Cheemerla S, Balakrishnan M. World epidemiology of continual liver illness. Clin Liver Dis (Hoboken). 2021;17(5):365–70.
Facilities for illness management and prevention. nationwide middle for well being statistics. quick stats homepage. Continual liver illness. Obtainable at: https://www.cdc.gov/nchs/fastats/liver-disease.htm. Final accessed on March 16, 2024.
Li M, Wang ZQ, Zhang L, Zheng H, Liu DW, Zhou MG. Burden of cirrhosis and different continual liver ailments brought on by particular etiologies in China, 1990–2016: Findings from the World Burden of Illness Research 2016. Biomed Environ Sci. 2020;33(1):1–10.
Zhai M, Liu Z, Lengthy J, Zhou Q, Yang L, Zhou Q, et al. The incidence developments of liver cirrhosis brought on by nonalcoholic steatohepatitis through the GBD examine 2017. Sci Rep. 2021;11:5195. https://doi.org/10.1038/s41598-021-84577-z.
Angulo P, Kleiner DE, Dam-Larsen S, Adams LA, Bjornsson ES, Charatcharoenwitthaya P, et al. liver fibrosis, however no different histologic options, is related to long-term outcomes of sufferers with nonalcoholic fatty liver illness. Gastroenterology. 2015;149(2):389–97.
Choi YS, Beltran TA, Calder SA, Padilla CR, Berry-Caban CS, Salyer KR. Prevalence of hepatic steatosis and fibrosis in the US. Metab Syndr Relat Disord. 2022;20(3):141–7.
GBD 2017 Cirrhosis Collaborators. The worldwide, regional, and nationwide burden of cirrhosis by trigger in 195 international locations and territories, 1990–2017: a scientific evaluation for the World Burden of Illness Research 2017. Lancet Gastroenterol Hepatol. 2020;5(3):245–66.
Chu LC, Park S, Kawamoto S, Yuille AL, Hruban RH, Fishman EK. Present standing of radiomics and deep studying in liver imaging. J Comput Help Tomogr. 2021;45:343–51.
Manning DS, Afdhal NH. Prognosis and quantitation of fibrosis. Gastroenterology. 2008;134(6):1670–81.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: pictures are greater than footage, they’re information. Radiology. 2016;278:563–77.
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalised medication. Nat Rev Clin Oncol. 2017;14(12):749–62. https://doi.org/10.1038/nrclinonc.2017.141.
Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. Radiol Med. 2021;126(10):1296–311.
Lan GY, Guo Y, Zhang XY, Cai XL, Shi Y. Worth of radiomic evaluation of knowledge from magnetic resonance elastography for diagnosing fibrosis levels in sufferers with hepatitis B/C. Chin J Acad Radiol. 2019;1:74–84.
Park HJ, Park B, Lee SS. Radiomics and deep studying: hepatic functions. Korean J Radiol. 2020;21:387–401.
Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, et al. The functions of radiomics in precision analysis and remedy of oncology: Alternatives and challenges. Theranostics. 2019;9(5):1303–22.
Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, et al. Synthetic intelligence-driven radiomics examine in most cancers: the position of characteristic engineering and modeling. Mil Med Res. 2023;10(1):22. https://doi.org/10.1186/s40779-023-00458-8.
Neisius U, El-Rewaidy H, Nakamori S, Rodriguez J, Manning WJ, Nezafat R. Radiomic evaluation of myocardial native T1 imaging discriminates between hypertensive coronary heart illness and hypertrophic cardiomyopathy. JACC Cardiovasc Imaging. 2019;12(10):1946–54.
Neisius U, El-Rewaidy H, Kucukseymen S, Tsao CW, Mancio J, Nakamori S, et al. Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy sufferers with out scar. J Magn Reson Imaging. 2020;52(3):906–19.
Cuadra MB, Favre J, Omoumi P. Quantification in musculoskeletal imaging utilizing computational evaluation and machine studying: Segmentation and radiomics. Semin Musculoskelet Radiol. 2020;24(1):50–64. https://doi.org/10.1055/s-0039-3400268.
Chea P, Mandell JC. Present functions and future instructions of deep studying in musculoskeletal radiology. Skeletal Radiol. 2020;49(2):183–97.
Park YW, Choi D, Lee J, Ahn SS, Lee SK, Lee SH, et al. Differentiating sufferers with schizophrenia from wholesome controls by hippocampal subfields utilizing radiomics. Schizophr Res. 2020;223:337–44.
Park YW, Choi YS, Kim SE, Choi D, Han Ok, Kim H, et al. Radiomics options of hippocampal areas in magnetic resonance imaging can differentiate medial temporal lobe epilepsy sufferers from wholesome controls. Sci Rep. 2020;10:19567.
Bang M, Park YW, Eom J, Ahn SS, Kim J, Lee SK, et al. An interpretable radiomics mannequin for the analysis of panic dysfunction with or with out agoraphobia utilizing magnetic resonance imaging. J Have an effect on Disord. 2022;305:47–54.
Zhao Ok, Ding Y, Han Y, Fan Y, Alexander-Bloch AF, Han T, et al. Impartial and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s illness: analysis, longitudinal progress and organic foundation. Sci Bull. 2020;65:1103–13.
Liu F, Ning Z, Liu Y, Liu D, Tian J, Luo H, et al. Improvement and validation of a radiomics signature for clinically vital portal hypertension in cirrhosis (CHESS1701): a potential multicenter examine. EBioMedicine. 2018;36:151–8.
Chen ZW, Tang Ok, Zhao YF, Chen YZ, Tang LJ, Li G, et al. Radiomics primarily based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot examine. Int J Med Sci. 2021;18:3624–30.
Hu P, Chen L, Zhong Y, Lin Y, Yu X, Hu X, et al. Results of slice thickness on CT radiomics options and fashions for staging liver fibrosis brought on by continual liver illness. Japanese J Radiol. 2022;40:1061–8.
Li W, Huang Y, Zhuang BW, Liu GJ, Hu HT, Li X, et al. Multiparametric ultrasomics of serious liver fibrosis: A machine learning-based evaluation. Eur Radiol. 2019;29:1496–506.
Lu X, Zhou H, Wang Ok, Jin J, Meng F, Mu X, et al. Evaluating radiomics fashions with completely different inputs for correct analysis of serious fibrosis in continual liver illness. Eur Radiol. 2021;31:8743–54.
Park HJ, Lee SS, Park B, Yun J, Sung YS, Shim WH, et al. Radiomics evaluation of Gadoxetic Acid–enhanced MRI for staging liver fibrosis. Radiology. 2019;290:380–7.
Qiu QT, Zhang J, Duan JH, Wu SZ, Ding JL, Yin Y. Improvement and validation of radiomics mannequin constructed by incorporating machine studying for figuring out liver fibrosis and early-stage cirrhosis. Chin Med J. 2020;133(22):2653–9.
Sim KC, Kim MJ, Cho Y, Kim HJ, Park BJ, Sung DJ, et al. Diagnostic feasibility of magnetic resonance elastography radiomics evaluation for the evaluation of hepatic fibrosis in sufferers with nonalcoholic fatty liver illness. J Comput Help Tomogr. 2022;46:505–13.
Wang Ok, Lu X, Zhou H, Gao Y, Zheng J, Tong M, et al. Deep studying Radiomics of shear wave elastography considerably improved diagnostic efficiency for assessing liver fibrosis in continual hepatitis B: a potential multicentre examine. Intestine. 2019;68:729–41.
Wang J, Tang S, Mao Y, Wu J, Xu S, Yue Q, et al. Radiomics evaluation of contrast-enhanced CT for staging liver fibrosis: an replace for picture biomarker. Hepatol Int. 2022;16:627–39.
Xue LY, Jiang ZY, Fu TT, Wang QM, Zhu YL, Dai M, et al. Switch studying radiomics primarily based on multimodal ultrasound imaging for staging liver fibrosis. Eur Radiol. 2020;30:2973–83.
Yin Y, Yakar D, Dierckx RAJO, Mouridsen KB, Kwee TC, de Haas RJ. Combining hepatic and splenic CT radiomic options improves radiomic evaluation efficiency for liver fibrosis staging. Diagnostics. 2022;12:550. https://doi.org/10.3390/diagnostics12020550.
Zhang D, Cao Y, Solar Y, Xia Zhao X, Peng C, Zhao J, et al. Radiomics nomograms primarily based on R2* mapping and medical biomarkers for staging of liver fibrosis in sufferers with continual hepatitis B: a single-center retrospective examine. Eur Radiol. 2023;33:1653–67.
Zhao R, Zhao H, Ge YQ, Zhou FF, Wang LS, Yu HZ, et al. Usefulness of noncontrast MRI-based radiomics mixed clinic biomarkers in stratification of liver fibrosis. Canadian J Gastroenterol Hepatol. 2022;2022:2249447. Article ID 2249447.
Zheng R, Shi C, Wang C, Shi N, Qiu T, Chen W, et al. Imaging-based staging of hepatic fibrosis in sufferers with hepatitis B: A dynamic radiomics mannequin primarily based on Gd-EOB-DTPA-enhanced MRI. Biomolecules. 2021;11:307.
Cui E, Lengthy W, Wu J, Li Q, Ma C, Lei Y, et al. Predicting the levels of liver fibrosis with multiphase CT radiomics primarily based on volumetric options. Abdom Radiol. 2021;46:3866–76.
Duan YY, Qin J, Qiu WQ, Li SY, Li C, Liu AS, et al. Efficiency of a generative adversarial community utilizing ultrasound pictures to stage liver fibrosis and predict cirrhosis primarily based on a deep-learning radiomics nomogram. Clin Radiol. 2022;77(10):e723–31.
Zhou Z, Zhang Z, Gao A, Tai DI, Wu S, Tsui PH. Liver fibrosis evaluation utilizing radiomics of ultrasound homodyned-Ok imaging primarily based on the unreal neural community estimator. Ultrasonic Imaging. 2022;44(5–6):229–41.
Yasaka Ok, Akai H, Kunimatsu A, Abe O, Kiryu S. Liver fibrosis: deep convolutional neural community for staging through the use of gadoxetic acid–enhanced hepatobiliary part MR pictures. Radiology. 2018;287:146–55.
Choi KJ, Jang JK, Lee SS, Sung YS, Shim WH, Kim HS, et al. Improvement and validation of a deep studying system for staging liver fibrosis through the use of distinction agent–enhanced CT pictures within the liver. Radiology. 2018;289:688–97.
Zheng W, Guo W, Xiong M, Chen X, Gao L, Music Y, et al. Clinic-radiological options and radiomics signatures primarily based on Gd-BOPTA-enhanced MRI for predicting superior liver fibrosis. European Radiology. 2023;33:633–44.
Yamada A, Kamagata Ok, Hirata Ok, Ito R, Nakaura T, Ueda D, et al. Scientific functions of synthetic intelligence in liver imaging. Radiol Med. 2023;128(6):655–67.
Poynard T, Halfon P, Castera L, Charlotte F, Le Bail B, Munteanu M, et al. Variability of the world below the receiver working attribute curves within the diagnostic analysis of liver fibrosis markers: impression of biopsy size and fragmentation. Aliment Pharmacol Ther. 2007;25(6):733–9.
Guha IN, Myers RP, Patel Ok, Talwalkar JA. Biomarkers of liver fibrosis: What lies beneath the receiver working attribute curve? Hepatology. 2011;54:1454–62.
