[Non-invasive diagnosis of brain gliomas by histological type using neuroradiomics in standardized regions of interest: towards digital biopsy]. / Neinvazivnaya diagnostika gliom golovnogo mozga po gistologicheskomu tipu s pomoshch'yu neiroradiomiki v standartizirovannykh zonakh interesa: na puti k tsifrovoi biopsii.

The future of contemporary neuroimaging does not solely lie in novel image-capturing technologies, but also in better methods for extraction of useful information from these images. Scientists see great promise in radiomics, i.e. the methodology for analysis of multiple features in medical image. However, there are certain issues in this field impairing reproducibility of results. One such issue is no standards in establishing the regions of interest. OBJECTIVE: To introduce a standardized method for identification of regions of interest when analyzing MR images using radiomics; to test the hypothesis that this approach is effective for distinguishing different histological types of gliomas. MATERIAL AND METHODS: We analyzed preoperative MR data in 83 adults with various gliomas (WHO classification, 2016), i.e. oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma, and glioblastoma. Radiomic features were computed for T1, T1-enhanced, T2 and T2-FLAIR modalities in four standardized volumetric regions of interest by 356 voxels (46.93 mm3): 1) contrast enhancement; 2) edema-infiltration; 3) area adjacent to edema-infiltration; 4) reference area in contralateral hemisphere. Subsequently, mathematical models were trained to classify MR-images of glioma depending on histological type and quantitative features. RESULTS: Mean accuracy of differential diagnosis of 4 histological types of gliomas in experiments with machine learning was 81.6%, mean accuracy of identification of tumor types - from 94.1% to 99.5%. The best results were obtained using support vector machines and random forest model. CONCLUSION: In a pilot study, the proposed standardization of regions of interest demonstrated high effectiveness for MR-based differential diagnosis of oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma and glioblastoma. There are grounds for applying and improving this methodology in further studies.

[Metastatic brain lesion following gastrointestinal stromal tumor of the stomach complicated by coma.( A case report and literature review)]. / Metastaticheskoe porazhenie golovnogo mozga gastrointestinal'noi stromal'noi opukhol'yu zheludka, oslozhnivsheesya razvitiem komatoznogo sostoyaniya. (Opisanie sluchaya i obzor literatury).

The authors present an extremely rare case of metastatic brain lesion in a patient with gastrointestinal stromal tumor of the stomach. There are literature data on 23 cases of metastatic lesions of the brain, skull and soft tissues of the head in similar patients. Atypical localization of metastases can lead to some diagnostic difficulties, unreasonable cancellation of chemotherapy and delayed surgical treatment. A feature of our observation was postoperative coma determined by the features of the underlying disease.

[MR-guided non-invasive typing of brain gliomas using machine learning]. / Pervye rezul'taty neinvazivnogo tipirovaniya gliom golovnogo mozga po dannym magnitno-rezonansnoi tomografii s pomoshch'yu mashinnogo obucheniya.

Gliomas are the most common neuroepithelial brain tumors. The modern classification of tumors of central nervous system and treatment approaches are based on tissue and molecular features of a particular neoplasm. Today, histological and molecular genetic typing of tumors can only be carried out through invasive procedures. In this regard, non-invasive preoperative diagnosis in neurooncology is appreclated. One of the perspective areas is artificial intelligence applied for neuroimaging to identify significant patterns associated with histological and molecular profiles of tumors and not obvlous for a specialist. OBJECTIVE: To evaluate diagnostic accuracy of deep learning methods for glioma typing according to the 2007 WHO classification based on preoperative magnetic resonance imaging (MRI) data. MATERIAL AND METHODS: The study included MR scans of patients with glial tumors undergoing neurosurgical treatment at the Burdenko National Medical Research Center for Neurosurgery. All patients underwent preoperative contrast-enhanced MRI. 2D and 3D MR scans were used for learning of artificial neural networks with two architectures (Resnest200e and DenseNet, respectively) in classifying tumors into 4 categories (WHO grades I-IV). Learning was provided on 80% of random examinations. Classification quality metrics were evaluated in other 20% of examinations (validation and test samples). RESULTS: Analysis included 707 contrast-enhanced T1 welghted images. 3D classification based on DenseNet model showed the best result in predicting WHO tumor grade (accuracy 83%, AUC 0.95). Other authors reported similar results for other methods. CONCLUSION: The first results of our study confirmed the fundamental possibility of grading axial contrast-enhanced T1 images according to the 2007 WHO classes using deep learning models.

[Anaplastic pleomorphic xanthoastrocytoma - single-center analysis of 42 patients]. / Anaplasticheskaya pleomorfnaya ksantoastrotsitoma ­ analiz 42 nablyudenii iz odnoi kliniki.

Anaplastic pleomorphic xanthoastrocytoma is a rare tumor. There are still no objective data on the incidence of its diagnosis. OBJECTIVE: To study neuroimaging, morphological features of tumors, as well as factors affecting treatment and prognosis. MATERIAL AND METHODS: A retrospective study enrolled 42 patients operated on at the Burdenko Neurosurgery Center between 2003 and 2020. MR characteristics of anaplastic pleomorphic xanthoastrocytoma were analyzed. All patients underwent resection of tumor (total resection in 83.3% of cases). Redo surgeries were performed in 1/3 of patients. Mutational status of BRAF V600E was assessed in all patients. Adjuvant radio- and chemotherapy was performed in more than 80% of cases. Tyrosine kinase inhibitors were administered in 19% of cases. The follow-up period was 152 months (median 34 months). RESULTS: We found no pathognomonic MR signs of this disease. Indeed, anaplastic pleomorphic xanthoastrocytoma have the same signal characteristics as other malignant gliomas. The BRAF V600E mutation status was positive in 54.8% of cases. None patient had IDH-1 mutation. Mean Ki-67 index was 12.5%. The overall survival was 79 months (range 4-152). Seven (17%) patients are alive for more than 90 months. Only Ki-67 index and BRAF mutation significantly influenced the treatment prognosis and overall survival regardless the use of tyrosine kinase inhibitors. CONCLUSION: Such well-known factors for malignant glioma as patient age, total resection and adjuvant therapy did not significantly affect overall survival. Perhaps, searching for new molecular genetic features will reveal additional significant factors of prognosis in patients with anaplastic pleomorphic xanthoastrocytoma.

Experimental substantiation and clinical use of a combination of dermal matrix with allogenic or autologous cells for the treatment of extensive traumatic wounds.

We carried out a comparative experimental and clinical study of the efficiency of the combination of dermal matrix with autologous and allogenic cells for creation of an optimal biological wound coating. Experiments on outbred mice have demonstrated that the use of dermal matrix in combination with allogeneic or autologous cells shortens the inflammation phase and accelerated maturation of the granulation tissue. Clinical application of biological wound dressing based on dermal matrix in combination with autologous or allogeneic mesenchymal multipotent stromal cells prevented septic complications and shortened the time of preparing extensive traumatic wounds with skin defect to autodermoplasty.