4.6 Article

Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures

Journal

DIAGNOSTICS
Volume 12, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/diagnostics12112678

Keywords

tissue elasticity imaging; kidney prognosis; regression analysis; machine learning

Funding

  1. National Natural Science Foundation of China [81970574, 82170685]
  2. Shanghai Municipal Commission of Health and Family Planning [ZY(2021-2023)-0208, ZY(2021-2023)-0302, 2018JP005]
  3. Open Project Program Foundation of Key Laboratory of Liver and Kidney Diseases (Shanghai University of Traditional Chinese Medicine, Ministry of Education) [GS2022-01]

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Shear wave elastography ultrasound (SWE) can predict kidney injury progression by radiomics and Cox regression modeling with improved performance.
Background: Shear wave elastography ultrasound (SWE) is an emerging non-invasive candidate for assessing kidney stiffness. However, its prognostic value regarding kidney injury is unclear. Methods: A prospective cohort was created from kidney biopsy patients in our hospital from May 2019 to June 2020. The primary outcome was the initiation of renal replacement therapy or death, while the secondary outcome was eGFR < 60 mL/min/1.73 m(2). Ultrasound, biochemical, and biopsy examinations were performed on the same day. Radiomics signatures were extracted from the SWE images. Results: In total, 187 patients were included and followed up for 24.57 +/- 5.52 months. The median SWE value of the left kidney cortex (L_C_median) is an independent risk factor for kidney prognosis for stage 3 or over (HR 0.890 (0.796-0.994), p < 0.05). The inclusion of 9 out of 2511 extracted radiomics signatures improved the prognostic performance of the Cox regression models containing the SWE and the traditional index (chi-square test, p < 0.001). The traditional Cox regression model had a c-index of 0.9051 (0.8460-0.9196), which was no worse than the machine learning models, Support Vector Machine (SVM), SurvivalTree, Random survival forest (RSF), Coxboost, and Deepsurv. Conclusions: SWE can predict kidney injury progression with an improved performance by radiomics and Cox regression modeling.

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