Literature DB >> 32969110

Hemorrhagic Cysts and Other MR Biomarkers for Predicting Renal Dysfunction Progression in Autosomal Dominant Polycystic Kidney Disease.

Sadjad Riyahi1, Hreedi Dev1, Jon D Blumenfeld2,3, Hanna Rennert4, Xiaorui Yin1, Hanieh Attari1, Irina Barash2,3, Ines Chicos2, Warren Bobb2, Stephanie Donahue2, Martin R Prince1,5.   

Abstract

BACKGROUND: Screening for rapidly progressing autosomal dominant polycystic kidney disease (ADPKD) is necessary for assigning and monitoring therapies. Height-adjusted total kidney volume (ht-TKV) is an accepted biomarker for clinical prognostication, but represents only a small fraction of information on abdominal MRI.
PURPOSE: To investigate the utility of other MR features of ADPKD to predict progression. STUDY TYPE: Single-center retrospective. POPULATION: Longitudinal data from 186 ADPKD subjects with baseline serum creatinine, PKD gene testing, abdominal MRI measurements, and ≥2 follow-up serum creatinine were reviewed. FIELD STRENGTH/SEQUENCE: 1.5T, T2 -weighted single-shot fast spin echo, T1 -weighted 3D spoiled gradient echo (liver accelerated volume acquisition) and 2D cine velocity encoded gradient echo (phase contrast MRA). ASSESSMENT: Ht-TKV, renal blood flow (RBF), number and fraction of renal and hepatic cysts, bright T1 hemorrhagic renal cysts, and liver and spleen volumes were independently assessed by three observers blinded to estimated glomerular filtration rate (eGFR) data. STATISTICAL TESTS: Linear mixed-effect models were applied to predict eGFR over time using MRI features at baseline adjusted for confounders. Validation was performed in 158 patients who had follow-up MRI using receiver operator characteristic, sensitivity, and specificity.
RESULTS: Hemorrhagic cysts, fraction of renal and hepatic cysts, height-adjusted liver and spleen volumes were significant independent predictors of future eGFR (final prediction model R2 = 0.88 P < 0.05). The number of hemorrhagic cysts significantly improved the prediction compared to ht-TKV in predicting future eGFR (area under the curve [AUC] = 0.94, 95% confidence interval [CI]: 0.9-0.94 vs. R2 = 0.9, 95% CI: 0.85-0.9, P = 0.045). For baseline eGFR ≥60 ml/min/1.73m2 , sensitivity for predicting eGFR<45 ml/min/1.73m2 by ht-TKV alone was 29%. Sensitivity increased to 72% with all MRI variables in the model (P < 0.05 = 0.019), whereas specificity was unchanged, 100% vs. 99%. DATA
CONCLUSION: Combining multiple MR features including hemorrhagic renal cysts, renal cyst fraction, liver and spleen volume, hepatic cyst fraction, and renal blood flow enhanced sensitivity for predicting eGFR decline in ADPKD compared to the standard model including only ht-TKV. Level of Evidence 2 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:564-576.
© 2020 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  ADPKD; TKV; hemorrhagic cyst; renal blood flow

Mesh:

Substances:

Year:  2020        PMID: 32969110     DOI: 10.1002/jmri.27360

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  2 in total

1.  A MRI-based radiomics nomogram for evaluation of renal function in ADPKD.

Authors:  Xiaojiao Li; Qingwei Liu; Jingxu Xu; Chencui Huang; Qianqian Hua; Haili Wang; Teng Ma; Zhaoqin Huang
Journal:  Abdom Radiol (NY)       Date:  2022-02-13

2.  Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.

Authors:  Arman Sharbatdaran; Dominick Romano; Kurt Teichman; Hreedi Dev; Syed I Raza; Akshay Goel; Mina C Moghadam; Jon D Blumenfeld; James M Chevalier; Daniil Shimonov; George Shih; Yi Wang; Martin R Prince
Journal:  Tomography       Date:  2022-07-13
  2 in total

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