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ORIGINAL RESEARCH

Noninvasive Evaluation of Lupus Nephritis Activity Using a Radiomics Machine Learning Model Based on Ultrasound

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Pages 433-441 | Received 23 Nov 2022, Accepted 27 Jan 2023, Published online: 03 Feb 2023

References

  • Hanly JG, O’Keeffe AG, Su L, et al. The frequency and outcome of lupus nephritis: results from an international inception cohort study. Rheumatology. 2016;55:252–262. doi:10.1093/rheumatology/kev311
  • Parikh SV, Almaani S, Brodsky S, Rovin BH. Update on lupus nephritis: core curriculum 2020. Am J Kidney Dis. 2020;76:265–281. doi:10.1053/j.ajkd.2019.10.017
  • Pan BP, Feng ZJ, Li XL, et al. An analysis of the correlation between clinical indexes and pathological classifications in 202 patients with lupus nephritis. J Inflamm Res. 2021;14:6917–6927. doi:10.2147/JIR.S339744
  • Alforaih N, Whittall-Garcia L, Touma Z. A review of lupus nephritis. J Appl Lab Med. 2022;7(6):1450–1467. doi:10.1093/jalm/jfac036
  • Gatto M, Radice F, Saccon F, et al. Clinical and histological findings at second but not at first kidney biopsy predict end-stage kidney disease in a large multicentric cohort of patients with active lupus nephritis. Lupus Sci Med. 2022;9:e000689.
  • De Rosa M, Rocha AS, De Rosa G, Dubinsky D, Almaani SJ, Rovin BH. Low-grade proteinuria does not exclude significant kidney injury in lupus nephritis. Kidney Int Rep. 2020;5:1066–1068. doi:10.1016/j.ekir.2020.04.005
  • Aljaberi N, Wenderfer SE, Mathur A, et al. Clinical measurement of lupus nephritis activity is inferior to biomarker-based activity assessment using the renal activity index for lupus nephritis in childhood-onset systemic lupus erythematosus. Lupus Sci Med. 2022;9:e000631.
  • Anders HJ, Rovin B. A pathophysiology-based approach to the diagnosis and treatment of lupus nephritis. Kidney Int. 2016;90:493–501. doi:10.1016/j.kint.2016.05.017
  • Singla RK, Kadatz M, Rohling R, Nguan C. Kidney ultrasound for nephrologists: a review. Kidney Med. 2022;4:100464. doi:10.1016/j.xkme.2022.100464
  • Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol. 2021;72:238–250. doi:10.1016/j.semcancer.2020.04.002
  • Mukherjee S, Patra A, Khasawneh H, et al. Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic CTs at a substantial lead time prior to clinical diagnosis. Gastroenterology. 2022;163(5):1435–1446.e3. doi:10.1053/j.gastro.2022.06.066
  • Bandara MS, Gurunayaka B, Lakraj G, Pallewatte A, Siribaddana S, Wansapura J. Ultrasound based radiomics features of chronic kidney disease. Acad Radiol. 2022;29:229–235. doi:10.1016/j.acra.2021.01.006
  • Zhu L, Huang R, Li M, et al. Machine learning-based ultrasound radiomics for evaluating the function of transplanted kidneys. Ultrasound Med Biol. 2022;48:1441–1452. doi:10.1016/j.ultrasmedbio.2022.03.007
  • Kliewer MA, Tupler RH, Carroll BA, et al. Renal artery stenosis: analysis of Doppler waveform parameters and tardus-parvus pattern. Radiology. 1993;189:779–787. doi:10.1148/radiology.189.3.8234704
  • Weening JJ, D’Agati VD, Schwartz MM, et al. The classification of glomerulonephritis in systemic lupus erythematosus revisited. Kidney Int. 2004;65:521–530. doi:10.1111/j.1523-1755.2004.00443.x
  • Austin HA, Boumpas DT, Vaughan EM, Balow JE. Predicting renal outcomes in severe lupus nephritis: contributions of clinical and histologic data. Kidney Int. 1994;45:544–550. doi:10.1038/ki.1994.70
  • Austin HA, Muenz LR, Joyce KM, et al. Prognostic factors in lupus nephritis. Contribution of renal histologic data. Am J Med. 1983;75:382–391. doi:10.1016/0002-9343(83)90338-8
  • Anders HJ, Jayne DR, Rovin BH. Hurdles to the introduction of new therapies for immune-mediated kidney diseases. Nat Rev Nephrol. 2016;12:205–216. doi:10.1038/nrneph.2015.206
  • Moroni G, Porata G, Raffiotta F, et al. Beyond ISN/RPS lupus nephritis classification: adding chronicity index to clinical variables predicts kidney survival. Kidney. 2022;(3):122–132. doi:10.34067/KID.0005512021
  • Petrucci I, Clementi A, Sessa C, Torrisi I, Meola M. Ultrasound and color Doppler applications in chronic kidney disease. J Nephrol. 2018;31:863–879. doi:10.1007/s40620-018-0531-1
  • Page JE, Morgan SH, Eastwood JB, et al. Ultrasound findings in renal parenchymal disease: comparison with histological appearances. Clin Radiol. 1994;49:867–870. doi:10.1016/S0009-9260(05)82877-6
  • Nakazato T, Ikehira H, Imasawa T. Determinants of renal shape in chronic kidney disease patients. Clin Exp Nephrol. 2016;20:748–756. doi:10.1007/s10157-015-1220-1
  • Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–577. doi:10.1148/radiol.2015151169
  • Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–762. doi:10.1038/nrclinonc.2017.141
  • Zhang L, Chen Z, Feng L, et al. Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy. BMC Med Imaging. 2021;21:115. doi:10.1186/s12880-021-00647-8