The Assessment of Glioblastoma Metabolic Activity via 11C-Methionine PET and Radiomics.

Nowadays, the quantitative analysis of PET/CT data in patients with glioblastoma is not strictly standardized in the clinic and does not exclude the human factor. This study aimed to evaluate the relationship between the radiomic features of glioblastoma 11C-methionine PET images and the tumor-to-normal brain (T/N) ratio determined by radiologists in clinical routine. PET/CT data were obtained for 40 patients (mean age 55 ± 12 years; 77.5% men) with a histologically confirmed diagnosis of glioblastoma. Radiomic features were calculated for the whole brain and tumor-containing regions of interest using the RIA package for R. We redesigned the original RIA functions for GLCM and GLRLM calculation to reduce computation time significantly. Machine learning over radiomic features was applied to predict T/N with the best median correlation between the true and predicted values of 0.73 (p = 0.01). The present study showed a reproducible linear relationship between 11C-methionine PET radiomic features and a T/N indicator routinely assessed in brain tumors. Radiomics enabled utilizing texture properties of PET/CT neuroimaging that may reflect the biological activity of glioblastoma and can potentially augment the radiological assessment.

Minimally Invasive Resection of a Gangliocytic Paraganglioma of the Cauda Equina: A Case Report and Review of Literature.

Gangliocytic paraganglioma (GP) is considered a rare neuroendocrine tumor (NET) most often located in the distal half of the duodenum. Insufficient reports describe tumors of this histological type located in the distal parts of the spinal canal, the conus medullaris and cauda equina. To date, nine cases of GP of the cauda equina and one case of GP of conus medullaris have been described. After analyzing all available treatment reports of GP, a study described it as a tumor with an extremely good prognosis in cases of total tumor removal. Here, we present a case of a female patient with a GP at the level of the L4 vertebra treated at Burdenko Neurosurgical Center using a minimally invasive approach through a tubular retractor. The tumor was removed en bloc through an intralaminar opening, and the patient was discharged two days after surgery with total regression of symptoms.

Noninvasive Glioma Grading with Deep Learning: A Pilot Study.

Gliomas are the most common neuroepithelial brain tumors, different by various biological tissue types and prognosis. They could be graded with four levels according to the 2007 WHO classification. The emergence of non-invasive histological and molecular diagnostics for nervous system neoplasms can revolutionize the efficacy and safety of medical care and radically reduce healthcare costs. Our pilot study aimed to evaluate the diagnostic accuracy of deep learning (DL) in subtyping gliomas by WHO grades (I-IV) based on preoperative magnetic resonance imaging (MRI) from Burdenko Neurosurgery Center's database. A total of 707 MRI studies was included. A "3D classification" approach predicting tumor type for the entire patient's MRI data showed the best result (accuracy = 83%, ROC AUC = 0.95), consistent with that of other authors who used different methodologies. Our preliminary results proved the separability of MR T1 axial images with contrast enhancement by WHO grade using DL.

Study of Simple Immunohistochemical Cytocolorimetric Assay Application for More Accurate Assessment of Prognosis in Patients with Pituitary Adenomas.

BACKGROUND: Immunohistochemistry is a basic diagnostic technique. Immunohistochemical examination results reflect mainly qualitative and less quantitative characteristics of proteomic status of cells. A combined approach with complex quantitative evaluation of marker expression using colorimetric analysis and computer technologies can expand the diagnostic capabilities of immunohistochemistry. We studied such an approach developed by using expression of the proliferative marker Ki-67 in pituitary adenomas. METHODS: A retrospective, blind, randomized, comparative study was performed of Ki-67 expression activity in pituitary adenomas using the traditional Ki-67 labeling index and a simple immunohistochemical cytocolorimetric analysis developed by us with immunohistochemical cytocolorimetric index (ICI) estimation as predictors of relapse, assessing the relationships of these indicators with the time before relapse. RESULTS: Mean Ki-67 labeling index was 3.87% ± 0.29% in the relapse-free group and 4.01% ± 0.29% in the relapse group; the difference was not statistically significant. The average Ki-67 ICI was 24.16% ± 0.51% in the relapse-free group and 30.68% ± 0.64% in the relapse group; the difference was statistically significant. The correlation coefficient of ICI values and time before relapse was -0.302, indicating the presence of a weak negative correlation. CONCLUSIONS: We successfully tested an ICI estimation method developed by us to assess Ki-67 expression in pituitary adenomas. The ICI technique can be used both as a prognostic factor for relapse and, in combination with other modern proteomic and genetic methods, as the basis for creation of new multimodal analyzing systems for functional state assessment of cells and tissues.