Search Results (3)

Cancer metabolism is characterized by an increased utilization of fermentable fuels, such as glucose and glutamine, which support cancer cell survival by increasing resistance to both oxidative stress and the inherent immune system in humans. Dialysis has the power to shift the patient from a state dependent on glucose and glutamine to a ketogenic condition (KC) combined with low glutamine levels—thereby forcing ATP production through the Krebs cycle. By the force of dialysis, the cancer cells will be deprived of their preferred fermentable fuels, disrupting major metabolic pathways important for the ability of the cancer cells to survive. Dialysis has the potential to reduce glucose levels below physiological levels, concurrently increase blood ketone body levels and reduce glutamine levels, which may further reinforce the impact of the KC. Importantly, ketones also induce epigenetic changes imposed by histone deacetylates (HDAC) activity (Class I and Class IIa) known to play an important role in cancer metabolism. Thus, dialysis could be an impactful and safe adjuvant treatment, sensitizing cancer cells to traditional cancer treatments (TCTs), potentially making these significantly more efficient. Full article

Background: Multidisciplinary team (MDT) meetings provide treatment recommendations based on available information and collective decision-making in teams with complementary professions, disciplines and skills. We aimed to map ancillary medical and nonmedical patient information during case presentations and case discussions in MDT meetings in cancer care. Methods: Through a nonparticipant, observational approach, we mapped verbal information on medical, nonmedical and patient-related characteristics and classified these based on content. Data were collected from 336 case discussions in three MDTs for neuro-oncology, sarcoma and hepato-biliary cancer. Results: Information on physical status was presented in 48.2% of the case discussions, psychological status in 8.9% and comorbidity in 48.5% of the cases. Nonmedical factors, such as family relations, occupation, country of origin and abode were referred to in 3.6–7.7% of the cases, and patient preferences were reported in 4.2%. Conclusions: Provision of information on comorbidities in half of the cases and on patient characteristics and treatment preferences in <10% of case discussions suggest a need to define data elements and develop reporting standards to support robust MDT decision-making. Full article

The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) between LGG and HGG, and when these metrics were combined, HGG could be distinguished from LGG with a sensitivity and specificity of 100%. The only metric to distinguish HGG from MET was the normalized-ADC-E with a sensitivity of 68.8% and a specificity of 80%. LGG can be distinguished from MET by combining edema volume (Vol-E), Vol-E/tumor volume (Vol-T), nADC-T, nCBF-T, nCBV-T, and nADC-E with a sensitivity of 93.3% and a specificity of 100%. The present study confirms the usability of a multibiometric approach including volume, perfusion, and diffusion metrics in differentially diagnosing brain tumors in preoperative patients and adds to the growing body of evidence in the clinical field in need of validation and standardization. Full article