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Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2:133-140. [DOI: 10.35712/aig.v2.i5.133] [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: 06/30/2021] [Revised: 08/09/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Infectious or noninfectious liver disease has inexorably risen as one of the leading causes of global death and disease burden. There were an estimated 2.14 million liver-related deaths in 2017, representing an 11.4% increase since 2012. Traditional diagnosis and treatment methods have various dilemmas in different causes of liver disease. As a hot research topic in recent years, the application of artificial intelligence (AI) in different fields has attracted extensive attention, and new technologies have brought more ideas for the diagnosis and treatment of some liver diseases. Machine learning (ML) is the core of AI and the basic way to make a computer intelligent. ML technology has many potential uses in hepatology, ranging from exploring new noninvasive means to predict or diagnose different liver diseases to automated image analysis. The application of ML in liver diseases can help clinical staff to diagnose and treat different liver diseases quickly, accurately and scientifically, which is of importance for reducing the incidence and mortality of liver diseases, reducing medical errors, and promoting the development of medicine. This paper reviews the application and prospects of AI in liver diseases, and aims to improve clinicians’ awareness of the importance of AI in the diagnosis and treatment of liver diseases.
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Affiliation(s)
- Qiong Li
- The First Clinical Medical College, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Jun-Feng Li
- Department of Infectious Diseases & Institute of Infectious Diseases, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Xiao-Rong Mao
- Department of Infectious Diseases, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
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Perez AA, Noe-Kim V, Lubner MG, Graffy PM, Garrett JW, Elton DC, Summers RM, Pickhardt PJ. Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly. Radiology 2021; 302:336-342. [PMID: 34698566 PMCID: PMC8805660 DOI: 10.1148/radiol.2021210531] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (n = 1960) or renal donor evaluation (n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.
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Affiliation(s)
- Alberto A. Perez
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Victoria Noe-Kim
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Meghan G. Lubner
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Peter M. Graffy
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - John W. Garrett
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Daniel C. Elton
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Ronald M. Summers
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Perry J. Pickhardt
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
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Koç U, Taydaş O. Evaluation of pancreatic steatosis prevalence and anthropometric measurements using non-contrast computed tomography. TURKISH JOURNAL OF GASTROENTEROLOGY 2021; 31:640-648. [PMID: 33090101 DOI: 10.5152/tjg.2020.19434] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND/AIMS Pancreatic steatosis (PS) is a subject of current interest and its prevalence has been reported to range from 16.1% to 30.7% using various radiological methods. This study aimed to evaluate PS prevalence with non-contrast computed tomography (CT). MATERIALS AND METHODS The non-contrast CT scans taken in 2016 and 2017 in our hospital were retrospectively screened. A total of 637 cases (320 males, 317 females) were included in the study. CT number measurements were performed from three anatomic regions of the pancreas using regions of interest (ROI) of approximately 1 cm2. The cases with a <0.7 ratio of the pancreatic over splenic CT number were accepted as quantitatively steatosis-positive. Anthropometric evaluations were undertaken by determining various parameters defined on CT. RESULTS PS was determined visually in 30.6% of the males and 29% of the females, and quantitatively in 32.8% and 30.6%, respectively. A positive agreement was determined between the quantitative and visual evaluations of steatosis (Cohen's kappa coefficient=0.587, p<0.001). Although PS was seen to be mostly diffuse, the tail region of the pancreas was determined to be the area with most steatosis. CONCLUSION PS is usually overlooked in radiology practice but it has a clinical presentation with non-insignificant prevalence. Current radiological methods are adequate in the evaluation of PS. The determination of the cut-off values for various criteria on non-contrast CT can provide more objective evaluations.
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Affiliation(s)
- Ural Koç
- Department of Radiology, Erzincan Mengucek Gazi Training and Research Hospital, Erzincan, Turkey
| | - Onur Taydaş
- Department of Radiology, Erzincan Mengucek Gazi Training and Research Hospital, Erzincan, Turkey
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Zver T, Calame P, Koch S, Aubry S, Vuitton L, Delabrousse E. Early Prediction of Acute Biliary Pancreatitis Using Clinical and Abdominal CT Features. Radiology 2021; 302:118-126. [PMID: 34636635 DOI: 10.1148/radiol.2021210607] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Assessment of the biliary origin of acute pancreatitis (AP) is crucial because it affects patient treatment to avoid recurrence. Although CT is systematically performed to determine severity in AP, its usefulness in assessing AP biliary origin has not been evaluated. Purpose To assess abdominal CT features associated with acute biliary pancreatitis (ABP) and to evaluate the predictive value of a combination of CT and clinical data for determining a biliary origin in a first episode of AP. Materials and Methods From December 2014 to May 2019, all consecutive patients who presented with a first episode of AP and with at least 6 months of follow-up were retrospectively reviewed. Evidence of gallstones was mandatory for a clinical diagnosis of ABP. Abdominal CT images were reviewed by two abdominal radiologists. Univariable and multivariable statistical analyses were performed, and a nomogram was constructed on the basis of the combination of clinical and CT features. This nomogram was validated in a further independent internal cohort of patients. Results A total of 271 patients (mean age ± standard deviation, 56 years ± 20; 160 men) were evaluated. Of these, 170 (63%) had ABP. At multivariable analysis, age (odds ratio [OR], 1.06; 95% CI: 1.03, 1.09; P < .001), alanine aminotransferase level (OR, 1.00; 95% CI: 1.00, 1.01; P = .009), gallbladder gallstone (OR, 15.59; 95% CI: 4.61, 68.62; P < .001), choledochal ring sign (OR, 5.73; 95% CI: 2.11, 17.05; P < .001), liver spontaneous attenuation (OR, 1.07; 95% CI: 1.04, 1.11; P < .001), and duodenal thickening (OR, 0.17; 95% CI: 0.03, 0.61; P = .01) were independently associated with ABP. The matching nomogram combining both clinical and CT features displayed an area under the curve of 0.94 (95% CI: 0.91, 0.97) in the study sample (n = 271) and 0.91 (95% CI: 0.84, 0.99) in the validation cohort (n = 51). Conclusion Abdominal CT provided useful features for diagnosis of acute biliary pancreatitis (ABP). Combining CT and clinical features in a nomogram showed good diagnostic performance for early diagnosis of ABP. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chang in this issue.
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Affiliation(s)
- Thibaut Zver
- From the Department of Radiology (T.Z., P.C., S.A., E.D.), EA 4662 Nanomedicine Laboratory, Imagery and Therapeutics (P.C., S.A., E.D.), and Department of Gastroenterology (S.K., L.V.), University of Bourgogne Franche-Comté, CHRU Besançon, 3 boulevard Alexandre Fleming, 25030 Besançon, France
| | - Paul Calame
- From the Department of Radiology (T.Z., P.C., S.A., E.D.), EA 4662 Nanomedicine Laboratory, Imagery and Therapeutics (P.C., S.A., E.D.), and Department of Gastroenterology (S.K., L.V.), University of Bourgogne Franche-Comté, CHRU Besançon, 3 boulevard Alexandre Fleming, 25030 Besançon, France
| | - Stéphane Koch
- From the Department of Radiology (T.Z., P.C., S.A., E.D.), EA 4662 Nanomedicine Laboratory, Imagery and Therapeutics (P.C., S.A., E.D.), and Department of Gastroenterology (S.K., L.V.), University of Bourgogne Franche-Comté, CHRU Besançon, 3 boulevard Alexandre Fleming, 25030 Besançon, France
| | - Sébastien Aubry
- From the Department of Radiology (T.Z., P.C., S.A., E.D.), EA 4662 Nanomedicine Laboratory, Imagery and Therapeutics (P.C., S.A., E.D.), and Department of Gastroenterology (S.K., L.V.), University of Bourgogne Franche-Comté, CHRU Besançon, 3 boulevard Alexandre Fleming, 25030 Besançon, France
| | - Lucine Vuitton
- From the Department of Radiology (T.Z., P.C., S.A., E.D.), EA 4662 Nanomedicine Laboratory, Imagery and Therapeutics (P.C., S.A., E.D.), and Department of Gastroenterology (S.K., L.V.), University of Bourgogne Franche-Comté, CHRU Besançon, 3 boulevard Alexandre Fleming, 25030 Besançon, France
| | - Eric Delabrousse
- From the Department of Radiology (T.Z., P.C., S.A., E.D.), EA 4662 Nanomedicine Laboratory, Imagery and Therapeutics (P.C., S.A., E.D.), and Department of Gastroenterology (S.K., L.V.), University of Bourgogne Franche-Comté, CHRU Besançon, 3 boulevard Alexandre Fleming, 25030 Besançon, France
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Jirapatnakul A, Yip R, Branch AD, Lewis S, Crane M, Yankelevitz DF, Henschke CI. Dose-response relationship between World Trade Center dust exposure and hepatic steatosis. Am J Ind Med 2021; 64:837-844. [PMID: 34328231 DOI: 10.1002/ajim.23269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/04/2021] [Accepted: 05/17/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND The World Trade Center (WTC) attack exposed thousands of workers to toxic chemicals that have been linked to liver diseases and cancers. This study examined the relationship between the intensity of WTC dust exposure and the risk of hepatic steatosis in the WTC General Responders Cohort (GRC). METHODS All low-dose computed tomography (CT) scans of the chest performed on the WTC GRC between September 11, 2001 and December 31, 2018, collected as part of the World Trade Center Health Program, were reviewed. WTC dust exposure was categorized into five groups based on WTC arrival time. CT liver density was estimated using an automated algorithm, statistics-based liver density estimation from imaging. The relationship between the intensity of WTC dust exposure and the risk of hepatic steatosis was examined using univariate and multivariable regression analyses. RESULTS Of the 1788 WTC responders, 258 (14.4%) had liver attenuation less than 40 Hounsfield units (HU < 40) on their earliest CT. Median time after September 11, 2001 and the earliest available CT was 11.3 years (interquartile range: 8.0-14.9 years). Prevalence of liver attenuation less than 40 HU was 17.0% for arrivals on September 11, 2001, 16.0% for arrivals on (September 12, 2001 or September 13, 2001), 10.9% for arrivals on September 14-30, 2001, and 9.0% for arrivals on January 10, 2001 or later (p = 0.0015). A statistically significant trend of increasing liver steatosis was observed with earlier arrival times (p < 0.0001). WTC arrival time remained a significant independent factor for decreased liver attenuation after controlling for other covariates. CONCLUSIONS Early arrival at the WTC site was significantly associated with increasing hepatic steatosis.
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Affiliation(s)
- Artit Jirapatnakul
- Department of Radiology Icahn School of Medicine at Mount Sinai New York New York USA
| | - Rowena Yip
- Department of Radiology Icahn School of Medicine at Mount Sinai New York New York USA
| | - Andrea D. Branch
- Division of Liver Diseases, Department of Medicine Icahn School of Medicine at Mount Sinai New York New York USA
| | - Sara Lewis
- Department of Radiology Icahn School of Medicine at Mount Sinai New York New York USA
| | - Michael Crane
- Department of Environmental Medicine and Public Health Icahn School of Medicine at Mount Sinai New York New York USA
| | - David F. Yankelevitz
- Department of Radiology Icahn School of Medicine at Mount Sinai New York New York USA
| | - Claudia I. Henschke
- Department of Radiology Icahn School of Medicine at Mount Sinai New York New York USA
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Ziaee A, Azarkar G, Ziaee M. Role of fatty liver in coronavirus disease 2019 patients' disease severity and hospitalization length: a case-control study. Eur J Med Res 2021; 26:115. [PMID: 34565475 PMCID: PMC8474852 DOI: 10.1186/s40001-021-00590-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/16/2021] [Indexed: 02/08/2023] Open
Abstract
Background and purpose Fatty liver is one of the most common pre-existing illnesses; it can cause liver injury, leading to further complications in coronavirus disease 2019 patients. Our goal is to determine if pre-existing fatty liver is more prevalent in hospitalized COVID-19 patients compared to patients admitted before the SARS-CoV-2 pandemic and determine the disease severity among fatty liver patients. Experimental approach This retrospective study involves a case and a control group consisting of 1162 patients; the case group contains hospitalized COVID-19 patients with positive PCR tests and available chest CT-scan; the control group contains patients with available chest CT-scan previous to the COVID-19 pandemic. Patients’ data such as liver Hounsfield unit, hospitalization length, number of affected lobes, and total lungs involvement score were extracted and compared between the patients. Results The findings indicate that 37.9% of hospitalized COVID-19 patients have a pre-existing fatty liver, which is significantly higher (P < 0.001) than the prevalence of pre-existing fatty liver in control group patients (9.02%). In comparison to hospitalized non-fatty liver COVID-19 patients, data from hospitalized COVID-19 patients with fatty liver indicate a longer hospitalization length (6.81 ± 4.76 P = 0.02), a higher total lungs involvement score (8.73 ± 5.28 P < 0.001), and an increased number of affected lobes (4.42 ± 1.2 P < 0.001). Conclusion The statistical analysis shows fatty liver is significantly more prevalent among COVID-19 against non-COVID-19 patients, and they develop more severe disease and tend to be hospitalized for more extended periods.
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Affiliation(s)
- Arash Ziaee
- Neuroscience Department, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghodsiyeh Azarkar
- Infectious Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran. .,Infectious Diseases Research Center, Radiology Department, Birjand University of Medical Sciences, Birjand, Iran.
| | - Masood Ziaee
- Infectious Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
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Anconina R, Ortega C, Metser U, Liu ZA, Suzuki C, McInnis M, Darling GE, Wong R, Taylor K, Yeung J, Chen EX, Swallow CJ, Bajwa J, Jang RW, Elimova E, Veit-Haibach P. Influence of sarcopenia, clinical data, and 2-[ 18F] FDG PET/CT in outcome prediction of patients with early-stage adenocarcinoma esophageal cancer. Eur J Nucl Med Mol Imaging 2021; 49:1012-1020. [PMID: 34491404 DOI: 10.1007/s00259-021-05514-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 07/28/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE To determine the prognostic value of sarcopenia measurements done on staging 2-[18F] FDG PET/CT together with metabolic activity of the tumor in patients with adenocarcinoma esophagogastric cancer with surgical treatment. METHODS Patients with early-stage, surgically treated esophageal adenocarcinoma and available pre-treatment 2-[18F] FDG PET/CT were included. The standard uptake value (SUV) and SUV normalized by lean body mass (SUL) were recorded. Skeletal muscle index (SMI) was measured at the L3 level on the CT component of the PET/CT. Sarcopenia was defined as SMI < 34.4cm2/m2 in women and < 45.4cm2/m2 in men. RESULTS Of the included 145 patients. 30% were sarcopenic at baseline. On the univariable Cox proportional hazards analysis, ECOG, surgical T and N staging, lymphovascular invasion (LVI) positive lymph nodes, and sarcopenia were significant prognostic factors concerning RFS and OS. On multivariable Cox regression analysis, surgical N staging (p = 0.025) and sarcopenia (p = 0.022) remained significant poor prognostic factors for OS and RFS. Combining the clinical parameters with the imaging-derived nutritional evaluation of the patient but not metabolic parameters of the tumor showed improved predictive ability for OS and RFS. CONCLUSION Combining the patients' imaging-derived sarcopenic status with standard clinical data, but not metabolic parameters, offered an overall improved prognostic value concerning OS and RFS.
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Affiliation(s)
- Reut Anconina
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada.
| | - Claudia Ortega
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada
| | - Ur Metser
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada
| | - Zhihui Amy Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Chihiro Suzuki
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Micheal McInnis
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada
| | - Gail E Darling
- Division of Thoracic Surgery, Department of Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada
| | - Rebecca Wong
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Kirsty Taylor
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Jonathan Yeung
- Division of Thoracic Surgery, Department of Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada
| | - Eric X Chen
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Carol J Swallow
- Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network and Sinai Health System, University of Toronto, Toronto, Canada
| | | | - Raymond W Jang
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Elena Elimova
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada
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A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography. J Comput Assist Tomogr 2021; 45:870-876. [PMID: 34469906 DOI: 10.1097/rct.0000000000001215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Poor contrast enhancement is related to issues with examination execution, contrast prescription, computed tomography (CT) protocols, and patient conditions. Currently, our community has no metric to monitor true enhancement on routine single-phase examinations because this requires knowledge of both pre- and postcontrast CT number. PURPOSE We propose an automatable solution to quantifying contrast enhancement without requiring a dedicated noncontrast series. METHODS The difference in CT number between a target region in an enhanced and unenhanced image defines the metric "quantification of iodine contrast enhancement" (Q-ICE). Quantification of iodine contrast enhancement uses the noncontrast bolus tracking baseline image from routine abdominal examinations, which mitigates the need for a dedicated noncontrast series. We applied this method retrospectively to 312 patient livers from 2 sites between 2017 and 2020. Each site used a weight-based contrast injection protocol for weights 60 to 113 kg and a constant volume less than 60 kg and greater than 113 kg. Hypothesis testing was performed to compare Q-ICE between sites and detect Q-ICE dependence on weight and kilovoltage (kV). RESULTS Mean Q-ICE differed between sites (P = 0.004) by 4.96 Hounsfield unit with 95% confidence interval (1.63-8.28), albeit this difference was roughly 2 times smaller than the SD in Q-ICE across patients at a single site. For patients between 60 and 113 kg, we did not observe evidence of Q-ICE varying with patient weight (P = 0.920 and 0.064 for 120 and 140 kV, respectively). The Q-ICE did vary with patient weight for patients less than 60 kg (P = 0.003) and greater than 113 kg (P = 0.04). We observed a roughly 10 Hounsfield unit reduction in Q-ICE liver for patients scanned with 140 versus 120 kV. We observed several underenhancing examinations with an arterial phase appearance motivating our CT protocol optimization team to consider increasing the delay for slowly enhancing patients. CONCLUSIONS A quality metric for quantifying CT contrast enhancement was developed and suggested tangible opportunities for quality improvement and potential financial savings.
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Accuracy of Dual-Energy Computed Tomography Techniques for Fat Quantification in Comparison With Magnetic Resonance Proton Density Fat Fraction and Single-Energy Computed Tomography in an Anthropomorphic Phantom Environment. J Comput Assist Tomogr 2021; 45:877-887. [PMID: 34469903 DOI: 10.1097/rct.0000000000001193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To investigate in an anthropomorphic phantom study the accuracy of dual-energy computed tomography (DECT) techniques for fat quantification in comparison with magnetic resonance (MR) proton density fat fraction (PDFF) and single-energy computed tomography (SECT), using known fat content as reference standard. METHODS Between August 2018 and November 2020, organic material-based cylinders, composed of mixtures of lean and fat tissues mimics, iodine, and iron, were constructed to simulate varying fat content levels (0%, 10%, 15%, 25%, 50%, 75%, and 100%) in a parenchymal organ and were embedded into an anthropomorphic phantom simulating 3 patient sizes (circumference, 91, 126, and 161 cm). The phantom was imaged with multiecho MR, DECT, and SECT. Magnetic resonance PDFF, DECT fat fraction, and computed tomography (CT) numbers (SECT polychromatic and DECT monochromatic data, virtual unenhanced images) were estimated. Performances of MR PDFF and CT techniques to detect differences in fat content were measured using the area under the curve (AUC). Noninferiority of each CT technique relative to MR PDFF was tested using a noninferiority margin of -0.1. RESULTS MR PDFF, DECT 140 keV monochromatic data, and fat fraction most closely correlated with known fat content (R2 = 0.98, 0.98, and 0.96, respectively). Unlike SECT and all other DECT techniques, DECT fat fraction was not affected by presence of iodine (mean difference, 0.3%; 95% confidence interval [CI], -0.9% to 1.5%). Dual-energy computed tomography fat fraction showed noninferiority to MR PDFF in detecting differences of 5% in fat content in medium-sized phantoms (ΔAUC, -0.05; 95% CI, -0.08 to -0.01), and 7% in large (ΔAUC, -0.04; 95% CI, -0.0 to 0.00) or extralarge sized phantoms (ΔAUC, -0.02; 95% CI, -0.07 to 0.00). CONCLUSIONS Dual-energy computed tomography fat fraction shows linear correlation with true fat content in the range up to 50% fat fraction. Dual-energy computed tomography fat fraction has comparable estimation error and shows noninferiority to MR PDFF in detecting small differences in fat content across different body sizes.
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Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes. AJR Am J Roentgenol 2021; 218:124-131. [PMID: 34406056 DOI: 10.2214/ajr.21.26486] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND. Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. OBJECTIVE. The purpose of this article is to compare the utility of fully automated deep learning CT-based muscle quantitation at the L1 versus L3 level for predicting future hip fractures and death. METHODS. This retrospective study included 9223 asymptomatic adults (mean age, 57 ± 8 [SD] years; 4071 men, 5152 women) who underwent unenhanced low-dose abdominal CT. A previously validated fully automated deep learning tool was used to assess muscle for myosteatosis (by mean attenuation) and myopenia (by cross-sectional area) at the L1 and L3 levels. Performance for predicting hip fractures and death was compared between L1 and L3 measures. Performance for predicting hip fractures and death was also evaluated using the established clinical risk scores from the fracture risk assessment tool (FRAX) and Framingham risk score (FRS), respectively. RESULTS. Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.1-11.6 years), yielding hip fractures and death in 219 (2.4%) and 549 (6.0%) patients, respectively. L1-level and L3-level muscle attenuation measurements were not different in 2-, 5-, or 10-year AUC for hip fracture (p = .18-.98) or death (p = .19-.95). For hip fracture, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRAX score were 0.717, 0.709, and 0.708, respectively. For death, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRS were 0.737, 0.721, and 0.688, respectively. Lowest quartile hazard ratios (HRs) for hip fracture were 2.20 (L1 attenuation), 2.45 (L3 attenuation), and 2.53 (FRAX score), and for death were 3.25 (L1 attenuation), 3.58 (L3 attenuation), and 2.82 (FRS). CT-based muscle cross-sectional area measurements at L1 and L3 were less predictive for hip fracture and death (5-year AUC ≤ 0.571; HR ≤ 1.56). CONCLUSION. Automated CT-based measurements of muscle attenuation for myosteatosis at the L1 level compare favorably with previously established L3-level measurements and clinical risk scores for predicting hip fracture and death. Assessment for myopenia was less predictive of outcomes at both levels. CLINICAL IMPACT. Alternative use of the L1 rather than L3 level for CT-based muscle measurements allows sarcopenia assessment using both chest and abdominal CT scans, greatly increasing the potential yield of opportunistic CT screening.
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Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets. PLoS One 2021; 16:e0255605. [PMID: 34375365 PMCID: PMC8354440 DOI: 10.1371/journal.pone.0255605] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 07/19/2021] [Indexed: 02/08/2023] Open
Abstract
The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datasets from a radiological perspective. Training and testing datasets were acquired retrospectively from two hospitals of Taiwan. The training dataset contained 10,130 images of liver tumor densities of 11,258 regions of interest (ROIs). The positive testing dataset contained 1,833 images of liver tumor densities with 1,874 ROIs, and negative testing data comprised 20,283 images without abnormal densities in liver parenchyma. The Mask R-CNN was used to generate a medical model, and areas under the curve, true positive rates, false positive rates, and Dice coefficients were evaluated. For abnormal liver CT density detection, in each image, we identified the mean area under the curve, true positive rate, and false positive rate, which were 0.9490, 91.99%, and 13.68%, respectively. For segmentation ability, the highest mean Dice coefficient obtained was 0.8041. This study trained a Mask R-CNN on various HCC images to construct a medical model that serves as an auxiliary tool for alerting radiologists to abnormal CT density in liver scans; this model can simultaneously detect liver lesions and perform automatic instance segmentation.
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van Dijk DP, Zhao J, Kemter K, Baracos VE, Dejong CH, Rensen SS, Olde Damink SW. Ectopic fat in liver and skeletal muscle is associated with shorter overall survival in patients with colorectal liver metastases. J Cachexia Sarcopenia Muscle 2021; 12:983-992. [PMID: 34061469 PMCID: PMC8350209 DOI: 10.1002/jcsm.12723] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 02/27/2021] [Accepted: 05/03/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Myosteatosis has been associated with shorter overall survival in cancer patients. The increase in ectopic fat might not be limited to skeletal muscle only and might also extend to other sites such as the liver, resulting in non-alcoholic fatty liver disease (NAFLD). In this study, we assessed the relationship between myosteatosis and NAFLD and their association with overall survival in patients with colorectal liver metastases undergoing partial hepatectomy. METHODS Patients were selected from a prospective cohort of 289 consecutive patients with colorectal liver metastases. All patients with a preoperative computed tomography (CT)-scan and liver biopsy obtained during surgery were included. If available a second pre-operative CT scan was used to calculate changes in body composition over time. Muscle radiation attenuation was defined as the average Hounsfield units on CT of all muscle tissue at the L3 level. Liver biopsies were graded by a liver pathologist using the steatosis, activity, and fibrosis scoring system for NAFLD. RESULTS Two-hundred and eighteen patients had an available liver biopsy of which 131 patients had two available pre-operative CT scans with an average time interval of 3.2 months. One-hundred and thirty-five (62%) biopsies were classified as NAFLD. In multivariable Cox-regression analysis, NAFLD [hazard ratio (HR): 1.8, 95%-confidence interval (CI) 1.0-3.0, P = 0.037], increase in myosteatosis (HR 1.8, 95%-CI 1.1-2.9, P = 0.018), and skeletal muscle loss (HR 1.7, 95%-CI 1.0-2.9, P = 0.035) were independently associated with shorter overall survival while high visceral adipose tissue fat content was associated with longer overall survival (HR: 0.7, 95%-CI 0.5-0.9, P = 0.014). CONCLUSIONS Ectopic fat content of liver as well as skeletal muscle tissue is independently associated with shorter overall survival in patients with colorectal liver metastases, while increased visceral adipose tissue fat content is associated with longer overall survival.
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Affiliation(s)
- David P.J. van Dijk
- Department of SurgeryMaastricht University Medical CentreMaastrichtThe Netherlands
- NUTRIM School of Nutrition and Translational Research in MetabolismMaastricht UniversityMaastrichtThe Netherlands
| | - Junfang Zhao
- Department of SurgeryMaastricht University Medical CentreMaastrichtThe Netherlands
- NUTRIM School of Nutrition and Translational Research in MetabolismMaastricht UniversityMaastrichtThe Netherlands
- Department of Biliary and Pancreatic SurgeryTongji Hospital of Tongji Medical CollegeWuhanChina
| | - Katrin Kemter
- Department of SurgeryMaastricht University Medical CentreMaastrichtThe Netherlands
- NUTRIM School of Nutrition and Translational Research in MetabolismMaastricht UniversityMaastrichtThe Netherlands
| | | | - Cornelis H.C. Dejong
- Department of SurgeryMaastricht University Medical CentreMaastrichtThe Netherlands
- NUTRIM School of Nutrition and Translational Research in MetabolismMaastricht UniversityMaastrichtThe Netherlands
- GROW School for Oncology and Developmental BiologyMaastricht UniversityMaastrichtThe Netherlands
- Department of General, Visceral and Transplantation SurgeryRWTH University Hospital AachenAachenGermany
| | - Sander S. Rensen
- Department of SurgeryMaastricht University Medical CentreMaastrichtThe Netherlands
- NUTRIM School of Nutrition and Translational Research in MetabolismMaastricht UniversityMaastrichtThe Netherlands
| | - Steven W.M. Olde Damink
- Department of SurgeryMaastricht University Medical CentreMaastrichtThe Netherlands
- NUTRIM School of Nutrition and Translational Research in MetabolismMaastricht UniversityMaastrichtThe Netherlands
- Department of General, Visceral and Transplantation SurgeryRWTH University Hospital AachenAachenGermany
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Abstract
ABSTRACT Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.
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Burian E, Sollmann N, Mei K, Dieckmeyer M, Juncker D, Löffler M, Greve T, Zimmer C, Kirschke JS, Baum T, Noël PB. Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on quantitative paraspinal muscle assessment. Quant Imaging Med Surg 2021; 11:3042-3050. [PMID: 34249633 DOI: 10.21037/qims-20-1220] [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: 11/01/2020] [Accepted: 02/18/2021] [Indexed: 11/06/2022]
Abstract
Background Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment. Methods MDCT scans of eighteen subjects (6 males, age: 71.5±15.9 years, and 12 females, age: 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images. Segmentations were superimposed on all low-dose scans and muscle density (MD) extracted. Results Sparse sampling derived mean MD showed no significant changes (P=0.57 and P=0.22) down to 5% of the original projections in the erector spinae and psoas muscles, respectively. All virtually reduced tube current series showed significantly different (P>0.05) mean MD in the psoas and erector spinae muscles as compared to the original dose except for the images of 5% of the original tube current in the erector spinae muscle. Conclusions Our findings demonstrated the possibility of considerable radiation dose reduction using MDCT scans for assessing the composition of the paravertebral musculature. The sparse sampling approach seems to be promising and a potentially superior technique for dose reduction as compared to tube current reduction.
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Affiliation(s)
- Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Kai Mei
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Daniela Juncker
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Maximilian Löffler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Tobias Greve
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Neurosurgery, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Peter B Noël
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
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Popa SL, Ismaiel A, Cristina P, Cristina M, Chiarioni G, David L, Dumitrascu DL. Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review. Diagnostics (Basel) 2021; 11:diagnostics11061078. [PMID: 34204822 PMCID: PMC8231502 DOI: 10.3390/diagnostics11061078] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/05/2021] [Accepted: 06/10/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging. Methods: PubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records). Results: Our search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software. Conclusions: We found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.
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Affiliation(s)
- Stefan L. Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
- Correspondence:
| | - Pop Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; (P.C.); (M.C.)
| | - Mogosan Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; (P.C.); (M.C.)
| | - Giuseppe Chiarioni
- Division of Gastroenterology, University of Verona, 1-37126 AOUI Verona, Italy;
| | - Liliana David
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Dan L. Dumitrascu
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
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Graffy PM, Summers RM, Perez AA, Sandfort V, Zea R, Pickhardt PJ. Automated assessment of longitudinal biomarker changes at abdominal CT: correlation with subsequent cardiovascular events in an asymptomatic adult screening cohort. Abdom Radiol (NY) 2021; 46:2976-2984. [PMID: 33388896 DOI: 10.1007/s00261-020-02885-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/25/2020] [Accepted: 11/27/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Cardiovascular (CV) disease is a major public health concern, and automated methods can potentially capture relevant longitudinal changes on CT for opportunistic CV screening purposes. METHODS Fully-automated and validated algorithms that quantify abdominal fat, muscle, bone, liver, and aortic calcium were retrospectively applied to a longitudinal adult screening cohort undergoing serial non-contrast CT examination between 2005 and 2016. Downstream major adverse events (MI/CVA/CHF/death) were identified via algorithmic EHR search. Logistic regression, ROC curve, and Cox survival analyses assessed for associations between changes in CT variables and adverse events. RESULTS Final cohort included 1949 adults (942 M/1007F; mean age, 56.2 ± 6.2 years at initial CT). Mean interval between CT scans was 5.8 ± 2.0 years. Mean clinical follow-up interval from initial CT was 10.4 ± 2.7 years. Major CV events occurred after follow-up CT in 230 total subjects (11.8%). Mean change in aortic calcium Agatston score was significantly higher in CV(+) cohort (591.6 ± 1095.3 vs. 261.1 ± 764.3), as was annualized Agatston change (120.5 ± 263.6 vs. 46.7 ± 143.9) (p < 0.001 for both). 5-year area under the ROC curve (AUC) for Agatston change was 0.611. Hazard ratio for Agatston score change > 500 was 2.8 (95% CI 1.5-4.0) relative to < 500. Agatston score change was the only significant univariate CT biomarker in the survival analysis. Changes in fat and bone measures added no meaningful prediction. CONCLUSION Interval change in automated CT-based abdominal aortic calcium load represents a promising predictive longitudinal tool for assessing cardiovascular and mortality risks. Changes in other body composition measures were less predictive of adverse events.
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Affiliation(s)
- Peter M Graffy
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Ronald M Summers
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Alberto A Perez
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Veit Sandfort
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
- E3/311 Clinical Science Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792‑3252, USA.
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Basso L, Baldi D, Mannelli L, Cavaliere C, Salvatore M, Brancato V. Investigating Dual-Energy CT Post-Contrast Phases for Liver Iron Quantification: A Preliminary Study. Dose Response 2021; 19:15593258211011359. [PMID: 34121963 PMCID: PMC8173994 DOI: 10.1177/15593258211011359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/17/2021] [Accepted: 03/29/2021] [Indexed: 12/03/2022] Open
Abstract
Background and Purpose: Quantification of hepatic virtual iron content (VIC) by using Multidetector Dual Energy Computed Tomography (DECT) has been recently investigated since this technique could offer a good compromise between accuracy and non-invasiveness for liver iron content quantification. The aim of our study is to investigate differences in VIC at different DECT time points (namely baseline and arterial, venous and tardive phases), identifying the most reliable and also exploring the underlying temporal trend of these values. Materials and Methods: Eleven patients who underwent DECT examination and were characterized by low liver fat content were included in this retrospective study. By using the Syngo.via Frontier–DE IronVNC tool, regions of interest (ROI) were placed on the VIC images at 3 hepatic levels, both in left and right liver lobes, at each DECT time point. Friedman’s test followed by Bonferroni-adjusted Wilcoxon signed-rank test for post-hoc analysis was performed to assess differences between DECT timepoints. Page’s L test was performed to test the temporal trend of VIC across the 4 examined timepoints. Results: For both liver lobes, Friedman’s test followed by Bonferroni-adjusted Wilcoxon signed-rank test revealed that VIC values differed significantly when extracted from ROIs placed at the 4 different timepoints. The Page’s L test for multiple comparison revealed a significant growing trend for VIC, from baseline acquisition to the fourth and last time point post-contrast agent injection. Conclusions: The extraction of hepatic VIC in healthy subjects was found to be significantly influenced by the DECT time point chosen for the extrapolation of the VIC values.
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Zhou SK, Greenspan H, Davatzikos C, Duncan JS, van Ginneken B, Madabhushi A, Prince JL, Rueckert D, Summers RM. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2021; 109:820-838. [PMID: 37786449 PMCID: PMC10544772 DOI: 10.1109/jproc.2021.3054390] [Citation(s) in RCA: 202] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
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Affiliation(s)
- S Kevin Zhou
- School of Biomedical Engineering, University of Science and Technology of China and Institute of Computing Technology, Chinese Academy of Sciences
| | - Hayit Greenspan
- Biomedical Engineering Department, Tel-Aviv University, Israel
| | - Christos Davatzikos
- Radiology Department and Electrical and Systems Engineering Department, University of Pennsylvania, USA
| | - James S Duncan
- Departments of Biomedical Engineering and Radiology & Biomedical Imaging, Yale University
| | | | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University and Louis Stokes Cleveland Veterans Administration Medical Center, USA
| | - Jerry L Prince
- Electrical and Computer Engineering Department, Johns Hopkins University, USA
| | - Daniel Rueckert
- Klinikum rechts der Isar, TU Munich, Germany and Department of Computing, Imperial College, UK
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Automatisierte CT-Quantifizierung von Leberfett. ROFO-FORTSCHR RONTG 2021. [DOI: 10.1055/a-1072-8874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Perez AA, Pickhardt PJ, Elton DC, Sandfort V, Summers RM. Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast. Abdom Radiol (NY) 2021; 46:1229-1235. [PMID: 32948910 DOI: 10.1007/s00261-020-02755-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/31/2020] [Accepted: 09/03/2020] [Indexed: 01/28/2023]
Abstract
PURPOSE Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures. MATERIALS AND METHODS Initial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r2) was generated for each comparison. RESULTS Mean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm2, 18.8 ± 6.0 HU, - 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r2 = 0.72), MA (pre = 0.98 × post; r2 = 0.92), M-HU (pre = 0.75 × post + 5.7; r2 = 0.75), and V/S (pre = 1.11 × post; r2 = 0.94); p < 0.001 for all r2 values. There were no significant trends according to patient age or gender that required further correction. CONCLUSION Fully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.
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Affiliation(s)
- Alberto A Perez
- The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Perry J Pickhardt
- The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI, 53792-3252, USA.
| | - Daniel C Elton
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Veit Sandfort
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 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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Pickhardt PJ, Graffy PM, Perez AA, Lubner MG, Elton DC, Summers RM. Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value. Radiographics 2021; 41:524-542. [PMID: 33646902 DOI: 10.1148/rg.2021200056] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ©RSNA, 2021.
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Affiliation(s)
- Perry J Pickhardt
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., A.A.P., M.G.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Peter M Graffy
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., A.A.P., M.G.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Alberto A Perez
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., A.A.P., M.G.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Meghan G Lubner
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., A.A.P., M.G.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Daniel C Elton
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., A.A.P., M.G.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
| | - Ronald M Summers
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., A.A.P., M.G.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.)
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Sung YS, Park B, Park HJ, Lee SS. Radiomics and deep learning in liver diseases. J Gastroenterol Hepatol 2021; 36:561-568. [PMID: 33709608 DOI: 10.1111/jgh.15414] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/17/2021] [Accepted: 01/19/2021] [Indexed: 12/14/2022]
Abstract
Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.
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Affiliation(s)
- Yu Sub Sung
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Bumwoo Park
- Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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Lennartz S, Dratsch T, Zopfs D, Persigehl T, Maintz D, Große Hokamp N, Pinto Dos Santos D. Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives. J Med Internet Res 2021; 23:e24221. [PMID: 33595451 PMCID: PMC7929746 DOI: 10.2196/24221] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/01/2020] [Accepted: 11/30/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce. OBJECTIVE This study aimed to investigate patients' opinions on the use of AI in different aspects of the medical workflow and the level of control and supervision under which they would deem the application of AI in medicine acceptable. METHODS Patients scheduled for computed tomography or magnetic resonance imaging voluntarily participated in an anonymized questionnaire between February 10, 2020, and May 24, 2020. Patient information, confidence in physicians vs AI in different clinical tasks, opinions on the control of AI, preference in cases of disagreement between AI and physicians, and acceptance of the use of AI for diagnosing and treating diseases of different severity were recorded. RESULTS In total, 229 patients participated. Patients favored physicians over AI for all clinical tasks except for treatment planning based on current scientific evidence. In case of disagreement between physicians and AI regarding diagnosis and treatment planning, most patients preferred the physician's opinion to AI (96.2% [153/159] vs 3.8% [6/159] and 94.8% [146/154] vs 5.2% [8/154], respectively; P=.001). AI supervised by a physician was considered more acceptable than AI without physician supervision at diagnosis (confidence rating 3.90 [SD 1.20] vs 1.64 [SD 1.03], respectively; P=.001) and therapy (3.77 [SD 1.18] vs 1.57 [SD 0.96], respectively; P=.001). CONCLUSIONS Patients favored physicians over AI in most clinical tasks and strongly preferred an application of AI with physician supervision. However, patients acknowledged that AI could help physicians integrate the most recent scientific evidence into medical care. Application of AI in medicine should be disclosed and controlled to protect patient interests and meet ethical standards.
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Affiliation(s)
- Simon Lennartz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Thomas Dratsch
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - David Zopfs
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nils Große Hokamp
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Daniel Pinto Dos Santos
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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Abstract
OBJECTIVE. The purpose of this study was to evaluate the utility of laboratory and CT metrics in identifying patients with high-risk nonalcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS. Patients with biopsy-proven NAFLD who underwent CT within 1 year of biopsy were included. Histopathologic review was performed by an experienced gastrointestinal pathologist to determine steatosis, inflammation, and fibrosis. The presence of any lobular inflammation and hepatocyte ballooning was categorized as nonalcoholic steatohepatitis (NASH). Patients with NAFLD and advanced fibrosis (stage F3 or higher) were categorized as having high-risk NAFLD. Aspartate transaminase to platelet ratio index and Fibrosis-4 (FIB-4) laboratory scores were calculated. CT metrics included hepatic attenuation, liver segmental volume ratio (LSVR), splenic volume, liver surface nodularity score, and selected texture features. In addition, two readers subjectively assessed the presence of NASH (present or not present) and fibrosis (stages F0-F4). RESULTS. A total of 186 patients with NAFLD (mean age, 49 years; 74 men and 112 women) were included. Of these, 87 (47%) had NASH and 112 (60%) had moderate to severe steatosis. A total of 51 patients were classified as fibrosis stage F0, 42 as F1, 23 as F2, 37 as F3, and 33 as F4. Additionally, 70 (38%) had advanced fibrosis (stage F3 or F4) and were considered to have high-risk NAFLD. FIB-4 score correlated with fibrosis (ROC AUC of 0.75 for identifying high-risk NAFLD). Of the individual CT parameters, LSVR and splenic volume performed best (AUC of 0.69 for both for detecting high-risk NAFLD). Subjective reader assessment performed best among all parameters (AUCs of 0.78 for reader 1 and 0.79 for reader 2 for detecting high-risk NAFLD). FIB-4 and subjective scores were complementary (combined AUC of 0.82 for detecting high-risk NAFLD). For NASH assessment, FIB-4 performed best (AUC of 0.68), whereas the AUCs were less than 0.60 for all individual CT features and subjective assessments. CONCLUSION. FIB-4 and multiple CT findings can identify patients with high-risk NAFLD (advanced fibrosis or cirrhosis). However, the presence of NASH is elusive on CT.
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Winkel DJ, Breit HC, Weikert TJ, Stieltjes B. Building Large-Scale Quantitative Imaging Databases with Multi-Scale Deep Reinforcement Learning: Initial Experience with Whole-Body Organ Volumetric Analyses. J Digit Imaging 2021; 34:124-133. [PMID: 33469724 PMCID: PMC7887142 DOI: 10.1007/s10278-020-00398-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/21/2020] [Accepted: 11/10/2020] [Indexed: 11/27/2022] Open
Abstract
To explore the feasibility of a fully automated workflow for whole-body volumetric analyses based on deep reinforcement learning (DRL) and to investigate the influence of contrast-phase (CP) and slice thickness (ST) on the calculated organ volume. This retrospective study included 431 multiphasic CT datasets—including three CP and two ST reconstructions for abdominal organs—totaling 10,508 organ volumes (10,344 abdominal organ volumes: liver, spleen, and kidneys, 164 lung volumes). Whole-body organ volumes were determined using multi-scale DRL for 3D anatomical landmark detection and 3D organ segmentation. Total processing time for all volumes and mean calculation time per case were recorded. Repeated measures analyses of variance (ANOVA) were conducted to test for robustness considering CP and ST. The algorithm calculated organ volumes for the liver, spleen, and right and left kidney (mean volumes in milliliter (interquartile range), portal venous CP, 5 mm ST: 1868.6 (1426.9, 2157.8), 350.19 (45.46, 395.26), 186.30 (147.05, 214.99) and 181.91 (143.22, 210.35), respectively), and for the right and left lung (2363.1 (1746.3, 2851.3) and 1950.9 (1335.2, 2414.2)). We found no statistically significant effects of the variable contrast phase or the variable slice thickness on the organ volumes. Mean computational time per case was 10 seconds. The evaluated approach, using state-of-the art DRL, enables a fast processing of substantial amounts irrespective of CP and ST, allowing building up organ-specific volumetric databases. The thus derived volumes may serve as reference for quantitative imaging follow-up.
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Affiliation(s)
- David J Winkel
- Department of Radiology, University Hospital of Basel, Basel, Basel-Stadt, Switzerland.
| | - Hanns-Christian Breit
- Department of Radiology, University Hospital of Basel, Basel, Basel-Stadt, Switzerland
| | - Thomas J Weikert
- Department of Radiology, University Hospital of Basel, Basel, Basel-Stadt, Switzerland
| | - Bram Stieltjes
- Department of Radiology, University Hospital of Basel, Basel, Basel-Stadt, Switzerland
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Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms. AJR Am J Roentgenol 2020; 216:85-92. [PMID: 32603223 DOI: 10.2214/ajr.20.23049] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Metabolic syndrome describes a constellation of reversible cardiometabolic abnormalities associated with cardiovascular risk and diabetes. The present study investigates the use of fully automated abdominal CT-based biometric measures for opportunistic identification of metabolic syndrome in adults without symptoms. MATERIALS AND METHODS International Diabetes Federation criteria were applied to a cohort of 9223 adults without symptoms who underwent unenhanced abdominal CT. After patients with insufficient clinical data for diagnosis were excluded, the final cohort consisted of 7785 adults (mean age, 57.0 years; 4361 women and 3424 men). Previously validated and fully automated CT-based algorithms for quantifying muscle, visceral and subcutaneous fat, liver fat, and abdominal aortic calcification were applied to this final cohort. RESULTS A total of 738 subjects (9.5% of all subjects; mean age, 56.7 years; 372 women and 366 men) met the clinical criteria for metabolic syndrome. Subsequent major cardiovascular events occurred more frequently in the cohort with metabolic syndrome (p < 0.001). Significant differences were observed between the two groups for all CT-based biomarkers (p < 0.001). Univariate L1-level total abdominal fat (area under the ROC curve [AUROC] = 0.909; odds ratio [OR] = 27.2), L3-level skeletal muscle index (AUROC = 0.776; OR = 5.8), and volumetric liver attenuation (AUROC = 0.738; OR = 5.1) performed well when compared with abdominal aortic calcification scoring (AUROC = 0.578; OR = 1.6). An L1-level total abdominal fat threshold of 460.6 cm2 was 80.1% sensitive and 85.4% specific for metabolic syndrome. For women, the AUROC was 0.930 when fat and muscle measures were combined. CONCLUSION Fully automated quantitative tissue measures of fat, muscle, and liver derived from abdominal CT scans can help identify individuals who are at risk for metabolic syndrome. These visceral measures can be opportunistically applied to CT scans obtained for other clinical indications, and they may ultimately provide a more direct and useful definition of metabolic syndrome.
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Pickhardt PJ, Graffy PM, Zea R, Lee SJ, Liu J, Sandfort V, Summers RM. Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults. Radiology 2020; 297:64-72. [PMID: 32780005 PMCID: PMC7526945 DOI: 10.1148/radiol.2020200466] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 06/05/2020] [Accepted: 06/10/2020] [Indexed: 12/13/2022]
Abstract
Background Body composition data from abdominal CT scans have the potential to opportunistically identify those at risk for future fracture. Purpose To apply automated bone, muscle, and fat tools to noncontrast CT to assess performance for predicting major osteoporotic fractures and to compare with the Fracture Risk Assessment Tool (FRAX) reference standard. Materials and Methods Fully automated bone attenuation (L1-level attenuation), muscle attenuation (L3-level attenuation), and fat (L1-level visceral-to-subcutaneous [V/S] ratio) measures were derived from noncontrast low-dose abdominal CT scans in a generally healthy asymptomatic adult outpatient cohort from 2004 to 2016. The FRAX score was calculated from data derived from an algorithmic electronic health record search. The cohort was assessed for subsequent future fragility fractures. Subset analysis was performed for patients evaluated with dual x-ray absorptiometry (n = 2106). Hazard ratios (HRs) and receiver operating characteristic curve analyses were performed. Results A total of 9223 adults were evaluated (mean age, 57 years ± 8 [standard deviation]; 5152 women) at CT and were followed over a median time of 8.8 years (interquartile range, 5.1-11.6 years), with documented subsequent major osteoporotic fractures in 7.4% (n = 686), including hip fractures in 2.4% (n = 219). Comparing the highest-risk quartile with the other three quartiles, HRs for bone attenuation, muscle attenuation, V/S fat ratio, and FRAX were 2.1, 1.9, 0.98, and 2.5 for any fragility fracture and 2.0, 2.5, 1.1, and 2.5 for femoral fractures, respectively (P < .001 for all except V/S ratio, which was P ≥ .51). Area under the receiver operating characteristic curve (AUC) values for fragility fracture were 0.71, 0.65, 0.51, and 0.72 at 2 years and 0.63, 0.62, 0.52, and 0.65 at 10 years, respectively. For hip fractures, 2-year AUC for muscle attenuation alone was 0.75 compared with 0.73 for FRAX (P = .43). Multivariable 2-year AUC combining bone and muscle attenuation was 0.73 for any fragility fracture and 0.76 for hip fractures, respectively (P ≥ .73 compared with FRAX). For the subset with dual x-ray absorptiometry T-scores, 2-year AUC was 0.74 for bone attenuation and 0.65 for FRAX (P = .11). Conclusion Automated bone and muscle imaging biomarkers derived from CT scans provided comparable performance to Fracture Risk Assessment Tool score for presymptomatic prediction of future osteoporotic fractures. Muscle attenuation alone provided effective hip fracture prediction. © RSNA, 2020 See also the editorial by Smith in this issue.
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Affiliation(s)
- Perry J. Pickhardt
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., R.Z., S.J.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (J.L., V.S., R.M.S.)
| | - Peter M. Graffy
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., R.Z., S.J.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (J.L., V.S., R.M.S.)
| | - Ryan Zea
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., R.Z., S.J.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (J.L., V.S., R.M.S.)
| | - Scott J. Lee
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., R.Z., S.J.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (J.L., V.S., R.M.S.)
| | - Jiamin Liu
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., R.Z., S.J.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (J.L., V.S., R.M.S.)
| | - Veit Sandfort
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., R.Z., S.J.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (J.L., V.S., R.M.S.)
| | - Ronald M. Summers
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., P.M.G., R.Z., S.J.L.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (J.L., V.S., R.M.S.)
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Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard. AJR Am J Roentgenol 2020; 217:359-367. [PMID: 32936018 DOI: 10.2214/ajr.20.24415] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.
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Value-Added Opportunistic CT: Insights Into Osteoporosis and Sarcopenia. AJR Am J Roentgenol 2020; 215:582-594. [DOI: 10.2214/ajr.20.22874] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Pickhardt PJ, Graffy PM, Weigman B, Deiss-Yehiely N, Hassan C, Weiss JM. Diagnostic Performance of Multitarget Stool DNA and CT Colonography for Noninvasive Colorectal Cancer Screening. Radiology 2020; 297:120-129. [PMID: 32779997 DOI: 10.1148/radiol.2020201018] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BackgroundMultitarget stool DNA (mt-sDNA) screening has increased rapidly since simultaneous approval by the U.S. Food and Drug Administration and Centers for Medicare and Medicaid Services in 2014, whereas CT colonography screening remains underused and is not covered by Centers for Medicare and Medicaid Services.PurposeTo report postapproval clinical experience with mt-sDNA screening for colorectal cancer (CRC) and compare results with CT colonography screening at the same center.Materials and MethodsIn this retrospective cohort study, asymptomatic adults underwent clinical mt-sDNA screening during a 5-year interval (2014-2019). Electronic medical records were searched to verify test results and document subsequent optical colonoscopy and histopathologic findings. A similar analysis was performed for CT colonography screening during a 15-year interval (2004-2019), with consideration of thresholds for positivity of both 6-mm and 10-mm polyp sizes. χ2 or two-sample t tests were used for group comparisons.ResultsA total of 3987 asymptomatic adult patients (mean age, 64 years ± 9 [standard deviation]; 2567 women) underwent mt-sDNA screening and 9656 patients (mean age, 57 years ± 8; 5200 women) underwent CT colonography. Test-positive rates for mt-sDNA and for 6-mm- and 10-mm-threshold CT colonography were 15.2%, 16.4%, and 6.7%, respectively. Optical colonoscopy follow-up rates for positive results of mt-sDNA and 6-mm- and 10-mm-threshold CT colonography were 13.1%, 12.3%, and 5.9%, respectively. Positive predictive values (PPVs) for any neoplasm 6 mm or greater, advanced neoplasia, and CRC for mt-sDNA were 54.2%, 22.7%, and 1.9% respectively; for 6-mm-threshold CT colonography, PPVs were 76.8%, 44.3%, and 2.7%; for 10-mm-threshold CT colonography, PPVs were 84.5%, 75.2%, and 5.2%, respectively (P < .001 for mt-sDNA vs CT colonography for all except 6-mm CRC at CT colonography). For mt-sDNA versus 6-mm-threshold CT colonography, overall detection rates for advanced neoplasia were 2.7% and 5.0%, respectively (P < .001); corresponding detection rates for CRC were 0.23% and 0.31%, respectively (P = .43).ConclusionThe detection rates of advanced neoplasia at CT colonography screening were greater than those of multitarget stool DNA. Detection rates were similar for colorectal cancer.© RSNA, 2020See also the editorial by Yee in this issue.
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Affiliation(s)
- Perry J Pickhardt
- From the Department of Radiology (P.J.P., P.M.G., B.W.) and the Department of Medicine (N.D.Y., J.M.W.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; and Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy (C.H.)
| | - Peter M Graffy
- From the Department of Radiology (P.J.P., P.M.G., B.W.) and the Department of Medicine (N.D.Y., J.M.W.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; and Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy (C.H.)
| | - Benjamin Weigman
- From the Department of Radiology (P.J.P., P.M.G., B.W.) and the Department of Medicine (N.D.Y., J.M.W.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; and Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy (C.H.)
| | - Nimrod Deiss-Yehiely
- From the Department of Radiology (P.J.P., P.M.G., B.W.) and the Department of Medicine (N.D.Y., J.M.W.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; and Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy (C.H.)
| | - Cesare Hassan
- From the Department of Radiology (P.J.P., P.M.G., B.W.) and the Department of Medicine (N.D.Y., J.M.W.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; and Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy (C.H.)
| | - Jennifer M Weiss
- From the Department of Radiology (P.J.P., P.M.G., B.W.) and the Department of Medicine (N.D.Y., J.M.W.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; and Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy (C.H.)
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Gregory J, Dioguardi Burgio M, Corrias G, Vilgrain V, Ronot M. Evaluation of liver tumour response by imaging. JHEP Rep 2020; 2:100100. [PMID: 32514496 PMCID: PMC7267412 DOI: 10.1016/j.jhepr.2020.100100] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 03/10/2020] [Accepted: 03/10/2020] [Indexed: 12/12/2022] Open
Abstract
The goal of assessing tumour response on imaging is to identify patients who are likely to benefit - or not - from anticancer treatment, especially in relation to survival. The World Health Organization was the first to develop assessment criteria. This early score, which assessed tumour burden by standardising lesion size measurements, laid the groundwork for many of the criteria that followed. This was then improved by the Response Evaluation Criteria in Solid Tumours (RECIST) which was quickly adopted by the oncology community. At the same time, many interventional oncology treatments were developed to target specific features of liver tumours that result in significant changes in tumours but have little effect on tumour size. New criteria focusing on the viable part of tumours were therefore designed to provide more appropriate feedback to guide patient management. Targeted therapy has resulted in a breakthrough that challenges conventional response criteria due to the non-linear relationship between response and tumour size, requiring the development of methods that emphasize the appearance of tumours. More recently, research into functional and quantitative imaging has created new opportunities in liver imaging. These results have suggested that certain parameters could serve as early predictors of response or could predict later tumour response at baseline. These approaches have now been extended by machine learning and deep learning. This clinical review focuses on the progress made in the evaluation of liver tumours on imaging, discussing the rationale for this approach, addressing challenges and controversies in the field, and suggesting possible future developments.
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Key Words
- (c)TACE, (conventional) transarterial chemoembolisation
- (m)RECIST, (modified) Response Evaluation Criteria in Solid Tumours
- 18F-FDG, 18F-fluorodeoxyglucose
- 90Y, yttrium-90
- ADC, apparent diffusion coefficient
- APHE, arterial phase hyperenhancement
- CEUS, contrast-enhanced ultrasound
- CRLM, colorectal liver metastases
- DWI, diffusion-weighted imaging
- EASL
- EASL, European Association for the Study of the Liver criteria
- GIST, gastrointestinal stromal tumours
- HCC, hepatocellular carcinoma
- HU, Hounsfield unit
- Imaging
- LI-RADS
- LI-RADS, Liver Imaging Reporting And Data System
- Liver
- Metastases
- PD, progressive disease
- PET, positron emission tomography
- PR, partial response
- RECIST
- SD, stable disease
- SIRT, selective internal radiotherapy
- TR, treatment response
- Tumours
- WHO, World Health Organization
- mRECIST
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Affiliation(s)
- Jules Gregory
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
| | - Marco Dioguardi Burgio
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
| | - Giuseppe Corrias
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
| | - Valérie Vilgrain
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
| | - Maxime Ronot
- Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France
- University of Paris, Paris, France
- INSERM U1149, CRI, Paris, France
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Pickhardt PJ, Graffy PM, Zea R, Lee SJ, Liu J, Sandfort V, Summers RM. Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. LANCET DIGITAL HEALTH 2020; 2:e192-e200. [PMID: 32864598 DOI: 10.1016/s2589-7500(20)30025-x] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Background Body CT scans are frequently performed for a wide variety of clinical indications, but potentially valuable biometric information typically goes unused. We investigated the prognostic ability of automated CT-based body composition biomarkers derived from previously-developed deep-learning and feature-based algorithms for predicting major cardiovascular events and overall survival in an adult screening cohort, compared with clinical parameters. Methods Mature and fully-automated CT-based algorithms with pre-defined metrics for quantifying aortic calcification, muscle density, visceral/subcutaneous fat, liver fat, and bone mineral density (BMD) were applied to a generally-healthy asymptomatic outpatient cohort of 9223 adults (mean age, 57.1 years; 5152 women) undergoing abdominal CT for routine colorectal cancer screening. Longitudinal clinical follow-up (median, 8.8 years; IQR, 5.1-11.6 years) documented subsequent major cardiovascular events or death in 19.7% (n=1831). Predictive ability of CT-based biomarkers was compared against the Framingham Risk Score (FRS) and body mass index (BMI). Findings Significant differences were observed for all five automated CT-based body composition measures according to adverse events (p<0.001). Univariate 5-year AUROC (with 95% CI) for automated CT-based aortic calcification, muscle density, visceral/subcutaneous fat ratio, liver density, and vertebral density for predicting death were 0.743(0.705-0.780)/0.721(0.683-0.759)/0.661(0.625-0.697)/0.619 (0.582-0.656)/0.646(0.603-0.688), respectively, compared with 0.499(0.454-0.544) for BMI and 0.688(0.650-0.727) for FRS (p<0.05 for aortic calcification vs. FRS and BMI); all trends were similar for 2-year and 10-year ROC analyses. Univariate hazard ratios (with 95% CIs) for highest-risk quartile versus others for these same CT measures were 4.53(3.82-5.37) /3.58(3.02-4.23)/2.28(1.92-2.71)/1.82(1.52-2.17)/2.73(2.31-3.23), compared with 1.36(1.13-1.64) and 2.82(2.36-3.37) for BMI and FRS, respectively. Similar significant trends were observed for cardiovascular events. Multivariate combinations of CT biomarkers further improved prediction over clinical parameters (p<0.05 for AUROCs). For example, by combining aortic calcification, muscle density, and liver density, the 2-year AUROC for predicting overall survival was 0.811 (0.761-0.860). Interpretation Fully-automated quantitative tissue biomarkers derived from CT scans can outperform established clinical parameters for pre-symptomatic risk stratification for future serious adverse events, and add opportunistic value to CT scans performed for other indications.
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Affiliation(s)
- Perry J Pickhardt
- The University of Wisconsin School of Medicine & Public Health, Madison, WI
| | - Peter M Graffy
- The University of Wisconsin School of Medicine & Public Health, Madison, WI
| | - Ryan Zea
- The University of Wisconsin School of Medicine & Public Health, Madison, WI
| | - Scott J Lee
- The University of Wisconsin School of Medicine & Public Health, Madison, WI
| | - Jiamin Liu
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
| | - Veit Sandfort
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
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Computed Tomography Measurements of Hepatic Steatosis in Cholelitihiasis and Cholecystectomy Cases Using Unenhanced Images. J Med Imaging Radiat Sci 2020; 51:137-144. [PMID: 32007481 DOI: 10.1016/j.jmir.2019.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 11/28/2019] [Accepted: 12/04/2019] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Computed tomography (CT) measurements of hepatic steatosis can be performed using unenhanced CT images. The purpose of this study was to assess the occurrence of hepatic steatosis using unenhanced CT images for patients undergoing cholecystectomy or having cholelithiasis. METHODS A total of 143 unenhanced CT cases from a single centre were retrospectively examined. The CT number of liver, ratio of CT number of liver to spleen, and CT number of liver minus CT number of spleen were measured in three groups: (1) patients undergoing cholecystectomy, (2) patients having cholelithiasis, and (3) control group. Abdominal circumference, anterior subcutaneous fat tissue thickness, and body mass index were obtained. RESULTS Mean CT number of liver was significantly different between the group of patients with cholecystectomy and cholelithiasis and the control group (P < .001) and also between cases of cholecystectomy and cholelithiasis (P = .041), with the lowest CT number of liver in the cholecystectomy group. The mean CT number of liver minus CT number of spleen and mean CT number (liver/spleen) ratios, evaluated separately for both lobes, were not different comparing the cholelithiasis and cholecystectomy groups. The mean CT number of liver minus CT number of spleen and mean CT number (liver/spleen) ratios differed significantly between the control group and both patient groups (P < .001). Positive correlations were identified between abdominal circumference, subcutaneous fat depth, body mass index, and liver size and hepatic steatosis. CONCLUSION There was an increased occurrence of hepatic steatosis in patients who have undergone a cholecystectomy compared with patients treated for cholelithiasis and the control group.
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El Naqa I, Haider MA, Giger ML, Ten Haken RK. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol 2020; 93:20190855. [PMID: 31965813 PMCID: PMC7055429 DOI: 10.1259/bjr.20190855] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 01/12/2020] [Accepted: 01/13/2020] [Indexed: 12/15/2022] Open
Abstract
Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Masoom A Haider
- Department of Medical Imaging and Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Mashayekhi R, Parekh VS, Faghih M, Singh VK, Jacobs MA, Zaheer A. Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis. Eur J Radiol 2019; 123:108778. [PMID: 31846864 DOI: 10.1016/j.ejrad.2019.108778] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 11/25/2019] [Accepted: 11/30/2019] [Indexed: 02/07/2023]
Abstract
PURPOSE Patients with recurrent abdominal pain and pancreatic enzyme elevations may be diagnosed clinically with recurrent acute pancreatitis (RAP) even with normal imaging or no imaging at all. Since neither abdominal pain nor enzyme elevations are specific for acute pancreatitis (AP), and patients with RAP often have a normal appearing pancreas on CT after resolution of an AP episode, RAP diagnosis can be challenging. This study aims to determine if quantitative radiomic features of the pancreas on CT can differentiate patients with functional abdominal pain, RAP, and chronic pancreatitis (CP). METHOD Contrast enhanced CT abdominal images of adult patients evaluated in a pancreatitis clinic from 2010 to 2018 with the diagnosis of RAP, functional abdominal pain, or CP were retrospectively reviewed. The pancreas was outlined by drawing region of interest (ROI) on images. 54 radiomic features were extracted from each ROI and were compared between the patient groups. A one-vs-one Isomap and Support Vector Machine (IsoSVM) classifier was also trained and tested to classify patients into one of the three diagnostic groups based on their radiomic features. RESULTS Among the study's 56 patients, 20 (35.7 %) had RAP, 19 (33.9 %) had functional abdominal pain, and 17 (30.4 %) had CP. On univariate analysis, 11 radiomic features (10 GLCM features and one NGTDM feature) were significantly different between the patient groups. The IsoSVM classifier for prediction of patient diagnosis had an overall accuracy of 82.1 %. CONCLUSIONS Certain radiomic features on CT imaging can differentiate patients with functional abdominal pain, RAP, and CP.
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Affiliation(s)
- Rouzbeh Mashayekhi
- The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Vishwa S Parekh
- The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21208, USA.
| | - Mahya Faghih
- Pancreatitis Center, Division of Gastroenterology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Vikesh K Singh
- Pancreatitis Center, Division of Gastroenterology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Michael A Jacobs
- The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Atif Zaheer
- The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA; Pancreatitis Center, Division of Gastroenterology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
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