Literature DB >> 31239109

Quantitative spleen and liver volume changes predict survival of patients with primary sclerosing cholangitis.

P Khoshpouri1, B Hazhirkarzar1, S Ameli1, A Pandey1, M Ghadimi1, R Rezvani Habibabadi1, M Aliyari Ghasabeh1, P Pandey1, M Shaghaghi1, I R Kamel2.   

Abstract

AIM: To assess the value of quantitative spleen and liver volume changes in predicting the survival of patients with primary sclerosing cholangitis (PSC).
MATERIALS AND METHODS: This institutional review board-approved single-centre study included 89 PSC patients with baseline and follow-up liver imaging studies and laboratory data between 2000 and 2018. Change in spleen, total and lobar liver volumes, and lobar-to-total liver volume ratio was compared between patients with and without adverse outcome (liver transplantation, transplant waiting list, and death). Receiver operating characteristic (ROC) and Kaplan-Meier analysis were performed to identify the volumetric threshold for prediction of outcome and show how these thresholds predict survival, respectively. A p-value of <0.05 was considered statistically significant.
RESULTS: The present cohort included 53 men (60%), with mean age of 42 years at baseline. The only volumetric parameters with significant differences in change between patients with and without adverse outcome were spleen volume (p<0.001) and left-to-total liver volume ratio (L/T; p=0.025). The probability of transplant-free survival at 36 months was 59.1% versus 11.9% for patients with spleen volume change <50 ml versus ≥50 ml, respectively (AUC=0.731); and 61.3% versus 13.8% for patients with L/T change <0.04 versus ≥0.04, respectively (AUC=0.638). The patients with changes below the cut-off in both spleen volume and L/T, had a higher probability of transplant-free survival at 36 months (76.8%), compared to those with change at or below the cut-offs in one or both of these two parameters (36.7%, 15%, respectively; p=0.001).
CONCLUSION: Spleen volume change and L/T change might be useful biomarkers for prediction of transplant-free survival in patients with PSC.
Copyright © 2019. Published by Elsevier Ltd.

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Year:  2019        PMID: 31239109     DOI: 10.1016/j.crad.2019.05.018

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  1 in total

1.  Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks.

Authors:  Yiqiao Liu; Madhusudhana Gargesha; Bryan Scott; Arthure Olivia Tchilibou Wane; David L Wilson
Journal:  Sci Rep       Date:  2022-09-07       Impact factor: 4.996

  1 in total

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