Nanocomposite for comprehensive pharmacokinetic and antitumor potential using transferrin for bypassing BBB and anti-nestin antibody as a homing moiety against glioma.

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

The aggressive and recurrent nature of glioblastoma is multifactorial and has been attributed to its biological heterogeneity, dysfunctional metabolic signaling pathways, rigid blood–brain barrier, inherent resistance to standard therapy due to the stemness property of the gliomas cells, immunosuppressive tumor microenvironment, hypoxia and neoangiogenesis which are very well orchestrated and create the tumor’s own highly pro-tumorigenic milieu. Once the relay of events starts amongst these components, eventually it becomes difficult to control the cascade using only the balanced contemporary care of treatment consisting of maximal resection, radiotherapy and chemotherapy with temozolamide. Over the past few decades, implementation of contemporary treatment modalities has shown benefit to some extent, but no significant overall survival benefit is achieved. Therefore, there is an unmet need for advanced multifaceted combinatorial strategies. Recent advances in molecular biology, development of innovative therapeutics and novel delivery platforms over the years has resulted in a paradigm shift in gliomas therapeutics. Decades of research has led to emergence of several treatment molecules, including immunotherapies such as immune checkpoint blockade, oncolytic virotherapy, adoptive cell therapy, nanoparticles, CED and BNCT, each with the unique proficiency to overcome the mentioned challenges, present research. Recent years are seeing innovative combinatorial strategies to overcome the multifactorial resistance put forth by the GBM cell and its TME. This review discusses the contemporary and the investigational combinatorial strategies being employed to treat GBM and summarizes the evidence accumulated till date. Graphical Abstract Glioblastoma is a form of brain tumor which typically leads to death in almost all patients. Over the last two decades, traditional treatment strategies such as surgery, radiotherapy and chemotherapy have been combined as standard therapy. Together, these aggressive treatment strategies have provided modest survival benefit with acceptable toxicity. However, relapse is the invariable norm resulting in death in the overwhelming majority of patients. Relapse occurs due to multiple factors such as inability of drugs to cross blood–brain barrier, immunosuppressive tumor microenvironment, stemness nature of glioma cells, tumor heterogeneity and enhanced hypoxia and angiogenic factors. Therefore, there is an urgent need to develop an innovative treatment approach to treat glioblastoma. Recently, several treatment strategies known as immunotherapies including CAR T cell therapy, dendritic cell vaccines, immune checkpoints blockade and oncolytic virus, nano particles and gene editing/silencing technology have demonstrated promising results in preclinical and few clinical trials. Furthermore, to increase the efficacy of these novel strategies, combinatorial approaches are being implemented for the treatment. This includes CAR T cell therapy in combination with small molecules, immune checkpoint inhibitors and oncolytic virus and nanoparticles plus gene editing, silencing or immune checkpoints inhibitors. These treatments have shown exciting results in preclinical settings and few of these trials are in progress. The review summarizes these combinatorial novel approaches and discusses them in detail.

Advancements in assay design, detection techniques, signal transduction and enhancement strategies using smart nanomaterials.

Background: Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. Recent surge of interest in predictive analytics, using machine learning (ML) from radiomic features and deep learning (DL) for diagnosing, predicting response and prognosticating disease has evinced interest among radiologists and clinicians. The objective of this systematic review and meta-analysis was to evaluate the deep learning & ML algorithms in classifying PCNSL from GBM.Methods: The authors performed a systematic review of the literature from MEDLINE, EMBASE and the Cochrane central trials register for the search strategy in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL that for differentiating PCNSL from GBM on MR imaging were included. These studies were further narrowed down to focus on works published between 2018 and 2021. Two researchers independently conducted the literature screening, database extraction and risk bias assessment. The extracted data was synthesised and analysed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity and balanced accuracy.Results: Ten articles meeting the eligibility criteria were identified addressing use of ML and DL in training and validation classifiers to distinguish PCNSL from GBM on MR imaging. The total sample size was 1311 in the included studies. ML approach was used in 6 studies while DL in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL was directly compared with the gold standard histopathology. The lowest reported specificity was 87% while the highest reported specificity was 100%. The highest reported balanced accuracy was 100% and the lowest was 84%.Conclusions: Extensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the currently published studies, Both DL & ML algorithms have demonstrated encouraging results and certainly have the potential to aid neurooncologists in taking preoperative decisions in the future leading to not only reduction in morbidities but also be cost effective.