Brain Tumor Imaging

Development of Magnetic Resonance-Compatible Head Immobilization Device and Initial Experience of Magnetic Resonance-Guided Radiation Therapy for Central Nervous System Tumors

PURPOSE

We aimed to develop and investigate positional reproducibility using a fixation device (Unity Brain tumor Immobilization Device [UBID]) in patients with brain tumor undergoing magnetic resonance (MR)-guided radiation therapy (RT) with a 1.5 Tesla (T) MR-linear accelerator (MR-LINAC) to evaluate its feasibility in clinical practice and report representative cases of patients with central nervous system (CNS) tumor.

MATERIALS AND METHODS

Quantitative analysis was performed by comparing images obtained by placing only the MR phantom on the couch with those obtained by placing UBID next to the MR phantom. Twenty patients who underwent RT for CNS tumors using 1.5T MR-LINAC between June and October 2022 were retrospectively analyzed. Among them, 5 did not use UBID, whereas 15 used UBID. The positional reproducibility of UBID was evaluated using the median interfractional and intrafractional errors in the first 10 fractions.

RESULTS

Each MR quality factor of the MR phantom with UBID satisfied the criteria presented by Elekta. Median values of median shifts in the mediolateral, anteroposterior, and craniocaudal axes for interfractional errors were 2.98, 2.35, and 1.40 mm, respectively. For intrafractional errors, the median values were 0.05, 0.03, and 0.06 mm, respectively. The median values of the median rotations in pitch, roll, and yaw for both interfractional and intrafractional rotations were 0.00°. One patient diagnosed with an optic nerve sheath meningioma received RT with motion monitoring during irradiation. In 2 patients, changes in the tumor cavity and residual lesions were observed in the MRI obtained using 1.5T MR-LINAC on the day of the first treatment and immediately before the 21st fraction, respectively; therefore, offline/online adaptation was performed.

CONCLUSIONS

The reproducible and immobile UBID is clinically feasible in patients with CNS tumors receiving RT with 1.5T MR-LINAC. Based on our initial experience, we developed a workflow for 1.5T MR-LINAC treatment of CNS tumors.

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive computational markers from primary tumor and BM data at [Formula: see text] and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are [Formula: see text], the proliferation rate of a single tumor cell; and [Formula: see text], the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at [Formula: see text] for 20 patients. Parameters [Formula: see text] and [Formula: see text] were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p = 0.0029 and HR 1.95 (1.31-2.91) p = 0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p < 0.0001). We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.

Nanotechnology for brain tumor imaging and therapy based on π-conjugated materials: state-of-the-art advances and prospects

In contemporary biomedical research, the development of nanotechnology has brought forth numerous possibilities for brain tumor imaging and therapy. Among these, π-conjugated materials have garnered significant attention as a special class of nanomaterials in brain tumor-related studies. With their excellent optical and electronic properties, π-conjugated materials can be tailored in structure and nature to facilitate applications in multimodal imaging, nano-drug delivery, photothermal therapy, and other related fields. This review focuses on presenting the cutting-edge advances and application prospects of π-conjugated materials in brain tumor imaging and therapeutic nanotechnology.

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals' data. All models' median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74-0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.

Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Retinoblastoma: present scenario and future challenges

With an average incidence of 1 in every 18,000 live births, retinoblastoma is a rare type of intraocular tumour found to affect patients during their early childhood. It is curable if diagnosed at earlier stages but can become life-threateningly malignant if not treated timely. With no racial or gender predisposition, or even environmental factors known to have been involved in the incidence of the disease, retinoblastoma is often considered a clinical success story in pediatric oncology. The survival rate in highly developed countries is higher than 95% and they have achieved this because of the advancement in the development of diagnostics and treatment techniques. This includes developing the already existing techniques like chemotherapy and embarking on new strategies like enucleation, thermotherapy, cryotherapy, etc. Early diagnosis, studies on the etiopathogenesis and genetics of the disease are the need of the hour for improving the survival rates. According to the Knudson hypothesis, also known as the two hit hypothesis, two hits on the retinoblastoma susceptibility (RB) gene is often considered as the initiating event in the development of the disease. Studies on the molecular basis of the disease have also led to deciphering the downstream events and thus in the discovery of biomarkers and related targeted therapies. Furthermore, improvements in molecular biology techniques enhanced the development of efficient methods for early diagnosis, genetic counseling, and prevention of the disease. In this review, we discuss the genetic and molecular features of retinoblastoma with a special emphasis on the mutation leading to the dysregulation of key signaling pathways involved in cell proliferation, DNA repair, and cellular plasticity. Also, we describe the classification, clinical and epidemiological relevance of the disease, with an emphasis on both the traditional and innovative treatments to tackle retinoblastoma. Video Abstract.

Exploring the Power of Deep Learning: Fine-Tuned Vision Transformer for Accurate and Efficient Brain Tumor Detection in MRI Scans

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.

Microfluidic Technology, Artificial Intelligence, and Biosensors As Advanced Technologies in Cancer Screening: A Review Article

Cancer screening techniques aim to detect premalignant lesions and enable early intervention to delay the onset of cancer while keeping incidence constant. Technology advancements have led to the development of powerful tools such as microfluidic technology, artificial intelligence, machine learning algorithms, and electrochemical biosensors to aid in early cancer detection. Non-invasive cancer screening methods like virtual colonoscopy and endoscopic ultrasonography have also been developed to provide comprehensive pictures of organs and detect cancer early. This review article provides an overview of recent advances in cancer screening in microfluidic technology, artificial intelligence, and biomarkers through a narrative literature search. Microfluidic devices enable easy handling of sub-microliter volumes and have become a promising tool for cancer detection, drug screening, and modeling angiogenesis and metastasis in cancer research. Machine learning and artificial intelligence have shown high accuracy in oncology-related diagnostic imaging, reducing the manual steps in lesion detection and providing standardized and accurate results, with potential for global standardization in areas like colon polyps, breast cancer, and primary and metastatic brain cancer. A biomarker-based cancer diagnosis is promising for early detection and effective therapy, and electrochemical biosensors integrated with nanoparticles offer multiplexing and amplification capabilities. Understanding these advanced technologies' basics, achievements, and challenges is crucial for advancing their use in oncology.

The Role of Molecular Imaging in Patients with Brain Metastases: A Literature Review

Over the last several years, molecular imaging has gained a primary role in the evaluation of patients with brain metastases (BM). Therefore, the "Response Assessment in Neuro-Oncology" (RANO) group recommends amino acid radiotracers for the assessment of BM. Our review summarizes the current use of positron emission tomography (PET) radiotracers in patients with BM, ranging from present to future perspectives with new PET radiotracers, including the role of radiomics and potential theranostics approaches. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2022 were reviewed. Current evidence confirms the important role of amino acid PET radiotracers for the delineation of BM extension, for the assessment of response to therapy, and particularly for the differentiation between tumor progression and radionecrosis. The newer radiotracers explore non-invasively different biological tumor processes, although more consistent findings in larger clinical trials are necessary to confirm preliminary results. Our review illustrates the role of molecular imaging in patients with BM. Along with magnetic resonance imaging (MRI), the gold standard for diagnosis of BM, PET is a useful complementary technique for processes that otherwise cannot be obtained from anatomical MRI alone.

Balancing Risk and Efficiency in Drug Development for Rare and Challenging Tumors: A New Paradigm for Glioma

The process of developing cancer therapies is well established and has enabled the incorporation of many new drugs and classes of agents into the standard of care for common cancers. Clinical drug development is fundamentally different for rare and difficult-to-treat solid tumors, such as glioma or pancreatic cancer. The failure to develop effective new agents for the latter diseases has discouraged the development of therapeutics for these cancers. Using glioma as an example, we describe a process toward obtaining more reliable early-stage signals of drug activity and a process toward translating those signals into clinical benefits with more efficient late-stage development. If linked together, these processes should increase the likelihood of benefit in late-stage settings at a lower cost and encourage more drug development for patients with rare and difficult-to-treat cancers.

Imaging Findings of 18F-Choline and 18F-DOPA PET/MRI in a Case of Glioblastoma Multiforme Pseudoprogression: Correlation with Clinical Outcome

We describe the case of 74-year-old-male, previously treated with fronto-parietal craniotomy due to primary glioblastoma multiforme (GBM), followed by concurrent radiation therapy (RT) and temozolomide (TMZ) chemotherapy. Magnetic resonance imaging (MRI) of the brain, at 1 month after completing RT + TMZ, depicted partial response. Three months later, the patient was submitted to a further brain MRI, that resulted doubtful for therapy induced changes (i.e., pseudoprogression). The patient, who had been previously treated with prostatectomy for prostate cancer (PC), underwent a positron emission tomography/computed tomography (PET/CT) scan with 18F-choline for PC biochemical recurrence. 18F-choline whole body PET/CT resulted negative for PC relapse, while segmental brain PET, co-registered with MRI, demonstrated increased tracer uptake corresponding to tumor boundaries. In order to solve differential diagnosis between pseudoprogression and GBM recurrence, brain PET/CT with 18F-L-dihydroxy-phenil-alanine (18F-DOPA) was subsequently performed: fused axial PET/MRI images showed increased 18F-DOPA incorporation in the peri-tumoral edema, but not in tumor boundaries, consistent with the suspicion of GBM pseudoprogression, as then confirmed by clinical and radiological follow-up.

Molecular imaging in oncology: Current impact and future directions

The authors define molecular imaging, according to the Society of Nuclear Medicine and Molecular Imaging, as the visualization, characterization, and measurement of biological processes at the molecular and cellular levels in humans and other living systems. Although practiced for many years clinically in nuclear medicine, expansion to other imaging modalities began roughly 25 years ago and has accelerated since. That acceleration derives from the continual appearance of new and highly relevant animal models of human disease, increasingly sensitive imaging devices, high-throughput methods to discover and optimize affinity agents to key cellular targets, new ways to manipulate genetic material, and expanded use of cloud computing. Greater interest by scientists in allied fields, such as chemistry, biomedical engineering, and immunology, as well as increased attention by the pharmaceutical industry, have likewise contributed to the boom in activity in recent years. Whereas researchers and clinicians have applied molecular imaging to a variety of physiologic processes and disease states, here, the authors focus on oncology, arguably where it has made its greatest impact. The main purpose of imaging in oncology is early detection to enable interception if not prevention of full-blown disease, such as the appearance of metastases. Because biochemical changes occur before changes in anatomy, molecular imaging-particularly when combined with liquid biopsy for screening purposes-promises especially early localization of disease for optimum management. Here, the authors introduce the ways and indications in which molecular imaging can be undertaken, the tools used and under development, and near-term challenges and opportunities in oncology.

Advancements in Oncology with Artificial Intelligence—A Review Article

Simple Summary

With the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its function, as well as comprehensively summarize its evolution and role in breast, colorectal, and central nervous system cancers. Understanding the origin and current accomplishments might be essential to improve the quality, accuracy, generalizability, cost-effectiveness, and reliability of artificial intelligence models that can be used in worldwide clinical practice. Students and researchers in the medical field will benefit from a deeper understanding of how to use integrative AI in oncology for innovation and research.

Abstract

Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.

Hemodynamic molecular imaging of tumor-associated enzyme activity in the living brain

Molecular imaging could have great utility for detecting, classifying, and guiding treatment of brain disorders, but existing probes offer limited capability for assessing relevant physiological parameters. Here, we describe a potent approach for noninvasive mapping of cancer-associated enzyme activity using a molecular sensor that acts on the vasculature, providing a diagnostic readout via local changes in hemodynamic image contrast. The sensor is targeted at the fibroblast activation protein (FAP), an extracellular dipeptidase and clinically relevant biomarker of brain tumor biology. Optimal FAP sensor variants were identified by screening a series of prototypes for responsiveness in a cell-based bioassay. The best variant was then applied for quantitative neuroimaging of FAP activity in rats, where it reveals nanomolar-scale FAP expression by xenografted cells. The activated probe also induces robust hemodynamic contrast in nonhuman primate brain. This work thus demonstrates a potentially translatable strategy for ultrasensitive functional imaging of molecular targets in neuromedicine.

Brain metastases: the role of clinical imaging

Imaging of brain metastases (BMs) has advanced greatly over the past decade. In this review, we discuss the main challenges that BMs pose in clinical practice and describe the role of imaging.Firstly, we describe the increased incidence of BMs of different primary tumours and the rationale for screening. A challenge lies in selecting the right patients for screening: not all cancer patients develop BMs in their disease course.Secondly, we discuss the imaging techniques to detect BMs. A three-dimensional (3D) T1W MRI sequence is the golden standard for BM detection, but additional anatomical (susceptibility weighted imaging, diffusion weighted imaging), functional (perfusion MRI) and metabolic (MR spectroscopy, positron emission tomography) information can help to differentiate BMs from other intracranial aetiologies.Thirdly, we describe the role of imaging before, during and after treatment of BMs. For surgical resection, imaging is used to select surgical patients, but also to assist intraoperatively (neuronavigation, fluorescence-guided surgery, ultrasound). For treatment planning of stereotactic radiosurgery, MRI is combined with CT. For surveillance after both local and systemic therapies, conventional MRI is used. However, advanced imaging is increasingly performed to distinguish true tumour progression from pseudoprogression.FInally, future perspectives are discussed, including radiomics, new biomarkers, new endogenous contrast agents and theranostics.

18F-Trifluoromethylated D-Cysteine as a Promising New PET Tracer for Glioma Imaging: Comparative Analysis With MRI and Histopathology in Orthotopic C6 Models

Comparing MRI and histopathology, this study aims to comprehensively explore the potential application of ¹⁸F-trifluoromethylated D-cysteine (S-[¹⁸F]CF₃-D-CYS) in evaluating glioma by using orthotopic C6 glioma models. Sprague-Dawley (SD) rats (n = 9) were implanted with C6 glioma cells. Tumor growth was monitored every week by multiparameter MRI [including dynamic contrast-enhanced MRI (DCE-MRI)], [¹⁸F]FDG, S-[¹⁸F]CF₃-D-CYS, and [¹⁸F]FDOPA PET imaging. Repeated scans of the same rat with the two or three [¹⁸F]-labeled radiotracers were investigated. Initial regions of interest were manually delineated on T₂WI and set on the same level of PET images, and tumor-to-normal brain uptake ratios (TNRs) were calculated to semiquantitatively assess the tracer accumulation in the tumor. The tumor volume in PET and histopathology was calculated. HE and Ki67 immunohistochemical staining were further performed. The correlations between the uptake of S-[¹⁸F]CF₃-D-CYS and Ki67 were analyzed. Dynamic S-[¹⁸F]CF₃-D-CYS PET imaging showed tumor uptake rapidly reached a peak, maintained plateau during 10-30 min after injection, then decreased slowly. Compared with [¹⁸F]FDG and [¹⁸F]FDOPA PET imaging, S-[¹⁸F]CF₃-D-CYS PET demonstrated the highest TNRs (P < 0.05). There were no significant differences in the tumor volume measured on S-[¹⁸F]CF₃-D-CYS PET or HE specimen. Furthermore, our results showed that the uptake of S-[¹⁸F]CF₃-D-CYS was significantly positively correlated with tumor Ki67, and the poor accumulated S-[¹⁸F]CF₃-D-CYS was consistent with tumor hemorrhage. There was no significant correlation between the S-[¹⁸F]CF₃-D-CYS uptakes and the K^trans values derived from DCE-MRI. In comparison with MRI and histopathology, S-[¹⁸F]CF₃-D-CYS PET performs well in the diagnosis and evaluation of glioma. S-[¹⁸F]CF₃-D-CYS PET may serve as a valuable tool in the clinical management of gliomas.

Use of modified Delphi introduces the risk of chronological bias during clinical research interventions

BACKGROUND

A study aimed at reducing the time spent on the phone obtaining insurance preauthorisation in a neurosurgical clinic was successfully completed. However, the researchers were unable to reject the null hypothesis because of a combination of chronological bias and the Hawthorne effect.

AIM

To increase nurse researchers' awareness of the potential to introduce a chronological bias as a confounder in clinical research and suggest potential alternative approaches to study design.

DISCUSSION

The researcher shared the study's purpose, design and outcome measure with the participants before collecting the baseline data. This enabled the participants to alter their practice before the intervention was implemented (a chronological bias) and change their behaviour surrounding the outcome (the Hawthorne effect).

CONCLUSION

The use of the Delphi method became a catalyst for change before the collection of baseline data, the combination of chronological bias and the Hawthorne effect affecting the study's results.

IMPLICATIONS FOR PRACTICE

Nurse researchers seeking to improve practice should collect baseline data before informing participants and consider the risks and benefits of blinding (concealment) surrounding the outcome.

Measuring 129 Xe transfer across the blood-brain barrier using MR spectroscopy

PURPOSE

This study develops a tracer kinetic model of xenon uptake in the human brain to determine the transfer rate of inhaled hyperpolarized ¹²⁹ Xe from cerebral blood to gray matter that accounts for the effects of cerebral physiology, perfusion and magnetization dynamics. The ¹²⁹ Xe transfer rate is expressed using a tracer transfer coefficient, which estimates the quantity of hyperpolarized ¹²⁹ Xe dissolved in cerebral blood under exchange with depolarized ¹²⁹ Xe dissolved in gray matter under equilibrium of concentration.

THEORY AND METHODS

Time-resolved MR spectra of hyperpolarized ¹²⁹ Xe dissolved in the human brain were acquired from three healthy volunteers. Acquired spectra were numerically fitted with five Lorentzian peaks in accordance with known ¹²⁹ Xe brain spectral peaks. The signal dynamics of spectral peaks for gray matter and red blood cells were quantified, and correction for the ¹²⁹ Xe T₁ dependence upon blood oxygenation was applied. ¹²⁹ Xe transfer dynamics determined from the ratio of the peaks for gray matter and red blood cells was numerically fitted with the developed tracer kinetic model.

RESULTS

For all the acquired NMR spectra, the developed tracer kinetic model fitted the data with tracer transfer coefficients between 0.1 and 0.14.

CONCLUSION

In this study, a tracer kinetic model was developed and validated that estimates the transfer rate of HP ¹²⁹ Xe from cerebral blood to gray matter in the human brain.

Bombesin Receptor Family Activation and CNS/Neural Tumors: Review of Evidence Supporting Possible Role for Novel Targeted Therapy

G-protein-coupled receptors (GPCRs) are increasingly being considered as possible therapeutic targets in cancers. Activation of GPCR on tumors can have prominent growth effects, and GPCRs are frequently over-/ectopically expressed on tumors and thus can be used for targeted therapy. CNS/neural tumors are receiving increasing attention using this approach. Gliomas are the most frequent primary malignant brain/CNS tumor with glioblastoma having a 10-year survival <1%; neuroblastomas are the most common extracranial solid tumor in children with long-term survival<40%, and medulloblastomas are less common, but one subgroup has a 5-year survival <60%. Thus, there is an increased need for more effective treatments of these tumors. The Bombesin-receptor family (BnRs) is one of the GPCRs that are most frequently over/ectopically expressed by common tumors and is receiving particular attention as a possible therapeutic target in several tumors, particularly in prostate, breast, and lung cancer. We review in this paper evidence suggesting why a similar approach in some CNS/neural tumors (gliomas, neuroblastomas, medulloblastomas) should also be considered.

Magnetic resonance spectroscopic imaging in gliomas: clinical diagnosis and radiotherapy planning

The reprogramming of cellular metabolism is a hallmark of cancer diagnosis and prognosis. Proton magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic technique for investigating brain metabolism to establish cancer diagnosis and IDH gene mutation diagnosis as well as facilitate pre-operative planning and treatment response monitoring. By allowing tissue metabolism to be quantified, MRSI provides added value to conventional MRI. MRSI can generate metabolite maps from a single volume or multiple volume elements within the whole brain. Metabolites such as NAA, Cho and Cr, as well as their ratios Cho:NAA ratio and Cho:Cr ratio, have been used to provide tumor diagnosis and aid in radiation therapy planning as well as treatment assessment. In addition to these common metabolites, 2-hydroxygluterate (2HG) has also been quantified using MRSI following the recent discovery of IDH mutations in gliomas. This has opened up targeted drug development to inhibit the mutant IDH pathway. This review provides guidance on MRSI in brain gliomas, including its acquisition, analysis methods, and evolving clinical applications.

Biomarkers of Brain Damage Induced by Radiotherapy

Radiotherapy remains currently a critical component for both primary and metastatic brain tumors either alone or in combination with surgery, chemotherapy, and molecularly targeted agents, while it could cause simultaneously normal brain tissue injury leading to serious health consequences, that is, development of cognitive impairments following cranial radiotherapy is considered as a critical clinical disadvantage especially for the whole brain radiotherapy. Biomarkers can help to detect the accurate physiology or conditions of patients with brain tumor and develop effective treatment procedures for these patients. In the near future, biomarkers will become one of the prime driving forces of cancer treatment. In this minireview, we analyze the documented work on the acute brain damage and late consequences induced by radiotherapy, identify the biomarkers, in particular, the predictive biomarkers for the damage, and summarize the biological significance of the biomarkers. It is expected that translation of these research advance to radiotherapy would assist stratifying patients for optimized treatment and improving therapeutic efficacy and the quality of life.

Management of glioblastoma: State of the art and future directions

Glioblastoma is the most common malignant primary brain tumor. Overall, the prognosis for patients with this disease is poor, with a median survival of <2 years. There is a slight predominance in males, and incidence increases with age. The standard approach to therapy in the newly diagnosed setting includes surgery followed by concurrent radiotherapy with temozolomide and further adjuvant temozolomide. Tumor-treating fields, delivering low-intensity alternating electric fields, can also be given concurrently with adjuvant temozolomide. At recurrence, there is no standard of care; however, surgery, radiotherapy, and systemic therapy with chemotherapy or bevacizumab are all potential options, depending on the patient's circumstances. Supportive and palliative care remain important considerations throughout the disease course in the multimodality approach to management. The recently revised classification of glioblastoma based on molecular profiling, notably isocitrate dehydrogenase (IDH) mutation status, is a result of enhanced understanding of the underlying pathogenesis of disease. There is a clear need for better therapeutic options, and there have been substantial efforts exploring immunotherapy and precision oncology approaches. In contrast to other solid tumors, however, biological factors, such as the blood-brain barrier and the unique tumor and immune microenvironment, represent significant challenges in the development of novel therapies. Innovative clinical trial designs with biomarker-enrichment strategies are needed to ultimately improve the outcome of patients with glioblastoma.

Understanding the Uptake of Nanomedicines at Different Stages of Brain Cancer Using a Modular Nanocarrier Platform and Precision Bispecific Antibodies

Increasing accumulation and retention of nanomedicines within tumor tissue is a significant challenge, particularly in the case of brain tumors where access to the tumor through the vasculature is restricted by the blood-brain barrier (BBB). This makes the application of nanomedicines in neuro-oncology often considered unfeasible, with efficacy limited to regions of significant disease progression and compromised BBB. However, little is understood about how the evolving tumor-brain physiology during disease progression affects the permeability and retention of designer nanomedicines. We report here the development of a modular nanomedicine platform that, when used in conjunction with a unique model of how tumorigenesis affects BBB integrity, allows investigation of how nanomaterial properties affect uptake and retention in brain tissue. By combining different in vivo longitudinal imaging techniques (including positron emission tomography and magnetic resonance imaging), we have evaluated the retention of nanomedicines with predefined physicochemical properties (size and surface functionality) and established a relationship between structure and tissue accumulation as a function of a new parameter that measures BBB leakiness; this offers significant advancements in our ability to relate tumor accumulation of nanomedicines to more physiologically relevant parameters. Our data show that accumulation of nanomedicines in brain tumor tissue is better correlated with the leakiness of the BBB than actual tumor volume. This was evaluated by establishing brain tumors using a spontaneous and endogenously derived glioblastoma model providing a unique opportunity to assess these parameters individually and compare the results across multiple mice. We also quantitatively demonstrate that smaller nanomedicines (20 nm) can indeed cross the BBB and accumulate in tumors at earlier stages of the disease than larger analogues, therefore opening the possibility of developing patient-specific nanoparticle treatment interventions in earlier stages of the disease. Importantly, these results provide a more predictive approach for designing efficacious personalized nanomedicines based on a particular patient's condition.

Rise of Raman spectroscopy in neurosurgery: a review

SIGNIFICANCE

Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons.

AIM

We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices.

APPROACH

We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting.

RESULTS

The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing.

CONCLUSIONS

The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

Positron emission tomography with computed tomography imaging (PET/CT) for the radiotherapy planning definition of the biological target volume: PART 1

AIM

Functional and molecular imaging, including positron emission tomography with computed tomography imaging (PET/CT) is increasing for radiotherapy (RT) definition of the target volume. This expert review summarizes existing data of functional imaging modalities and RT management, in terms of target volume delineation, for the following anatomical districts: brain (for primary and secondary tumors), head/neck and lung.

MATERIALS AND METHODS

A collection of available published data was made, by PubMed a search. Only original articles were carefully and critically revised.

RESULTS

For primary and secondary brain tumors, amino acid PET radiotracers could be useful to identify microscopic residual areas and to differ between recurrence and treatment-related alterations in case of re-irradiation. As for head and neck neoplasms may benefit from precise PET/CT-based target delineation, due to the major capability to identify high-risk RT areas. In primary and secondary lung cancer, PET/CT could be useful both to delimit a tumor and collapsed lungs and as a predictive parameter of treatment response.

CONCLUSION

Taken together, molecular and functional imaging approaches offer a major step to individualize radiotherapeutic care going forward. Nevertheless, several uncertainties remain on the standard method to properly assess the target volume definition including PET information for primary and secondary brain tumors.

Challenges to curing primary brain tumours

Despite decades of research, brain tumours remain among the deadliest of all forms of cancer. The ability of these tumours to resist almost all conventional and novel treatments relates, in part, to the unique cell-intrinsic and microenvironmental properties of neural tissues. In an attempt to encourage progress in our understanding and ability to successfully treat patients with brain tumours, Cancer Research UK convened an international panel of clinicians and laboratory-based scientists to identify challenges that must be overcome if we are to cure all patients with a brain tumour. The seven key challenges summarized in this Position Paper are intended to serve as foci for future research and investment.

Current and Future Imaging Methods for Evaluating Response to Immunotherapy in Neuro-Oncology

Imaging plays a central role in evaluating responses to therapy in neuro-oncology patients. The advancing clinical use of immunotherapies has demonstrated that treatment-related inflammatory responses mimic tumor growth via conventional imaging, thus spurring the development of new imaging approaches to adequately distinguish between pseudoprogression and progressive disease. To this end, an increasing number of advanced imaging techniques are being evaluated in preclinical and clinical studies. These novel molecular imaging approaches will serve to complement conventional response assessments during immunotherapy. The goal of these techniques is to provide definitive metrics of tumor response at earlier time points to inform treatment decisions, which has the potential to improve patient outcomes. This review summarizes the available immunotherapy regimens, clinical response criteria, current state-of-the-art imaging approaches, and groundbreaking strategies for future implementation to evaluate the anti-tumor and immune responses to immunotherapy in neuro-oncology applications.

Direct comparison of [11C] choline and [18F] FET PET to detect glioma infiltration: a diagnostic accuracy study in eight patients

Background

Positron emission tomography (PET) is increasingly used to guide local treatment in glioma. The purpose of this study was a direct comparison of two potential tracers for detecting glioma infiltration, O-(2-[¹⁸F]-fluoroethyl)-l-tyrosine ([¹⁸F] FET) and [¹¹C] choline.

Methods

Eight consecutive patients with newly diagnosed diffuse glioma underwent dynamic [¹¹C] choline and [¹⁸F] FET PET scans. Preceding craniotomy, multiple stereotactic biopsies were obtained from regions inside and outside PET abnormalities. Biopsies were assessed independently for tumour presence by two neuropathologists. Imaging measurements were derived at the biopsy locations from 10 to 40 min [¹¹C] choline and 20–40, 40–60 and 60–90 min [¹⁸F] FET intervals, as standardized uptake value (SUV) and tumour-to-brain ratio (TBR). Diagnostic accuracies of both tracers were compared using receiver operating characteristic analysis and generalized linear mixed modelling with consensus histopathological assessment as reference.

Results

Of the 74 biopsies, 54 (73%) contained tumour. [¹¹C] choline SUV and [¹⁸F] FET SUV and TBR at all intervals were higher in tumour than in normal samples. For [¹⁸F] FET, the diagnostic accuracy of TBR was higher than that of SUV for intervals 40–60 min (area under the curve: 0.88 versus 0.81, p = 0.026) and 60–90 min (0.90 versus 0.81, p = 0.047). The diagnostic accuracy of [¹⁸F] FET TBR 60–90 min was higher than that of [¹¹C] choline SUV 20–40 min (0.87 versus 0.67, p = 0.005).

Conclusions

[¹⁸F] FET was more accurate than [¹¹C] choline for detecting glioma infiltration. Highest accuracy was found for [¹⁸F] FET TBR for the interval 60–90 min post-injection.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0523-8) contains supplementary material, which is available to authorized users.

Scalable dissolution-dynamic nuclear polarization with rapid transfer of a polarized solid

In dissolution-dynamic nuclear polarization, nuclear spins are hyperpolarized at cryogenic temperatures using radicals and microwave irradiation. The hyperpolarized solid is dissolved with hot solvent and the solution is transferred to a secondary magnet where strongly enhanced magnetic resonance signals are observed. Here we present a method for transferring the hyperpolarized solid. A bullet containing the frozen, hyperpolarized sample is ejected using pressurized helium gas, and shot into a receiving structure in the secondary magnet, where the bullet is retained and the polarized solid is dissolved rapidly. The transfer takes approximately 70 ms. A solenoid, wound along the entire transfer path ensures adiabatic transfer and limits radical-induced low-field relaxation. The method is fast and scalable towards small volumes suitable for high-resolution nuclear magnetic resonance spectroscopy while maintaining high concentrations of the target molecule. Polarization levels of approximately 30% have been observed for 1-¹³C-labelled pyruvic acid in solution.

Dissolution-dynamic nuclear polarization is able to enhance nuclear magnetic resonance signals, but requires complex procedures to generate hyperpolarized nuclear spins. Here the authors establish a fast and facile method to transfer hyperpolarized samples into the liquid solution where the measurement is performed.

APT-weighted MRI: Techniques, current neuro applications, and challenging issues

Amide proton transfer-weighted (APTw) imaging is a molecular MRI technique that generates image contrast based predominantly on the amide protons in mobile cellular proteins and peptides that are endogenous in tissue. This technique, the most studied type of chemical exchange saturation transfer imaging, has been used successfully for imaging of protein content and pH, the latter being possible due to the strong dependence of the amide proton exchange rate on pH. In this article we briefly review the basic principles and recent technical advances of APTw imaging, which is showing promise clinically, especially for characterizing brain tumors and distinguishing recurrent tumor from treatment effects. Early applications of this approach to stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury are also illustrated. Finally, we outline the technical challenges for clinical APT-based imaging and discuss several controversies regarding the origin of APTw imaging signals in vivo. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:347-364.

Retinoblastoma, the visible CNS tumor: A review

The pediatric ocular cancer retinoblastoma is the only central nervous system (CNS) tumor readily observed without specialized equipment: it can be seen by, and in, the naked eye. This accessibility enables unique imaging modalities. Here, we review this cancer for a neuroscience audience, highlighting these clinical and research imaging options, including fundus imaging, optical coherence tomography, ultrasound, and magnetic resonance imaging. We also discuss the subtype of retinoblastoma driven by the MYCN oncogene more commonly associated with neuroblastoma, and consider trilateral retinoblastoma, in which an intracranial tumor arises along with ocular tumors in patients with germline RB1 gene mutations. Retinoblastoma research and clinical care can offer insights applicable to CNS malignancies, and also benefit from approaches developed elsewhere in the CNS.

Primary brain tumours in adults

Primary CNS tumours refer to a heterogeneous group of tumours arising from cells within the CNS, and can be benign or malignant. Malignant primary brain tumours remain among the most difficult cancers to treat, with a 5 year overall survival no greater than 35%. The most common malignant primary brain tumours in adults are gliomas. Recent advances in molecular biology have improved understanding of glioma pathogenesis, and several clinically significant genetic alterations have been described. A number of these (IDH, 1p/19q codeletion, H3 Lys27Met, and RELA-fusion) are now combined with histology in the revised 2016 WHO classification of CNS tumours. It is likely that understanding such molecular alterations will contribute to the diagnosis, grading, and treatment of brain tumours. This progress in genomics, along with significant advances in cancer and CNS immunology, has defined a new era in neuro-oncology and holds promise for diagntic and therapeutic improvement. The challenge at present is to translate these advances into effective treatments. Current efforts are focused on developing molecular targeted therapies, immunotherapies, gene therapies, and novel drug-delivery technologies. Results with single-agent therapies have been disappointing so far, and combination therapies seem to be required to achieve a broad and durable antitumour response. Biomarker-targeted clinical trials could improve efficiencies of therapeutic development.

Radiotherapy of Glioblastoma 15 Years after the Landmark Stupp's Trial: More Controversies than Standards?

Background

The current standard of care of glioblastoma, the most common primary brain tumor in adults, has remained unchanged for over a decade. Nevertheless, some improvements in patient outcomes have occurred as a consequence of modern surgery, improved radiotherapy and up-to-date management of toxicity. Patients from control arms (receiving standard concurrent chemoradiotherapy and adjuvant chemotherapy with temozolomide) of recent clinical trials achieve better outcomes compared to the median survival of 14.6 months reported in Stupp’s landmark clinical trial in 2005. The approach to radiotherapy that emerged from Stupp’s trial, which continues to be a basis for the current standard of care, is no longer applicable and there is a need to develop updated guidelines for radiotherapy within the daily clinical practice that address or at least acknowledge existing controversies in the planning of radiotherapy.

The goal of this review is to provoke critical thinking about potentially controversial aspects in the radiotherapy of glioblastoma, including among others the issue of target definitions, simultaneously integrated boost technique, and hippocampal sparing.

Conclusions

In conjunction with new treatment approaches such as tumor-treating fields (TTF) and immunotherapy, the role of adjuvant radiotherapy will be further defined. The personalized approach in daily radiotherapy practice is enabled with modern radiotherapy systems.

The vast potential and bright future of neuroimaging

Significant advances in anatomical and functional neuroimaging techniques have allowed researchers and clinicians to visualize the brain in action. The field of neuroimaging currently includes newer and faster scanners, improved image quality, higher spatial and temporal resolution and diverse methods of acquisition and analysis. Beyond simply imaging brain structures, these developments enable quantitative assessment of the microstructural and functional architecture, perfusion and metabolism of the brain. The resultant highly granular data have the potential to greatly improve characterization of neurological, neurosurgical and psychiatric disorders without invasive neurosurgery. However, the surge in neuroimaging data that can be collected over a relatively short acquisition period has led to a "big data" problem, where novel methods are needed to appropriately extract and analyze information and integrate data with other types of big data, such as genomic and proteomic data. Another challenge is the translation of these new technologies from basic science into clinical practice, so that they can be leveraged to improve patient outcomes and alleviate human disease. Critical to this endeavor is research comparing the effectiveness and outcomes of these advancements to allow widespread acceptance in the modern, economically constrained healthcare system. This review aims to illustrate the different facets of cutting edge neuroimaging techniques, as well as the potential role of these methods as clinical tools for evaluating the breadth of diseases that affect the brain.

Gold Nanoparticles for Brain Tumor Imaging: A Systematic Review

Background

Demarcation of malignant brain tumor boundaries is critical to achieve complete resection and to improve patient survival. Contrast-enhanced brain magnetic resonance imaging (MRI) is the gold standard for diagnosis and pre-surgical planning, despite limitations of gadolinium (Gd)-based contrast agents to depict tumor margins. Recently, solid metal-based nanoparticles (NPs) have shown potential as diagnostic probes for brain tumors. Gold nanoparticles (GNPs) emerged among those, because of their unique physical and chemical properties and biocompatibility. The aim of the present study is to review the application of GNPs for in vitro and in vivo brain tumor diagnosis.

Methods

We performed a PubMed search of reports exploring the application of GNPs in the diagnosis of brain tumors in biological models including cells, animals, primates, and humans. The search words were "gold" AND "NP" AND "brain tumor." Two reviewers performed eligibility assessment independently in an unblinded standardized manner. The following data were extracted from each paper: first author, year of publication, animal/cellular model, GNP geometry, GNP size, GNP coating [i.e., polyethylene glycol (PEG) and Gd], blood-brain barrier (BBB) crossing aids, imaging modalities, and therapeutic agents conjugated to the GNPs.

Results

The PubMed search provided 100 items. A total of 16 studies, published between the 2011 and 2017, were included in our review. No studies on humans were found. Thirteen studies were conducted in vivo on rodent models. The most common shape was a nanosphere (12 studies). The size of GNPs ranged between 20 and 120 nm. In eight studies, the GNPs were covered in PEG. The BBB penetration was increased by surface molecules (nine studies) or by means of external energy sources (in two studies). The most commonly used imaging modalities were MRI (four studies), surface-enhanced Raman scattering (three studies), and fluorescent microscopy (three studies). In two studies, the GNPs were conjugated with therapeutic agents.

Conclusion

Experimental studies demonstrated that GNPs might be versatile, persistent, and safe contrast agents for multimodality imaging, thus enhancing the tumor edges pre-, intra-, and post-operatively improving microscopic precision. The diagnostic GNPs might also be used for multiple therapeutic approaches, namely as "theranostic" NPs.