Brain Cancer Imaging Research Articles

Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights.

Artificial intelligence (AI) is increasingly being used in the medical field, specifically for brain cancer imaging. In this review, we explore how AI-powered medical imaging can impact the diagnosis, prognosis, and treatment of brain cancer. We discuss various AI techniques, including deep learning and causality learning, and their relevance. Additionally, we examine current applications that provide practical solutions for detecting, classifying, segmenting, and registering brain tumors. Although challenges such as data quality, availability, interpretability, transparency, and ethics persist, we emphasise the enormous potential of intelligent applications in standardising procedures and enhancing personalised treatment, leading to improved patient outcomes. Innovative AI solutions have the power to revolutionise neuro-oncology by enhancing the quality of routine clinical practice.

A Novel Technique for Brain Tumor Detection and Classification using T1-Weighted MR Image

Brain tumors are particularly perilous because they form when cells in the brain multiply uncontrollably within the skull. Therefore, a fast and accurate method of diagnosing tumors is crucial for the patient’s health. This study proposes a method for evaluating brain cancer images. The phases of implementation for the proposed work are as follows: In the first phase, we compiled a set of specialized feature vector descriptions for advanced classification tasks by employing both deep learning (DL) and conventional feature extraction techniques. In the second phase, we employ a proposed convolutional neural network (CNN) approach and a traditional subset of features from a genetic algorithm (GA) to select our deep features. The third phase involves using the fusion method to merge the prioritized features. Finally, determine whether the brain image is normal or abnormal. The results showed that the proposed method successfully classified objects accurately and revealed their robustness across different ages and acquisition protocols. According to the results, the classification accuracy of the support vector machines (SVM) classifier has significantly improved by combining conventional features and deep learning features (DLF), achieving an accuracy of up to 86.50% using the T1 weighted brain MR image.

Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review.

Transformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. This study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. This review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. Of the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer-based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. It can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer.

Optimization of Brain Cancer Images with Some Noise Models

Optimization of Brain Cancer Images with Some Noise Models

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification.

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

Dual-targeted transferrin and AS1411 aptamer conjugated micelles for improved therapeutic efficacy and imaging of brain cancer

Brain tumors represent an aggressive form of cancer, posing significant challenges in achieving complete remission. Development of advanced therapies is crucial for improving clinical outcomes in cancer patients. This study aimed to create a novel treatment approach using dual-targeted transferrin (TF) and AS1411 conjugated micelles, designed to enhance therapeutic effectiveness of docetaxel (DTX) and facilitate gadolinium (Gd) based imaging in brain cancer. Micelles were prepared using a slightly modified solvent-casting method, and the dual-targeting ligands were attached to the micelle’s surface through a physical adsorption process. Average particle size of micelles ranged from 117.49 ± 3.90–170.38 ± 3.39 nm, with a low polydispersity index. Zeta potential ranged from − 1.5 ± 0.02 to − 18.7 ± 0.04 mV. Encapsulation efficiency of DTX in micelles varied from 92.64 ± 4.22–79.77 ± 4.13 %. Simultaneously, encapsulation of Gd in micelles was found to be 48.27 ± 3.18–58.52 ± 3.17, respectively. In-vitro drug release studies showed a biphasic sustained release profile, with DTX and Gd release continuing up to 72 h with their t50 % at 4.95, 11.29, and 24.14 h for GDTP, GDTP-TF and GDTP-TF-AS1411 micelles, respectively. Cytotoxicity effect of GDTP-TF-AS1411 micelles has shown significant improvement (P < 0.001) and reduced IC50 value up to 0.19 ± 0.14 μg/ml compared to Taxotere® (2.73 ± 0.73 μg/ml). Theranostic study revealed higher accumulation of GDTP-TF and GDTP-TF-AS1411 micelles free GD treated animal brains. The AUC of GDTP-TF-AS1411 micelles exhibited 23.79 ± 17.82 μg.h/ml higher than Taxotere® (14.14 ± 10.59 μg.h/ml). These findings direct enhanced effectiveness in brain cancer therapy leading to improved therapeutics in brain cancer patients. The combined targeted ligands and therapeutic agents strategy can direct advancement in brain cancer therapy and offer improved therapy for patients.

Brain tumor diagnosis using a step-by-step methodology based on courtship learning-based water strider algorithm

Medical imaging plays an essential function in the management of brain tumors for diagnosis and assortment. Today, MRI images are used to diagnose brain tumors because they show the structure of a normal brain in great detail. Brain tumor segmentation from MRI is a challenging procedure that has certain ups and downs. The tumor to be diagnosed has a flexible and complex structure in the image, it is completely different in size and location and from disease to patient, and accordingly, various algorithms were suggested. In this paper, an automated method is introduced to achieve higher speed and appropriate accuracy in the diagnosis of brain tumors. The present study proposed a new pipeline technique to automatic diagnosis of the brain cancer images from the MRI. Extraction of a feature was conducted to the input images followed by preprocessing to reduce the complexity of the system. The features are then entered into an optimal ANN to provide an efficient diagnosis system. Both feature selection and classification are established by an improved metaheuristic, named courtship learning-based water strider algorithm. The proposed method is then enforced on the “Brain-Tumor-Progression” database and the outcomes have been validated by comparing with some formerly published methods. Simulation consequences indicated the higher efficiency of the suggested method against the other analyzed procedures.

Smart Nanoformulations for Brain Cancer Theranostics: Challenges and Promises.

Despite their low prevalence, brain tumors are among the most lethal cancers. They are extremely difficult to diagnose, monitor and treat. Conventional anti-cancer strategies such as radio- and chemotherapy have largely failed, and to date, the development of even a single effective therapeutic strategy against central nervous system (CNS) tumors has remained elusive. There are several factors responsible for this. Brain cancers are a heterogeneous group of diseases with variable origins, biochemical properties and degrees of invasiveness. High-grade gliomas are amongst the most metastatic and invasive cancers, which is another reason for therapeutic failure in their case. Moreover, crossing the blood brain and the blood brain tumor barriers has been a significant hindrance in the development of efficient CNS therapeutics. Cancer nanomedicine, which encompasses the application of nanotechnology for diagnosis, monitoring and therapy of cancers, is a rapidly evolving field of translational medicine. Nanoformulations, because of their extreme versatility and manipulative potential, are emerging candidates for tumor targeting, penetration and treatment in the brain. Moreover, suitable nanocarriers can be commissioned for theranostics, a combinatorial personalized approach for simultaneous imaging and therapy. This review first details the recent advances in novel bioengineering techniques that provide promising avenues for circumventing the hurdles of delivering the diagnostic/therapeutic agent to the CNS. The authors then describe in detail the tremendous potential of utilizing nanotechnology, particularly nano-theranostics for brain cancer imaging and therapy, and outline the different categories of recently developed next-generation smart nanoformulations that have exceptional potential for making a breakthrough in clinical neuro-oncology therapeutics.

M-UNet for Segmentation of Brain Images

A mass or progress of unusual cells in the brain is termed as brain tumor. Several categories of brain tumors occur in human. Certain types of brain tumors are non-cancerous which is indicated as benign, whereas certain brain tumors are cancerous, called malignant. In this paper, images are segmented using Modified- Universal Education and Training, Ltd. (M-UNet). The main aim is to investigate network architectures (MUNet) based on deep learning which is used for enhanced classification and segmentation of brain tumor images. Segmentation of brain cancer images is the procedure of splitting the tumor from usual brain muscles; in medical routine, it offers valuable information for analysis and treatment planning. It is still a complex job due to the asymmetrical arrangement and perplexing borders of tumors. The Convolutional Neural Network (CNN) and Universal Education and Training, Ltd.(UNet) are considered to be notable techniques in segmentation of images. The concept of CNN is a dominant technique for recognition of images and forecasts. CNN is typically utilized for brain cancer separation, classification, and estimate of existence period for infected people. UNet is a familiar image separation method established mainly for analyzing clinical images that can exactly divide images using an unusual quantity of preparation facts. These qualities make UNet efficient in clinical imaging forum and support wide-ranging implementation of UNet in performing separation jobs in therapeutic imaging. M-UNet is recommended in this paper to slice the given input images in a well-defined manner. Experimental results have shown that the proposed M-UNet achieves accuracy of 97% which is notably better when compared to the existing CNN and UNet techniques. The results are also compared based on Dice Coefficient, Jaccard Coefficient and time period. It is evident that the M-UNet outperforms the existing techniques on all assessment parameters. A novel frame work using M-UNet that includes extraction of both global and local features is proposed to increase the segmentation accuracy. The outcomes show better performance in segmenting the 5 tumor areas on the huge BRATs 2018 dataset. The performance of the network is assessed by comparing the forecast segmentation of tumor areas to the ground truth offered by the dataset. Dice Similarity Coefficient (DSC) and Jaccard Coefficient (JC) give the like nessamid the anticipated tumor area and ground truth by associating the overlay areas. In this paper, brain image segmentation is performed using UNet and M-UNet methods and the proposed method efficiently predicts the border of then segmentation pixel..

A Novel Approach in Bio-Medical Image Segmentation for Analyzing Brain Cancer Images with U-NET Semantic Segmentation and TPLD Models Using SVM

A Novel Approach in Bio-Medical Image Segmentation for Analyzing Brain Cancer Images with U-NET Semantic Segmentation and TPLD Models Using SVM

Vasoprobe for brain cancer imaging.

Vasoprobe for brain cancer imaging.

Assessment of Hybrid Gene Selection Using the VIKOR Method

One of the crucial processes in the classification of microarray data is gene selection. Particle swarm optimization is gaining popularity as a powerful method for genetic selection since it uses fewer parameters and doesn't use complicated evolution operators. Some particle swarm optimization-based gene selection techniques may select non-optimal genes with a high probability because particle swarm optimization is likely to converge to local minima, which may result in premature convergence. It is still difficult to choose prognostic genes with low redundancy without excluding crucial genes.Research significance: Exposure to ionising radiation has been associated to leukaemia, and gene selection often involves two phases. All of the survivors of the World War II atomic bombings in Japan have been shown to have a significant incidence of leukaemia. Functional brain cancer imaging with positron emission tomography and magnetic resonance imaging is widely used to map regional changes in the brain function advanced over Last decade. Appreciating unique contribution of aerobics glycol sis new insights into the role of brain energy metabolism atropines signal transduction have emerged. Diagnoses of colon cancer in the United States the subject of this recommendation is colon cancer. In a different recommendation, rectal cancer is discussed. Smoking status is the most powerful predictor lung function Decrease in smokers with COPD. If you are a smoker advised older people are more likely to quit because they cannot stop smoking or their lungs are already damaged. Similarly, there is a nihilistic approach to that intervenes in smoking cessation. The incidence of Hodgkin's lymphoma, a rare form of cancer, rises in the third decade in women and subsequently drops, whereas in men it stays largely steady after this point. The presence of Hodgkin/Reed-Sternberg cells serves as the basis for the diagnosis of Hodgkin's lymphoma.VIKOR method used in multi-criteria analysis (MCDM). Although a popular method, mcdm problems there are some problems with solving. This study is traditional discussed the problems with VIKOR method. This intention of the take a look at is to resolve issues with the traditional VIKOR technique to keep away from numerical troubles in fixing is modified VIKOR approach to create. From the result it is seen that Leukemia is showing the highest value for SRBCT is showing the lowest value. Resulting in Leukemia ranked first, there SRBCT has low rank

SP-015 - Preliminary evaluation of a novel carbon-11 labeled brain-penetrant EGFR-TKI radiotracer for brain cancer imaging

SP-015 - Preliminary evaluation of a novel carbon-11 labeled brain-penetrant EGFR-TKI radiotracer for brain cancer imaging

In Vivo Positive Magnetic Resonance Imaging of Brain Cancer (U87MG) Using Folic Acid-Conjugated Polyacrylic Acid-Coated Ultrasmall Manganese Oxide Nanoparticles

Ultrasmall nanoparticles are potential candidates for application as high-performance imaging agents. Herein, we present the synthesis and characterization of folic acid (FA)-conjugated polyacrylic acid (PAA)-coated MnO nanoparticles with an average particle diameter of 2.7 nm. FA conferred cancer-targeting ability, while PAA conferred good colloidal stability and low cellular cytotoxicity on the FA-PAA-coated MnO nanoparticles. Further, the nanoparticles exhibited a high relaxivity (r1) value of 9.3 s−1mM−1 (r2/r1 = 2.2). Their application potential as cancer-targeting T1 magnetic resonance imaging contrast agents was confirmed by their enhanced T1 contrast enhancements at the brain cancer (U87MG) site upon intravenous administration to mice tails.

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey.

In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

Multimodal quantitative imaging of brain cancer in cultured cells.

Fluorescence emission, polarization and subcellular localization of methylene blue (MB) were studied in four cancerous and two normal human brain cell lines. Fluorescence emission and polarization images were acquired and analyzed. The co-localization of MB with mitochondria, lysosomes and nuclei of the cells was evaluated. Glioblastoma cells exhibited significantly higher MB fluorescence polarization compared to normal astrocytes. Preferential accumulation of MB in mitochondria of glioblastoma cells may explain higher fluorescence polarization values in cancer cells as compared to normal. These findings may lead to the development of a quantitative method for the detection of brain cancer in single cells.

Self‐Assembled pH‐Sensitive Fluoromagnetic Nanotubes as Archetype System for Multimodal Imaging of Brain Cancer

AbstractFluoromagnetic systems are recognized as an emerging class of materials with great potential in the biomedical field. Here, it is shown how to fabricate fluoromagnetic nanotubes that can serve as multimodal probes for the imaging and targeting of brain cancer. An ionic self‐assembly strategy is used to functionalize the surface of synthetic chrysotile nanotubes with pH‐sensitive fluorescent chromophores and ferromagnetic nanoparticles. The acquired magnetic properties permit their use as contrast agent for magnetic resonance imaging, and enable the tracking of tumor cell migration and infiltration responsible for metastatic growth and disease recurrence. Their organic component, changing its fluorescence attitude as a function of local pH, targets the cancer distinctive acidity, and allows localizing and monitoring the tumor occurrence and progression by mapping the acidic spatial distribution within biopsy tissues. The fluoromagnetic properties of nanotubes are preserved from the in vitro to the in vivo condition and they show the ability to migrate across the blood brain barrier, thus spontaneously reaching the brain tumor after injection. The simplicity of the synthesis route of these geomimetic nanomaterials combined with their demonstrated affinity with the in vivo condition strongly highlights their potential for developing effective functional materials for multimodal theranostics of brain cancer.

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.

Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.

RGD-TPGS decorated theranostic liposomes for brain targeted delivery

The aim of this work was to formulate RGD-TPGS decorated theranostic liposomes, which contain both docetaxel (DTX) and quantum dots (QDs) for brain cancer imaging and therapy. RGD conjugated TPGS (RGD-TPGS) was synthesized and conjugation was confirmed by Fourier transform infrared (FTIR) spectroscopy and electrospray ionisation (ESI) mass spectroscopy (ESI–MS). The theranostic liposomes were prepared by the solvent injection method and characterized for their particle size, polydispersity, zeta-potential, surface morphology, drug encapsulation efficiency, and in-vitro release study. Biocompatibility and safety of theranostic liposomes were studied by reactive oxygen species (ROS) generation study and histopathology of brain. In-vivo study was performed for determination of brain theranostic effects in comparison with marketed formulation (Docel™) and free QDs. The particle sizes of the non-targeted and targeted theranostic liposomes were found in between 100 and 200nm. About 70% of drug encapsulation efficiency was achieved with liposomes. The drug release from RGD-TPGS decorated liposomes was sustained for more than 72h with 80% of drug release. The in-vivo results demonstrated that RGD-TPGS decorated theranostic liposomes were 6.47- and 6.98-fold more effective than Docel™ after 2h and 4h treatments, respectively. Further, RGD-TPGS decorated theranostic liposomes has reduced ROS generation effectively, and did not show any signs of brain damage or edema in brain histopathology. The results of this study have indicated that RGD-TPGS decorated theranostic liposomes are promising carrier for brain theranostics.

Appropriate Contrast Enhancement Measures for Brain and Breast Cancer Images.

Medical imaging systems often produce images that require enhancement, such as improving the image contrast as they are poor in contrast. Therefore, they must be enhanced before they are examined by medical professionals. This is necessary for proper diagnosis and subsequent treatment. We do have various enhancement algorithms which enhance the medical images to different extents. We also have various quantitative metrics or measures which evaluate the quality of an image. This paper suggests the most appropriate measures for two of the medical images, namely, brain cancer images and breast cancer images.