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For: Graffy PM, Sandfort V, Summers RM, Pickhardt PJ. Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment. Radiology 2019;293:334-42. [PMID: 31526254 DOI: 10.1148/radiol.2019190512] [Cited by in Crossref: 31] [Cited by in F6Publishing: 30] [Article Influence: 10.3] [Reference Citation Analysis]
Number Citing Articles
1 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;11:3042-50. [PMID: 34249633 DOI: 10.21037/qims-20-1220] [Reference Citation Analysis]
2 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;46:1229-35. [PMID: 32948910 DOI: 10.1007/s00261-020-02755-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
3 van Dijk DPJ, Zhao J, Kemter K, Baracos VE, Dejong CHC, Rensen SS, Olde Damink SWM. Ectopic fat in liver and skeletal muscle is associated with shorter overall survival in patients with colorectal liver metastases. J Cachexia Sarcopenia Muscle 2021;12:983-92. [PMID: 34061469 DOI: 10.1002/jcsm.12723] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 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;34:124-33. [PMID: 33469724 DOI: 10.1007/s10278-020-00398-y] [Reference Citation Analysis]
5 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 DOI: 10.1259/bjr.20190855] [Cited by in Crossref: 22] [Cited by in F6Publishing: 17] [Article Influence: 11.0] [Reference Citation Analysis]
6 Lubner MG, Graffy PM, Said A, Watson R, Zea R, Malecki KM, Pickhardt PJ. Utility of Multiparametric CT for Identification of High-Risk NAFLD. AJR Am J Roentgenol 2021;216:659-68. [PMID: 33474981 DOI: 10.2214/AJR.20.22842] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2021. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Reference Citation Analysis]
8 Pickhardt PJ, Blake GM, Graffy PM, Sandfort V, Elton DC, Perez AA, Summers RM. 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 2021;217:359-67. [PMID: 32936018 DOI: 10.2214/AJR.20.24415] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
9 Starekova J, Hernando D, Pickhardt PJ, Reeder SB. Quantification of Liver Fat Content with CT and MRI: State of the Art. Radiology 2021;301:250-62. [PMID: 34546125 DOI: 10.1148/radiol.2021204288] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 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 DOI: 10.3390/diagnostics11071155] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Boutin RD, Lenchik L. Value-Added Opportunistic CT: Insights Into Osteoporosis and Sarcopenia. American Journal of Roentgenology 2020;215:582-94. [DOI: 10.2214/ajr.20.22874] [Cited by in Crossref: 13] [Cited by in F6Publishing: 3] [Article Influence: 6.5] [Reference Citation Analysis]
12 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;297:120-9. [PMID: 32779997 DOI: 10.1148/radiol.2020201018] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
13 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 Digit Health 2020;2:e192-200. [PMID: 32864598 DOI: 10.1016/S2589-7500(20)30025-X] [Cited by in Crossref: 31] [Cited by in F6Publishing: 14] [Article Influence: 15.5] [Reference Citation Analysis]
14 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. [PMID: 34328231 DOI: 10.1002/ajim.23269] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
15 Szczykutowicz TP, Viggiano B, Rose S, Pickhardt PJ, Lubner MG. A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography. J Comput Assist Tomogr 2021;45:870-6. [PMID: 34469906 DOI: 10.1097/RCT.0000000000001215] [Reference Citation Analysis]
16 Yang CJ, Wang CK, Fang YD, Wang JY, Su FC, Tsai HM, Lin YJ, Tsai HW, Yeh LR. 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 DOI: 10.1371/journal.pone.0255605] [Reference Citation Analysis]
17 Pickhardt PJ, Graffy PM, Zea R, Lee SJ, Liu J, Sandfort V, Summers RM. Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms. AJR Am J Roentgenol 2021;216:85-92. [PMID: 32603223 DOI: 10.2214/AJR.20.23049] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
18 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 DOI: 10.1016/j.jhepr.2020.100100] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
19 Sung YS, Park B, Park HJ, Lee SS. Radiomics and deep learning in liver diseases. J Gastroenterol Hepatol 2021;36:561-8. [PMID: 33709608 DOI: 10.1111/jgh.15414] [Reference Citation Analysis]
20 Çam İ, Koc U, Genez S, Güneş A. Computed Tomography Measurements of Hepatic Steatosis in Cholelitihiasis and Cholecystectomy Cases Using Unenhanced Images. J Med Imaging Radiat Sci 2020;51:137-44. [PMID: 32007481 DOI: 10.1016/j.jmir.2019.12.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
22 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;:210531. [PMID: 34698566 DOI: 10.1148/radiol.2021210531] [Reference Citation Analysis]
23 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 DOI: 10.1186/s40001-021-00590-y] [Reference Citation Analysis]
24 Koç U, Taydaş O. Evaluation of pancreatic steatosis prevalence and anthropometric measurements using non-contrast computed tomography. Turk J Gastroenterol 2020;31:640-8. [PMID: 33090101 DOI: 10.5152/tjg.2020.19434] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 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. [PMID: 34491404 DOI: 10.1007/s00259-021-05514-w] [Reference Citation Analysis]
26 Guo Z, Blake GM, Graffy PM, Sandfort V, Summers RM, Li K, Xu S, Chen Y, Zhou J, Shao J, Jiang Y, Qu H, Li B, Cheng X, Pickhardt PJ. Hepatic Steatosis: CT-based Prevalence in Adults in China and the United States and Associations with Age, Sex, and Body Mass Index. AJR Am J Roentgenol 2021. [PMID: 34817193 DOI: 10.2214/AJR.21.26728] [Reference Citation Analysis]
27 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 DOI: 10.2196/24221] [Reference Citation Analysis]
28 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;:210607. [PMID: 34636635 DOI: 10.1148/radiol.2021210607] [Reference Citation Analysis]
29 Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F; Special Interest Group (SIG) Artificial Intelligence and Liver Diseases; Italian Association for the Study of the Liver (AISF). The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2021:S1590-8658(21)00317-0. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Reference Citation Analysis]
30 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 DOI: 10.1177/15593258211011359] [Reference Citation Analysis]
31 Zhang QH, Zhao Y, Tian SF, Xie LH, Chen LH, Chen AL, Wang N, Song QW, Zhang HN, Xie LZ, Shen ZW, Liu AL. Hepatic fat quantification of magnetic resonance imaging whole-liver segmentation for assessing the severity of nonalcoholic fatty liver disease: comparison with a region of interest sampling method. Quant Imaging Med Surg 2021;11:2933-42. [PMID: 34249624 DOI: 10.21037/qims-20-989] [Reference Citation Analysis]
32 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. 2020;123:108778. [PMID: 31846864 DOI: 10.1016/j.ejrad.2019.108778] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 4.3] [Reference Citation Analysis]
33 Pickhardt PJ, Perez AA, Garrett JW, Graffy PM, Zea R, Summers RM. 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 2022;218:124-31. [PMID: 34406056 DOI: 10.2214/AJR.21.26486] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 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;11:1078. [PMID: 34204822 DOI: 10.3390/diagnostics11061078] [Reference Citation Analysis]
35 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 DOI: 10.1148/radiol.2020200466] [Cited by in Crossref: 17] [Cited by in F6Publishing: 10] [Article Influence: 8.5] [Reference Citation Analysis]
36 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;41:524-42. [PMID: 33646902 DOI: 10.1148/rg.2021200056] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]