Imaging Advances for Central Nervous System Tumors

Challenges, limitations, and pitfalls of PET and advanced MRI in patients with brain tumors: A report of the PET/RANO group

Brain tumor diagnostics have significantly evolved with the use of positron emission tomography (PET) and advanced magnetic resonance imaging (MRI) techniques. In addition to anatomical MRI, these modalities may provide valuable information for several clinical applications such as differential diagnosis, delineation of tumor extent, prognostication, differentiation between tumor relapse and treatment-related changes, and the evaluation of response to anticancer therapy. In particular, joint recommendations of the Response Assessment in Neuro-Oncology (RANO) Group, the European Association of Neuro-oncology, and major European and American Nuclear Medicine societies highlighted that the additional clinical value of radiolabeled amino acids compared to anatomical MRI alone is outstanding and that its widespread clinical use should be supported. For advanced MRI and its steadily increasing use in clinical practice, the Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition provided more recently an updated acquisition protocol for the widely used dynamic susceptibility contrast perfusion MRI. Besides amino acid PET and perfusion MRI, other PET tracers and advanced MRI techniques (e.g. MR spectroscopy) are of considerable clinical interest and are increasingly integrated into everyday clinical practice. Nevertheless, these modalities have shortcomings which should be considered in clinical routine. This comprehensive review provides an overview of potential challenges, limitations, and pitfalls associated with PET imaging and advanced MRI techniques in patients with gliomas or brain metastases. Despite these issues, PET imaging and advanced MRI techniques continue to play an indispensable role in brain tumor management. Acknowledging and mitigating these challenges through interdisciplinary collaboration, standardized protocols, and continuous innovation will further enhance the utility of these modalities in guiding optimal patient care.

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.

Primary central nervous system tumors survival in children in ten Colombian cities: a VIGICANCER report

Purpose

Primary central nervous system (CNS) tumors are the second most common cancer in children and adolescents, leading to premature death and disability. Population-based survival estimates aid decision-making in cancer control, however data on survival for primary CNS tumors in Latin America is lacking. We describe survival rates for children with primary CNS tumors treated in ten Colombian cities.

Methods

We analyzed data from children and adolescents newly diagnosed with cancer between 2012 and 2021, participating in the Childhood Cancer Clinical Outcomes Surveillance System (VIGICANCER) in ten cities in Colombia. VIGICANCER collects information on clinical outcomes from twenty-seven pediatric oncology units and conducts active follow-up every three months. VIGICANCER does not register craniopharyngiomas; we excluded intracranial germ cell tumors for this report. We used the Kaplan-Meier method to estimate the overall survival probability, stratified by sociodemographic variables, topography, WHO grading, receipt of radiation therapy, and type of surgical resection. We analyzed the prognostic capacity of variables using multivariate proportional Cox's regression, stratified by city and year of diagnosis.

Results

During the study period, VIGICANCER included 989 primary CNS tumors in 879 children and 110 adolescents. The cohort median age was 9 years; 53% of patients were males, and 8% were Afro-descendants. Most common tumors were supratentorial astrocytomas (47%), astrocytic tumors (35%), medulloblastomas (20%), ependymomas (11%), and mixed and unspecified gliomas (10%). Five-year overall survival of the entire cohort was 54% (95% CI, 51-58); for supratentorial gliomas, WHO grade I was 77%, II was 62%, III-IV was 27%, respectively, and for medulloblastoma was 61%. The adjusted hazard rate ratio for patients with WHO grade III and IV, for those with subtotal resection, for brainstem location, and for those not receiving radiation therapy was 7.4 (95% CI, 4.7-11.8), 6.4 (95% CI, 4.2-9.8), 2.8 (95% 2.1-3.8), 2.0 (95% CI, 1.3-2.8) and 2.3 (95% CI, 1.7-3.0), respectively.

Conclusion

We found that half of Colombia's children and adolescents with primary CNS tumors survive five years, compared to 70% to 80% in high-income countries. In addition to tumor biology and location, gross total resection was crucial for improved survival in this cohort. Systematic monitoring of survival and its determinants provides empirical data for guiding cancer control policies.

Nanotechnology-Inspired Extracellular Vesicles Theranostics for Diagnosis and Therapy of Central Nervous System Diseases

Shuttling various bioactive substances across the blood-brain barrier (BBB) bidirectionally, extracellular vesicles (EVs) have been opening new frontiers for the diagnosis and therapy of central nervous system (CNS) diseases. However, clinical translation of EV-based theranostics remains challenging due to difficulties in effective EV engineering for superior imaging/therapeutic potential, ultrasensitive EV detection for small sample volume, as well as scale-up and standardized EV production. In the past decade, continuous advancement in nanotechnology provided extensive concepts and strategies for EV engineering and analysis, which inspired the application of EVs for CNS diseases. Here we will review the existing types of EV-nanomaterial hybrid systems with improved diagnostic and therapeutic efficacy for CNS diseases. A summary of recent progress in the incorporation of nanomaterials and nanostructures in EV production, separation, and analysis will also be provided. Moreover, the convergence between nanotechnology and microfluidics for integrated EV engineering and liquid biopsy of CNS diseases will be discussed.

An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach

BACKGROUND

The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI.

METHODS

In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models.

RESULTS

The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction.

CONCLUSION

The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.