Magnetic Resonance Imaging Tumor Research Articles

NIR Light-Activated and RGD-Conjugated Ultrasmall Fe/PPy Nanopolymers for Enhanced Tumor Photothermal Ferrotherapy and MR Imaging.

Iron-based nanomaterials have shown great promise for tumor ferrotherapy in recent years. However, nanoparticle-induced ferroptosis has low therapeutic efficacy owing to unsatisfactory Fenton reaction activity in a typical tumor microenvironment. In this study, NIR light-activated Fe/PPy-RGD nanopolymers were developed to combine photothermal therapy and ferrotherapy and achieve enhanced antitumor activity. Importantly, Fe/PPy-RGD exhibited excellent therapeutic performance under NIR light activation both in vitro and in vivo. Under irradiation with NIR light, the heat generated by Fe/PPy-RGD not only induced a therapeutic photothermal effect but also enhanced the release of iron ions and the Fenton reaction by inducing ferroptosis. Additionally, by virtue of RGD conjugation and its ultrasmall size, Fe/PPy-RGD could effectively accumulate at tumor sites in living mice after systemic administration and could be monitored via MR imaging. Hence, this study provides a promising approach for integrating ferrotherapy with photothermal therapy to achieve enhanced tumor treatment.

MRI Tumor Regression Grade Combined with T2-Weighted Volumetry May Predict Histopathological Response in Locally Advanced Rectal Cancer following Neoadjuvant Chemoradiotherapy-A New Scoring System Proposal.

Modern studies focus on the discovery of innovative methods to improve the value of post-treatment magnetic resonance imaging (MRI) in the prediction of pathological responses to preoperative neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). The aim of this study was to assess the potential benefits of combining magnetic resonance tumor regression grade (mrTRG) with T2-weighted volumetry in the prediction of pathological responses to nCRT in LARC. This was a cohort study conducted on patients with histopathologically confirmed LARC in a period from 2020 to 2022. After histopathological verification, all patients underwent initial MRI studies, while the follow-up MRI was performed after nCRT. Tumor characteristics, MRI estimated tumor regression grade (mrTRG) and tumor volumetry were evaluated both initially and at follow-up. All patients were classified into responders and non-responders according to pathological tumor regression grade (pTRG) and mrTRG. A total of 71 patients, mostly male (66.2%) were included in the study. The median tumor volume reduction rate was significantly higher in nCRT-responders compared to non-responders (79.9% vs. 63.3%) (p = 0.003). Based on ROC analysis, optimal cut-off value for tumor volume reduction rate was determined with an area under the curve (AUC) value of 0.724 (p = 0.003). Using the tumor volume reduction rate ≥75% with the addition of response to nCRT according to mrTRG, a new scoring system for prediction of pTRG to preoperative nCRT in LARC was developed. Diagnostic performance of prediction score was tested and the sensitivity, PPV, specificity, and NPV were 81.8%, 56.3%, 71.4%, and 89.7%, respectively. The combination of mrTRG and T2-weighted volumetry increases the MRI-based prediction of pTRG to preoperative nCRT in LARC. The proposed scoring system could aid in distinguishing responders to nCRT, as these patients could benefit from organ-preserving treatment and a "watch and wait" strategy.

Deep learning‐based robust hybrid approaches for brain tumor classification in magnetic resonance images

AbstractThe classification of brain images is an immensely challenging task and one of the most valuable and frequently employed procedures in the field of medical diagnostics. Deep Learning (DL), which falls under the umbrella of artificial intelligence, has introduced novel techniques for automated analysis of medical images. The objective of this study was to develop two hybrid DL models for the segmentation and classification of brain magnetic resonance imaging (MRI) data. The first hybrid model combines fully convolutional networks (FCNs) and residual networks (ResNets) for both segmentation and classification purposes. The second model is a hybrid of SegNet for segmentation and MobileNet for classification. Rest assured, it will provide you with a dependable and promising performance. The analysis was conducted on a labeled dataset comprising images of glioma, meningioma, pituitary, and no‐tumor cases. Python was utilized to implement the aforementioned hybrid models. The proposed models achieved remarkable accuracies of 93.9% and 91.3%, respectively, when evaluated on a brain tumor MRI dataset. Additionally, a comprehensive evaluation of the proposed models was conducted, comparing them with other existing hybrid models in terms of metrics such as the dice score, Jaccard index, positive predictive value (PPV), false predictive value (FPV), testing time, precision, recall, specificity, and F1 score. The results demonstrated that the proposed DL technique can significantly assist doctors and radiologists in the early detection of brain tumor cells.

Automated Identification and Localization of Brain Tumor in MRI Using U-Net Segmentation and CNN-LSTM Classification

Nowadays, the use of computers to evaluate medical images automatically is critical part of the life. Today's treatment method relies heavily on early diagnosis and accurate disease identification, which were formerly difficult for medical research to achieve. Brain Magnetic Resonance Imaging (MRI) is essential to the detection and treatment of brain tumor (BT). Tumor of the brain are the result of brain cell division that has gone awry or is otherwise out of control. The manual MRI segmentation of BT is a difficult and time-consuming process. The most critical factor in the effective treatment and identification of BT is the ability to accurately locate the tumor. The detection of BT is regarded as a difficult task in medical image processing. For analysing and interpreting MRI, there are semi-automatic and fully automated systems that require large-scale professional input and evaluation, with varying degrees of effectiveness. Automated identification and extraction of the tumor's localization from brain MRI will be proposed in this paper. To achieve this goal, the data collected from Kaggle and the collected data are processed. Then the U-Net is employed to segment the tumor region from the MRI. Next, the MRI is classified using DL models like Convolutional Neural Network (CNN), and the hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). Both process segmentation and classification are evaluated using the metrics. From the evaluation, it is identified that CNN-LSTM outperforms the CNN model.

TumorDetNet: A unified deep learning model for brain tumor detection and classification.

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, magnetic resonance imaging (MRI) scans are frequently employed to identify tumors. The manual identification of tumors is difficult and requires considerable time due to the large number of three-dimensional images that an MRI scan of one patient's brain produces from various angles. Moreover, the variations in location, size, and shape of the brain tumor also make it challenging to detect and classify different types of tumors. Thus, computer-aided diagnostics (CAD) systems have been proposed for the detection of brain tumors. In this paper, we proposed a novel unified end-to-end deep learning model named TumorDetNet for brain tumor detection and classification. Our TumorDetNet framework employs 48 convolution layers with leaky ReLU (LReLU) and ReLU activation functions to compute the most distinctive deep feature maps. Moreover, average pooling and a dropout layer are also used to learn distinctive patterns and reduce overfitting. Finally, one fully connected and a softmax layer are employed to detect and classify the brain tumor into multiple types. We assessed the performance of our method on six standard Kaggle brain tumor MRI datasets for brain tumor detection and classification into (malignant and benign), and (glioma, pituitary, and meningioma). Our model successfully identified brain tumors with remarkable accuracy of 99.83%, classified benign and malignant brain tumors with an ideal accuracy of 100%, and meningiomas, pituitary, and gliomas tumors with an accuracy of 99.27%. These outcomes demonstrate the potency of the suggested methodology for the reliable identification and categorization of brain tumors.

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

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

BRAIN TUMOR DETECTION WITH MRI IMAGE USING ANISOTROPHIC FILTER AND SEGMENTATION

The early detection of cancer can be helpful in complete curing the disease. According to the most research in developed countries shows results that just because of inaccurate detection the numbers of people who have brain tumor were died. As the use of digital images has rapidly increased over the past decade, Radiologists by using computed Tomography (CT scan) and Magnetic Resonance Imaging (MRI) examine the patient physically. In surgical & medical assessments, brain tumor segmentation using MRI images is very difficult and important task. For diagnosis of brain tumor MR image is visually examined by the physician. However, this method of manual detection resists accurate tumor detection and more time consuming. To overcome these problems, this paper uses computer aided techniques such as SVM for extraction of tumor is key component to automate specific radiological tasks for the characterization of anatomical structures and regions of interest and AD algorithm to locate tumor area on the MRI images. At the end of process, the tumor detected from the MR image and its exact position and the shape also determined. This technique allows the segmentation of brain tumor tissue with accuracy, improved performance and robustness; it also reduces the effect of noise. Key Words: MRI, Anisotrophic filtering ,SVM, Future Extraction,Segmentation,Processing

Associations of Multiparametric Breast MRI Features, Tumor-Infiltrating Lymphocytes, and Immune Gene Signature Scores Following a Single Dose of Trastuzumab in HER2-Positive Early-Stage Breast Cancer.

Dynamic biomarkers that permit the real-time monitoring of the tumor microenvironment response to therapy are an unmet need in breast cancer. Breast magnetic resonance imaging (MRI) has demonstrated value as a predictor of pathologic complete response and may reflect immune cell changes in the tumor microenvironment. The purpose of this pilot study was to investigate the value of breast MRI features as early markers of treatment-induced immune response. Fourteen patients with early HER2+ breast cancer were enrolled in a window-of-opportunity study where a single dose of trastuzumab was administered and both tissue and MRIs were obtained at the pre- and post-treatment stages. Functional diffusion-weighted and dynamic contrast-enhanced MRI tumor measures were compared with tumor-infiltrating lymphocytes (TILs) and RNA immune signature scores. Both the pre-treatment apparent diffusion coefficient (ADC) and the change in peak percent enhancement (DPE) were associated with increased tumor-infiltrating lymphocytes with trastuzumab therapy (r = -0.67 and -0.69, p < 0.01 and p < 0.01, respectively). Low pre-treatment ADC and a greater decrease in PE in response to treatment were also associated with immune-activated tumor microenvironments as defined by RNA immune signatures. Breast MRI features hold promise as biomarkers of early immune response to treatment in HER2+ breast cancer.

Potential clinical feasibility of synthetic MRI in bladder tumors: a comparative study with conventional MRI.

Synthetic magnetic resonance imaging (MRI) can provide quantitative information about inherent tissue properties and synthesize tailored contrast-weighted images simultaneously in a single scan. This study aimed to investigate the clinical feasibility of synthetic MRI in bladder tumors. A total of 47 patients (37 males; mean age: 66±10 years old) with postoperative pathology-confirmed papillary urothelial neoplasms of the bladder were enrolled in this retrospective study. A 2-dimensional (2D) multi-dynamic multi-echo pulse sequence was performed for synthetic MRI at 3T. The overall image quality, lesion conspicuity, contrast resolution, resolution of subtle anatomic structures, motion artifact, blurring, and graininess of images were subjectively evaluated by 2 radiologists independently using a 5-point Likert scale for qualitative analysis. The signal intensity ratio (SIR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for quantitative analysis. Linear weighted Kappa, Wilcoxon's signed-rank test, and the Mann-Whitney U-test were used for statistical analysis. The interobserver consistency was excellent (κ values: 0.607-1). Synthetic T1-weighted (syn-T1w) and synthetic T2-weighted (syn-T2w) images obtained scores of 4 in most subjective terms, which were relatively smaller than those of conventional images. The SIR and SNR of syn-T1w were significantly higher than those of con-T1w images (SIR 2.37±0.86 vs. 1.47±0.20, P<0.001; SNR 21.83±9.43 vs. 14.81±3.30, P<0.001). No difference was found in SIR between syn-T2w and conventional T2-weighted (con-T2w) images, whereas the SNR of the syn-T2w was significantly lower (8.79±4.06 vs. 26.49±6.80, P<0.001). Additionally, the CNR of synthetic images was significantly lower than that of conventional images (T1w 1.41±0.72 vs. 2.68±1.04; T2w 1.40±0.87 vs. 4.03±1.55, all P<0.001). Synthetic MRI generates morphologic magnetic resonance (MR) images with diagnostically acceptable image quality in bladder tumors, especially T1-weighted images with high image contrast of tumors relative to urine. Further technological improvements are needed for synthetic MRI to reduce noise. Combined with T1, T2, and proton density (PD) quantitative data, synthetic MRI has potential for clinical application in bladder tumors.

A Biomimetic Nanogel System Restores Macrophage Phagocytosis for Magnetic Resonance Imaging-Guided Synergistic Chemoimmunotherapy of Breast Cancer.

Novel strategies to facilitate tumor-specific drug delivery and restore immune attacks remain to be developed to overcome the current limitations of chemotherapy. Herein, a cancer cell membrane (CM)-camouflaged and ultrasmall iron oxide nanoparticles (USIO NPs)-loaded polyethylenimine nanogel (NG) system is reported to co-deliver docetaxel (DTX) and CD47 siRNA (siCD47). The prepared co-delivery system exhibits good colloidal stability, biocompatibility, and r1 relaxivity (1.35 mM-1 s-1 ) and enables redox-responsive release of the loaded DTX in the tumor microenvironment. The NG system realizes homologous targeting delivery of DTX and siCD47 to murine breast cancer cells (4T1 cells) for efficient chemotherapy and gene silencing; thus, inducing immunogenic cell death (ICD) and restoring macrophage phagocytic effect through downregulation of "don't eat me" signals on cancer cells. Likewise, the co-delivery system can also act on macrophages to promote their M1 polarization, which can be combined with DTX-mediated ICD and antibody-mediated immune checkpoint blockade to generate effector T cells for robust chemoimmunotherapy. Further, the USIO NPs-incorporated NG system also allows for magnetic resonance imaging of tumors. The developed biomimetic NG system acting on both cancer cells and macrophages holds a promising potential for macrophage phagocytosis-restored chemoimmunotherapy.

Diagnostic accuracy of pre-operative breast magnetic resonance imaging (MRI) in predicting axillary lymph node metastasis: variations in intrinsic subtypes, and strategy to improve negative predictive value—an analysis of 2473 invasive breast cancer patients

BackgroundThe value and utility of axillary lymph node (ALN) evaluation with MRI in breast cancer were not clear for various intrinsic subtypes. The aim of the current study is to test the potential of combining breast MRI and clinicopathologic factors to identify low-risk groups of ALN metastasis and improve diagnostic performance.Material and methodsPatients with primary operable invasive breast cancer with pre-operative breast MRI and post-operative pathologic reports were retrospectively collected from January 2009 to December 2021 in a single institute. The concordance of MRI and pathology of ALN status were determined, and also analyzed in different intrinsic subtypes. A stepwise strategy was designed to improve MRI-negative predictive value (NPV) on ALN metastasis.Results2473 patients were enrolled. The diagnostic performance of MRI in detecting metastatic ALN was significantly different between intrinsic subtypes (p = 0.007). Multivariate analysis identified tumor size and histologic type as independent predictive factors of ALN metastases. Patients with HER-2 (MRI tumor size ≤ 2 cm), or TNBC (MRI tumor size ≤ 2 cm) were found to have MRI–ALN-NPV higher than 90%, and these false cases were limited to low axillary tumor burden.ConclusionThe diagnostic performance of MRI to predict ALN metastasis varied according to the intrinsic subtype. Combined pre-operative clinicopathologic factors and intrinsic subtypes may increase ALN MRI NPV, and further identify some groups of patients with low risks of ALN metastasis, high NPV, and low burdens of axillary disease even in false-negative cases.

Preoperative Prediction of Microvascular Invasion with Gadoxetic Acid-Enhanced Magnetic Resonance Imaging in Patients with Single Hepatocellular Carcinoma: The Implication of Surgical Decision on the Extent of Liver Resection

Introduction: Microvascular invasion (MVI) is one of the most important prognostic factors for hepatocellular carcinoma (HCC) recurrence, but its application in preoperative clinical decisions is limited. This study aimed to identify preoperative predictive factors for MVI in HCC and further evaluate oncologic outcomes of different types and extents of hepatectomy according to stratified risk of MVI. Methods: Patients with surgically resected single HCC (≤5 cm) who underwent preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI) were included in a single-center retrospective study. Two radiologists reviewed the images with no clinical, pathological, or prognostic information. Significant predictive factors for MVI were identified using logistic regression analysis against pathologic MVI and used to stratify patients. In the subgroup analysis, long-term outcomes of the stratified patients were analyzed using the Kaplan-Meier method with log-rank test and compared between anatomical and nonanatomical or major and minor resection. Results: A total of 408 patients, 318 men and 90 women, with a mean age of 56.7 years were included. Elevated levels of tumor markers (alpha-fetoprotein [α-FP] ≥25 ng/mL and PIVKA-II ≥40 mAU/mL) and three MRI features (tumor size ≥3 cm, non-smooth tumor margin, and arterial peritumoral enhancement) were independent predictive factors for MVI. As the MVI risk increased from low (no predictive factor) and intermediate (1–2 factors) to high-risk (3–4 factors), recurrence-free and overall survival of each group significantly decreased (p = 0.001). In the high MVI risk group, 5-year cumulative recurrence rate was significantly lower in patients who underwent major compared to minor hepatectomy (26.6 vs. 59.8%, p = 0.027). Conclusion: Tumor markers and MRI features can predict the risk of MVI and prognosis after hepatectomy. Patients with high MVI risk had the worst prognosis among the three groups, and major hepatectomy improved long-term outcomes in these high-risk patients.

A tumor microenvironment-responsive core-shell tecto dendrimer nanoplatform for magnetic resonance imaging-guided and cuproptosis-promoted chemo-chemodynamic therapy.

Theranostic nanoplatforms for combination tumor therapy have gained lots of attention recently due to the optimized therapeutic efficiency and simultaneous diagnosis performance. Herein, a novel tumor microenvironment (TME)-responsive core-shell tecto dendrimer (CSTD) was assembled by phenylboronic acid- and mannose-modified poly(amidoamine) dendrimers via the phenylboronic ester bonds that are responsive to low pH and reactive oxygen species (ROS), and efficiently loaded with copper ions and chemotherapeutic drug disulfiram (DSF) for tumor-targeted magnetic resonance (MR) imaging and cuproptosis-promoted chemo-chemodynamic therapy. The formed CSTD-Cu(II)@DSF could be specifically taken up by MCF-7 breast cancer cells, accumulated to the tumor model after circulation, and released drugs in response to the weakly acidic TME with overexpressed ROS. The enriched intracellular Cu(II) ions could induce the oligomerization of lipoylated proteins and proteotoxic stress for cuproptosis, and lipid peroxidation for chemodynamic therapy as well. Moreover, the CSTD-Cu(II)@DSF could cause the dysfunction of mitochondria and arrest the cell cycle at the G2/M phase, leading to enhanced DSF-mediated cell apoptosis. As a result, CSTD-Cu(II)@DSF could effectively inhibit the growth of MCF-7 tumors by a combination therapy strategy integrating chemotherapy with cuproptosis and chemodynamic therapy. Lastly, the CSTD-Cu(II)@DSF also displays Cu(II)-associated r1 relaxivity, allowing for T1-weighted real-time MR imaging of tumors in vivo. The developed tumor-targeted and TME-responsive CSTD-based nanomedicine formulation may be developed for accurate diagnosis and synergistic treatment of other cancer types. STATEMENT OF SIGNIFICANCE: Constructing an effective nanoplatform for the combination of therapeutic effects and real-time tumor imaging remains a challenge. In this study, we reported for the first time an all-in-one tumor-targeted and tumor microenvironment (TME) responsive nanoplatform based on core-shell tecto dendrimer (CSTD) for the cuproptosis-promoted chemo-chemodynamic therapy and enhanced MR imaging. The efficient loading, selective tumor-targeting, and TME-responsive release of Cu(II) and disulfiram could enhance the intracellular accumulation of drugs, induce cuproptosis of cancer cells, and amplify the synergistic chemo-chemodynamic therapeutic effect, resulting in enhanced MR imaging and accelerated tumor eradication. This study sheds new light on the development of theranostic nanoplatforms for early accurate diagnosis and effective treatment of cancers.

Physico-chemical and MR relaxometry study of bovine serum albumin-coated magneto-plasmonic nanoparticles designed for potential use in cancer nanotheranostics

PurposeIn recent years, the use of nanoparticles has been developed to improve MRI contrast. To improve the contrast agents in image-guided therapy by Multifunctional nanoparticles, in this study, we synthesized a theranostic magneto-plasmonic nanocomplex based on magnetic iron oxide nanoparticles and bovine serum albumin-modified gold nanorod (Au@BSA-Fe3O4@CMD). The purpose of synthesizing these nanoparticles was to use them as MRI contrast agent and photothermal agents in in vitro and in vivo experiments. Materials and methodsInitially, the properties of the synthesized nanoparticles were investigated by methods such as DLS, TEM, FTIR. MTT assay was used to evaluate the toxicity of nanoparticles. Finally, to evaluate the contrast ability of nanoparticles, MRI images were taken in in vitro and in vivo conditions and then the images were analyzed. ResultsMTT test results on CT26 cell line showed no significant cytotoxicity for Au@BSA-Fe3O4@CMD nanoparticles at concentrations up to 20 ppm. The in vitro results demonstrated that the Au@BSA-Fe3O4@CMD nanocomplex has high T2 relaxation rate and great relaxivities (r2 = 140.14 mM−1 s−1, r1 = 2.066 mM−1 s−1, r2/r1 = 67.83). For in vivo conditions, a decrease in T2 signal of 9.64 and 11.01, respectively, was observed for intratumoral and intraperitoneal injection of nanoparticles. ConclusionThese in vitro and in vivo studies show that Au @ BSA-Fe3O4@CMD nanoparticles can significantly reduce the signal intensity of T2-weight MRI images, and therefore can offer significant potential as a theranostic platform for effective tumor MR imaging.

Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system

Most primary brain malignancies are malignant tumors characterized by masses of abnormal tissue that grow uncontrollably. Recently, deep transfer learning (DTL) has been considered in the automated clinical application of magnetic resonance imaging (MRI) for determining brain tumor characteristics. Several previous studies have shown that tumors analyzed in MR images usually have local features, are extensive, and are associated with a high level of uncertainty. Accordingly, we introduce a two-branch parallel model that integrates the Transformer Module (TM) with the Self-Attention Unit (SAU) and Convolutional Neural Networks (CNN) to classify brain tumors in MR images. We also propose a novel approach that combines local and global features derived from CNNs and TMs to improve classification accuracy through a cross-fusion strategy. Hybrid architecture combined with cross-fusion allows parallel systems to be merged between branches, resulting in a pattern that identifies various types of tumors. Additionally, we developed a lightweight and improved CNN architecture (iResNet) that distinguishes tumor features based on MR images. From the 3064 slices in the four-class MR images from the Figshare dataset, 20 % were considered unseen images. The remainder was divided into 60 %, 20 %, and 20 % slices for training, validation, and testing. The overall structure was combined with other similar CNN networks (i.e., DenseNet, VGG, and ResNet), and we found that the accuracy of iVGG, iDensNet, and iResNet is 98.59, 98.94, and 99.30 %, respectively. Using local and global features, we developed an accurate and generalizable model to detect brain tumors in MRI, allowing rapid and accurate diagnosis.

Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

Developing a Gadolinium(III) Compound Based on Apoferritin for Targeted Magnetic Resonance Imaging and Dual-Modal Therapy of Cancer.

To integrate targeted diagnosis and treatment of cancer, we proposed to develop a gadolinium (Gd) agent based on the properties of apoferritin (AFt). To this end, we not only optimized a series of Gd(III) 8-hydroxyquinoline-2-carboxaldehyde-thiosemicarbazone compounds to obtain a Gd(III) compound (C4) with remarkable T1-weighted magnetic resonance imaging (MRI) performance and cytotoxicity to cancer cells in vitro but also constructed an AFt-C4 nanoparticle (NP) delivery system. Importantly, AFt-C4 NPs improved the targeting ability of C4 in vivo and showed enhanced MRI performance and tumor growth inhibition ratio relative to C4 alone. Furthermore, we confirmed that C4 and AFt-C4 NPs inhibited tumor growth through apoptosis, ferroptosis, and ferroptosis-induced immune response.

Evaluation of Gliomas with Magnetic Resonance Fingerprinting with PET Correlation—A Comparative Study

Objectives: Advanced MR imaging of brain tumors is still mainly based on qualitative imaging. PET imaging offers additive metabolic information, and MR fingerprinting (MRF) offers a novel approach to quantitative data acquisition. The purpose of this study was to evaluate the ability of MRF to predict tumor regions and grading in combination with PET. Methods: Seventeen patients with histologically verified infiltrating gliomas and available amino-acid PET data were enrolled. ROIs for solid tumor parts (SPo), perifocal edema (ED1), and normal-appearing white matter (NAWM) were selected on conventional MRI sequences and aligned to the MRF and PET images. The predictability of gliomas by region and grading as well as intermodal correlations were assessed. Results: For MRF, we calculated an overall predictability by region (SPo, ED1, and NAWM) for all of the MRF parameters of 76.5%, 47.1%, and 94.1%, respectively. The overall ability to distinguish low- from high-grade gliomas using MRF was 88.9% for LGG and 75% for HGG, with an accuracy of 82.4%, a ppV of 85.71%, and an npV of 80%. PET positivity was found in 13/17 patients for solid tumor parts, and in 3/17 patients for the edema region. However, there was no significant difference in region-specific MRF values between PET positive and PET negative patients. Conclusions: MRF and PET provide quantitative measurements of the tumor tissue characteristics of gliomas, with good predictability. Nonetheless, the results are dissimilar, reflecting the different underlying mechanisms of each method.

Designing Smart Iron Oxide Nanoparticles for MR Imaging of Tumors.

Iron oxide nanoparticles (IONPs) possess unique magnetism and good biocompatibility, and they have been widely applied as contrast agents (CAs) for magnetic resonance imaging (MRI). Traditional CAs typically show a fixed enhanced signal, thus exhibiting the limitations of low sensitivity and a lack of specificity. Nowadays, the progress of stimulus-responsive IONPs allows alteration of the relaxation signal in response to internal stimuli of the tumor, or external stimuli, thus providing an opportunity to overcome those limitations. This review summarizes the current status of smart IONPs as tumor imaging MRI CAs that exhibit responsiveness to endogenous stimuli, such as pH, hypoxia, glutathione, and enzymes, or exogenous stimuli, such as magnets, light, and so on. We discuss the challenges and future opportunities for IONPs as MRI CAs and comprehensively illustrate the applications of these stimuli-responsive IONPs. This review will help provide guidance for designing IONPs as MRI CAs and further promote the reasonable design of magnetic nanoparticles and achieve early and accurate tumor detection.

Multifunctional Ultrasmall Theranostic Nanohybrids Developed by Ultrasonic Atomizer for Drug Delivery and Magnetic Resonance Imaging.

Theranostic nanoparticulate systems (TNPs) have shown potential in addressing problems related to spatial localization and temporally controlled release of drugs with the capabilities of real-time imaging to evaluate the progress of therapy. The current study reports the ultrasonic atomization-led synthesis of in vitro and in vivo evaluations of ultrasmall chitosan-based theranostic nanohybrid formulations with encapsulated doxorubicin (DOX) and iron-oxide magnetic nanoparticles. The nanohybrid particles are characterized using transmission electron microscopy, powder X-ray diffraction, FTIR, DOX encapsulation efficiency, in vitro release, cellular uptake, and toxicity. These formulations were also tested for the capability of invivo tumor reduction and simultaneous magnetic resonance imaging using Swiss albino mice. Ultrasonic atomizer-led synthesis resulted in chitosan-magnetic nanohybrids (CMNPs) having sizes of 15 ± 3 nm which comprise MNP of 10 ± 3 nm. The encapsulation of DOX in CMNP was around 25%, resulting in an 80% sustained release over 10 days at pH 5 and 7. CMNP was also found to be an efficient DOX delivery vehicle tested on cancer cells (HeLa). The CMNPs were able to reduce the tumor volume by 60% in 15 days. The inherent magnetic property and nanoscale size of CMNPs also provided for enhanced contrast efficiency in magnetic resonance imaging of tumors. Thus, such multifunctional theranostic nanoparticles can be an efficient tool for targeted diagnostic and therapeutic success.