The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks.

The pharmacokinetics of contrast media (CM) will determine how long safe waiting intervals between successive CT or MRI examinations should be. The Contrast Media Safety Committee has reviewed the data on pharmacokinetics of contrast media to suggest safe waiting intervals between successive contrast-enhanced imaging studies in relation to the renal function of the patient. Clinical relevance statement: Consider a waiting time between elective contrast-enhanced CT and (coronary) angiography with successive iodine-based contrast media administrations in patients with normal renal function (eGFR > 60 mL/min/1.73 m²) of optimally 12 h (near complete clearance of the previously administered iodine-based contrast media) and minimally 4 h (if clinical indication requires rapid follow-up). Key Points: • Pharmacokinetics of contrast media will guide safe waiting times between successive administrations. • Safe waiting times increase with increasing renal insufficiency. • Iodine-based contrast media influence MRI signal intensities and gadolinium-based contrast agents influence CT attenuation.

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 with MM or MG, in which 12 unconfounded cases of PC-AKI were 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 PC-AKI. 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 • Monoclonal gammopathies including multiple myeloma are a large spectrum of disorders. • In monoclonal gammopathy with normal renal function, PC-AKI risk is not increased. • Renal function is often reduced in myeloma, increasing the risk of PC-AKI. • Correction of hypercalcaemia is necessary in myeloma before iodine-based contrast medium administration. • Bence-Jones proteinuria assessment in myeloma is unnecessary before iodine-based contrast medium administration.

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.

The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.

Objectives: Hysterosalpingography (HSG) is widely used for evaluating the fallopian tubes; however, controversies regarding the use of water- or oil-based iodine-based contrast media (CM) remain. The aim of this work was (1) to discuss reported pregnancy rates related to the CM type used, (2) to validate the used CM in published literature, (3) to discuss possible complications and side effects of CM in HSG, and (4) to develop guidelines on the use of oil-based CM in HSG. Methods: A systematic literature search was conducted for original RCT studies or review/meta-analyses on using water-based and oil-based CM in HSG with fertility outcomes and complications. Nine randomized controlled trials (RCTs) and 10 reviews/meta-analyses were analyzed. Grading of the literature was performed based on the Oxford Centre for Evidence-Based Medicine (OCEBM) 2011 classification. Results: An approximately 10% higher pregnancy rate is reported for oil-based CM. Side effects are rare, but oil-based CM have potentially more side effects on the maternal thyroid function and the peritoneum. Conclusions: 1. HSG with oil-based CM gives approximately 10% higher pregnancy rates. 2. External validity is limited, as in five of nine RCTs, the CM used is no longer on the market. 3. Oil-based CM have potentially more side effects on the maternal thyroid function and on the peritoneum. 4. Guideline: Maternal thyroid function should be tested before HSG with oil-based CM and monitored for 6 months after. Clinical relevance statement: Oil-based CM is associated with an approximately 10% higher chance of pregnancy compared to water-based CM after HSG. Although side effects are rare, higher iodine concentration and slower clearance of oil-based CM may induce maternal thyroid function disturbance and peritoneal inflammation and granuloma formation. Key Points: • It is unknown which type of contrast medium, oil-based or water-based, is the optimal for HSG. • Oil-based contrast media give a 10% higher chance of pregnancy after HSG, compared to water-based contrast media. • From the safety perspective, oil-based CM can cause thyroid dysfunction and an intra-abdominal inflammatory response in the patient.