Multimodality Brain Tumor Imaging: MR Imaging, PET, and PET/MR Imaging

Challenges and material innovations in drug delivery to central nervous system tumors

Central nervous system (CNS) tumors, encompassing a diverse array of neoplasms in the brain and spinal cord, pose significant therapeutic challenges due to their intricate anatomy and the protective presence of the blood-brain barrier (BBB). The primary treatment obstacle is the effective delivery of therapeutics to the tumor site, which is hindered by multiple physiological, biological, and technical barriers, including the BBB. This comprehensive review highlights recent advancements in material science and nanotechnology aimed at surmounting these delivery challenges, with a focus on the development and application of nanomaterials. Nanomaterials emerge as potent tools in designing innovative drug delivery systems that demonstrate the potential to overcome the limitations posed by CNS tumors. The review delves into various strategies, including the use of lipid nanoparticles, polymeric nanoparticles, and inorganic nanoparticles, all of which are engineered to enhance drug stability, BBB penetration, and targeted tumor delivery. Additionally, this review highlights the burgeoning role of theranostic nanoparticles, integrating therapeutic and diagnostic functionalities to optimize treatment efficacy. The exploration extends to biocompatible materials like biodegradable polymers, liposomes, and advanced material-integrated delivery systems such as implantable drug-eluting devices and microfabricated devices. Despite promising preclinical results, the translation of these material-based strategies into clinical practice necessitates further research and optimization.

Radiological Predictors of Cognitive Impairment in Paediatric Brain Tumours Using Multiparametric Magnetic Resonance Imaging: A Review of Current Practice, Challenges and Future Directions

Paediatric brain tumours and their treatments are associated with long-term cognitive impairment. While the aetiology of cognitive impairment is complex and multifactorial, multiparametric Magnetic Resonance Imaging (MRI) can identify many risk factors including tumour location, damage to eloquent structures and tumour phenotype. Hydrocephalus and raised intracranial pressure can be observed, along with risk factors for post-operative paediatric cerebellar mutism syndrome or epilepsy. MRI can also identify complications of surgery or radiotherapy and monitor treatment response. Advanced imaging sequences provide valuable information about tumour and brain physiology, but clinical use is limited by extended scanning times and difficulties in processing and analysis. Brain eloquence classifications exist, but focus on adults with neurological deficits and are outdated. For the analysis of childhood tumours, limited numbers within tumour subgroups and the investigation of long-term outcomes necessitate using historical scans and/or multi-site collaboration. Variable imaging quality and differing acquisition parameters limit the use of segmentation algorithms and radiomic analysis. Harmonisation can standardise imaging in collaborative research, but can be challenging, while data-sharing produces further logistical challenges. Consequently, most research consists of small single-centre studies limited to regional analyses of tumour location. Technological advances reducing scanning times increase the feasibility of clinical acquisition of high-resolution standardised imaging including advanced physiological sequences. The RAPNO and SIOPE paediatric brain tumour imaging guidelines have improved image standardisation, which will benefit future collaborative imaging research. Modern machine learning techniques provide more nuanced approaches for integration and analysis of the complex and multifactorial data involved in cognitive outcome prediction.

Application of a CYP1B1-Targeted NIR Probe for Breast Cancer Diagnosis, Surgical Navigation, and CYP1B1-Associated Chemotherapy Resistance Monitoring

Early detection and precise treatment for breast cancer are crucial, given its high global incidence rate. Hence, the development of novel imaging targets is essential for diagnosing and monitoring resistance to chemotherapy, which is pivotal for achieving precise and personalized treatment for breast cancer patients. In our previous work, we successfully developed a near-infrared (NIR) probe 1 for CYP1B1-targeted imaging. In this study, we aimed to investigate the utility of the probe as a NIR fluorescence and photoacoustic dual-mode imaging probe for the detection and surveillance of breast cancer. Western blotting of cancer cell lines has confirmed that CYP1B1 is widely expressed in breast cancer and gynecological cancer. In vitro NIR fluorescence imaging capability of the probe for tracking CYP1B1-positive tumor cells was validated by using confocal microscopy. Further studies, including in vivo fluorescence and photoacoustic dual-model imaging and ex vivo biological distribution analysis on a triple-negative breast cancer xenograft mouse model, demonstrated that the probe selectively accumulated in tumor tissue within as early as 0.5 h postinjection. The results of the surgical resection experiment revealed that the tumor could be entirely removed under the guidance of NIR imaging, thereby indicating the probe's efficacy in surgical navigation. CYP1B1 expression was found to be upregulated in adriamycin (ADR)-resistant breast cancer cells, MCF-7/ADR. Consequently, the sensitivity of CYP1B1 overexpressed cells, MCF-7/1B1, to ADR was significantly reduced, with an IC50 value of 0.586 ± 0.0934 μM, compared to the parental MCF-7 cells with an IC50 value of 0.183 ± 0.0444 μM. In vivo and ex vivo imaging assays conducted on MCF-7/ADR tumor-bearing mice demonstrated that the probe was specifically enriched in tumor sites, suggesting its potential for monitoring chemotherapy resistance in breast cancer. This study expands the scope of application for NIR probe 1, establishing its utility in breast cancer diagnosis through fluorescence-photoacoustic dual-model imaging, monitoring of chemotherapy resistance, and guidance for surgical resection. This strategy paves the way for novel approaches to precise and personalized treatment for breast cancer patients.

Advancements in nanotheranostics for glioma therapy

Gliomas are brain tumors mainly derived from glial cells that are difficult to treat and cause high mortality. Radiation, chemotherapy, and surgical excision are the conventional treatments for gliomas. Patients who have surgery or have undergone chemotherapy for glioma treatment have poor prognosis with tumor recurrence. In particular, for glioblastoma, the 5-year average survival rate is 4-7%, and the median survival is 12-18 months. A number of issues hinder effective treatment such as, poor surgical resection, tumor heterogeneity, insufficient drug penetration across the blood-brain barrier, multidrug resistance, and difficulties with drug specificity. Nanotheranostic-mediated drug delivery is becoming a well-researched consideration, and an efficient non-invasive method for delivering chemotherapeutic drugs to the target area. Theranostic nanomedicines, which incorporate therapeutic drugs and imaging agents for personalized therapies, can be used for preventing overdose of non-responders. Through the identification of massive and complicated information from next-generation sequencing, machine learning enables for precise prediction of therapeutic outcomes and post-treatment management for patients with cancer. This article gives a thorough overview of nanocarrier-mediated drug delivery with a brief introduction to drug delivery challenges. In addition, this assessment offers a current summary of preclinical and clinical research on nanomedicines for gliomas. In the future, nanotheranostics will provide personalized treatment for gliomas and other treatable cancers.

Intraoperative variations in intra-axial brain tumor size after craniotomy: a prospective study with histopathological and tumor tissue composition correlation, introducing concepts of tumor expansion and tumor surfacing

Intraoperative assessment of tumor margins can be challenging; as neoplastic cells may extend beyond the margins seen on preoperative imaging. Real-time intraoperative ultrasonography (IOUS) has emerged as a valuable tool for delineating tumor boundaries during surgery. However, concerns remain regarding its ability to accurately distinguish between tumor margins, peritumoral edema, and normal brain tissue. Preoperative contrast-enhanced MRI (CEMRI) and contrast-enhanced CT (CECT) were performed to assess tumor dimensions, and IOUS was used intraoperatively to further evaluate tumor characteristics. Tumor volume was estimated using the prolate ellipsoid formula. Statistical analysis was conducted to compare tumor dimensions between imaging modalities and assess tumor expansion post-craniotomy. Our study included 51 patients with intracranial tumors. IOUS revealed larger tumor dimensions compared to preoperative CEMRI and CECT, with significant differences observed in surface area and volume. Tumors exhibited varied echogenicity on IOUS, with most showing mixed echogenicity. Histopathological analysis revealed a range of tumor grades, with gliomas being the most common. Statistical analysis indicated significant differences in tumor dimensions between imaging modalities, with tumor expansion observed post-craniotomy. Tumor type and grade were predictive factors for tumor volume expansion. Tumors exhibit expansion after craniotomy, with both tumor volume and surface area increasing. This expansion phenomenon, not solely attributed to tumor edema, underscores the importance of considering tumor mass expansion during surgical planning and intraoperative decision-making. These findings highlight the utility of IOUS in accurately delineating tumor boundaries and optimizing surgical outcomes in the management of intracranial tumors.

Enhancing Glioblastoma Resection with NIR Fluorescence Imaging: A Systematic Review

Glioblastoma (GB) is among the most aggressive and difficult-to-treat brain tumors, with a median survival of only 12-15 months despite maximal treatments, including surgery, radiotherapy, and chemotherapy. Extensive surgical resection improves survival in glioblastoma patients; however, achieving complete resection is often hindered by limitations in neurosurgical guidance technologies for accurate tumor margin detection. Recent advancements in fluorescence-guided surgery (FGS) and imaging techniques have significantly enhanced the precision and extent of glioblastoma resections. This study evaluates the impact of NIR fluorescence imaging on tumor visualization, surgical precision, cost-effectiveness, and patient survival. A systematic review of PubMed, Scopus, Google Scholar, and Embase was conducted to identify studies on the role of NIR fluorescence in glioblastoma surgery. A total of 135 studies were included, comprising 10 reviews, three clinical studies, 10 randomized controlled trials (RCTs), 10 preclinical studies, and four case reports, all focused on NIR fluorescence imaging in glioblastoma surgery. The findings indicate that NIR fluorescence imaging significantly improves tumor visualization, resulting in an 18-22% increase in gross total resection (GTR) rates in clinical studies. NIR fluorescence provides continuous real-time feedback, minimizing repeat imaging, reducing operational costs, and increasing GTR. These improvements contribute to better patient outcomes, including extended progression-free survival, improved overall survival, and reduced postoperative neurological deficits. This review underscores the potential of NIR imaging to establish a new standard for intraoperative glioblastoma management.

Nuclear Medicine Imaging Techniques in Glioblastomas

Glioblastomas are the most common primary malignant grade 4 tumors of the central nervous system (CNS). The treatment and management of such tumors requires a multidisciplinary approach and nuclear medicine techniques play an important role in this process. Glioblastoma, which recurs despite current treatments and becomes resistant to treatments, is among the tumors with the lowest survival rate, with a survival rate of approximately 8 months. Currently, the standard treatment of glioblastoma is adjuvant chemoradiotherapy after surgical resection. There have been many recent advances in the field of Nuclear Medicine in glioblastoma. PET scans are critical in determining tumor localization, pre-surgical planning, evaluation of post-treatment response and detection of recurrence. Advances in the treatment of glioblastoma and a better understanding of the biological characteristics of the disease have contributed to the development of nuclear medicine techniques. This review, in addition to other studies, is intended as a general imaging summary guide and includes some new expressions discovered in glioblastoma. This review discusses recent advances in nuclear medicine in glioblastoma.

Synergy in Neuroimaging: PET-CT and MRI Fusion for Enhanced Characterization of Brain Pathology

BACKGROUND

Accurate diagnosis and understanding of brain disorders are crucial for the best treatment. While multimodal neuroimaging is essential, it has its limitations. Conventional computed tomography (CT) and magnetic resonance imaging (MRI) provide detailed anatomical information but lack molecular insights, while 18F-fluorodeoxyglucose-positron emission tomography (FDG PET) offers metabolic data but often has limited spatial resolution.

OBJECTIVE

This study aimed to assess the potential of combining 18F-FDG PET with MRI to characterize brain disorders compared to standard imaging methods.

METHODS

Fifty patients suspected of having brain tumors underwent 18F-FDG PET-CT and then MRI (including 3D fluid-attenuated inversion recovery (FLAIR)) after CT scans revealed suspicious lesions. The images were combined and PET-MRI findings were compared to the initial CT interpretations.

RESULTS

The combination of PET-MRI significantly improved diagnostic accuracy in 20 out of the 50 patients (40%). Importantly, it identified and characterized brain lesions missed by CT in two patients (4%). In patients with known dementia or epilepsy, PET-MRI revealed specific metabolic patterns in affected brain areas.

CONCLUSION

18F-FDG PET-MRI fusion shows greater sensitivity and specificity than standard imaging techniques for various brain disorders. It provides valuable insights into structural and functional abnormalities, potentially leading to improved diagnosis, treatment planning, and patient outcomes.

Breaking the barrier: Nanoparticle-enhanced radiotherapy as the new vanguard in brain tumor treatment

The pursuit of effective treatments for brain tumors has increasingly focused on the promising area of nanoparticle-enhanced radiotherapy (NERT). This review elucidates the context and significance of NERT, with a particular emphasis on its application in brain tumor therapy-a field where traditional treatments often encounter obstacles due to the blood-brain barrier (BBB) and tumor cells' inherent resistance. The aims of this review include synthesizing recent advancements, analyzing action mechanisms, and assessing the clinical potential and challenges associated with nanoparticle (NP) use in radiotherapy enhancement. Preliminary preclinical studies have established a foundation for NERT, demonstrating that nanoparticles (NPs) can serve as radiosensitizers, thereby intensifying radiotherapy's efficacy. Investigations into various NP types, such as metallic, magnetic, and polymeric, have each unveiled distinct interactions with ionizing radiation, leading to an augmented destruction of tumor cells. These interactions, encompassing physical dose enhancement and biological and chemical radio sensitization, are crucial to the NERT strategy. Although clinical studies are in their early phases, initial trials have shown promising results in terms of tumor response rates and survival, albeit with mindful consideration of toxicity profiles. This review examines pivotal studies affirming NERT's efficacy and safety. NPs have the potential to revolutionize radiotherapy by overcoming challenges in targeted delivery, reducing off-target effects, and harmonizing with other modalities. Future directions include refining NP formulations, personalizing therapies, and navigating regulatory pathways. NERT holds promise to transform brain tumor treatment and provide hope for patients.

Correlation Between CT Density, Incidence, Mortality, and Mortality-to-Incidence Ratio in Central Nervous System Cancers: An Exploratory Analysis of Global Data

BACKGROUND/AIM

Cancers of the central nervous system (CNS) pose a significant burden, despite their relatively low incidence compared to other types of cancers. The mortality-to-incidence ratio (MIR) is a crucial indicator of long-term survival and healthcare system performance. Computed tomography (CT) plays a crucial role in the screening, diagnosis, and monitoring of brain tumors, enabling early intervention and treatment. This study aimed to explore the relationship between CT density, CNS cancer incidence, mortality, and MIR to investigate regional variations in CT utilization and their impact on CNS cancer mortality rates.

PATIENTS AND METHODS

Changes in MIR, referred to as δMIR, were calculated based on data from 2012 and 2018. CT density data for the year 2013 were retrieved from the Global Health Observatory data repository. The association between variables was analyzed using Spearman's rank correlation coefficient.

RESULTS

Analysis of data from 107 countries revealed a positive association between CT density and both CNS cancer incidence and mortality. However, a trend was observed between CT density and MIR. These findings suggest that in countries with greater accessibility to CT imaging, CNS cancer cases may be detected earlier and lower mortality rates can be achieved.

CONCLUSION

Our research contributes to the understanding of the impact of CT imaging on the management and outcomes of CNS cancers. It informs healthcare strategies and resource allocation to improve patient care.

Designing a deep hybridized residual and SE model for MRI image-based brain tumor prediction

Deep learning techniques have become crucial in the detection of brain tumors but classifying numerous images is time-consuming and error-prone, impacting timely diagnosis. This can hinder the effectiveness of these techniques in detecting brain tumors in a timely manner. To address this limitation, this study introduces a novel brain tumor detection system. The main objective is to overcome the challenges associated with acquiring a large and well-classified dataset. The proposed approach involves generating synthetic Magnetic Resonance Imaging (MRI) images that mimic the patterns commonly found in brain MRI images. The system utilizes a dataset consisting of small images that are unbalanced in terms of class distribution. To enhance the accuracy of tumor detection, two deep learning models are employed. Using a hybrid ResNet+SE model, we capture feature distributions within unbalanced classes, creating a more balanced dataset. The second model, a tailored classifier identifies brain tumors in MRI images. The proposed method has shown promising results, achieving a high detection accuracy of 98.79%. This highlights the potential of the model as an efficient and cost-effective system for brain tumor detection.

Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions

Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care.

The Role of Mesenchymal Reprogramming in Malignant Clonal Evolution and Intra-Tumoral Heterogeneity in Glioblastoma

Glioblastoma (GBM) is the most common yet uniformly fatal adult brain cancer. Intra-tumoral molecular and cellular heterogeneities are major contributory factors to therapeutic refractoriness and futility in GBM. Molecular heterogeneity is represented through molecular subtype clusters whereby the proneural (PN) subtype is associated with significantly increased long-term survival compared to the highly resistant mesenchymal (MES) subtype. Furthermore, it is universally recognized that a small subset of GBM cells known as GBM stem cells (GSCs) serve as reservoirs for tumor recurrence and progression. The clonal evolution of GSC molecular subtypes in response to therapy drives intra-tumoral heterogeneity and remains a critical determinant of GBM outcomes. In particular, the intra-tumoral MES reprogramming of GSCs using current GBM therapies has emerged as a leading hypothesis for therapeutic refractoriness. Preventing the intra-tumoral divergent evolution of GBM toward the MES subtype via new treatments would dramatically improve long-term survival for GBM patients and have a significant impact on GBM outcomes. In this review, we examine the challenges of the role of MES reprogramming in the malignant clonal evolution of glioblastoma and provide future perspectives for addressing the unmet therapeutic need to overcome resistance in GBM.

IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI

Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious "black boxes". The opaqueness of the model and the reasoning process make it difficult for health workers to decide whether to trust the prediction outcomes. In this study, we develop an interpretable multi-part attention network (IMPA-Net) for brain tumor classification to enhance the interpretability and trustworthiness of classification outcomes. The proposed model not only predicts the tumor grade but also provides a global explanation for the model interpretability and a local explanation as justification for the proffered prediction. Global explanation is represented as a group of feature patterns that the model learns to distinguish high-grade glioma (HGG) and low-grade glioma (LGG) classes. Local explanation interprets the reasoning process of an individual prediction by calculating the similarity between the prototypical parts of the image and a group of pre-learned task-related features. Experiments conducted on the BraTS2017 dataset demonstrate that IMPA-Net is a verifiable model for the classification task. A percentage of 86% of feature patterns were assessed by two radiologists to be valid for representing task-relevant medical features. The model shows a classification accuracy of 92.12%, of which 81.17% were evaluated as trustworthy based on local explanations. Our interpretable model is a trustworthy model that can be used for decision aids for glioma classification. Compared with black-box CNNs, it allows health workers and patients to understand the reasoning process and trust the prediction outcomes.

Advances in determining the gross tumor target volume for radiotherapy of brain metastases

Brain metastases (BMs) are the most prevalent intracranial malignant tumors in adults and are the leading cause of mortality attributed to malignant brain diseases. Radiotherapy (RT) plays a critical role in the treatment of BMs, with local RT techniques such as stereotactic radiosurgery (SRS)/stereotactic body radiotherapy (SBRT) showing remarkable therapeutic effectiveness. The precise determination of gross tumor target volume (GTV) is crucial for ensuring the effectiveness of SRS/SBRT. Multimodal imaging techniques such as CT, MRI, and PET are extensively used for the diagnosis of BMs and GTV determination. With the development of functional imaging and artificial intelligence (AI) technology, there are more innovative ways to determine GTV for BMs, which significantly improve the accuracy and efficiency of the determination. This article provides an overview of the progress in GTV determination for RT in BMs.

Biodistribution Assessment of a Novel 68Ga-Labeled Radiopharmaceutical in a Cancer Overexpressing CCK2R Mouse Model: Conventional and Radiomics Methods for Analysis

The aim of the present study consists of the evaluation of the biodistribution of a novel 68Ga-labeled radiopharmaceutical, [68Ga]Ga-NODAGA-Z360, injected into Balb/c nude mice through histopathological analysis on bioptic samples and radiomics analysis of positron emission tomography/computed tomography (PET/CT) images. The 68Ga-labeled radiopharmaceutical was designed to specifically bind to the cholecystokinin receptor (CCK2R). This receptor, naturally present in healthy tissues such as the stomach, is a biomarker for numerous tumors when overexpressed. In this experiment, Balb/c nude mice were xenografted with a human epidermoid carcinoma A431 cell line (A431 WT) and overexpressing CCK2R (A431 CCK2R+), while controls received a wild-type cell line. PET images were processed, segmented after atlas-based co-registration and, consequently, 112 radiomics features were extracted for each investigated organ / tissue. To confirm the histopathology at the tissue level and correlate it with the degree of PET uptake, the studies were supported by digital pathology. As a result of the analyses, the differences in radiomics features in different body districts confirmed the correct targeting of the radiopharmaceutical. In preclinical imaging, the methodology confirms the importance of a decision-support system based on artificial intelligence algorithms for the assessment of radiopharmaceutical biodistribution.

Proteomic landscape of primary and metastatic brain tumors for heterogeneity discovery

PURPOSE

Despite recent advancements in our understanding of driver gene mutations and heterogeneity within brain tumors, whether primary or metastatic (also known as secondary), our comprehension of proteomic changes remains inadequate. The aim of this study is to provide an informative source for brain tumor researches, and distinguish primary brain tumors and secondary brain tumors from extracranial origins based on proteomic analysis.

EXPERIMENTAL DESIGN

We assembled the most frequent brain tumors as follows: gliomas from WHO grade 2 to 4, with IDH1 mutations and wildtypes; brain metastases (BrMs) originating from lung cancer (LC), breast cancer (BC), ovarian cancer (OC), and colorectal cancer (CC). A total of 29 tissue samples were analyzed by label free quantitative mass spectrometry-based proteomics.

RESULTS

In total, 8165 protein groups were quantified, of which 4383 proteins were filtered at 50% valid intensity values for downstream analysis. Proteomic analysis of BrMs reveals conserved features shared among multiple origins. While proteomic heterogeneities were found for discriminating different grades of gliomas, as well as IDH1 mutant and wildtype gliomas. In addition, notable distinctions were observed at the pathway level between BrMs and gliomas. Specifically, BrMs exhibited characteristic pathways focused on proliferation and immunomodulation after colonizing the brain, whereas gliomas primarily engaged in invasion processes.

CONCLUSIONS AND CLINICAL RELEVANCE

We characterized an extensive proteomic landscape of BrMs and gliomas. These findings have promising implications for the development of targeted therapies for BrMs and gliomas.

Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy

BACKGROUND

Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients.

METHODS

Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors.

RESULTS

Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %.

CONCLUSIONS

Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.

PSMA PET/MR is a New Imaging Option for Identifying Glioma Recurrence and Predicting Prognosis

BACKGROUND

Glioma is characterized by a high recurrence rate, while the results of the traditional imaging methods (including magnetic resonance imaging, MRI) to distinguish recurrence from treatment-related changes (TRCs) are poor. Prostate-specific membrane antigen (PSMA) (US10815200B2, Deutsches Krebsforschungszentrum, German Cancer Research Center) is a type II transmembrane glycoprotein overexpressed in glioma vascular endothelium, and it is a promising target for imaging and therapy.

OBJECTIVE

The study aimed to assess the performance of PSMA positron emission tomography/ magnetic resonance (PET/MR) for diagnosing recurrence and predicting prognosis in glioma patients.

MATERIALS AND METHODS

Patients suspected of glioma recurrence who underwent 18F-PSMA-1007 PET/MR were prospectively enrolled. Eight metabolic parameters and fifteen texture features of the lesion were extracted from PSMA PET/MR. The ability of PSMA PET/MR to diagnose glioma recurrence was investigated and compared with conventional MRI. The diagnostic agreement was assessed using Cohen κ scores and the predictive parameters of PSMA PET/MR were obtained. Kaplan-Meier method and Cox proportional hazard model were used to analyze recurrence- free survival (RFS) and overall survival (OS). Finally, the expression of PSMA was analyzed by immunohistochemistry (IHC).

RESULTS

Nineteen patients with a mean age of 48.11±15.72 were assessed. The maximum tumorto- parotid ratio (TPRmax) and texture features extracted from PET and T1-weighted contrast enhancement (T1-CE) MR showed differences between recurrence and TRCs (all p <0.05). PSMA PET/MR and conventional MRI exhibited comparable power in diagnosing recurrence with specificity and PPV of 100%. The interobserver concordance was fair between the two modalities (κ = 0.542, p = 0.072). The optimal cutoffs of metabolic parameters, including standardized uptake value (SUV, SUVmax, SUVmean, and SUVpeak) and TPRmax for predicting recurrence were 3.35, 1.73, 1.99, and 0.17 respectively, with the area under the curve (AUC) ranging from 0.767 to 0.817 (all p <0.05). In grade 4 glioblastoma (GBM) patients, SUVmax, SUVmean, SUVpeak, TBRmax, TBRmean, and TPRmax showed improved performance of AUC (0.833-0.867, p <0.05). Patients with SUVmax, SUVmean, or SUVpeak more than the cutoff value had significantly shorter RFS (all p <0.05). In addition, patients with SUVmean, SUVpeak, or TPRmax more than the cutoff value had significantly shorter OS (all p <0.05). PSMA expression of glioma vascular endothelium was observed in ten (10/11, 90.9%) patients with moderate-to-high levels in all GBM cases (n = 6/6, 100%).

CONCLUSION

This primitive study shows multiparameter PSMA PET/MR to be useful in identifying glioma (especially GBM) recurrence by providing excellent tumor background comparison, tumor heterogeneity, recurrence prediction and prognosis information, although it did not improve the diagnostic performance compared to conventional MRI. Further and larger studies are required to define its potential clinical application in this setting.

The LMIT: Light-mediated minimally-invasive theranostics in oncology

Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

A review on brain tumor segmentation based on deep learning methods with federated learning techniques

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues.

Approach to diagnosis, evaluation, and treatment of generalized and nonlocal dysesthesia: A review

Dysesthesia is an abnormal sensation in the skin that occurs in the absence of any extraordinary stimulus or other primary cutaneous disorders, excluding any delusions or tactile hallucinations. Clinicians have characterized dysesthesias to include sensations such as burning, tingling, pruritus, allodynia, hyperesthesia, or anesthesia. The etiology and pathogenesis of various generalized dysesthesias is largely unknown, though many dysesthesias have been associated with systemic pathologies including malignancy, infection, autoimmune disorders, and neuropathies. Dermatologists are often the first-line clinicians for patients presenting with such cutaneous findings, thus it is crucial for these physicians to be able to methodically work-up generalized dysesthesias to build a working differential diagnosis, follow up with key labs and/or imaging, and offer patients evidence-based treatment to relieve their symptoms. This broad literature review is an attempt to centralize key studies, cases, and series to help guide dermatologists in their assessment and evaluation of complaints of abnormal cutaneous sensations.

A novel deep learning model for glioma epilepsy associated with the identification of human cytomegalovirus infection injuries based on head MR

Purpose

In this study, a deep learning model was established based on head MRI to predict a crucial evaluation parameter in the assessment of injuries resulting from human cytomegalovirus infection: the occurrence of glioma-related epilepsy. The relationship between glioma and epilepsy was investigated, which serves as a significant indicator of labor force impairment.

Methods

This study enrolled 142 glioma patients, including 127 from Shengjing Hospital of China Medical University, and 15 from the Second Affiliated Hospital of Dalian Medical University. T1 and T2 sequence images of patients' head MRIs were utilized to predict the occurrence of glioma-associated epilepsy. To validate the model's performance, the results of machine learning and deep learning models were compared. The machine learning model employed manually annotated texture features from tumor regions for modeling. On the other hand, the deep learning model utilized fused data consisting of tumor-containing T1 and T2 sequence images for modeling.

Results

The neural network based on MobileNet_v3 performed the best, achieving an accuracy of 86.96% on the validation set and 75.89% on the test set. The performance of this neural network model significantly surpassed all the machine learning models, both on the validation and test sets.

Conclusion

In this study, we have developed a neural network utilizing head MRI, which can predict the likelihood of glioma-associated epilepsy in untreated glioma patients based on T1 and T2 sequence images. This advancement provides forensic support for the assessment of injuries related to human cytomegalovirus infection.

Amyloids and brain cancer: molecular linkages and crossovers

Amyloids are high-order proteinaceous formations deposited in both intra- and extracellular spaces. These aggregates have tendencies to deregulate cellular physiology in multiple ways; for example, altered metabolism, mitochondrial dysfunctions, immune modulation, etc. When amyloids are formed in brain tissues, the endpoint often is death of neurons. However, interesting but least understood is a close connection of amyloids with another set of conditions in which brain cells proliferate at an extraordinary rate and form tumor inside brain. Glioblastoma is one such condition. Increasing number of evidence indicate a possible link between amyloid formation and depositions in brain tumors. Several proteins associated with cell cycle regulation and apoptotic pathways themselves have shown to possess high tendencies to form amyloids. Tumor suppressor protein p53 is one prominent example that mutate, oligomerize and form amyloids leading to loss- or gain-of-functions and cause increased cell proliferation and malignancies. In this review article, we present available examples, genetic links and common pathways that indicate that possibly the two distantly placed pathways: amyloid formation and developing cancers in the brain have similarities and are mechanistically intertwined together.

A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques

The risk of discovering an intracranial aneurysm during the initial screening and follow-up screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these mass effects, unruptured aneurysms frequently generate symptoms, however, the real hazard occurs when an aneurysm ruptures and results in a cerebral hemorrhage known as a subarachnoid hemorrhage. The objective is to study the multiple kinds of hemorrhage and aneurysm detection problems and develop machine and deep learning models to recognise them. Due to its early stage, subarachnoid hemorrhage, the most typical symptom after aneurysm rupture, is an important medical condition. It frequently results in severe neurological emergencies or even death. Although most aneurysms are asymptomatic and won't burst, because of their unpredictable growth, even small aneurysms are susceptible. A timely diagnosis is essential to prevent early mortality because a large percentage of hemorrhage cases present can be fatal. Physiological/imaging markers and the degree of the subarachnoid hemorrhage can be used as indicators for potential early treatments in hemorrhage. The hemodynamic pathomechanisms and microcellular environment should remain a priority for academics and medical professionals. There is still disagreement about how and when to care for aneurysms that have not ruptured despite studies reporting on the risk of rupture and outcomes. We are optimistic that with the progress in our understanding of the pathophysiology of hemorrhages and aneurysms and the advancement of artificial intelligence has made it feasible to conduct analyses with a high degree of precision, effectiveness and reliability.

Evaluation of Artifact Appearance and Burden in Pediatric Brain Tumor MR Imaging with Compressed Sensing in Comparison to Conventional Parallel Imaging Acceleration

Clinical magnetic resonance imaging (MRI) aims for the highest possible image quality, while balancing the need for acceptable examination time, reasonable signal-to-noise ratio (SNR), and lowest artifact burden. With a recently introduced imaging acceleration technique, compressed sensing, the acquisition speed and image quality of pediatric brain tumor exams can be improved. However, little attention has been paid to its impact on method-related artifacts in pediatric brain MRI. This study assessed the overall artifact burden and artifact appearances in a standardized pediatric brain tumor MRI by comparing conventional parallel imaging acceleration with compressed sensing. This showed that compressed sensing resulted in fewer physiological artifacts in the FLAIR sequence, and a reduction in technical artifacts in the 3D T1 TFE sequences. Only a slight difference was noted in the T2 TSE sequence. A relatively new range of artifacts, which are likely technique-related, was noted in the 3D T1 TFE sequences. In conclusion, by equipping a basic pediatric brain tumor protocol for 3T MRI with compressed sensing, the overall burden of common artifacts can be reduced. However, attention should be paid to novel compressed-sensing-specific artifacts.

Effects of proton therapy on regional [18F]FDG uptake in non-tumor brain regions of patients treated for head and neck cancer

Background and purpose

Previous pre-clinical research using [¹⁸F]FDG-PET has shown that whole-brain photon-based radiotherapy can affect brain glucose metabolism. This study, aimed to investigate how these findings translate into regional changes in brain [¹⁸F]FDG uptake in patients with head and neck cancer treated with intensity-modulated proton therapy (IMPT).

Materials and methods

Twenty-three head and neck cancer patients treated with IMPT and available [¹⁸F]FDG scans before and at 3 months follow-up were retrospectively evaluated. Regional assessment of the [¹⁸F]FDG standardized uptake value (SUV) parameters and radiation dose in the left (L) and right (R) hippocampi, L and R occipital lobes, cerebellum, temporal lobe, L and R parietal lobes and frontal lobe were evaluated to understand the relationship between regional changes in SUV metrics and radiation dose.

Results

Three months after IMPT, [¹⁸F]FDG brain uptake calculated using SUVmean and SUVmax, was significantly higher than that before IMPT. The absolute SUVmean after IMPT was significantly higher than before IMPT in seven regions of the brain (p ≤ 0.01), except for the R (p = 0.11) and L (p = 0.15) hippocampi. Absolute and relative changes were variably correlated with the regional maximum and mean doses received in most of the brain regions.

Conclusion

Our findings suggest that 3 months after completion of IMPT for head and neck cancer, significant increases in the uptake of [¹⁸F]FDG (reflected by SUVmean and SUVmax) can be detected in several individual key brain regions, and when evaluated jointly, it shows a negative correlation with the mean dose. Future studies are needed to assess whether and how these results could be used for the early identification of patients at risk for adverse cognitive effects of radiation doses in non-tumor tissues.

Case Report: Brain tumor's pitfalls: two cases of high-grade brain tumors mimicking autoimmune encephalitis with positive onconeuronal antibodies

Background

Glioblastoma (GBM) is the most common primary brain tumor in adulthood. Initial diagnosis is generally based on clinical and MRI findings, which may be misinterpreted as other neurological pictures, including autoimmune encephalitis (AE). AE is a heterogeneous group of neuroinflammatory diseases due to the presence of auto-antibodies targeting antigens on neuronal synaptic or cell surface. In the present report, we describe two peculiar cases of GBM initially misdiagnosed as AE, focusing on the diagnostic pitfalls and the treatment strategies.

Methods

We report the case of two patients with high-grade brain tumors, initially misdiagnosed and treated for AE. Clinical, laboratory, and neuroradiological data are discussed in terms of differential diagnosis between AE and GBM.

Results

The presence of atypical brain MRI findings and the unresponsiveness to immunosuppressive treatment are major red flags in the differential diagnosis between AE and GBM. In these cases, a brain biopsy is necessary to confirm the diagnosis.

Conclusions

Atypical brain tumor presentation causes a diagnostic and therapeutic delay. A positive onconeural autoantibodies result should always be interpreted cautiously, considering the possibility of a false-positive test. A brain biopsy is mandatory for a definite diagnosis.

Advances in the application of neuroinflammatory molecular imaging in brain malignancies

The prevalence of brain cancer has been increasing in recent decades, posing significant healthcare challenges. The introduction of immunotherapies has brought forth notable diagnostic imaging challenges for brain tumors. The tumor microenvironment undergoes substantial changes in induced immunosuppression and immune responses following the development of primary brain tumor and brain metastasis, affecting the progression and metastasis of brain tumors. Consequently, effective and accurate neuroimaging techniques are necessary for clinical practice and monitoring. However, patients with brain tumors might experience radiation-induced necrosis or other neuroinflammation. Currently, positron emission tomography and various magnetic resonance imaging techniques play a crucial role in diagnosing and evaluating brain tumors. Nevertheless, differentiating between brain tumors and necrotic lesions or inflamed tissues remains a significant challenge in the clinical diagnosis of the advancements in immunotherapeutics and precision oncology have underscored the importance of clinically applicable imaging measures for diagnosing and monitoring neuroinflammation. This review summarizes recent advances in neuroimaging methods aimed at enhancing the specificity of brain tumor diagnosis and evaluating inflamed lesions.

Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model

PURPOSE

The accurate preoperative histopathological grade diagnosis of adult gliomas is of great significance for the formulation of a surgical plan and the implementation of a subsequent treatment. The aim of this study is to establish a predictive model for classifying adult gliomas into grades 2-4 based on preoperative conventional multimodal MRI radiomics.

PATIENTS AND METHODS

Patients with pathologically confirmed gliomas at Huashan Hospital, Fudan University, between February 2017 and July 2019 were retrospectively analyzed. Two regions of interest (ROIs), called the maximum anomaly region (ROI1) and the tumor region (ROI2), were delineated on the patients' preoperative MRIs utilizing the tool ITK-SNAP, and Pyradiomics 3.0 was applied to execute feature extraction. Feature selection was performed utilizing a least absolute shrinkage and selection operator (LASSO) filter. Six classifiers, including Gaussian naive Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) with a linear kernel, adaptive boosting (AB), and multilayer perceptron (MLP) were used to establish predictive models, and the predictive performance of the six classifiers was evaluated through five-fold cross-validation. The performance of the predictive models was evaluated using the AUC and other metrics. After that, the model with the best predictive performance was tested using the external data from The Cancer Imaging Archive (TCIA).

RESULTS

According to the inclusion and exclusion criteria, 240 patients with gliomas were identified for inclusion in the study, including 106 grade 2, 68 grade 3, and 66 grade 4 gliomas. A total of 150 features was selected, and the MLP classifier had the best predictive performance among the six classifiers based on T2-FLAIR (mean AUC of 0.80 ± 0.07). The SVM classifier had the best predictive performance among the six classifiers based on DWI (mean AUC of 0.84 ± 0.05); the SVM classifier had the best predictive performance among the six classifiers based on CE-T1WI (mean AUC of 0.85 ± 0.06). Among the six classifiers, based on ROI1, the MLP classifier had the best prediction performance (mean AUC of 0.78 ± 0.07); among the six classifiers, based on ROI2, the SVM classifier had the best prediction performance (mean AUC of 0.82 ± 0.07). Among the six classifiers, based on the multimodal MRI of all the ROIs, the SVM classifier had the best prediction performance (average AUC of 0.85 ± 0.04). The SVM classifier, based on the multimodal MRI of all the ROIs, achieved an AUC of 0.81 using the external data from TCIA.

CONCLUSIONS

The prediction model, based on preoperative conventional multimodal MRI radiomics, established in this study can conveniently, accurately, and noninvasively classify adult gliomas into grades 2-4, providing certain assistance for the precise diagnosis and treatment of patients and optimizing their clinical management.

Imaging the brain and vascular reactions to headache treatments: a systematic review

BACKGROUND

Neuroimaging studies have made an important contribution to our understanding of headache pathophysiology. This systematic review aims to provide a comprehensive overview and critical appraisal of mechanisms of actions of headache treatments and potential biomarkers of treatment response disclosed by imaging studies.

MAIN BODY

We performed a systematic literature search on PubMed and Embase databases for imaging studies investigating central and vascular effects of pharmacological and non-pharmacological treatments used to abort and prevent headache attacks. Sixty-three studies were included in the final qualitative analysis. Of these, 54 investigated migraine patients, 4 cluster headache patients and 5 patients with medication overuse headache. Most studies used functional magnetic resonance imaging (MRI) (n = 33) or molecular imaging (n = 14). Eleven studies employed structural MRI and a few used arterial spin labeling (n = 3), magnetic resonance spectroscopy (n = 3) or magnetic resonance angiography (n = 2). Different imaging modalities were combined in eight studies. Despite of the variety of imaging approaches and results, some findings were consistent. This systematic review suggests that triptans may cross the blood-brain barrier to some extent, though perhaps not sufficiently to alter the intracranial cerebral blood flow. Acupuncture in migraine, neuromodulation in migraine and cluster headache patients, and medication withdrawal in patients with medication overuse headache could promote headache improvement by reverting headache-affected pain processing brain areas. Yet, there is currently no clear evidence for where each treatment acts, and no firm imaging predictors of efficacy. This is mainly due to a scarcity of studies and heterogeneous treatment schemes, study designs, subjects, and imaging techniques. In addition, most studies used small sample sizes and inadequate statistical approaches, which precludes generalizable conclusions.

CONCLUSION

Several aspects of headache treatments remain to be elucidated using imaging approaches, such as how pharmacological preventive therapies work, whether treatment-related brain changes may influence therapy effectiveness, and imaging biomarkers of clinical response. In the future, well-designed studies with homogeneous study populations, adequate sample sizes and statistical approaches are needed.

uRP: An integrated research platform for one-stop analysis of medical images

Introduction

Medical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible.

Methods

We present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable.

Results and Discussion

The uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.

Advances in the intraoperative delineation of malignant glioma margin

Surgery plays a critical role in the treatment of malignant glioma. However, due to the infiltrative growth and brain shift, it is difficult for neurosurgeons to distinguish malignant glioma margins with the naked eye and with preoperative examinations. Therefore, several technologies were developed to determine precise tumor margins intraoperatively. Here, we introduced four intraoperative technologies to delineate malignant glioma margin, namely, magnetic resonance imaging, fluorescence-guided surgery, Raman histology, and mass spectrometry. By tracing their detecting principles and developments, we reviewed their advantages and disadvantages respectively and imagined future trends.

Nanotechnology: A Promising Targeted Drug Delivery System for Brain Tumours and Alzheimer's Disease

Nanotechnology is the process of modulating shape and size at the nanoscale to design and manufacture structures, devices, and systems. Nanotechnology's prospective breakthroughs are incredible, and some cannot even be comprehended right now. The blood-brain barrier, which is a prominent physiological barrier in the brain, limits the adequate elimination of malignant cells by changing the concentration of therapeutic agents at the target tissue. Nanotechnology has sparked interest in recent years as a way to solve these issues and improve drug delivery. Inorganic and organic nanomaterials have been found to be beneficial for bioimaging approaches and controlled drug delivery systems. Brain cancer (BC) and Alzheimer's disease (AD) are two of the prominent disorders of the brain. Even though the pathophysiology and pathways for both disorders are different, nanotechnology with common features can deliver drugs over the BBB, advancing the treatment of both disorders. This innovative technology could provide a foundation for combining diagnostics, treatments, and delivery of targeted drugs to the tumour site, further supervising the response and designing and delivering materials by employing atomic and molecular elements. There is currently limited treatment for Alzheimer's disease, and reversing further progression is difficult. Recently, various nanocarriers have been investigated to improve the bioavailability and efficacy of many AD treatment drugs. Nanotechnology-assisted drugs can penetrate the BBB and reach the target tissue. However, further research is required in this field to ensure the safety and efficacy of drug-loaded nanoparticles. The application of nanotechnology in the diagnosis and treatment of brain tumours and Alzheimer's disease is briefly discussed in this review.

Application of nanomaterials in diagnosis and treatment of glioblastoma

Diagnosing and treating glioblastoma patients is currently hindered by several obstacles, such as tumor heterogeneity, the blood-brain barrier, tumor complexity, drug efflux pumps, and tumor immune escape mechanisms. Combining multiple methods can increase benefits against these challenges. For example, nanomaterials can improve the curative effect of glioblastoma treatments, and the synergistic combination of different drugs can markedly reduce their side effects. In this review, we discuss the progression and main issues regarding glioblastoma diagnosis and treatment, the classification of nanomaterials, and the delivery mechanisms of nanomedicines. We also examine tumor targeting and promising nano-diagnosis or treatment principles based on nanomedicine. We also summarize the progress made on the advanced application of combined nanomaterial-based diagnosis and treatment tools and discuss their clinical prospects. This review aims to provide a better understanding of nano-drug combinations, nano-diagnosis, and treatment options for glioblastoma, as well as insights for developing new tools.

Towards differentiation of brain tumor from radiation necrosis using multi-parametric MRI: Preliminary results at 4.7 T using rodent models

BACKGROUND

It remains a clinical challenge to differentiate brain tumors from radiation-induced necrosis in the brain. Despite significant improvements, no single MRI method has been validated adequately in the clinical setting.

METHODS

Multi-parametric MRI (mpMRI) was performed to differentiate 9L gliosarcoma from radiation necrosis in animal models. Five types of MRI methods probed complementary information on different scales i.e., T2 (relaxation), CEST based APT (probing mobile proteins/peptides) and rNOE (mobile macromolecules), qMT (macromolecules), diffusion based ADC (cell density) and SSIFT iAUC (cell size), and perfusion based DSC (blood volume and flow).

RESULTS

For single MRI parameters, iAUC and ADC provide the best discrimination of radiation necrosis and brain tumor. For mpMRI, a combination of iAUC, ADC, and APT shows the best classification performance based on a two-step analysis with the Lasso and Ridge regressions.

CONCLUSION

A general mpMRI approach is introduced to choosing candidate multiple MRI methods, identifying the most effective parameters from all the mpMRI parameters, and finding the appropriate combination of chosen parameters to maximize the classification performance to differentiate tumors from radiation necrosis.

Fluorescein-guided resection for pediatric low-grade gliomas: institutional experience on two cases and a narrative literature review

PURPOSE

Low-grade gliomas compose 30% of pediatric central nervous system tumors and outcomes of disease-free progression, and survival is directly correlated to the extent of resection. The use of sodium fluorescein (Na-Fl) is an intraoperative method in the localization of tumor cells in adult patients to optimize resection. Our purpose is to describe the use of Na-Fl in pediatric low-grade gliomas and its outcomes.

METHODS

Patients under 18 years of age with low-grade gliomas at the author's institution underwent resection with the use of Na-Fl, with review of preoperative imaging findings, intraoperative results, and follow-up. Then, a comprehensive, narrative literature review of the use of Na-Fl in pediatric low-grade glioma was performed.

RESULTS

Our single-institution use of Na-Fl in pediatric patients with suspected low-grade glioma demonstrated excellent results of intraoperative enhancement of tumor cells as well as gross total resection. The literature demonstrated 84% Na-Fl staining and 59.2% of gross total resection in pediatric low-grade gliomas with few small case studies, a range of reported findings, and few side effects.

CONCLUSION

Na-Fl has a promising use in low-grade glioma resection in the pediatric patient population. Further research is warranted, such as randomized controlled studies, to assess Na-Fl as a potential tool in improving resection and long-term favorable outcomes.

Changes in survival over time for primary brain and other CNS tumors in the United States, 2004-2017

Purpose

Despite advances in cancer diagnosis and clinical care, survival for many primary brain and other central nervous system (CNS) tumors remain poor. This study performs a comprehensive survival analysis on these tumors.

Methods

Survival differences were determined utilizing the National Program of Cancer Registries Survival Analytic file for primary brain and CNS tumors. Overall survival and survival of the 5 most common histopathologies, within specific age groups, were determined. Overall survival was compared for three time periods: 2004–2007, 2008–2012, and 2013–2017. Survival differences were evaluated using Kaplan–Meier and multivariable Cox proportional hazards models. Models were adjusted for sex, race/ethnicity, and treatment. Malignant and non-malignant brain tumors were assessed separately.

Results

Among malignant brain and CNS tumor patients overall, there were notable differences in survival by time period among all age groups. Similar differences were noted in non-malignant brain and CNS tumor patients, except for adults (aged 40–64 years), where no survival changes were observed. Survival differences varied within specific histopathologies across age groups. There were improvements in survival in 2008–2012 and 2013–2017, when compared to 2004–2007, in children, AYA, and older adults with malignant tumors, and among older adults with non-malignant tumors.

Conclusion

Overall survival for malignant brain and other CNS tumors improved slightly in 2013–2017 for all age groups as compared to 2004–2007. Significant changes were observed for non-malignant brain and other CNS tumors among older adults. Information regarding survival over time can be utilized to identify population level effects of diagnostic and treatment improvements.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11060-022-04138-w.

Exploring the role of nanomedicines for the therapeutic approach of central nervous system dysfunction: At a glance

In recent decades, research scientists, molecular biologists, and pharmacologists have placed a strong emphasis on cutting-edge nanostructured materials technologies to increase medicine delivery to the central nervous system (CNS). The application of nanoscience for the treatment of neurodegenerative diseases (NDs) such as Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), Huntington’s disease (HD), brain cancer, and hemorrhage has the potential to transform care. Multiple studies have indicated that nanomaterials can be used to successfully treat CNS disorders in the case of neurodegeneration. Nanomedicine development for the cure of degenerative and inflammatory diseases of the nervous system is critical. Nanoparticles may act as a drug transporter that can precisely target sick brain sub-regions, boosting therapy success. It is important to develop strategies that can penetrate the blood–brain barrier (BBB) and improve the effectiveness of medications. One of the probable tactics is the use of different nanoscale materials. These nano-based pharmaceuticals offer low toxicity, tailored delivery, high stability, and drug loading capacity. They may also increase therapeutic effectiveness. A few examples of the many different kinds and forms of nanomaterials that have been widely employed to treat neurological diseases include quantum dots, dendrimers, metallic nanoparticles, polymeric nanoparticles, carbon nanotubes, liposomes, and micelles. These unique qualities, including sensitivity, selectivity, and ability to traverse the BBB when employed in nano-sized particles, make these nanoparticles useful for imaging studies and treatment of NDs. Multifunctional nanoparticles carrying pharmacological medications serve two purposes: they improve medication distribution while also enabling cell dynamics imaging and pharmacokinetic study. However, because of the potential for wide-ranging clinical implications, safety concerns persist, limiting any potential for translation. The evidence for using nanotechnology to create drug delivery systems that could pass across the BBB and deliver therapeutic chemicals to CNS was examined in this study.

Superiority of 11 C-Choline PET/CT in the Delineation of a Rare Intracranial Diffuse Embryonic Tumor

ABSTRACT

Intracranial diffuse embryonal tumor in the adult is rare. We report a young woman with a diffuse embryonal malignancy in the saddle area, which was depicted well by 11 C-choline PET/CT, superior to 18 F-FDG PET/CT and contrast-enhanced MRI. Under the guiding of 11 C-choline PET/CT, the biopsy was successfully performed and the diagnosis was established. This case highlights that 11 C-chione PET/CT may be useful to diagnose and delineate the intracranial diffuse embryonic tumors.

Role of Positron Emission Tomography in Primary Central Nervous System Lymphoma

Simple Summary

Primary central nervous system lymphoma (PCNSL) is a rare but highly aggressive lymphoma with increasing incidence in immunocompetent patients. To date, the only established biomarkers for survival are age and functional status. Currently, the magnetic resonance imaging (MRI) criteria of the International Collaborative Group on Primary Central Nervous System Lymphoma are the only ones recommended for follow-up. However, early occurrence of recurrence after treatment in patients with a complete response on MRI raises the question of its performance in assessing residual disease. While the use of ¹⁸F-fluorodeoxyglucose body positron emission tomography for identification of systemic disease has been established and can be pivotal in patient treatment decisions, the role of brain PET scan is less clear. Here we review the potential role of PET in the management of patients with PCNSL, both at diagnosis and for follow-up under treatment.

Abstract

The incidence of primary central nervous system lymphoma has increased over the past two decades in immunocompetent patients and the prognosis remains poor. A diagnosis and complete evaluation of the patient is needed without delay, but histologic evaluation is not always available and PCNSL can mimic a variety of brain lesions on MRI. In this article, we review the potential role of ¹⁸F-FDG PET for the diagnosis of PCNSL in immunocompetent and immunocompromised patients. Its contribution to systemic assessment at the time of diagnosis has been well established by expert societies over the past decade. In addition, ¹⁸F-FDG provides valuable information for differential diagnosis and outcome prediction. The literature also shows the potential role of ¹⁸F-FDG as a therapeutic evaluation tool during the treatment and the end of the treatment. Finally, we present several new radiotracers that may have a potential role in the management of PCNSL in the future.

Quantifying cerebrospinal fluid dynamics: A review of human neuroimaging contributions to CSF physiology and neurodegenerative disease

Cerebrospinal fluid (CSF), predominantly produced in the ventricles and circulating throughout the brain and spinal cord, is a key protective mechanism of the central nervous system (CNS). Physical cushioning, nutrient delivery, metabolic waste, including protein clearance, are key functions of the CSF in humans. CSF volume and flow dynamics regulate intracranial pressure and are fundamental to diagnosing disorders including normal pressure hydrocephalus, intracranial hypotension, CSF leaks, and possibly Alzheimer's disease (AD). The ability of CSF to clear normal and pathological proteins, such as amyloid-beta (Aβ), tau, alpha synuclein and others, implicates it production, circulation, and composition, in many neuropathologies. Several neuroimaging modalities have been developed to probe CSF fluid dynamics and better relate CSF volume and flow to anatomy and clinical conditions. Approaches include 2-photon microscopic techniques, MRI (tracer-based, gadolinium contrast, endogenous phase-contrast), and dynamic positron emission tomography (PET) using existing approved radiotracers. Here, we discuss CSF flow neuroimaging, from animal models to recent clinical-research advances, summarizing current endeavors to quantify and map CSF flow with implications towards pathophysiology, new biomarkers, and treatments of neurological diseases.

Image-guided/improved diseases management: From immune-strategies and beyond

Timely and accurate assessment and diagnosis are extremely important and beneficial for all diseases, especially for some of the major human disease, such as cancers, cardiovascular diseases, infectious diseases, and neurodegenerative diseases. Limited by the variable disease microenvironment, early imperceptible symptoms, complex immune system interactions, and delayed clinical phenotypes, disease diagnosis and treatment are difficult in most cases. Molecular imaging (MI) techniques can track therapeutic drugs and disease sites in vivo and in vitro in a non-invasive, real-time and visible strategies. Comprehensive visual imaging and quantitative analysis based on different levels can help to clarify the disease process, pathogenesis, drug pharmacokinetics, and further evaluate the therapeutic effects. This review summarizes the application of different MI techniques in the diagnosis and treatment of these major human diseases. It is hoped to shed a light on the development of related technologies and fields.

Optical molecular imaging and theranostics in neurological diseases based on aggregation-induced emission luminogens

Optical molecular imaging and image-guided theranostics benefit from special and specific imaging agents, for which aggregation-induced emission luminogens (AIEgens) have been regarded as good candidates in many biomedical applications. They display a large Stokes shift, high quantum yield, good biocompatibility, and resistance to photobleaching. Neurological diseases are becoming a substantial burden on individuals and society that affect over 50 million people worldwide. It is urgently needed to explore in more detail the brain structure and function, learn more about pathological processes of neurological diseases, and develop more efficient approaches for theranostics. Many AIEgens have been successfully designed, synthesized, and further applied for molecular imaging and image-guided theranostics in neurological diseases such as cerebrovascular disease, neurodegenerative disease, and brain tumor, which help us understand more about the pathophysiological state of brain through noninvasive optical imaging approaches. Herein, we focus on representative AIEgens investigated on brain vasculature imaging and theranostics in neurological diseases including cerebrovascular disease, neurodegenerative disease, and brain tumor. Considering different imaging modalities and various therapeutic functions, AIEgens have great potential to broaden neurological research and meet urgent needs in clinical practice. It will be inspiring to develop more practical and versatile AIEgens as molecular imaging agents for preclinical and clinical use on neurological diseases.

The influence of the blood-brain barrier in the treatment of brain tumours

Brain tumours have a poor prognosis and lack effective treatments. The blood-brain barrier (BBB) represents a major hurdle to drug delivery to brain tumours. In some locations in the tumour, the BBB may be disrupted to form the blood-brain tumour barrier (BBTB). This leaky BBTB enables diagnosis of brain tumours by contrast enhanced magnetic resonance imaging; however, this disruption is heterogeneous throughout the tumour. Thus, relying on the disrupted BBTB for achieving effective drug concentrations in brain tumours has met with little clinical success. Because of this, it would be beneficial to design drugs and drug delivery strategies to overcome the 'normal' BBB to effectively treat the brain tumours. In this review, we discuss the role of BBB/BBTB in brain tumour diagnosis and treatment highlighting the heterogeneity of the BBTB. We also discuss various strategies to improve drug delivery across the BBB/BBTB to treat both primary and metastatic brain tumours. Recognizing that the BBB represents a critical determinant of drug efficacy in central nervous system tumours will allow a more rapid translation from basic science to clinical application. A more complete understanding of the factors, such as BBB-limited drug delivery, that have hindered progress in treating both primary and metastatic brain tumours, is necessary to develop more effective therapies.

Glioblastoma Recurrence Versus Radiotherapy Injury: Combined Model of Diffusion Kurtosis Imaging and 11C-MET Using PET/MRI May Increase Accuracy of Differentiation

PURPOSE

To evaluate the diagnostic potential of decision-tree model of diffusion kurtosis imaging (DKI) and 11C-methionine (11C-MET) PET, for the differentiation of radiotherapy (RT) injury from glioblastoma recurrence.

METHODS

Eighty-six glioblastoma cases with suspected lesions after RT were retrospectively enrolled. Based on histopathology or follow-up, 48 patients were diagnosed with local glioblastoma recurrence, and 38 patients had RT injury between April 2014 and December 2019. All the patients underwent PET/MRI examinations. Multiple parameters were derived based on the ratio of tumor to normal control (TNR), including SUVmax and SUVmean, mean value of kurtosis and diffusivity (MK, MD) from DKI, and histogram parameters. The diagnostic models were established by decision trees. Receiver operating characteristic analysis was used for evaluating the diagnostic accuracy of each independent parameter and all the diagnostic models.

RESULTS

The intercluster correlations of DKI, PET, and texture parameters were relatively weak, whereas the intracluster correlations were strong. Compared with models of DKI alone (sensitivity =1.00, specificity = 0.70, area under the curve [AUC] = 0.85) and PET alone (sensitivity = 0.83, specificity = 0.90, AUC = 0.89), the combined model demonstrated the best diagnostic accuracy (sensitivity = 1.00, specificity = 0.90, AUC = 0.95).

CONCLUSIONS

Diffusion kurtosis imaging, 11C-MET PET, and histogram parameters provide complementary information about tissue. The decision-tree model combined with these parameters has the potential to further increase diagnostic accuracy for the discrimination between RT injury and glioblastoma recurrence over the standard Response Assessment in Neuro-Oncology criteria. 11C-MET PET/MRI may thus contribute to the management of glioblastoma patients with suspected lesions after RT.

Brainstem lesions: MRI review of standard morphological sequences

MRI signal changes in the brainstem are observed in a multitude of disorders including vascular diseases, neoplastic lesions, degenerative diseases, inflammatory disorders, metabolic diseases, infections, and trauma. In some diseases, brainstem involvement is typical and sometimes isolated, while in other diseases, brainstem lesions are only observed occasionally in the presence of other typical extra-brainstem abnormalities. In this review, we will discuss the MRI characteristics of brainstem lesions observed in different disorders associated with frequent and less frequent brainstem involvement. Identification of the origin of the brainstem lesion depends on the exact localisation of the lesion(s) inside the brainstem, the presence and the characteristics of associated lesions seen outside the brainstem, the signal changes on different MRI sequences, the evolution over time of the radiological abnormalities, the history and clinical state of the patient, and other radiological and non-radiological examinations.

Role of Molecular Imaging with PET/MR Imaging in the Diagnosis and Management of Brain Tumors

Gliomas are the most common primary brain tumors. Hybrid PET/MR imaging has revolutionized brain tumor imaging, allowing for noninvasive, simultaneous assessment of morphologic, functional, metabolic, and molecular parameters within the brain. Molecular information obtained from PET imaging may aid in the detection, classification, prognostication, and therapeutic decision making for gliomas. ¹⁸F-fluorodeoxyglucose (FDG) has been widely used in the setting of brain tumor imaging, and multiple techniques may be employed to optimize this methodology. More recently, a number of non-¹⁸F-FDG-PET radiotracers have been applied toward brain tumor imaging and are used in clinical practice.