ku, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-Ku, Okayama, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-Ku, Hiroshima City, Hiroshima, Japan
| | - Taiki Nozaki
- Department of Radiology, St. Luke's International Hospital, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Zou X, Zhou X, Li Y, Huang Q, Ni Y, Zhang R, Zhang F, Wen X, Cheng J, Yuan Y, Yu Y, Guo C, Xie G, Ji L. Gender-specific data-driven adiposity subtypes using deep-learning-based abdominal CT segmentation. Obesity (Silver Spring) 2023; 31:1600-1609. [PMID: 37157112 DOI: 10.1002/oby.23741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 01/11/2023] [Accepted: 01/11/2023] [Indexed: 05/10/2023]
Abstract
OBJECTIVE The aim of this study was to quantify abdominal adiposity and generate data-driven adiposity subtypes with different diabetes risks. METHODS A total of 3817 participants from the Pinggu Metabolic Disease Study were recruited. A deep-learning-based recognition model on abdominal computed tomography (CT) images (A-CT model) was developed and validated in 100 randomly selected cases. The volumes and proportions of subcutaneous fat, visceral fat, liver fat, and muscle fat were automatically recognized in all cases. K-means clustering was used to identify subgroups using the proportions of the four fat components. RESULTS The Dice indices among the measurements assessed by the A-CT model and manual evaluation to detect liver fat, muscle fat, and subcutaneous fat areas were 0.96, 0.95, and 0.92, respectively. Three subtypes were generated separately in men and women: visceral fat dominant type (VFD); subcutaneous fat dominant type (SFD); and intermuscular fat dominant type (MFD). Compared with the SFD group, the MFD group had similar diabetes risk, and the VFD group had a 60% higher diabetes risk when age and BMI were adjusted for in men. The adjusted odds ratio for diabetes was 1.92 (95% CI: 1.32-2.78) in the MFD group and 6.14 (95% CI: 4.18-9.03) in the VFD group in women. CONCLUSIONS This study identified gender-specific abdominal adiposity subgroups, which may help clinicians to distinguish diabetes risk quickly and automatically.
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Affiliation(s)
- Xiantong Zou
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Xianghai Zhou
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Yufeng Li
- Department of Endocrinology, Beijing Friendship Hospital Pinggu Campus, Capital Medical University, Beijing, China
| | - Qi Huang
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Yuan Ni
- Ping An Technology (Shenzhen) Co., Ltd., Shanghai, China
| | - Ruiming Zhang
- Ping An Technology (Shenzhen) Co., Ltd., Shanghai, China
| | - Fang Zhang
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Xin Wen
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Jiayu Cheng
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Yanping Yuan
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Yue Yu
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Chengcheng Guo
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Guotong Xie
- Ping An Technology (Shenzhen) Co., Ltd., Shanghai, China
| | - Linong Ji
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
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Correlation Between Computed Tomography Findings and the Laboratory Test-Derived Severity Score in Patients With Severe Acute Alcoholic Hepatitis. J Comput Assist Tomogr 2023:00004728-990000000-00153. [PMID: 36877790 DOI: 10.1097/rct.0000000000001459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
OBJECTIVE This study aimed to compare computed tomography (CT) findings between patients with severe and nonsevere acute alcoholic hepatitis (AAH). METHODS We included 96 patients diagnosed with AAH between January 2011 and October 2021 who underwent 4-phase liver CT and laboratory blood tests. Two radiologists reviewed the initial CT images with respect to distribution and grade of hepatic steatosis; transient parenchymal arterial enhancement (TPAE); and presence of cirrhosis, ascites, and hepatosplenomegaly. A Maddrey discriminant function score (4.6 × [patient's prothrombin time - control] + total bilirubin [mg/mL]) was used as cutoff indicator for severity, with a score of 32 or higher indicating severe disease. The image findings were compared between the severe (n = 24) and nonsevere (n = 72) groups using the χ2 test or Fisher exact test. After univariate analysis, the most significant factor was identified using a logistic regression analysis. RESULTS In the univariate analysis, there were significant between-group differences in the TPAE, liver cirrhosis, splenomegaly, and ascites (P < 0.0001, P < 0.0001, P = 0.0002, and P = 0.0163, respectively). Among them, TPAE was the only significant factor for severe AAH (P < 0.0001; odds ratio, 48.1; 95% confidence interval, 8.3-280.6). Using this single indicator, the estimated accuracy, positive predictive, and negative predictive values were 86%, 67%, and 97%, respectively. CONCLUSIONS Transient parenchymal arterial enhancement was the only significant CT finding in severe AAH.
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Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13050968. [PMID: 36900112 PMCID: PMC10000509 DOI: 10.3390/diagnostics13050968] [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: 12/23/2022] [Revised: 02/11/2023] [Accepted: 02/18/2023] [Indexed: 03/08/2023] Open
Abstract
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
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Pickhardt PJ, Correale L, Hassan C. AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis. Abdom Radiol (NY) 2023; 48:1181-1198. [PMID: 36670245 DOI: 10.1007/s00261-023-03800-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/02/2023] [Accepted: 01/04/2023] [Indexed: 01/22/2023]
Abstract
PURPOSE To assess the cost-effectiveness and clinical efficacy of AI-assisted abdominal CT-based opportunistic screening for atherosclerotic cardiovascular (CV) disease, osteoporosis, and sarcopenia using artificial intelligence (AI) body composition algorithms. METHODS Markov models were constructed and 10-year simulations were performed on hypothetical age- and sex-specific cohorts of 10,000 U.S. adults (base case: 55 year olds) undergoing abdominal CT. Using expected disease prevalence, transition probabilities between health states, associated healthcare costs, and treatment effectiveness related to relevant conditions (CV disease/osteoporosis/sarcopenia) were modified by three mutually exclusive screening models: (1) usual care ("treat none"; no intervention regardless of opportunistic CT findings), (2) universal statin therapy ("treat all" for CV prevention; again, no consideration of CT findings), and (3) AI-assisted abdominal CT-based opportunistic screening for CV disease, osteoporosis, and sarcopenia using automated quantitative algorithms for abdominal aortic calcification, bone mineral density, and skeletal muscle, respectively. Model validity was assessed against published clinical cohorts. RESULTS For the base-case scenarios of 55-year-old men and women modeled over 10 years, AI-assisted CT-based opportunistic screening was a cost-saving and more effective clinical strategy, unlike the "treat none" and "treat all" strategies that ignored incidental CT body composition data. Over a wide range of input assumptions beyond the base case, the CT-based opportunistic strategy was dominant over the other two scenarios, as it was both more clinically efficacious and more cost-effective. Cost savings and clinical improvement for opportunistic CT remained for AI tool costs up to $227/patient in men ($65 in women) from the $10/patient base-case scenario. CONCLUSION AI-assisted CT-based opportunistic screening appears to be a highly cost-effective and clinically efficacious strategy across a broad array of input assumptions, and was cost saving in most scenarios.
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Affiliation(s)
- Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Heatlh, 600 Highland Ave, Madison, WI, 53792, USA.
| | - Loredana Correale
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy
| | - Cesare Hassan
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
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Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction. Abdom Radiol (NY) 2023; 48:787-795. [PMID: 36369528 DOI: 10.1007/s00261-022-03728-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/23/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022]
Abstract
PURPOSE The purpose of this study is to compare fully automated CT-based measures of adipose tissue at the L1 level versus the standard L3 level for predicting mortality, which would allow for use at both chest (L1) and abdominal (L3) CT. METHODS This retrospective study of 9066 asymptomatic adults (mean age, 57.1 ± 7.8 [SD] years; 4020 men, 5046 women) undergoing unenhanced low-dose abdominal CT for colorectal cancer screening. A previously validated artificial intelligence (AI) tool was used to assess cross-sectional visceral and subcutaneous adipose tissue areas (SAT and VAT), as well as their ratio (VSR) at the L1 and L3 levels. Post-CT survival prediction was compared using area under the ROC curve (ROC AUC) and hazard ratios (HRs). RESULTS Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.2-11.6 years), during which 5.9% died (532/9066). No significant difference (p > 0.05) for mortality was observed between L1 and L3 VAT and SAT at 10-year ROC AUC. However, L3 measures were significantly better for VSR at 10-year AUC (p < 0.001). HRs comparing worst-to-best quartiles for mortality at L1 vs. L3 were 2.12 (95% CI, 1.65-2.72) and 2.22 (1.74-2.83) for VAT; 1.20 (0.95-1.52) and 1.16 (0.92-1.46) for SAT; and 2.26 (1.7-2.93) and 3.05 (2.32-4.01) for VSR. In women, the corresponding HRs for VSR were 2.58 (1.80-3.69) (L1) and 4.49 (2.98-6.78) (L3). CONCLUSION Automated CT-based measures of visceral fat (VAT and VSR) at L1 are predictive of survival, although overall measures of adiposity at L1 level are somewhat inferior to the standard L3-level measures. Utilizing predictive L1-level fat measures could expand opportunistic screening to chest CT imaging.
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Lee MH, Zea R, Garrett JW, Graffy PM, Summers RM, Pickhardt PJ. Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes. Radiology 2023; 306:e220574. [PMID: 36165792 PMCID: PMC9885340 DOI: 10.1148/radiol.220574] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/18/2022] [Accepted: 08/03/2022] [Indexed: 01/26/2023]
Abstract
Background CT-based body composition measures derived from fully automated artificial intelligence tools are promising for opportunistic screening. However, body composition thresholds associated with adverse clinical outcomes are lacking. Purpose To determine population and sex-specific thresholds for muscle, abdominal fat, and abdominal aortic calcium measures at abdominal CT for predicting risk of death, adverse cardiovascular events, and fragility fractures. Materials and Methods In this retrospective single-center study, fully automated algorithms for quantifying skeletal muscle (L3 level), abdominal fat (L3 level), and abdominal aortic calcium were applied to noncontrast abdominal CT scans from asymptomatic adults screened from 2004 to 2016. Longitudinal follow-up documented subsequent death, adverse cardiovascular events (myocardial infarction, cerebrovascular event, and heart failure), and fragility fractures. Receiver operating characteristic (ROC) curve analysis was performed to derive thresholds for body composition measures to achieve optimal ROC curve performance and high specificity (90%) for 10-year risks. Results A total of 9223 asymptomatic adults (mean age, 57 years ± 7 [SD]; 5152 women and 4071 men) were evaluated (median follow-up, 9 years). Muscle attenuation and aortic calcium had the highest diagnostic performance for predicting death, with areas under the ROC curve of 0.76 for men (95% CI: 0.72, 0.79) and 0.72 for women (95% CI: 0.69, 0.76) for muscle attenuation. Sex-specific thresholds were higher in men than women (P < .001 for muscle attenuation for all outcomes). The highest-performing markers for risk of death were muscle attenuation in men (31 HU; 71% sensitivity [164 of 232 patients]; 72% specificity [1114 of 1543 patients]) and aortic calcium in women (Agatston score, 167; 70% sensitivity [152 of 218 patients]; 70% specificity [1427 of 2034 patients]). Ninety-percent specificity thresholds for muscle attenuation for both risk of death and fragility fractures were 23 HU (men) and 13 HU (women). For aortic calcium and risk of death and adverse cardiovascular events, 90% specificity Agatston score thresholds were 1475 (men) and 735 (women). Conclusion Sex-specific thresholds for automated abdominal CT-based body composition measures can be used to predict risk of death, adverse cardiovascular events, and fragility fractures. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ohliger in this issue.
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Affiliation(s)
- Matthew H. Lee
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Ryan Zea
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - John W. Garrett
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Peter M. Graffy
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Ronald M. Summers
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Perry J. Pickhardt
- From the Departments of Radiology (M.H.L., R.Z., J.W.G., P.M.G.,
P.J.P.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine
and Public Health, 600 Highland Ave, Madison, WI 53792; and Imaging Biomarkers
and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
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Ziegelmayer S, Reischl S, Havrda H, Gawlitza J, Graf M, Lenhart N, Nehls N, Lemke T, Wilhelm D, Lohöfer F, Burian E, Neumann PA, Makowski M, Braren R. Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images. JAMA Netw Open 2023; 6:e2253370. [PMID: 36705919 DOI: 10.1001/jamanetworkopen.2022.53370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
IMPORTANCE Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care. OBJECTIVE To develop and evaluate a deep learning algorithm able to differentiate colon carcinoma (CC) and acute diverticulitis (AD) on CT images and analyze the impact of the AI-support system in a reader study. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic study, patients who underwent surgery between July 1, 2005, and October 1, 2020, for CC or AD were included. Three-dimensional (3-D) bounding boxes including the diseased bowel segment and surrounding mesentery were manually delineated and used to develop a 3-D convolutional neural network (CNN). A reader study with 10 observers of different experience levels was conducted. Readers were asked to classify the testing cohort under reading room conditions, first without and then with algorithmic support. MAIN OUTCOMES AND MEASURES To evaluate the diagnostic performance, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all readers and reader groups with and without AI support. Metrics were compared using the McNemar test and relative and absolute predictive value comparisons. RESULTS A total of 585 patients (AD: n = 267, CC: n = 318; mean [SD] age, 63.2 [13.4] years; 341 men [58.3%]) were included. The 3-D CNN reached a sensitivity of 83.3% (95% CI, 70.0%-96.6%) and specificity of 86.6% (95% CI, 74.5%-98.8%) for the test set, compared with the mean reader sensitivity of 77.6% (95% CI, 72.9%-82.3%) and specificity of 81.6% (95% CI, 77.2%-86.1%). The combined group of readers improved significantly with AI support from a sensitivity of 77.6% to 85.6% (95% CI, 81.3%-89.3%; P < .001) and a specificity of 81.6% to 91.3% (95% CI, 88.1%-94.5%; P < .001). Artificial intelligence support significantly reduced the number of false-negative and false-positive findings (NPV from 78.5% to 86.4% and PPV from 80.9% to 90.8%; P < .001). CONCLUSIONS AND RELEVANCE The findings of this study suggest that a deep learning model able to distinguish CC and AD in CT images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care.
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Affiliation(s)
- Sebastian Ziegelmayer
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Stefan Reischl
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Hannah Havrda
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Joshua Gawlitza
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Markus Graf
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Nicolas Lenhart
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Nadja Nehls
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Tristan Lemke
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Dirk Wilhelm
- Department of Surgery, Technical University of Munich, School of Medicine, Munich, Germany
| | - Fabian Lohöfer
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Egon Burian
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | | | - Marcus Makowski
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Rickmer Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
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Palm V, Norajitra T, von Stackelberg O, Heussel CP, Skornitzke S, Weinheimer O, Kopytova T, Klein A, Almeida SD, Baumgartner M, Bounias D, Scherer J, Kades K, Gao H, Jäger P, Nolden M, Tong E, Eckl K, Nattenmüller J, Nonnenmacher T, Naas O, Reuter J, Bischoff A, Kroschke J, Rengier F, Schlamp K, Debic M, Kauczor HU, Maier-Hein K, Wielpütz MO. AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine. Healthcare (Basel) 2022; 10:2166. [PMID: 36360507 PMCID: PMC9690402 DOI: 10.3390/healthcare10112166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 08/12/2023] Open
Abstract
Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.
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Affiliation(s)
- Viktoria Palm
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Tobias Norajitra
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Claus P. Heussel
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Stephan Skornitzke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Taisiya Kopytova
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Andre Klein
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Silvia D. Almeida
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Michael Baumgartner
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Dimitrios Bounias
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Jonas Scherer
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Klaus Kades
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Hanno Gao
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Paul Jäger
- Interactive Machine Learning Research Group, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Marco Nolden
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Elizabeth Tong
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Kira Eckl
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany
| | - Tobias Nonnenmacher
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Omar Naas
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Julia Reuter
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Arved Bischoff
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Jonas Kroschke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Fabian Rengier
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Manuel Debic
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Klaus Maier-Hein
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Mark O. Wielpütz
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
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Association between Metabolic Disorders and Cholangiocarcinoma: Impact of a Postulated Risk Factor with Rising Incidence. Cancers (Basel) 2022; 14:cancers14143483. [PMID: 35884542 PMCID: PMC9323381 DOI: 10.3390/cancers14143483] [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: 05/25/2022] [Revised: 07/09/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Introduction and objectives: The incidence of cholangiocarcinoma (CCA) has been increasing globally. Although a concomitant increase in the incidence of metabolic disorders might suggest a causal relationship, the data are scarce. We aimed to describe the prevalence of metabolic disorders in patients with CCA and report the clinical features and outcomes. Patients and Methods: Retrospective study including patients with CCA. Patients were divided into: (1) past history of diabetes or/and overweight/obesity (“metabolic disorder group”) and (2) without any of these features (“non-metabolic-disorder group”). A Cox regression model was used to determine the prognostic factors. Results: 122 patients were included. In total, 36 (29.5%) had overweight/obesity, 24 (19.7%) had diabetes, and 8 (6.6%) had both. A total of 29 (23.8%) patients had resectable disease and received upfront surgery. A total of 104 (85.2%) received chemotherapy for advanced/recurrent disease. The overall survival of the cohort was 14.3 months (95% CI: 10.1−17.3). ECOG-PS 0 (p < 0.0001), resectable disease (p = 0.018) and absence of vascular invasion (p = 0.048) were independently associated with better prognosis. The “metabolic disorder group” (n = 52) had a median survival of 15.5 months (95% CI 10.9−33.9) vs. 11.5 months (95% CI 8.4−16.5) in the “non-metabolic-disorder group” (n = 70) (HR: 1.10; 95% CI 0.62−1.94). Patients with resectable disease in the “metabolic group” had longer survival than patients in the “non-metabolic group” (43.4 months (95% CI 33.9-NR) vs. 21.8 months (95% CI 8.6−26.9); HR = 0.12, 95% CI 0.03−0.59). Conclusion: Metabolic disorders are frequent among CCA patients. Underlying metabolic comorbidities may be associated with prognosis in resectable CCA. There is a need to explore the mechanism that drives CCA carcinogenesis in a metabolic background.
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Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2022; 23:387-400. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2021] [Indexed: 10/20/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.
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Affiliation(s)
- Yifang Li
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Xuetao Wang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Jun Zhang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Shanshan Zhang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Jian Jiao
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China.
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CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation. AJR Am J Roentgenol 2022; 219:671-680. [PMID: 35642760 DOI: 10.2214/ajr.22.27749] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
CT-based body composition measures are well established in research settings as prognostic markers in oncologic patients. Numerous retrospective studies have shown the role of objective measurements extracted from abdominal CT images of skeletal muscle, abdominal fat, and bone mineral density in providing more accurate assessments of frailty and cancer cachexia in comparison with traditional clinical methods. Quantitative CT-based measurements of liver fat and aortic atherosclerotic calcification have received relatively less attention in cancer care but also provide prognostic information. Patients with cancer routinely undergo serial CT scans for staging, treatment response, and surveillance, providing the opportunity for performing quantitative body composition assessment as part of routine clinical care. The emergence of fully automated artificial intelligence-based segmentation and quantification tools to replace earlier time-consuming manual and semi-automated methods for body composition analysis will allow these opportunistic measures to transition from the research realm to clinical practice. With continued investigation, the measurements may ultimately be applied to achieve more precise risk stratification as a component of personalized oncologic care.
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Pickhardt PJ. Value-added Opportunistic CT Screening: State of the Art. Radiology 2022; 303:241-254. [PMID: 35289661 PMCID: PMC9083232 DOI: 10.1148/radiol.211561] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 12/13/2022]
Abstract
Opportunistic CT screening leverages robust imaging data embedded within abdominal and thoracic scans that are generally unrelated to the specific clinical indication and have heretofore gone largely unused. This incidental imaging information may prove beneficial to patients in terms of wellness, prevention, risk profiling, and presymptomatic detection of relevant disease. The growing interest in CT-based opportunistic screening relates to a confluence of factors: the objective and generalizable nature of CT-based body composition measures, the emergence of fully automated explainable AI solutions, the sheer volume of body CT scans performed, and the increasing emphasis on precision medicine and value-added initiatives. With a systematic approach to body composition and other useful CT markers, initial evidence suggests that their ability to help radiologists assess biologic age and predict future adverse cardiometabolic events rivals even the best available clinical reference standards. Emerging data suggest that standalone "intended" CT screening over an unorganized opportunistic approach may be justified, especially when combined with established cancer screening. This review will discuss the current status of opportunistic CT screening, including specific body composition markers and the various disease processes that may be impacted. The remaining hurdles to widespread clinical adoption include generalization to more diverse patient populations, disparate technical settings, and reimbursement.
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Affiliation(s)
- Perry J. Pickhardt
- From the Department of Radiology, The University of Wisconsin School
of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave,
Madison, WI 53792-3252
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Gunasekharan A, Jiang J, Nickerson A, Jalil S, Mumtaz K. Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis. Artif Intell Gastroenterol 2022; 3:46-53. [DOI: 10.35712/aig.v3.i2.46] [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: 12/31/2021] [Revised: 02/18/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide. It is critical to develop reliable methods of predicting progression to fibrosis, cirrhosis, and decompensated liver disease. Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data. Artificial intelligence (AI) provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis, and to identify patients at high risk for disease progression. We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis. Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis. We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction (MELD-Na score, liver biopsy, FIB-4 score, and biomarkers). They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy, for which there are currently no established prediction tools. These studies show that AI could be a helpful adjunct to existing techniques for diagnosing, monitoring, and treating both NAFLD and viral hepatitis.
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Affiliation(s)
| | - Joanna Jiang
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Ashley Nickerson
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Sajid Jalil
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Khalid Mumtaz
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
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Roussey B, Calame P, Revel L, Zver T, Konan A, Piton G, Koch S, Vuitton L, Delabrousse E. Liver spontaneous hypoattenuation on CT is an imaging biomarker of the severity of acute pancreatitis. Diagn Interv Imaging 2022; 103:401-407. [DOI: 10.1016/j.diii.2022.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 11/25/2022]
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Cai J, Guo X, Wang K, Zhang Y, Zhang D, Zhang X, Wang X. Automatic quantitative evaluation of normal pancreas based on deep learning in a Chinese adult population. Abdom Radiol (NY) 2022; 47:1082-1090. [PMID: 35064795 DOI: 10.1007/s00261-021-03327-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To develop a 3D U-Net-based model for the automatic segmentation of the pancreas using the diameters, volume, and density of normal pancreases among Chinese adults. METHODS A total of 2778 pancreas images (dataset 1) were retrospectively collected and randomly divided into training (n = 2252), validation (n = 245), and test (n = 281) datasets. The segmentation model for the pancreas was constructed through cascaded application of two 3D U-Net networks. The segmentation efficiency for the pancreas was evaluated by the Dice similarity coefficient (DSC). Another dataset of 3189 normal pancreas CT images (dataset 2) was obtained for external validation, including 1063 non-contrast images, 1063 arterial phase images, and 1063 portal venous phase images. The pancreas segmentation in dataset 2 was assessed objectively and manually revised by two radiologists. Then, the pancreatic volume, diameters, and average CT value for each phase of pancreas images in dataset 2 were calculated. The relationships between pancreas volume and age, sex, height, and weight were analyzed. RESULTS In dataset 1, a mean DSC of 0.94 for the test dataset was achieved. In dataset 2, the objective assessment yielded a 90% satisfaction rate for the automatic segmentation of the pancreas as external validation. The diameters of the pancreas were 43.71-44.28 mm, 67.40-68.15 mm, and 114.53-117.06 mm, respectively. The average pancreatic volume was 63,969.06-65,247.75 mm3, which was greatest at the age of 18-38 and then decreased to a minimum at the age of 69-85. The CT value of the pancreas also decreased with age, from a maximum value of 38.87 ± 9.70 HU to a minimum of 27.72 ± 10.85 HU. CONCLUSION The pancreas segmentation tool based on deep learning can segment the pancreas on CT images and measure its normal diameter, volume, and CT value accurately and effectively.
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Affiliation(s)
- Jinxiu Cai
- Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xiaochao Guo
- Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ke Wang
- Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co.Ltd, Beijing, China
| | - Dadou Zhang
- Beijing Smart Tree Medical Technology Co.Ltd, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing, 100034, China.
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [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: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Donners R, Zaugg C, Gehweiler JE, Boldanova T, Heim MH, Terracciano LM, Boll DT. Computed tomography (CT) and magnetic resonance imaging (MRI) of diffuse liver disease: a multiparametric predictive modelling algorithm can aid categorization of liver parenchyma. Quant Imaging Med Surg 2022; 12:1186-1197. [PMID: 35111615 DOI: 10.21037/qims-21-384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 09/06/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND Liver steatosis is common and tracking disease evolution to steatohepatitis and cirrhosis is essential for risk stratification and resultant patient management. Consequently, diagnostic tools allowing categorization of liver parenchyma based on routine imaging are desirable. The study objective was to compare established mono-factorial, dynamic single parameter and iterative multiparametric routine computed tomography (CT) and magnetic resonance imaging (MRI) analyses to distinguish between liver steatosis, steatohepatitis, cirrhosis and normal liver parenchyma. METHODS A total of 285 multi-phase contrast enhanced CT and 122 MRI studies with histopathological correlation of underlying parenchymal condition were retrospectively included. Parenchymal conditions were characterized based on CT Hounsfield units (HU) or MRI signal intensity (SI) measurements and calculated HU or SI ratios between non-contrast and contrast enhanced imaging time points. First, the diagnostic accuracy of mono-factorial analyses using established, static non-contrast HU and in- to opposed phase SI change cut-offs to distinguish between parenchymal conditions was established. Second, single dynamic discriminator analyses, with optimized non-contrast and enhancement HU and SI ratio cut-off values derived from the data, employing receiver operating characteristic (ROC) curve areas under the curve (AUCs) and the Youden index for maximum accuracy, were used for disease diagnosis. Third, multifactorial analyses, employing multiple non-contrast and contrast enhanced HU and SI ratio cut-offs in a nested, predictive-modelling algorithm were performed to distinguish between normal parenchyma, liver steatosis, steatohepatitis and cirrhosis. CT and MRI analyses were performed separately. RESULTS No single CT or MRI parameter showed significant difference between all four parenchymal conditions (each P>0.05). Mono-factorial static-CT-discriminator analyses identified liver steatosis with 75% accuracy. Mono-factorial MRI analyses identified steatosis with 89% accuracy. Single-dynamic CT parameter analyses identified normal parenchyma with 72% accuracy and cirrhosis with 75% accuracy. Single-dynamic MRI parameter analyses identified fatty parenchyma with 90% accuracy. Multifactorial CT analyzes identified normal parenchyma with 84%, liver steatosis with 95%, steatohepatitis with 95% and cirrhosis with 80% accuracy. Multifactorial predictive modelling of MRI parameters identified normal parenchyma with 79%, liver steatosis with 89%, steatohepatitis with 92% and cirrhosis with 89% accuracy. CONCLUSIONS Multiparametric analyses of quantitative measurements derived from routine CT and MRI, utilizing a predictive modelling algorithm, can help to distinguish between normal liver parenchyma, liver steatosis, steatohepatitis and cirrhosis.
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Affiliation(s)
- Ricardo Donners
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Carmen Zaugg
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Julian E Gehweiler
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Tuyana Boldanova
- Division of Gastroenterology and Hepatology, University Hospital Basel, Basel, Switzerland.,Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Markus H Heim
- Division of Gastroenterology and Hepatology, University Hospital Basel, Basel, Switzerland.,Department of Biomedicine, University of Basel, Basel, Switzerland
| | | | - Daniel T Boll
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
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Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2022; 4:100443. [PMID: 35243281 PMCID: PMC8867112 DOI: 10.1016/j.jhepr.2022.100443] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/26/2021] [Accepted: 01/11/2022] [Indexed: 12/18/2022]
Abstract
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
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Hepatic Steatosis: CT-based Prevalence in Adults in China and the United States and Associations with Age, Sex, and Body Mass Index. AJR Am J Roentgenol 2021; 218:846-857. [PMID: 34817193 DOI: 10.2214/ajr.21.26728] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Calibrated CT fat fraction (FFCT) measurements derived from non-enhanced abdominal CT reliably reflect liver fat content, allowing largescale population-level investigations of steatosis prevalence and associations. Objective: To compare prevalence of hepatic steatosis, assessed by calibrated CT measurements, between population-based Chinese and U.S. cohorts, and to investigate in these populations the relationship of steatosis with age, sex, and body mass index (BMI). Methods: This retrospective study included 3176 adults (1985 women, 1191 men) from seven Chinese provinces and 8748 adults (4834 women, 3914 men) from a single U.S. medical center, drawn from earlier studies. All participants were at least 40 years old and underwent unenhanced abdominal CT for the earlier studies. Liver fat content measurements on CT were cross-calibrated to MRI proton density fat fraction measurements using phantoms and expressed as adjusted FFCT. Mild, moderate, and severe steatosis were defined as adjusted FFCT of 5.0%-14.9%, 15.0%-24.9%, and ≥25.0%, respectively. The two cohorts were compared. Results: Median adjusted FFCT was for women 4.7% and 4.8%, and for men 5.8% and 6.2%, in the Chinese and U.S. cohorts, respectively. Steatosis prevalence was for women 46.3% and 48.7%, and for men 58.9% and 61.9%, in the Chinese and U.S. cohorts, respectively. Severe steatosis prevalence was for women 0.9% and 1.8%, and for men, 0.2% and 2.6%, in the Chinese and U.S. cohorts, respectively. Adjusted FFCT did not vary across age decades in women or men in the Chinese cohort, though increased across age decades in women and men in the U.S. cohort. Adjusted FFCT and BMI exhibited weak correlation (r=0.312-0.431). Among participants with normal BMI, 36.8% and 38.5% of those in the Chinese and U.S. cohorts had mild steatosis, and 3.0% and 1.5% had moderate or severe steatosis, respectively. Among U.S. participants with BMI ≥40.0, 17.7% had normal liver content. Conclusion: Steatosis and severe steatosis had higher prevalence in the U.S. than Chinese cohort in both women and men. BMI did not reliably predict steatosis. Clinical Impact: The findings provide new information on the dependence of hepatic steatosis on age, sex, and BMI.
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Pickhardt PJ, Summers RM, Garrett JW. Automated CT-Based Body Composition Analysis: A Golden Opportunity. Korean J Radiol 2021; 22:1934-1937. [PMID: 34719894 PMCID: PMC8628162 DOI: 10.3348/kjr.2021.0775] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 10/07/2021] [Indexed: 11/15/2022] Open
Affiliation(s)
- Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
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Starekova J, Hernando D, Pickhardt PJ, Reeder SB. Quantification of Liver Fat Content with CT and MRI: State of the Art. Radiology 2021; 301:250-262. [PMID: 34546125 PMCID: PMC8574059 DOI: 10.1148/radiol.2021204288] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hepatic steatosis is defined as pathologically elevated liver fat content and has many underlying causes. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an increasing prevalence among adults and children. Abnormal liver fat accumulation has serious consequences, including cirrhosis, liver failure, and hepatocellular carcinoma. In addition, hepatic steatosis is increasingly recognized as an independent risk factor for the metabolic syndrome, type 2 diabetes, and, most important, cardiovascular mortality. During the past 2 decades, noninvasive imaging-based methods for the evaluation of hepatic steatosis have been developed and disseminated. Chemical shift-encoded MRI is now established as the most accurate and precise method for liver fat quantification. CT is important for the detection and quantification of incidental steatosis and may play an increasingly prominent role in risk stratification, particularly with the emergence of CT-based screening and artificial intelligence. Quantitative imaging methods are increasingly used for diagnostic work-up and management of steatosis, including treatment monitoring. The purpose of this state-of-the-art review is to provide an overview of recent progress and current state of the art for liver fat quantification using CT and MRI, as well as important practical considerations related to clinical implementation.
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Affiliation(s)
- Jitka Starekova
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Diego Hernando
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Perry J Pickhardt
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Scott B Reeder
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
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