Dataset Information

Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma.

ABSTRACT: As the most common neoplasm arising from the kidney, renal cell carcinoma (RCC) continues to have a significant impact on global health. Conventional cross-sectional imaging has always served an important role in the staging of RCC. However, with recent advances in imaging techniques and postprocessing analysis, magnetic resonance imaging (MRI) now has the capability to function as a diagnostic, therapeutic, and prognostic biomarker for RCC. For this narrative literature review, a PubMed search was conducted to collect the most relevant and impactful studies from our perspectives as urologic oncologists, radiologists, and computational imaging specialists. We seek to cover advanced MR imaging and image analysis techniques that may improve the management of patients with small renal mass or metastatic renal cell carcinoma.

SUBMITTER: Wu Y

PROVIDER: S-EPMC4644550 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma.

Wu Yan Y Kwon Young Suk YS Labib Mina M Foran David J DJ Singer Eric A EA

Disease markers 20151101

As the most common neoplasm arising from the kidney, renal cell carcinoma (RCC) continues to have a significant impact on global health. Conventional cross-sectional imaging has always served an important role in the staging of RCC. However, with recent advances in imaging techniques and postprocessing analysis, magnetic resonance imaging (MRI) now has the capability to function as a diagnostic, therapeutic, and prognostic biomarker for RCC. For this narrative literature review, a PubMed search ...[more]

PMID: 26609190

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Project description:OBJECTIVES:(1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies. METHODS:In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed. RESULTS:Fifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93-0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27-9.12; p?<?0.001) for the differentiation of angiomyolipoma without visible fat from RCC. CONCLUSIONS:Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures. KEY POINTS:• Studies achieved an average Radiomics Quality Score of 10.8%. Common reasons for low Radiomics Quality Scores were unvalidated results, retrospective study design, absence of open science, and insufficient control for multiple comparisons. • A previous training phase allowed reaching almost perfect inter-rater agreement in the application of the Radiomics Quality Score. • Meta-analysis of radiomics studies distinguishing angiomyolipoma without visible fat from renal cell carcinoma show moderate diagnostic odds ratios of 6.24 and moderate methodological diversity.

Project description:BackgroundRenal fibrosis is a key driver of progression in chronic kidney disease (CKD). Recent advances in diagnostic imaging techniques have shown promising results for the noninvasive assessment of renal fibrosis. However, the specificity and accuracy of these techniques are controversial because they indirectly assess renal fibrosis. This limits fibrosis assessment by imaging in CKD for clinical practice. To validate magnetic resonance imaging (MRI) assessment for fibrosis, we derived representative models by mapping histology-proven renal fibrosis and imaging in CKD.MethodsNinety-seven adult Chinese CKD participants with histology were studied. The kidney cortex interstitial extracellular matrix volume was calculated by the Aperio ScanScope system using Masson's trichrome slices. The kidney cortex microcirculation was quantitatively assessed by peritubular capillary density using CD34 staining. The imaging techniques included intravoxel incoherent motion diffusion-weighted imaging and magnetic resonance elastography (MRE) imaging. Relevant analyses were performed to evaluate the correlations between MRI parameters and histology variables. Multiple linear regression models were used to describe the relationships between a response variable and other variables. The best-fit lines, which minimize the sum of squared residuals of the multiple linear regression models, were generated.ResultsMRE values were negatively associated with the interstitial extracellular matrix volume (Rho = -0.397, p < 0.001). The best mapping model of extracellular matrix volume with the MRE value and estimated glomerular filtration rate (eGFR) we obtained was as follows: Interstitial extracellular matrix volume = 218.504 - 14.651 × In(MRE) - 18.499 × In(eGFR). DWI-fraction values were positively associated with peritubular capillary density (Rho = 0.472, p < 0.001). The best mapping model of peritubular capillary density with DWI-fraction value and eGFR was as follows: Peritubular capillaries density = 17.914 + 9.403 × (DWI - fraction) + 0.112 × (eGFR).ConclusionsThe study provides histological evidence to support that MRI can effectively evaluate fibrosis in the kidney. These findings picture the graphs of the mapping model from imaging and eGFR into fibrosis, which has significant value for clinical implementation.

Dataset Information

Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma.

Publications

Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma.

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