Modern brain tumor imaging

Challenges, optimization strategies, and future horizons of advanced deep learning approaches for brain lesion segmentation

Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segmentation, object detection, and image classification. This paper offers an overview of recent DL algorithms for brain tumor and stroke segmentation, drawing on literature from 2021 to 2024. It highlights the strengths, limitations, current research challenges, and unexplored areas in imaging-based brain lesion classification based on insights from over 250 recent review papers. Techniques addressing difficulties like class imbalance and multi-modalities are presented. Optimization methods for improving performance regarding computational and structural complexity and processing speed are discussed. These include lightweight neural networks, multilayer architectures, and computationally efficient, highly accurate network designs. The paper also reviews generic and latest frameworks of different brain lesion detection techniques and highlights publicly available benchmark datasets and their issues. Furthermore, open research areas, application prospects, and future directions for DL-based brain lesion classification are discussed. Future directions include integrating neural architecture search methods with domain knowledge, predicting patient survival levels, and learning to separate brain lesions using patient statistics. To ensure patient privacy, future research is anticipated to explore privacy-preserving learning frameworks. Overall, the presented suggestions serve as a guideline for researchers and system designers involved in brain lesion detection and stroke segmentation tasks.

Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation

We have developed a family of biology-based mathematical models of high-grade glioma (HGG), capturing the key features of tumour growth and response to chemoradiation. We now seek to quantify the accuracy of parameter estimation and determine, when given a virtual patient cohort, which model was used to generate the tumours. In this way, we systematically test both the parameter and model identifiability. Virtual patients are generated from unique growth parameters whose growth dynamics are determined by the model family. We then assessed the ability to recover model parameters and select the model used to generate the tumour. We then evaluated the accuracy of predictions using the selected model at four weeks post-chemoradiation. We observed median parameter errors from 0.04% to 72.96%. Our model selection framework selected the model that was used to generate the data in 82% of the cases. Finally, we predicted the growth of the virtual tumours using the selected model resulting in low error at the voxel-level (concordance correlation coefficient (CCC) ranged from 0.66 to 0.99) and global level (percentage error in total tumour cellularity ranged from -12.35% to 0.07%). These results demonstrate the reliability of our framework to identify the most appropriate model under noisy conditions expected in the clinical setting.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

Diagnostic value and efficacy of multimodal magnetic resonance imaging in differentiating radiation necrosis from tumor recurrence in glioblastomas

BackgroundDistinguishing radiation necrosis (RN) from recurrent tumor (RT) in patients with gliomas treated with radiation therapy presents an important clinical dilemma.PurposeTo evaluate the diagnostic performance of multiparametric magnetic resonance imaging (MRI) techniques in distinguishing RN from RT in patients with histologically confirmed glioma treated previously with radiotherapy and chemotherapy or without chemotherapy using a combination of dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MRI, diffusion tensor imaging (DTI), and MR spectroscopy (MRS).Material and MethodsPatients with glioma who developed a new enhancing mass after standard treatment were retrospectively evaluated. Conventional MRI, DTI, DSC, and MRS were performed. The region of interest (ROI) was manually drawn in the enhancing lesions, peri-lesional white matter edema, and the contralateral normal-appearing white matter. Apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), N-acetylaspartate (NAA), choline (Cho), creatine (Cr), NAA/Cr, Cho/NAA, and Cho/Cr were calculated. Two-tailed t-test and receiver operating characteristic (ROC) curve analysis were performed.ResultsIn total, 34 patients with RT and 25 with RN met our inclusion criteria. FA, rCBF, rCBV, Cho/NAA, Cho/Cr were statistically significant between the two groups (P < 0.05). The sensitivity and specificity of FA, rCBF, rCBV, Cho/NAA, and Cho/Cr in the diagnosis of RT were 70.6%, 97.1%, 91.2%, 91.2%, and 82.4% and 64%, 100%, 100%, 96%, and 72% respectively.ConclusionDTI, DSC, and MRS are of great value in the differential diagnosis of RN and RT of glioma. The diagnostic performance of DSC is better than DTI and MRS.

Reducing clinical target volume margins for multifocal glioblastoma: a multi-institutional analysis of patterns of recurrence and treatment response

PURPOSE

No guidelines exist to delineate radiation therapy (RT) targets for the treatment of multiple glioblastoma (mGBM). This study analyzes margins around the gross tumor volume (GTV) to create a clinical target volume (CTV), comparing response parameters and modalities of recurrence. Material and Methods: One-hundred and three mGBM patients with a CTV margin of 2 cm (GTV + 2.0 cm) or 1 cm (GTV + 1.0 cm) were retrospectively analyzed. All patients received a total dose of 59.4-60 Gy in 1.8-2.0 Gy daily fractions, delivered from 4 to 8 weeks after surgery, concomitantly with temozolomide (75 mg/m2). Overall survival (OS) and progression-free survival (PFS) were calculated from the date of surgery until diagnosis of disease progression performed by magnetic resonance imaging and classified as marginal, in-field, or distant, comparing site of progression with dose distribution in RT plan.

RESULTS

OS in mGBM CTV1 group was 11.2 months (95% confidence interval [CI], 10.3-12.1), and 9.2 months in mGBM CTV2 group (95% CI, 9.0-11.3). PFS in mGBM CTV1 group occurred within 8.3 months (95% CI, 7.3-9.3), and 7.3 months in mGBM CTV2 group (95% CI, 6.4-8.1). No difference was observed between the two groups in terms of OS and PFS time distribution. Adjusted to a multivariate Cox risk model, epidermal growth factor receptor amplification resulted a negative prognostic factor for both OS and PFS.

CONCLUSION

In mGBM, the use of a 1 cm CTV expansion seems feasible as it does not significantly affect oncological outcomes and progression outcome.

Utility of Magnetic Resonance Spectroscopy and Perfusion Imaging in Differentiating Brain Tumors From Mimics in a Tertiary Hospital in the Philippines

BACKGROUND

A wide range of non-neoplastic brain lesions can mimic tumors on magnetic resonance imaging (MRI), creating diagnostic challenges. Although MRI is the gold standard for evaluating brain lesions, differentiating between neoplastic and non-neoplastic lesions, as well as high- and low-grade tumors, can be difficult, sometimes leading to unnecessary biopsies. Magnetic resonance spectroscopy (MRS) helps by analyzing biochemical and metabolic processes, especially when conventional MRI falls short. Perfusion MRI (MRP), sensitive to microvasculature, is used to classify tumors, detect strokes, and evaluate other conditions. Both methods are non-invasive alternatives to radiation-based imaging techniques.

METHODS

A retrospective, cross-sectional study of patients with intracranial lesions who underwent MRS and perfusion imaging in a tertiary hospital in the Philippines were analyzed.

RESULTS

The study included 37 patients (28 male, 9 female) aged 19-78, with MRS and MRP data collected. Seizures were the most common symptom (24%), followed by weakness, headache, dizziness, and visual changes. Notably, 24% were asymptomatic. Among all patients examined by MRI with MRS and MRP for intracranial mass lesions, 60% were neoplasm, 21.6% were radiation necrosis, 5.4% were demyelinating lesions, 2.7% were infection, and 2.7% were vascular lesions. Biopsies were performed on nine patients, with seven correlating to MR results. Thirty-five point one percent of patients showed no clinical change, while 18.9% fully recovered. Imaging revealed lesion reduction in 35.1% of patients, no change in 29.7%, and lesion growth in 18.9%.

CONCLUSION

 MRS and MRP complement conventional MRI in distinguishing neoplastic from non-neoplastic lesions, differentiation of types of malignancies, and differentiating tumor recurrence from radiation necrosis, offering a non-invasive way of catechizing the biochemical make-up of intracranial lesions.

Dilated SE-DenseNet for brain tumor MRI classification

In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks' attention mechanisms. This novel approach aims to improve upon state-of-the-art methods of tumor identification. Our model, trained and evaluated on a comprehensive Kaggle brain tumor dataset, demonstrated superior performance over established convolution-based and transformer-based models: ResNet-101, VGG-19, original DenseNet-121, MobileNet-V2, ViT-L/16, and Swin-B across key metrics: F1-score, accuracy, precision, and recall. The results underscore the effectiveness of our architectural enhancements in medical image analysis. Future research directions include optimizing dilation layers and exploring various architectural configurations. The study highlights the significant role of machine learning in improving diagnostic accuracy in medical imaging, with potential applications extending beyond brain tumor detection to other medical imaging tasks.

Applications of MR Finger printing derived T1 and T2 values in Adult brain: A Systematic review

Introduction

Magnetic resonance imaging (MRI) is essential for brain imaging, but conventional methods rely on qualitative contrast, are time-intensive, and prone to variability. Magnetic resonance finger printing (MRF) addresses these limitations by enabling fast, simultaneous mapping of multiple tissue properties like T1, T2. Using dynamic acquisition parameters and a precomputed signal dictionary, MRF provides robust, qualitative maps, improving diagnostic precision and expanding clinical and research applications in brain imaging.

Methods

Database searches were performed through PubMed, Embase, Scopus, Web of science to identify relevant articles focusing on the application of MR finger printing in the adult brain. We utilized the preferred reporting items for systematic reviews and meta-analysis guidelines to extract data from the selected studies.

Results

Nine articles were included in the final review, with a total sample size of 332 participants. In healthy brains, notable regional, sex, age, and hemispheric variations were identified, particularly in the corpus callosum and thalamus. MRF effectively differentiated meningioma subtypes, glioma grades, and IDH mutation status, with T2 values providing particularly predictive for glioma classification. In brain metastases, significant relaxometry differences were noted between normal and lesional tissues. For multiple sclerosis, MRF values correlated with clinical and disability measures, distinguishing relapsing-remitting secondary progressive forms. In traumatic brain injury, longitudinal T1 changes strongly correlated with clinical recovery, surpassing T2 values.

Conclusions

The systematic review highlighted MRD as a groundbreaking technique that enhances neurological diagnosis by simultaneously quantifying T1 and T2 relaxation times. With reduced acquisition times, MRF outperforms conventional MRI in detecting subtle pathologies, distinguishing properties, and providing reliable biomarkers.

Comparative analysis of intraoperative thermal and optical imaging for identification of the human primary sensory cortex

Significance

The precise identification and preservation of functional brain areas during neurosurgery are crucial for optimizing surgical outcomes and minimizing postoperative deficits. Intraoperative imaging plays a vital role in this context, offering insights that guide surgeons in protecting critical cortical regions.

Aim

We aim to evaluate and compare the efficacy of intraoperative thermal imaging (ITI) and intraoperative optical imaging (IOI) in detecting the primary somatosensory cortex, providing a detailed assessment of their potential integration into surgical practice.

Approach

Data from nine patients undergoing tumor resection in the region of the somatosensory cortex were analyzed. Both IOI and ITI were employed simultaneously, with a specific focus on the areas identified as the primary somatosensory cortex (S1 region). The methodologies included a combination of imaging techniques during distinct phases of rest and stimulation, confirmed by electrophysiological monitoring of somatosensory evoked potentials to verify the functional areas identified by both imaging methods. The data were analyzed using a Fourier-based analytical framework to distinguish physiological signals from background noise.

Results

Both ITI and IOI successfully generated reliable activity maps following median nerve stimulation. IOI showed greater consistency across various clinical scenarios, including those involving cortical tumors. Quantitative analysis revealed that IOI could more effectively differentiate genuine neuronal activity from artifacts compared with ITI, which was occasionally prone to false positives in the presence of cortical abnormalities.

Conclusions

ITI and IOI produce comparable functional maps with moderate agreement in Cohen's kappa values. Their distinct physiological mechanisms suggest complementary use in specific clinical scenarios, such as cortical tumors or impaired neurovascular coupling. IOI excels in spatial resolution and mapping reliability, whereas ITI provides additional insights into metabolic changes and tissue properties, especially in pathological areas. Combined, these modalities could enhance the understanding and analysis of functional and pathological processes in complex neurosurgical cases.

Lightweight MRI Brain Tumor Segmentation Enhanced by Hierarchical Feature Fusion

BACKGROUND

Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries.

METHODS

For this issue, a lightweight MRI brain tumor segmentation method, enhanced by hierarchical feature fusion (EHFF), is proposed. This method reduces model parameters while improving segmentation performance by integrating hierarchical features. Initially, a fine-grained feature adjustment network is crafted and guided by global contextual information, leading to the establishment of an adaptive feature learning (AFL) module. This module captures the global features of MRI brain tumor images through macro perception and micro focus, adjusting spatial granularity to enhance feature details and reduce computational complexity. Subsequently, a hierarchical feature weighting (HFW) module is constructed. This module extracts multi-scale refined features through multi-level weighting, enhancing the detailed features of spatial positions and alleviating the lack of attention to local position details in macro perception. Finally, a hierarchical feature retention (HFR) module is designed as a supplementary decoder. This module retains, up-samples, and fuses feature maps from each layer, thereby achieving better detail preservation and reconstruction.

RESULTS

Experimental results on the BraTS 2021 dataset demonstrate that the proposed method surpasses existing methods. Dice similarity coefficients (DSC) for the three semantic categories ET, TC, and WT are 88.57%, 91.53%, and 93.09%, respectively.

Clinical applications, safety profiles, and future developments of contrast agents in modern radiology: A comprehensive review

Contrast agents have transformed the field of medical imaging, significantly enhancing the visualisation of internal structures and improving diagnostic accuracy across X‐rays, computed tomography, magnetic resonance imaging (MRI), and ultrasound. This review explores the historical development, physicochemical properties, and mechanisms of action of iodinated, gadolinium‐based, barium sulfate, microbubble, and nanoparticle contrast agents. It highlights key advancements, including the transition from high‐osmolar to low‐ and iso‐osmolar iodinated agents, the integration of gadolinium in MRI, and the innovative use of microbubbles and nanoparticles. The review critically examines the safety profiles and adverse reactions of these contrast agents, categorising them into hypersensitivity and physiological reactions. It outlines risk factors, common misconceptions, and management strategies for adverse reactions, emphasising the importance of personalised approaches in clinical practice. Additionally, it delves into broader implications, including ethical considerations, environmental impact, and global accessibility of contrast media. The review also discusses technological advancements such as targeted contrast agents and the integration of artificial intelligence to optimise contrast dosage. By synthesising current knowledge and emerging trends, this review underscores the pivotal role of contrast agents in advancing medical imaging. It aims to equip clinicians, researchers, and policymakers with a thorough understanding to enhance diagnostic efficacy, ensure patient safety, and address ethical and environmental challenges, thereby informing future innovations and regulatory frameworks to promote equitable access to advanced imaging technologies globally.

Catalytic Nanoparticles in Biomedical Applications: Exploiting Advanced Nanozymes for Therapeutics and Diagnostics

Catalytic nanoparticles (CNPs) as heterogeneous catalyst reveals superior activity due to their physio-chemical features, such as high surface-to-volume ratio and unique optical, electric, and magnetic properties. The CNPs, based on their physio-chemical nature, can either increase the reactive oxygen species (ROS) level for tumor and antibacterial therapy or eliminate the ROS for cytoprotection, anti-inflammation, and anti-aging. In addition, the catalytic activity of nanozymes can specifically trigger a specific reaction accompanied by the optical feature change, presenting the feasibility of biosensor and bioimaging applications. Undoubtedly, CNPs play a pivotal role in pushing the evolution of technologies in medical and clinical fields, and advanced strategies and nanomaterials rely on the input of chemical experts to develop. Herein, a systematic and comprehensive review of the challenges and recent development of CNPs for biomedical applications is presented from the viewpoint of advanced nanomaterial with unique catalytic activity and additional functions. Furthermore, the biosafety issue of applying biodegradable and non-biodegradable nanozymes and future perspectives are critically discussed to guide a promising direction in developing span-new nanozymes and more intelligent strategies for overcoming the current clinical limitations.

Biological Activity of Natural and Synthetic Peptides as Anticancer Agents

Cancer is one of the leading causes of morbidity and death worldwide, making it a serious global health concern. Chemotherapy, radiotherapy, and surgical treatment are the most used conventional therapeutic approaches, although they show several side effects that limit their effectiveness. For these reasons, the discovery of new effective alternative therapies still represents an enormous challenge for the treatment of tumour diseases. Recently, anticancer peptides (ACPs) have gained attention for cancer diagnosis and treatment. ACPs are small bioactive molecules which selectively induce cancer cell death through a variety of mechanisms such as apoptosis, membrane disruption, DNA damage, immunomodulation, as well as inhibition of angiogenesis, cell survival, and proliferation pathways. ACPs can also be employed for the targeted delivery of drugs into cancer cells. With over 1000 clinical trials using ACPs, their potential for application in cancer therapy seems promising. Peptides can also be utilized in conjunction with imaging agents and molecular imaging methods, such as MRI, PET, CT, and NIR, improving the detection and the classification of cancer, and monitoring the treatment response. In this review we will provide an overview of the biological activity of some natural and synthetic peptides for the treatment of the most common and malignant tumours affecting people around the world.

Predictors for the Differentiation between Glioblastoma, Primary Central Nervous System Lymphoma, and Metastasis in Patients with a Solitary Enhancing Intracranial Mass

Introduction  Differentiation between glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastasis is important in decision-making before surgery. However, these malignant brain tumors have overlapping features. This study aimed to identify predictors differentiating between GBM, PCNSL, and metastasis. Materials and Methods  Patients with a solitary intracranial enhancing tumor and a histopathological diagnosis of GBM, PCNSL, or metastasis were investigated. All patients with intracranial lymphoma had PCNSL without extracranial involvement. Demographic, clinical, and radiographic data were analyzed to determine their associations with the tumor types. Results  The predictors associated with GBM were functional impairment ( p  = 0.001), large tumor size ( p  < 0.001), irregular tumor margin ( p  < 0.001), heterogeneous contrast enhancement ( p  < 0.001), central necrosis ( p  < 0.001), intratumoral hemorrhage ( p  = 0.018), abnormal flow void ( p  < 0.001), and hypodensity component on noncontrast cranial computed tomography (CT) scan ( p  < 0.001). The predictors associated with PCNSL comprised functional impairment ( p  = 0.005), deep-seated tumor location ( p  = 0.006), homogeneous contrast enhancement ( p  < 0.001), absence of cystic appearance ( p  = 0.008), presence of hypointensity component on precontrast cranial T1-weighted magnetic resonance imaging (MRI; p  = 0.027), and presence of isodensity component on noncontrast cranial CT ( p  < 0.008). Finally, the predictors for metastasis were an infratentorial ( p  < 0.001) or extra-axial tumor location ( p  = 0.035), smooth tumor margin ( p  < 0.001), and presence of isointensity component on cranial fluid-attenuated inversion recovery MRI ( p  = 0.047). Conclusion  These predictors may be used to differentiate between GBM, PCNSL, and metastasis, and they are useful in clinical management.

Epidemiology and Management Trends of Neuro-Oncology in Nigeria: A Systematic Review and Pooled Analysis

OBJECTIVE

Access to neuro-oncologic care in Nigeria has grown exponentially since the first reported cases in the mid-1960s. In this systematic review and pooled analysis, we characterize the growth of neurosurgical oncology in Nigeria and build a reference paper to direct efforts to expand this field.

METHODS

We performed an initial literature search of several article databases and gray literature sources. We included and subsequently screened articles published between 1962 and 2021. Several variables were extracted from each study, including the affiliated hospital, the number of patients treated, patient sex, tumor pathology, the types of imaging modalities used for diagnosis, and the interventions used for each individual. Change in these variables was assessed using Chi-squared independence tests and univariate linear regression when appropriate.

RESULTS

A total of 147 studies were identified, corresponding to 5,760 patients. Over 4000 cases were reported in the past 2 decades from 21 different Nigerian institutions. The types of tumors reported have increased over time, with increasingly more patients being evaluated via computed tomography (CT) and magnetic resonance imaging (MRI). There is also a prevalent use of radiotherapy, though chemotherapy remains an underreported treatment modality.

CONCLUSIONS

This study highlights key trends regarding the prevalence and management of neuro-oncologic pathologies within Nigeria. Further studies are needed to continue to learn and guide the future growth of this field in Nigeria.

A review on potential heterocycles for the treatment of glioblastoma targeting receptor tyrosine kinases

Glioblastoma, the most aggressive form of brain tumor, poses significant challenges in terms of treatment success and patient survival. Current treatment modalities for glioblastoma include radiation therapy, surgical intervention, and chemotherapy. Unfortunately, the median survival rate remains dishearteningly low at 12-15 months. One of the major obstacles in treating glioblastoma is the recurrence of tumors, making chemotherapy the primary approach for secondary glioma patients. However, the efficacy of drugs is hampered by the presence of the blood-brain barrier and multidrug resistance mechanisms. Consequently, considerable research efforts have been directed toward understanding the underlying signaling pathways involved in glioma and developing targeted drugs. To tackle glioma, numerous studies have examined kinase-downstream signaling pathways such as RAS-RAF-MEK-ERK-MPAK. By targeting specific signaling pathways, heterocyclic compounds have demonstrated efficacy in glioma therapeutics. Additionally, key kinases including phosphatidylinositol 3-kinase (PI3K), serine/threonine kinase, cytoplasmic tyrosine kinase (CTK), receptor tyrosine kinase (RTK) and lipid kinase (LK) have been considered for investigation. These pathways play crucial roles in drug effectiveness in glioma treatment. Heterocyclic compounds, encompassing pyrimidine, thiazole, quinazoline, imidazole, indole, acridone, triazine, and other derivatives, have shown promising results in targeting these pathways. As part of this review, we propose exploring novel structures with low toxicity and high potency for glioma treatment. The development of these compounds should strive to overcome multidrug resistance mechanisms and efficiently penetrate the blood-brain barrier. By optimizing the chemical properties and designing compounds with enhanced drug-like characteristics, we can maximize their therapeutic value and minimize adverse effects. Considering the complex nature of glioblastoma, these novel structures should be rigorously tested and evaluated for their efficacy and safety profiles.

Using a generative adversarial network to generate synthetic MRI images for multi-class automatic segmentation of brain tumors

Challenging tasks such as lesion segmentation, classification, and analysis for the assessment of disease progression can be automatically achieved using deep learning (DL)-based algorithms. DL techniques such as 3D convolutional neural networks are trained using heterogeneous volumetric imaging data such as MRI, CT, and PET, among others. However, DL-based methods are usually only applicable in the presence of the desired number of inputs. In the absence of one of the required inputs, the method cannot be used. By implementing a generative adversarial network (GAN), we aim to apply multi-label automatic segmentation of brain tumors to synthetic images when not all inputs are present. The implemented GAN is based on the Pix2Pix architecture and has been extended to a 3D framework named Pix2PixNIfTI. For this study, 1,251 patients of the BraTS2021 dataset comprising sequences such as T1w, T2w, T1CE, and FLAIR images equipped with respective multi-label segmentation were used. This dataset was used for training the Pix2PixNIfTI model for generating synthetic MRI images of all the image contrasts. The segmentation model, namely DeepMedic, was trained in a five-fold cross-validation manner for brain tumor segmentation and tested using the original inputs as the gold standard. The inference of trained segmentation models was later applied to synthetic images replacing missing input, in combination with other original images to identify the efficacy of generated images in achieving multi-class segmentation. For the multi-class segmentation using synthetic data or lesser inputs, the dice scores were observed to be significantly reduced but remained similar in range for the whole tumor when compared with evaluated original image segmentation (e.g. mean dice of synthetic T2w prediction NC, 0.74 ± 0.30; ED, 0.81 ± 0.15; CET, 0.84 ± 0.21; WT, 0.90 ± 0.08). A standard paired t-tests with multiple comparison correction were performed to assess the difference between all regions (p < 0.05). The study concludes that the use of Pix2PixNIfTI allows us to segment brain tumors when one input image is missing.

NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data

Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model's transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.

State of the neoadjuvant therapy for glioblastoma multiforme-Where do we stand?

Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor in adults. Despite several investigations in this field, maximal safe resection followed by chemoradiotherapy and adjuvant temozolomide with or without tumor-treating fields remains the standard of care with poor survival outcomes. Many endeavors have failed to make a dramatic change in the outcomes of GBM patients. This study aimed to review the available strategies for newly diagnosed GBM in the neoadjuvant setting, which have been mainly neglected in contrast to other solid tumors.

Least square-support vector machine based brain tumor classification system with multi model texture features

Radiologists confront formidable challenges when confronted with the intricate task of classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript introduces an innovative and highly effective methodology that capitalizes on the capabilities of Least Squares Support Vector Machines (LS-SVM) in tandem with the rich insights drawn from Multi-Scale Morphological Texture Features (MMTF) extracted from T1-weighted MR images. Our methodology underwent meticulous evaluation on a substantial dataset encompassing 139 cases, consisting of 119 cases of aberrant tumors and 20 cases of normal brain images. The outcomes we achieved are nothing short of extraordinary. Our LS-SVM-based approach vastly outperforms competing classifiers, demonstrating its dominance with an exceptional accuracy rate of 98.97%. This represents a substantial 3.97% improvement over alternative methods, accompanied by a notable 2.48% enhancement in Sensitivity and a substantial 10% increase in Specificity. These results conclusively surpass the performance of traditional classifiers such as Support Vector Machines (SVM), Radial Basis Function (RBF), and Artificial Neural Networks (ANN) in terms of classification accuracy. The outstanding performance of our model in the realm of brain tumor diagnosis signifies a substantial leap forward in the field, holding the promise of delivering more precise and dependable tools for radiologists and healthcare professionals in their pivotal role of identifying and classifying brain tumors using MRI imaging techniques.

Brain Tumor Imaging: Review of Conventional and Advanced Techniques

Approaches to central nervous system (CNS) tumor classification and evaluation have undergone multiple iterations over the past few decades, in large part due to our growing understanding of the influence of genetics on tumor behavior and our refinement of brain tumor imaging techniques. Computed tomography and magnetic resonance imaging (MRI) both play a critical role in the diagnosis and monitoring of brain tumors, although MRI has become especially important due to its superior soft tissue resolution. The purpose of this article will be to briefly review the fundamentals of conventional and advanced techniques used in brain tumor imaging. We will also highlight the applications of these imaging tools in the context of commonly encountered tumors based on the most recently updated 2021 World Health Organization (WHO) classification of CNS tumors framework.

DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping

BACKGROUND AND OBJECTIVE

The classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzing high-throughput omics data from different genomic layers. The development of a deep-learning framework enables the integration of multi-omics data to classify the glioma subtypes to support the clinical diagnosis.

RESULTS

Transcriptome and methylome data of glioma patients were preprocessed, and differentially expressed features from both datasets were identified. Subsequently, a Cox regression analysis determined genes and CpGs associated with survival. Gene set enrichment analysis was carried out to examine the biological significance of the features. Further, we identified CpG and gene pairs by mapping them in the promoter region of corresponding genes. The methylation and gene expression levels of these CpGs and genes were embedded in a lower-dimensional space with an autoencoder. Next, ANN and CNN were used to classify subtypes using the latent features from embedding space. CNN performs better than ANN for subtyping lower-grade gliomas (LGG) and glioblastoma multiforme (GBM). The subtyping accuracy of CNN was 98.03% (± 0.06) and 94.07% (± 0.01) in LGG and GBM, respectively. The precision of the models was 97.67% in LGG and 90.40% in GBM. The model sensitivity was 96.96% in LGG and 91.18% in GBM. Additionally, we observed the superior performance of CNN with external datasets. The genes and CpGs pairs used to develop the model showed better performance than the random CpGs-gene pairs, preprocessed data, and single omics data.

CONCLUSIONS

The current study showed that a novel feature selection and data integration strategy led to the development of DeepAutoGlioma, an effective framework for diagnosing glioma subtypes.

Predicting the WHO Grading of Pediatric Brain Tumors Based on Their MRI Appearance: A Retrospective Study

The treatment of central nervous system (CNS) tumors constitutes a significant part of a pediatric neurosurgeon's workload. The classification of such neoplasms spans many entities. These include low- and high-grade lesions, with both occurring in the population of patients under 18 years of age. Magnetic resonance imaging serves as the imaging method of choice for neoplastic lesions of the brain. Through its different modalities, such as T1, T2, T1 C+, apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), fluid-attenuated inversion recovery (FLAIR), etc., it allows the medical team to plan the therapeutic process accordingly while also possibly suggesting the specific tumor subtype prior to obtaining a definitive histological diagnosis. We conducted a retrospective study spanning 32 children treated surgically for brain tumors between July 2021 and January 2023 who had a precise histological diagnosis determined by using the 2021 WHO Classification of Tumors of the Central Nervous System. We divided them into two groups (high-grade and low-grade tumors, i.e., WHO grades 1 and 2, and grades 3 and 4, respectively) and analyzed their demographic data and preoperative MRI results. This was done using the following criteria: sub or supratentorial location of the tumor; lesion is circumscribed or infiltrating; solid, cystic, or mixed solid and cystic character of the tumor; number of compartments in cystic lesions; signal intensity (hypo-, iso-, hyperintensity sequences: T1, T2, T1 C+); presence of restricted diffusion; the largest diameter of the solid component and/or the largest diameter of the largest cyst in the transverse section. Then, we examined the results to find any correlation between the lesions' morphologies and their final assigned degree of malignancy. We found that the only radiological criteria correlating with the final WHO grade of the tumor were an infiltrative pattern of growth (25% of low-grade lesions, 75% of high-grade; p = 0.006) and the presence of a cystic component in the tumor (in 68.75% of low-grade tumors and 43.75% of high-grade tumors; p = 0.041). The only other feature close to attaining statistical significance was diffusion restriction (33.3% of low-grade tumors, 66.7% high-grade; p = 0.055). Older children tended to present with tumors of lower degrees of malignancy, and there was a predominance of female patients (21 female, 11 male).

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor

The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods.

Computed cancer magnetic susceptibility imaging (canχ): Computational inverse mappings of cancer MRI

PURPOSE

We report a new cancer imaging modality in the contrast of tissue intrinsic susceptibility property by computed inverse magnetic resonance imaging (CIMRI).

METHODS

In MRI physics, an MRI signal is formed from tissue magnetism source (primarily magnetic susceptibility χ) through a cascade of MRI-introduced transformations (e.g. dipole-convolved magnetization) involving MRI setting parameters (e.g. echo time). In two-step computational inverse mappings (from phase image to internal fieldmap to susceptibility source), we could remove the MRI transformations and imaging parameters, thereby obtaining χ-depicted cancer images (canχ) from MRI phase images. Canχ is computationally implemented from clinical cancer MRI phase image by CIMRI.

RESULTS

As a result of MRI effect removal through computational inverse mappings, the reconstructed χ map (canχ) could provide a new cancerous tissue depiction in contrast of tissue intrinsic magnetism property (i.e. diamagnetism vs paramagnetism) as in an off-scanner state (e.g. in absence of main field B₀).

CONCLUSION

Through retrospective clinical cancer MRI data analysis, we reported on the canχ method in technical details and demonstrated its feasibility of innovating cancer imaging in the contrast of tissue intrinsic paramagnetism/diamagnetism property (in a cancer tissue state free from MRI effect).

Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives

MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.

Performance of deep learning in classifying malignant primary and metastatic brain tumors using different MRI sequences: A medical analysis study

BACKGROUND

Malignant Primary Brain Tumor (MPBT) and Metastatic Brain Tumor (MBT) are the most common types of brain tumors, which require different management approaches. Magnetic Resonance Imaging (MRI) is the most frequently used modality for assessing the presence of these tumors. The utilization of Deep Learning (DL) is expected to assist clinicians in classifying MPBT and MBT more effectively.

OBJECTIVE

This study aims to examine the influence of MRI sequences on the classification performance of DL techniques for distinguishing between MPBT and MBT and analyze the results from a medical perspective.

METHODS

Total 1,360 images performed from 4 different MRI sequences were collected and preprocessed. VGG19 and ResNet101 models were trained and evaluated using consistent parameters. The performance of the models was assessed using accuracy, sensitivity, and other precision metrics based on a confusion matrix analysis.

RESULTS

The ResNet101 model achieves the highest accuracy of 83% for MPBT classification, correctly identifying 90 out of 102 images. The VGG19 model achieves an accuracy of 81% for MBT classification, accurately classifying 86 out of 102 images. T2 sequence shows the highest sensitivity for MPBT, while T1C and T1 sequences exhibit the highest sensitivity for MBT.

CONCLUSIONS

DL models, particularly ResNet101 and VGG19, demonstrate promising performance in classifying MPBT and MBT based on MRI images. The choice of MRI sequence can impact the sensitivity of tumor detection. These findings contribute to the advancement of DL-based brain tumor classification and its potential in improving patient outcomes and healthcare efficiency.

Understanding and modeling nerve-cancer interactions

The peripheral nervous system plays an important role in cancer progression. Studies in multiple cancer types have shown that higher intratumoral nerve density is associated with poor outcomes. Peripheral nerves have been shown to directly regulate tumor cell properties, such as growth and metastasis, as well as affect the local environment by modulating angiogenesis and the immune system. In this Review, we discuss the identity of nerves in organs in the periphery where solid tumors grow, the known mechanisms by which nerve density increases in tumors, and the effects these nerves have on cancer progression. We also discuss the strengths and weaknesses of current in vitro and in vivo models used to study nerve-cancer interactions. Increased understanding of the mechanisms by which nerves impact tumor progression and the development of new approaches to study nerve-cancer interactions will facilitate the discovery of novel treatment strategies to treat cancer by targeting nerves.

Brain tumor classification based on neural architecture search

Brain tumor is a life-threatening disease and causes about 0.25 million deaths worldwide in 2020. Magnetic Resonance Imaging (MRI) is frequently used for diagnosing brain tumors. In medically underdeveloped regions, physicians who can accurately diagnose and assess the severity of brain tumors from MRI are highly lacking. Deep learning methods have been developed to assist physicians in detecting brain tumors from MRI and determining their subtypes. In existing methods, neural architectures are manually designed by human experts, which is time-consuming and labor-intensive. To address this problem, we propose to automatically search for high-performance neural architectures for classifying brain tumors from MRIs, by leveraging a Learning-by-Self-Explanation (LeaSE) architecture search method. LeaSE consists of an explainer model and an audience model. The explainer aims at searching for a highly performant architecture by encouraging the architecture to generate high-fidelity explanations of prediction outcomes, where explanations' fidelity is evaluated by the audience model. LeaSE is formulated as a four-level optimization problem involving a sequence of four learning stages which are conducted end-to-end. We apply LeaSE for MRI-based brain tumor classification, including four classes: glioma, meningioma, pituitary tumor, and healthy, on a dataset containing 3264 MRI images. Results show that our method can search for neural architectures that achieve better classification accuracy than manually designed deep neural networks while having fewer model parameters. For example, our method achieves a test accuracy of 90.6% and an AUC of 95.6% with 3.75M parameters while the accuracy and AUC of a human-designed network-ResNet101-is 84.5% and 90.1% respectively with 42.56M parameters. In addition, our method outperforms state-of-the-art neural architecture search methods.

Temporal radiographic and histological study of necrosis development in a mouse glioblastoma model

Tumor necrosis is a poor prognostic marker in glioblastoma (GBM) and a variety of other solid cancers. Accumulating evidence supports that necrosis could facilitate tumor progression and resistance to therapeutics. GBM necrosis is typically first detected by magnetic resonance imaging (MRI), after prominent necrosis has already formed. Therefore, radiological appearances of early necrosis formation and the temporal-spatial development of necrosis alongside tumor progression remain poorly understood. This knowledge gap leads to a lack of reliable radiographic diagnostic/prognostic markers in early GBM progression to detect necrosis. Recently, we reported an orthotopic xenograft GBM murine model driven by hyperactivation of the Hippo pathway transcriptional coactivator with PDZ-binding motif (TAZ) which recapitulates the extent of GBM necrosis seen among patients. In this study, we utilized this model to perform a temporal radiographic and histological study of necrosis development. We observed tumor tissue actively undergoing necrosis first appears more brightly enhancing in the early stages of progression in comparison to the rest of the tumor tissue. Later stages of tumor progression lead to loss of enhancement and unenhancing signals in the necrotic central portion of tumors on T1-weighted post-contrast MRI. This central unenhancing portion coincides with the radiographic and clinical definition of necrosis among GBM patients. Moreover, as necrosis evolves, two relatively more contrast-enhancing rims are observed in relationship to the solid enhancing tumor surrounding the central necrosis in the later stages. The outer more prominently enhancing rim at the tumor border probably represents the infiltrating tumor edge, and the inner enhancing rim at the peri-necrotic region may represent locally infiltrating immune cells. The associated inflammation at the peri-necrotic region was further confirmed by immunohistochemical study of the temporal development of tumor necrosis. Neutrophils appear to be the predominant immune cell population in this region as necrosis evolves. This study shows central, brightly enhancing areas associated with inflammation in the tumor microenvironment may represent an early indication of necrosis development in GBM.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Current understanding and future potential applications of cerebral microvascular imaging in infants

Microvascular imaging is an advanced Doppler ultrasound technique that detects slow flow in microvessels by suppressing clutter signal and motion-related artifacts. The technique has been applied in several conditions to assess organ perfusion and lesion characteristics. In this pictorial review, we aim to describe current knowledge of the technique, particularly its diagnostic utility in the infant brain, and expand on the unexplored but promising clinical applications of microvascular imaging in the brain with case illustrations.

Comprehensive Account of Sodium Imaging and Spectroscopy for Brain Research

Sodium (²³Na) is a vital component of neuronal cells and plays a key role in various signal transmission processes. Hence, information on sodium distribution in the brain using magnetic resonance imaging (MRI) provides useful information on neuronal health. ²³Na MRI and MR spectroscopy (MRS) improve the diagnosis, prognosis, and clinical monitoring of neurological diseases but confront some inherent limitations that lead to low signal-to-noise ratio, longer scan time, and diminished partial volume effects. Recent advancements in multinuclear MR technology have helped in further exploration in this domain. We aim to provide a comprehensive description of ²³Na MRI and MRS for brain research including the following aspects: (a) theoretical background for understanding ²³Na MRI and MRS fundamentals; (b) technological advancements of ²³Na MRI with respect to pulse sequences, RF coils, and sodium compartmentalization; (c) applications of ²³Na MRI in the early diagnosis and prognosis of various neurological disorders; (d) structural-chronological evolution of sodium spectroscopy in terms of its numerous applications in human studies; (e) the data-processing tools utilized in the quantitation of sodium in the respective anatomical regions.

18F-FSPG PET/CT Imaging of System xC- Transporter Activity in Patients with Primary and Metastatic Brain Tumors

Background The PET tracer (4S)-4-(3-[¹⁸F]fluoropropyl)-l-glutamate (¹⁸F-FSPG) targets the system x_C^- cotransporter, which is overexpressed in various tumors. Purpose To assess the role of ¹⁸F-FSPG PET/CT in intracranial malignancies. Materials and Methods Twenty-six patients (mean age, 54 years ± 12; 17 men; 48 total lesions) with primary brain tumors (n = 17) or brain metastases (n = 9) were enrolled in this prospective, single-center study (ClinicalTrials.gov identifier: NCT02370563) between November 2014 and March 2016. A 30-minute dynamic brain ¹⁸F-FSPG PET/CT scan and a static whole-body (WB) ¹⁸F-FSPG PET/CT scan at 60-75 minutes were acquired. Moreover, all participants underwent MRI, and four participants underwent fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET imaging. PET parameters and their relative changes were obtained for all lesions. Kinetic modeling was used to estimate the ¹⁸F-FSPG tumor rate constants using the dynamic and dynamic plus WB PET data. Imaging parameters were correlated to lesion outcomes, as determined with follow-up MRI and/or pathologic examination. The Mann-Whitney U test or Student t test was used for group mean comparisons. Receiver operating characteristic curve analysis was used for performance comparison of different decision measures. Results ¹⁸F-FSPG PET/CT helped identify all 48 brain lesions. The mean tumor-to-background ratio (TBR) on the whole-brain PET images at the WB time point was 26.6 ± 24.9 (range: 2.6-150.3). When ¹⁸F-FDG PET was performed, ¹⁸F-FSPG permitted visualization of non-¹⁸F-FDG-avid lesions or allowed better lesion differentiation from surrounding tissues. In participants with primary brain tumors, the predictive accuracy of the relative changes in influx rate constant K_i and maximum standardized uptake value to discriminate between poor and good lesion outcomes were 89% and 81%, respectively. There were significant differences in the ¹⁸F-FSPG uptake curves of lesions with good versus poor outcomes in the primary brain tumor group (P < .05) but not in the brain metastases group. Conclusion PET/CT imaging with (4S)-4-(3-[¹⁸F]fluoropropyl)-l-glutamate (¹⁸F-FSPG) helped detect primary brain tumors and brain metastases with a high tumor-to-background ratio. Relative changes in ¹⁸F-FSPG uptake with multi-time-point PET appear to be helpful in predicting lesion outcomes. Clinical trial registration no. NCT02370563 © RSNA, 2022 Online supplemental material is available for this article.

Diagnostic performance of DSC perfusion MRI to distinguish tumor progression and treatment-related changes: a systematic review and meta-analysis

Background

In patients with high-grade glioma (HGG), true disease progression and treatment-related changes often appear similar on magnetic resonance imaging (MRI), making it challenging to evaluate therapeutic response. Dynamic susceptibility contrast (DSC) MRI has been extensively studied to differentiate between disease progression and treatment-related changes. This systematic review evaluated and synthesized the evidence for using DSC MRI to distinguish true progression from treatment-related changes.

Methods

We searched Ovid MEDLINE and the Ovid MEDLINE in-process file (January 2005-October 2019) and the reference lists. Studies on test performance of DSC MRI using relative cerebral blood volume in HGG patients were included. One investigator abstracted data, and a second investigator confirmed them; two investigators independently assessed study quality. Meta-analyses were conducted to quantitatively synthesize area under the receiver operating curve (AUROC), sensitivity, and specificity.

Results

We screened 1177 citations and included 28 studies with 638 patients with true tumor progression, and 430 patients with treatment-related changes. Nineteen studies reported AUROC and the combined AUROC is 0.85 (95% CI, 0.81-0.90). All studies contributed data for sensitivity and specificity, and the pooled sensitivity and specificity are 0.84 (95% CI, 0.80-0.88), and 0.78 (95% CI, 0.72-0.83). Extensive subgroup analyses based on study, treatment, and imaging characteristics generally showed similar results.

Conclusions

There is moderate strength of evidence that relative cerebral blood volume obtained from DSC imaging demonstrated "excellent" ability to discriminate true tumor progression from treatment-related changes, with robust sensitivity and specificity.

The Application of Image Segmentation to Determine the Ratio of Peritumoral Edema Area to Tumor Area on Primary Malignant Brain Tumor and Metastases through Conventional Magnetic Resonance Imaging

BACKGROUND: Primary malignant brain tumor and metastases on the brain have a similar pattern in conventional Magnetic Resonance Imaging (MRI), even though both require entirely different treatment and management. The pathophysiological difference of peritumoral edema can help to distinguish the case of primary malignant brain tumor and brain metastases. AIM: This study aimed to analyze the ratio of the area of peritumoral edema to the tumor using Otsu’s method of image segmentation technique with a user-friendly Graphical User Interface (GUI). METODS: Data was prepared by obtaining the examination results of Anatomical Pathology and MRI imaging. The area of peritumoral edema was identified from MRI image segmentation with T2/FLAIR sequence. While the area of tumor was identified using MRI image segmentation with T1 sequence. RESULTS: The Mann-Whitney test was employed to analyze the ratio of the area of peritumoral edema to tumor on both groups. Data testing produced a significance level of 0.013 (p < 0.05) with a median value (Nmax-Nmin) of 1.14 (3.31-0.08) for the primary malignant brain tumor group and a median value (Nmax-Nmin) of 1.17 (10.30-0.90) for the brain metastases group. CONCLUSIONS: There was a significant difference in the ratio of the area of peritumoral edema to the area of tumor from both groups, in which brain metastases have a greater value than the primary malignant brain tumor.

An application of neutrosophic hypersoft mapping to diagnose brain tumor and propose appropriate treatment

Brain tumors are one of the leading causes of death around the globe. More than 10 million people fall prey to it every year. This paper aims to characterize the discussions related to the diagnosis of tumors with their related problems. After examining the side effects of tumors, it encases similar indications, and it is hard to distinguish the precise type of tumors with their seriousness. Since in practical assessment, the indeterminacy and falsity parts are frequently dismissed, and because of this issue, it is hard to notice the precision in the patient’s progress history and cannot foresee the period of treatment. The Neutrosophic Hypersoft set (NHS) and the NHS mapping with its inverse mapping has been design to overcome this issue since it can deal with the parametric values of such disease in more detail considering the sub-parametric values; and their order and arrangement. These ideas are capable and essential to analyze the issue properly by interfacing it with scientific modeling. This investigation builds up a connection between symptoms and medicines, which diminishes the difficulty of the narrative. A table depending on a fuzzy interval between [0, 1] for the sorts of tumors is constructed. The calculation depends on NHS mapping to adequately recognize the disease and choose the best medication for each patient’s relating sickness. Finally, the generalized NHS mapping is presented, which will encourage a specialist to extricate the patient’s progress history and to foresee the time of treatment till the infection is relieved.

Stereotactic biopsy for lesions in brainstem and deep brain: a single-center experience of 72 cases

Stereotactic biopsies for lesions in the brainstem and deep brain are rare. This study aimed to summarize our 6-year experience in the accurate diagnosis of lesions in the brain stem and deep brain and to discuss the technical note and strategies. From December 2011 to January 2018, 72 cases of intracranial lesions in the brainstem or deep in the lobes undergoing stereotactic biopsy were retrospectively reviewed. An individualized puncture path was designed based on the lesion's location and the image characteristics. The most common biopsy targets were deep in the lobes (43 cases, 59.7%), including frontal lobe (33 cases, 45.8%), temporal lobe (4 cases, 5.6%), parietal lobe (3 cases, 4.2%), and occipital lobe (3 cases, 4.2 %). There were 12 cases (16.7%) of the brainstem, including 8 cases (11.1%) of midbrain, and 4 cases (5.6%) of pons or brachium pontis. Other targets included internal capsule (2 cases, 2.8%), thalamus (3 cases, 4.2%), and basal ganglion (12 cases, 16.7%). As for complications, one patient developed acute intracerebral hemorrhage in the biopsy area at 2 h post-operation, and one patient had delayed intracerebral hemorrhage at 7 days post-operation. The remaining patients recovered well after surgery. There was no surgery-related death. The CT-MRI-guided stereotactic biopsy of lesions in the brainstem or deep in the brain has the advantages of high safety, accurate diagnosis, and low incidence of complications. It plays a crucial role in the diagnosis of atypical, microscopic, diffuse, multiple, and refractory lesions.

A Neural Network Approach to Identify Glioblastoma Progression Phenotype from Multimodal MRI

The phenotypes of glioblastoma (GBM) progression after treatment are heterogeneous in both imaging and clinical prognosis. This study aims to apply radiomics and neural network analysis to preoperative multimodal MRI data to characterize tumor progression phenotypes. We retrospectively reviewed 41 patients with newly diagnosed cerebral GBM from 2009-2016 who comprised the machine learning training group, and prospectively included 18 patients from 2017-2018 for data validation. Preoperative MRI examinations included structural MRI, diffusion tensor imaging, and perfusion MRI. Tumor progression patterns were categorized as diffuse or localized. A supervised machine learning model and neural network-based models (VGG16 and ResNet50) were used to establish the prediction model of the pattern of progression. The diffuse progression pattern showed a significantly worse prognosis regarding overall survival (p = 0.032). A total of 153 of the 841 radiomic features were used to classify progression patterns using different machine learning models with an overall accuracy of 81% (range: 77.5-82.5%, AUC = 0.83-0.89). Further application of the pretrained ResNet50 and VGG 16 neural network models demonstrated an overall accuracy of 93.1 and 96.1%. The progression patterns of GBM are an important prognostic factor and can potentially be predicted by combining multimodal MR radiomics with machine learning.

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T1-weighted, and T2-FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: - 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.

Simultaneously acquired PET and ASL imaging biomarkers may be helpful in differentiating progression from pseudo-progression in treated gliomas

OBJECTIVES

The aim of this work was investigating the methods based on coupling cerebral perfusion (ASL) and amino acid metabolism ([¹⁸F]DOPA-PET) measurements to evaluate the diagnostic performance of PET/MRI in glioma follow-up.

METHODS

Images were acquired using a 3-T PET/MR system, on a prospective cohort of patients addressed for possible glioma progression. Data were preprocessed with statistical parametric mapping (SPM), including registration on T1-weighted images, spatial and intensity normalization, and tumor segmentation. As index tests, tumor isocontour maps of [¹⁸F]DOPA-PET and ASL T-maps were created and metabolic/perfusion abnormalities were evaluated with the asymmetry index z-score. SPM map analysis of significant size clusters and semi-quantitative PET and ASL map evaluation were performed and compared to the gold standard diagnosis. Lastly, ASL and PET topography of significant clusters was compared to that of the initial tumor.

RESULTS

Fifty-eight patients with unilateral treated glioma were included (34 progressions and 24 pseudo-progressions). The tumor isocontour maps and T-maps showed the highest specificity (100%) and sensitivity (94.1%) for ASL and [¹⁸F]DOPA analysis, respectively. The sensitivity of qualitative SPM maps and semi-quantitative rCBF and rSUV analyses were the highest for glioblastoma.

CONCLUSION

Tumor isocontour T-maps and combined analysis of CBF and [¹⁸F]DOPA-PET uptake allow achieving high diagnostic performance in differentiating between progression and pseudo-progression in treated gliomas. The sensitivity is particularly high for glioblastomas.

KEY POINTS

• Applied separately, MRI and PET imaging modalities may be insufficient to characterize the brain glioma post-therapeutic profile. • Combined ASL and [¹⁸F]DOPA-PET map analysis allows differentiating between tumor progression and pseudo-progression.

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.