Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease

References

• Bikbov B, Purcell CA, Levey AS, et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2020;395(10225):1–12.
   PubMed Web of Science ®Google Scholar
• Legrand K, Speyer E, Stengel B, et al. Perceived health and quality of life in patients with CKD, including those with kidney failure: findings from national surveys in France. Am J Kidney Dis. 2020;75(6):868–878.
   PubMed Web of Science ®Google Scholar
• Zhang L, Zhao MH, Zuo L, et al. China kidney disease network (CK-NET) 2016 annual data report. Kidney Int Suppl. 2020;10(2):e97–e185.
   PubMed Web of Science ®Google Scholar
• Romagnani P, Remuzzi G, Glassock R, et al. Chronic kidney disease. Nat Rev Dis Primers. 2017;3:17088.
   PubMed Web of Science ®Google Scholar
• Panizo S, Martinez-Arias L, Alonso-Montes C, et al. Fibrosis in chronic kidney disease: pathogenesis and consequences. Int J Mol Sci. 2021;22(1):408.
   PubMed Web of Science ®Google Scholar
• Ruiz-Ortega M, Rayego-Mateos S, Lamas S, et al. Targeting the progression of chronic kidney disease. Nat Rev Nephrol. 2020;16(5):269–288.
   PubMed Web of Science ®Google Scholar
• Morphology and evaluation of renal fibrosis. 2019.
   Google Scholar
• Humphreys BD. Mechanisms of renal fibrosis. Annu Rev Physiol. 2018;80:309–326.
   PubMed Web of Science ®Google Scholar
• Hogan JJ, Mocanu M, Berns JS. The native kidney biopsy: update and evidence for best practice. Clin J Am Soc Nephrol. 2016;11(2):354–362.
   PubMed Web of Science ®Google Scholar
• Halimi JM, Gatault P, Longuet H, et al. Major bleeding and risk of death after percutaneous native kidney biopsies: a French nationwide cohort study. Clin J Am Soc Nephrol. 2020;15(11):1587–1594.
   PubMed Web of Science ®Google Scholar
• Whittier WL, Korbet SM. Timing of complications in percutaneous renal biopsy. J Am Soc Nephrol. 2004;15(1):142–147.
   PubMed Web of Science ®Google Scholar
• Shiina T, Nightingale KR, Palmeri ML, et al. WFUMB guidelines and recommendations for clinical use of ultrasound elastography: part 1: basic principles and terminology. Ultrasound Med Biol. 2015;41(5):1126–1147.
   PubMed Web of Science ®Google Scholar
• Dietrich CF, Bamber J, Berzigotti A, et al. EFSUMB guidelines and recommendations on the clinical use of liver ultrasound elastography, update 2017 (long version). Ultraschall Med. 2017;38(4):e16–e47.
   PubMed Web of Science ®Google Scholar
• Săftoiu A, Gilja OH, Sidhu PS, et al. The EFSUMB guidelines and recommendations for the clinical practice of elastography in non-hepatic applications: update 2018. Ultraschall Med. 2019;40(4):425–453.
   PubMed Web of Science ®Google Scholar
• Chen Z, Chen J, Chen H, et al. Evaluation of renal fibrosis in patients with chronic kidney disease by shear wave elastography: a comparative analysis with pathological findings. Abdom Radiol (NY). 2022;47(2):738–745.
   PubMed Web of Science ®Google Scholar
• Grosu I, Bob F, Sporea I, et al. Two-dimensional shear-wave elastography for kidney stiffness assessment. Ultrasound Q. 2019;37(2):144–148.
   PubMed Web of Science ®Google Scholar
• Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358.
   PubMed Web of Science ®Google Scholar
• Ligeza A. Artificial intelligence: a modern approach. App Mech Mat. 2009;263(2):2829–2833.
   Google Scholar
• Yu J, Zhou Y, Yang Q, et al. Machine learning models for screening carotid atherosclerosis in asymptomatic adults. Sci Rep. 2021;11(1):22236.
   PubMed Web of Science ®Google Scholar
• Liang T, Shen J, Zhang S, et al. Using ultrasound-based multilayer perceptron to differentiate early breast mucinous cancer and its subtypes from fibroadenoma. Front Oncol. 2021;11:724656.
   PubMed Web of Science ®Google Scholar
• Yun J, Park JE, Lee H, et al. Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary Central nervous system lymphoma. Sci Rep. 2019;9(1):5746.
   PubMedGoogle Scholar
• Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825–830.
   PubMed Web of Science ®Google Scholar
• Goodfellow I, Bengio Y, Courville A. Deep learning[M]. MIT press; 2016. p. 98–164.
   Google Scholar
• Zha H-L, Zong M, Liu X-P, et al. Preoperative ultrasound-based radiomics score can improve the accuracy of the memorial sloan kettering cancer center nomogram for predicting sentinel lymph node metastasis in breast cancer. Eur J Radiol. 2021;135:109512.
   PubMed Web of Science ®Google Scholar
• Zheng Y, Liu X, Zhong Y, et al. A preliminary study for distinguish hormone-secreting functional adrenocortical adenoma subtypes using multiparametric CT radiomics-based machine learning model and nomogram. Front Oncol. 2020;10:570502.
   PubMed Web of Science ®Google Scholar
• Liu H, Jiang H, Wang X, et al. Treatment response prediction of rehabilitation program in children with cerebral palsy using radiomics strategy: protocol for a multicenter prospective cohort study in west China. Quant Imaging Med Surg. 2019;9(8):1402–1412.
   PubMed Web of Science ®Google Scholar
• Zhou ZH, Liu S. Machine learning[M]. Springer Nature; 2021. p. 25–56.
   Google Scholar
• Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20–29.
   PubMed Web of Science ®Google Scholar
• Earley A, Miskulin D, Lamb EJ, et al. Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med. 2012;156(11):785–795.
   PubMed Web of Science ®Google Scholar
• Schwandt A, Denkinger M, Fasching P, et al. Comparison of MDRD, CKD-EPI, and cockcroft-Gault equation in relation to measured glomerular filtration rate among a large cohort with diabetes. J Diabetes Complications. 2017;31(9):1376–1383.
   PubMed Web of Science ®Google Scholar
• Wang K, Lu X, Zhou H, et al. Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. GUT. 2019;68(4):729–741.
   PubMed Web of Science ®Google Scholar
• Powell MJ. Direct search algorithms for optimization calculations. Acta Numer. 1998;7:287–336.
   Google Scholar
• Kramer AA, Zimmerman JE. Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited. Crit Care Med. 2007;35(9):2052–2056.
   PubMed Web of Science ®Google Scholar
• Fitzgerald M, Saville BR, Lewis RJ. Decision curve analysis. Jama. 2015;313(4):409–410.
   PubMed Web of Science ®Google Scholar
• Kerr KF, Brown MD, Zhu K, et al. Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use. J Clin Oncol. 2016;34(21):2534–2540.
   PubMed Web of Science ®Google Scholar
• Kalantar-Zadeh K, Li PK. Strategies to prevent kidney disease and its progression. Nat Rev Nephrol. 2020;16(3):129–130.
   PubMed Web of Science ®Google Scholar
• Kistler BM, Moore LW, Benner D, et al. The international society of renal nutrition and metabolism commentary on the national kidney foundation and academy of nutrition and dietetics KDOQI clinical practice guideline for nutrition in chronic kidney disease. J Ren Nutr. 2021;31(2):116–120 e111.
   PubMed Web of Science ®Google Scholar
• Li PK, Garcia-Garcia G, Lui SF, et al. Kidney health for everyone everywhere-from prevention to detection and equitable access to care. J Ren Care. 2020;46(1):4–12.
   PubMed Web of Science ®Google Scholar
• Meng Y, Zhang H, Li Q, et al. Noncontrast magnetic resonance radiomics and multilayer perceptron network classifier: an approach for predicting fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma. J Magn Reson Imaging. 2021;54(5):1432–1443.
   PubMed Web of Science ®Google Scholar
• Zhang Z, Chen L, Xu P, et al. Predictive analytics with ensemble modeling in laparoscopic surgery: a technical note. Laparosc Endosc Robotic Surg. 2022;5(1):25–34.
   Google Scholar
• Davagdorj K, Pham VH, Theera-Umpon N, et al. XG boost-based framework for smoking-induced noncommunicable disease prediction. Int J Environ Res Public Health. 2020;17(18):6513.
   PubMed Web of Science ®Google Scholar
• Song W, Zhou X, Duan Q, et al. Using random forest algorithm for glomerular and tubular injury diagnosis. Front Med. 2022;9:911737.
   Web of Science ®Google Scholar
• Prieto A, Cabestany J, Sandoval F. Computational intelligence and bioinspired systems. Neurocomputing. 2007;70(16-18):2701–2703.
   Web of Science ®Google Scholar
• Kim Y. Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size. Expert Syst Appl. 2008;34(2):1227–1234.
   Web of Science ®Google Scholar
• Bercoff J, Tanter M, Fink M. Supersonic shear imaging: a new technique for soft tissue elasticity mapping. IEEE Trans Ultrason Ferroelectr Freq Control. 2004;51(4):396–409.
   PubMed Web of Science ®Google Scholar
• Hu Q, Wang XY, He HG, et al. Acoustic radiation force impulse imaging for non-invasive assessment of renal histopathology in chronic kidney disease. PLoS One. 2014;9(12):e115051.
   PubMed Web of Science ®Google Scholar
• Yang HC, Fogo AB. Fibrosis and renal aging. Kidney Int Suppl. 2014;4(1):75–78.
   Web of Science ®Google Scholar
• Brunskill NJ. Rapamycin: a new string to the antiproteinuric bow? J Am Soc Nephrol. 2005;16(7):1878–1879.
   PubMedGoogle Scholar
• Sugiura T, Wada A. Resistive index predicts renal prognosis in chronic kidney disease. Nephrol Dial Transplant. 2009;24(9):2780–2785.
   PubMed Web of Science ®Google Scholar
• Bigé N, Lévy PP, Callard P, et al. Renal arterial resistive index is associated with severe histological changes and poor renal outcome during chronic kidney disease. BMC Nephrol. 2012;13:139.
   PubMed Web of Science ®Google Scholar
• Makita A, Nagao T, Miyoshi KI, et al. The association between renal elasticity evaluated by real-time tissue elastography and renal fibrosis. Clin Exp Nephrol. 2021;25(9):981–987.
   PubMed Web of Science ®Google Scholar
• Chen Z, Chen J, Chen H, et al. A nomogram based on shear wave elastography for assessment of renal fibrosis in patients with chronic kidney disease. J Nephrol. 2022;36(3):719–729.
   Web of Science ®Google Scholar
• Zhu M, Ma L, Yang W, et al. Elastography ultrasound with machine learning improves the diagnostic performance of traditional ultrasound in predicting kidney fibrosis. J Formos Med Assoc. 2022;121(6):1062–1072.
   PubMed Web of Science ®Google Scholar
• Leong SS, Wong JHD, Md Shah MN, et al. Shear wave elastography accurately detects chronic changes in renal histopathology. Nephrology. 2021;26(1):38–45.
   PubMed Web of Science ®Google Scholar
• Bamber J, Cosgrove D, Dietrich CF, et al. EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 1: basic principles and technology. Ultraschall Med. 2013;34(2):169–184.
   PubMed Web of Science ®Google Scholar