Iodine-based contrast media, multiple myeloma and monoclonal gammopathies: literature review and ESUR Contrast Media Safety Committee guidelines

Objectives Many radiologists and clinicians still consider multiple myeloma (MM) and monoclonal gammopathies (MG) a contraindication for using iodine-based contrast media. The ESUR Contrast Media Safety Committee performed a systematic review of the incidence of post-contrast acute kidney injury (PC-AKI) in these patients. Methods A systematic search in Medline and Scopus databases was performed for renal function deterioration studies in patients with MM or MG following administration of iodine-based contrast media. Data collection and analysis were performed according to the PRISMA statement 2009. Eligibility criteria and methods of analysis were specified in advance. Cohort and case-control studies reporting changes in renal function were included. Results Thirteen studies were selected that reported 824 iodine-based contrast medium administrations in 642 patients withMMorMG, in which 12 unconfounded cases of PC-AKIwere found (1.6 %). The majority of patients had intravenous urography with high osmolality ionic contrast media after preparatory dehydration and purgation. Conclusions MM and MG alone are not risk factors for PCAKI. However, the risk of PC-AKI may become significant in dehydrated patients with impaired renal function. Hypercalcaemia may increase the risk of kidney damage, and should be corrected before contrast medium administration. Assessment for Bence-Jones proteinuria is not necessary. Key Points \u2022 Monoclonal gammopathies including multiple myeloma are a large spectrum of disorders. \u2022 In monoclonal gammopathy with normal renal function, PCAKI risk is not increased. \u2022 Renal function is often reduced in myeloma, increasing the risk of PC-AKI. \u2022 Correction of hypercalcaemia is necessary in myeloma before iodine-based contrast medium administration. \u2022 Bence-Jones proteinuria assessment in myeloma is unnecessary before iodine-based contrast medium administration

Comparison of 1.0 M gadobutrol and 0.5 M gadopentate dimeglumine-enhanced MRI in 471 patients with known or suspected renal lesions: Results of a multicenter, single-blind, interindividual, randomized clinical phase III trial

The purpose of this phase III clinical trial was to compare two different extracellular contrast agents, 1.0 M gadobutrol and 0.5 M gadopentate dimeglumine, for magnetic resonance imaging (MRI) in patients with known or suspected focal renal lesions. Using a multicenter, single-blind, interindividual, randomized study design, both contrast agents were compared in a total of 471 patients regarding their diagnostic accuracy, sensitivity, and specificity to correctly classify focal lesions of the kidney. To test for noninferiority the diagnostic accuracy rates for both contrast agents were compared with CT results based on a blinded reading. The average diagnostic accuracy across the three blinded readers ('average reader') was 83.7% for gadobutrol and 87.3% for gadopentate dimeglumine. The increase in accuracy from precontrast to combined precontrast and postcontrast MRI was 8.0% for gadobutrol and 6.9% for gadopentate dimeglumine. Sensitivity of the average reader was 85.2% for gadobutrol and 88.7% for gadopentate dimeglumine. Specificity of the average reader was 82.1% for gadobutrol and 86.1% for gadopentate dimeglumine. In conclusion, this study documents evidence for the noninferiority of a single i.v. bolus injection of 1.0 M gadobutrol compared with 0.5 M gadopentate dimeglumine in the diagnostic assessment of renal lesions with CE-MRI

A novel but frequent variant in LPA KIV-2 is associated with a pronounced Lp(a) and cardiovascular risk reduction

Aims Lp(a) concentrations represent a major cardiovascular risk factor and are almost entirely controlled by one single locus (LPA). However, many genetic factors in LPA governing the enormous variance of Lp(a) levels are still unknown. Since up to 70% of the LPA coding sequence are located in a difficult to access hypervariable copy number variation named KIV-2, we hypothesized that it may contain novel functional variants with pronounced effects on Lp(a) concentrations. We performed a large scale mutation analysis in the KIV-2 using an extreme phenotype approach Methods and results We compiled an discovery set of 123 samples showing discordance between LPA isoform phenotype and Lp(a) concentrations and controls. Using ultra-deep sequencing, we identified a splice site variant (G4925A) in preferential association with the smaller LPA isoforms. Follow-up in a European general population (n = 2892) revealed an exceptionally high carrier frequency of 22.1% in the general population. The variant explains 20.6% of the Lp(a) variance in carriers of low molecular weight (LMW) apo(a) isoforms (P = 5.75e-38) and reduces Lp(a) concentrations by 31.3 mg/dL. Accordingly the odds ratio for cardiovascular disease was reduced from 1.39 [95% confidence interval (CI): 1.17-1.66, P = 1.89e-04] for wildtype LMW individuals to 1.19 [95% CI: 0.92;1.56, P = 0.19] in LMW individuals who were additionally positive for G4925A. Functional studies point towards a reduction of splicing efficiency by this novel variant. Conclusion A highly frequent but until now undetected variant in the LPA KIV-2 region is strongly associated with reduced Lp(a) concentrations and reduced cardiovascular risk in LMW individuals

Magnetic resonance imaging biomarkers for clinical routine assessment of microvascular architecture in glioma

Knowledge about the topological and structural heterogeneity of the microvasculature is important for diagnosis and monitoring of glioma. A vessel caliber and type-dependent temporal shift in the magnetic resonance imaging signal forms the basis for vascular architecture mapping. This study introduced a clinically feasible approach for assessment of vascular pathologies in gliomas using vascular architecture mapping. Sixty consecutive patients with known or suspected gliomas were examined using vascular architecture mapping as part of the routine magnetic resonance imaging protocol. Maps of microvessel radius and density, which adapted to the vasculature-dependent temporal shift phenomenon, were calculated using a costume-made software tool. Microvessel radius and density were moderately to severely elevated in a heterogeneous, inversely correlated pattern within high-grade gliomas. Additionally, three new imaging biomarkers were introduced: Microvessel type indicator allowing differentiation between supplying arterial and draining venous microvasculature in high-grade gliomas. Vascular-induced bolus peak time shift may presumably be sensitive for early neovascularization in the infiltration zone. Surprisingly, curvature showed significant changes in peritumoral vasogenic edema which correlated with neovascularization in the tumor core of high-grade gliomas. These new magnetic resonance imaging biomarkers give insights into complexity and heterogeneity of vascular changes in glioma; however, histological validations in more well-defined patient populations are required

Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning

Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today’s clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism (“oxygen metabolic radiomics”) and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO2) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO2; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO2 and mitoPO2 values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis