Advances in Neuro-Oncology Imaging Techniques

Brain gliomas: Diagnostic and therapeutic issues and the prospects of drug-targeted nano-delivery technology

Glioma is the most common intracranial malignant tumor, with severe difficulty in treatment and a low patient survival rate. Due to the heterogeneity and invasiveness of tumors, lack of personalized clinical treatment design, and physiological barriers, it is often difficult to accurately distinguish gliomas, which dramatically affects the subsequent diagnosis, imaging treatment, and prognosis. Fortunately, nano-delivery systems have demonstrated unprecedented capabilities in diagnosing and treating gliomas in recent years. They have been modified and surface modified to efficiently traverse BBB/BBTB, target lesion sites, and intelligently release therapeutic or contrast agents, thereby achieving precise imaging and treatment. In this review, we focus on nano-delivery systems. Firstly, we provide an overview of the standard and emerging diagnostic and treatment technologies for glioma in clinical practice. After induction and analysis, we focus on summarizing the delivery methods of drug delivery systems, the design of nanoparticles, and their new advances in glioma imaging and treatment in recent years. Finally, we discussed the prospects and potential challenges of drug-delivery systems in diagnosing and treating glioma.

Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm

Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN's proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%.

Potential and limitations of radiomics in neuro-oncology

Radiomics seeks to apply classical methods of image processing to obtain quantitative parameters from imaging. Derived features are subsequently fed into algorithmic models to aid clinical decision making. The application of radiomics and machine learning techniques to clinical medicine remains in its infancy. The great potential of radiomics lies in its objective, granular approach to investigating clinical imaging. In neuro-oncology, advanced machine learning techniques, particularly deep learning, are at the forefront of new discoveries in the field. However, despite the great promise of machine learning aided radiomic approaches, the current use remains confined to scholarly research, without real-world deployment in neuro-oncology. The paucity of data, inconsistencies in preprocessing, radiomic feature instability, and the rarity of the events of interest are critical barriers to clinical translation. In this article, we will outline the major steps in the process of radiomics, as well as review advances and challenges in the field as they pertain to neuro-oncology.

Physiological Imaging Methods for Evaluating Response to Immunotherapies in Glioblastomas

Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive multi-modal therapy. Immunotherapy is currently being evaluated as an alternate treatment modality for recurrent GBMs in clinical trials. These immunotherapeutic approaches harness the patient's immune response to fight and eliminate tumor cells. Standard MR imaging is not adequate for response assessment to immunotherapy in GBM patients even after using refined response assessment criteria secondary to amplified immune response. Thus, there is an urgent need for the development of effective and alternative neuroimaging techniques for accurate response assessment. To this end, some groups have reported the potential of diffusion and perfusion MR imaging and amino acid-based positron emission tomography techniques in evaluating treatment response to different immunotherapeutic regimens in GBMs. The main goal of these techniques is to provide definitive metrics of treatment response at earlier time points for making informed decisions on future therapeutic interventions. This review provides an overview of available immunotherapeutic approaches used to treat GBMs. It discusses the limitations of conventional imaging and potential utilities of physiologic imaging techniques in the response assessment to immunotherapies. It also describes challenges associated with these imaging methods and potential solutions to avoid them.

Combined 18F-FET PET and diffusion kurtosis MRI in posttreatment glioblastoma: differentiation of true progression from treatment-related changes

Background

Radiological differentiation of tumor progression (TPR) from treatment-related changes (TRC) in pretreated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O-(2-[¹⁸F]-fluoroethyl)-l-tyrosine (¹⁸F-FET) PET for the differentiation of TPR from TRC in patients with pretreated glioblastoma.

Methods

Thirty-two patients with histomolecularly defined and pretreated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. Three-dimensional (3D) regions of interest were generated based on increased ¹⁸F-FET uptake using a tumor-to-brain ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions of interest using co-registered ¹⁸F-FET PET images, and advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions of interest. Diagnostic accuracy was analyzed by receiver operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression.

Results

Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumor-to-brain ratio of ¹⁸F-FET uptake and diffusion MRI kurtosis metrics.

Conclusions

The combined use of ¹⁸F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pretreated glioblastoma and warrants further investigation.

Headache in the Emergency Department: Avoiding Misdiagnosis of Dangerous Secondary Causes, An Update

In the initial assessment of the headache patient, the emergency physician must consider several dangerous secondary causes of headache. A thorough history and physical examination, along with consideration of a comprehensive differential diagnosis may alert the emergency physician to the diagnosis of a secondary headache particularly when the history is accompanied by any of the following clinical features: sudden/severe onset, focal neurologic deficits, altered mental status, advanced age, active or recent pregnancy, coagulopathy, malignancy, fever, visual deficits, and/or loss of consciousness.

Increasing emergency department utilization of brain imaging in patients with primary brain cancer

PURPOSE

To study changing emergency department (ED) brain imaging utilization in patients with primary brain cancers.

METHODS

Using 2006-2014 data from the Nationwide Emergency Department Sample (NEDS), we identified all patients with primary brain cancers visiting EDs and evaluated trends of head CT and brain MRI utilization. Multivariable logistic regression analyses were used to determine patient- and hospital-specific factors associated with brain imaging utilization.

RESULTS

A weighted cohort of 40,862 ED visits were included (mean age 55; 54% male), increasing from 3932 in 2006 to 5625 in 2014 (+ 43%). A total of 14.4% underwent brain imaging, with 13.2% undergoing CT, 2.3% undergoing MRI, and 1.1% undergoing both modalities. Between 2006 and 2014, there was a 104% increase in the rate of ED brain imaging (from 9.7% in 2006 to 19.8% in 2014). Factors associated with higher utilization of ED brain imaging in adults were non-teaching hospital status and Midwest and Northeast hospital regions (compared with the West). In pediatric patients, higher utilization was associated with older age, higher median household income of patient's ZIP code, and visits in rural, non-teaching hospitals located in the Midwest, South, and Northeast (compared with the West).

CONCLUSION

In US patients with primary brain cancer, the number of ED visits increased annually, and the utilization of ED head imaging examinations doubled in a recent 9-year period. A variety of sociodemographic characteristics are associated with a higher likelihood of imaging in both adult and pediatric patients.

The Molecular Effects of Ionizing Radiations on Brain Cells: Radiation Necrosis vs. Tumor Recurrence

The central nervous system (CNS) is generally resistant to the effects of radiation, but higher doses, such as those related to radiation therapy, can cause both acute and long-term brain damage. The most important results is a decline in cognitive function that follows, in most cases, cerebral radionecrosis. The essence of radio-induced brain damage is multifactorial, being linked to total administered dose, dose per fraction, tumor volume, duration of irradiation and dependent on complex interactions between multiple brain cell types. Cognitive impairment has been described following brain radiotherapy, but the mechanisms leading to this adverse event remain mostly unknown. In the event of a brain tumor, on follow-up radiological imaging often cannot clearly distinguish between recurrence and necrosis, while, especially in patients that underwent radiation therapy (RT) post-surgery, positron emission tomography (PET) functional imaging, is able to differentiate tumors from reactive phenomena. More recently, efforts have been done to combine both morphological and functional data in a single exam and acquisition thanks to the co-registration of PET/MRI. The future of PET imaging to differentiate between radionecrosis and tumor recurrence could be represented by a third-generation PET tracer already used to reveal the spatial extent of brain inflammation. The aim of the following review is to analyze the effect of ionizing radiations on CNS with specific regard to effect of radiotherapy, focusing the attention on the mechanism underling the radionecrosis and the brain damage, and show the role of nuclear medicine techniques to distinguish necrosis from recurrence and to early detect of cognitive decline after treatment.

[Multimodal image fusion technology for diagnosis and treatment of the skull base-infratemporal tumors]

OBJECTIVE

To explore the value of incorporated multimodal image fusion technology with computer-aided design of the skull base-infratemporal tumor treatment.

METHODS

A retrospective study was carried out to enroll seventeen patients with skull base-infratemporal tumors treated at Peking University Hospital of Stomatology from February 2011 to September 2018. Plain CT, enhanced CT and MRI data were imported into the iPlan 3.0 software (BrainLab navigation system), and the image fusion was performed for each patient preoperatively. Then the three-dimensional images of the tumor, vital vessels and craniofacial bones were reconstructed to prepare virtual operation design. We evaluated the application of multimodal image fusion technology that had been incorporated with computer-aided planning during the navigation-guided biopsy or surgery, through the analysis of the biopsy and operation data and regular follow-up postoperatively.

RESULTS

The mean age of 17 patients (7 males and 10 females) was 46 years. Primary tumors occurred in 11 cases, and recurrent tumors in 6 cases. The size of the 17 tumors ranged from 2.9 cm to 9 cm, and the mean size was 4.35 cm. There were 7 cases with skull base bone destruction and/or intracranial extension, and 10 cases with tumors adjacent to the skull base. High-quality multimodal fused images were obtained in all the 17 cases. The spatial-position relationships of the tumors, adjacent craniomaxillofacial bones and vital vessels labeled with different colors were displayed well on the generated fusion images. The multimodal image fusion technology that incorporated with computer-aided three-dimensional reconstruction and then applied in navigation-guided biopsy or surgery showed that, preoperative analysis and virtual operation design functioned with good results, especially in cases with small tumor size, recurrence or illdefined borders in the skull base-infratemporal region. Operation was carried out in 16 cases after preoperative diagnosis and assessment, and 1 case was performed by navigation-guided biopsy only. The proportions of navigation-guided surgery and biopsy were 70.6% (12/17) and 17.6% (3/17) individually. The positive rate of pathologic diagnosis using navigation-guided biopsy was 100% (3/3). All the navigation-guided biopsies or operations were carried out successfully. Complications included 1 case of cerebrospinal fluid leak from a recurred meningioma patient postoperatively, and 1 case of facial paralysis resulting from parotid-gland deep lobe tumor. Most (14/15) tumors got complete removal with safe boundary through intra-operative navigation verification and post-operative imaging confirmation, except for one case of subtotal resection to avoid the injury of cavernous sinus. The pathological results of the tumors could be classified to mesenchymal (10), adenogenous (3), neurogenic (3) or epithelial (1) resources. The follow-up time ranged from 3 to 94 months, with the median follow-up time of 9 months.

CONCLUSION

Taking full advantages of individualized multimodal images, could help analyze the three-dimensional spatial position relationship of tumors, vital vessels and craniofacial bones properly, and then complete the virtual operation design well. The incorporated multimodal image fusion technology with navigation technology may improve the accuracy and safety of core needle biopsy and surgical treatment of skull base-infratemporal tumors.