Background: The GALAD score is a serum biomarkerbased model that predicts the probability of having hepatocellular carcinoma (HCC) in patients with chronic liver disease. We aimed to assess the performance of the GALAD score in comparison with liver ultrasound for detection of HCC.Methods: A single-center cohort of 111 HCC patients and 180 controls with cirrhosis or chronic hepatitis B and a multicenter cohort of 233 early HCC and 412 cirrhosis patients from the Early Detection Research Network (EDRN) phase II HCC Study were analyzed.Results: The area under the ROC curve (AUC) of the GALAD score for HCC detection was 0.95 [95% confidence interval (CI), 0.93-97], which was higher than the AUC of ultrasound (0.82, P <0.01). At a cutoff of À0.76, the GALAD score had a sensitivity of 91% and a specificity of 85% for HCC detection. The AUC of the GALAD score for early-stage HCC detection remained high at 0.92 (95% CI, 0.88-0.96; cutoff À1.18, sensitivity 92%, specificity 79%). The AUC of the GALAD score for HCC detection was 0.88 (95% CI, 0.85-0.91) in the EDRN cohort. The combination of GALAD and ultrasound (GALADUS score) further improved the performance of the GALAD score in the single-center cohort, achieving an AUC of 0.98 (95% CI, 0.96-0.99; cutoff À0.18, sensitivity 95%, specificity 91%). a For the calculation of AUC, the continuous GALAD score is used, whereas for sensitivity and specificity, we used the GALAD cutoff. b P value looking at difference in AUC between GALAD and ultrasound.Yang et al.
BACKGROUND. A treatment option for autosomal dominant polycystic kidney disease (ADPKD) has highlighted the need to identify rapidly progressive patients. Kidney size/age and genotype have predictive power for renal outcomes, but their relative and additive value, plus associated trajectories of disease progression, are not well defined. METHODS. The value of genotypic and/or kidney imaging data (Mayo Imaging Class; MIC) to predict the time to functional (end-stage kidney disease [ESKD] or decline in estimated glomerular filtration rate [eGFR]) or structural (increase in height-adjusted total kidney volume [htTKV]) outcomes were evaluated in a Mayo Clinic PKD1/PKD2 population, and eGFR and htTKV trajectories from 20-65 years of age were modeled and independently validated in similarly defined CRISP and HALT PKD patients. RESULTS. Both genotypic and imaging groups strongly predicted ESKD and eGFR endpoints, with genotype improving the imaging predictions and vice versa; a multivariate model had strong discriminatory power (C-index = 0.845). However, imaging but not genotypic groups predicted htTKV growth, although more severe genotypic and imaging groups had larger kidneys at a young age. The trajectory of eGFR decline was linear from baseline in the most severe genotypic and imaging groups, but it was curvilinear in milder groups. Imaging class trajectories differentiated htTKV growth rates; severe classes had rapid early growth and large kidneys, but growth later slowed. CONCLUSION. The value of imaging, genotypic, and combined data to identify rapidly progressive patients was demonstrated, and reference values for clinical trials were provided. Our data indicate that differences in kidney growth rates before adulthood significantly define patients with severe disease.
Autosomal dominant polycystic kidney disease (ADPKD) is an inherited, progressive nephropathy accounting for 4-10% of end stage renal disease worldwide. PKD1 and PKD2 are the most common disease loci, but even accounting for other genetic causes, about 7% of families remain unresolved. Typically, these unsolved cases have relatively mild kidney disease and often have a negative family history. Mosaicism, due to de novo mutation in the early embryo, has rarely been identified by conventional genetic analysis of ADPKD families. Here we screened for mosaicism by employing two next generation sequencing screens, specific analysis of PKD1 and PKD2 employing long-range polymerase chain reaction, or targeted capture of cystogenes. We characterized mosaicism in 20 ADPKD families; the pathogenic variant was transmitted to the next generation in five families and sporadic in 15. The mosaic pathogenic variant was newly discovered by next generation sequencing in 13 families, and these methods precisely quantified the level of mosaicism in all. All of the mosaic cases had PKD1 mutations, 14 were deletions or insertions, and 16 occurred in females. Analysis of kidney size and function showed the mosaic cases had milder disease than a control PKD1 population, but only a few had clearly asymmetric disease. Thus, in a typical ADPKD population, readily detectable mosaicism by next generation sequencing accounts for about 1% of cases, and about 10% of genetically unresolved cases with an uncertain family history. Hence, identification of mosaicism is important to fully characterize ADPKD populations and provides informed prognostic information.
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