Magnetic Resonance Imaging Tumor Research Articles

Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization.

Brain tumor diagnosis is an important task in prognosing and treatment planning of the patients with brain cancer. in the meantime, using the Magnetic Resonance Imaging (MRI) as a commonly used non-invasive imaging technique provide the experts a helpful view for detecting the brain tumors. While deep learning methods have shown significant success in analyzing medical images, they often require careful design of architecture and tuning of hyperparameters to achieve optimal results. This study presents a new approach for diagnosing brain tumors in MRI scans using deep learning, focusing on Residual/Shuffle Networks. The designed network structures offer efficient results when compared to traditional deep learning models. To enhance the proposed network for brain tumor classification, a modified metaheuristic algorithm named Augmented Falcon Finch Optimization (AFFO) is introduced. AFFO utilizes bio-inspired principles to effectively search for the best hyperparameter configurations, thereby enhancing the reliability and accuracy of the deep learning model. The performance of the proposed method is evaluated on a standard brain tumor MRI dataset and compared with existing techniques, including ResNet, AlexNet, VGG-16, Inception V3, and U-Net to illustrate the effectiveness of combining Residual/Shuffle Networks with AFFO for brain tumor diagnosis.

MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism

AbstractAccurate segmentation of brain tumor magnetic resonance imaging (MRI) is crucial for treatment planning. Addressing the challenges of complex tumor structures and inadequate cross‐channel information utilization in Unet‐based segmentation, this paper proposes the multi‐scale residual brain tumor MRI segmentation network (MRS‐Net) incorporating an attention mechanism to enhance segmentation accuracy. First, the double residual feature fusion module is utilized to enhance the fusion of feature information between different levels. Second, the Atrous Spatial Pyramid Pooling is introduced as a bridging module of the network to capture the features at different scales of the image, so as to enhance the extraction capability of the network for detailed features. Finally, the inverted residual coordinate attention module replaces the direct splicing in Unet to fuse the large feature information at each level and scale, thus enhancing the model's ability to recognize the spatial location information of brain tumors. The Dice coefficients, positive predictive values (PPVs), sensitivities (Sensitivity) and Hausdorff distance (HD), which are the four evaluation indexes, reach 84.54%, 87.43%, 88.37% and 2.248, respectively, which are improved by 1.85%, 2.11%, 2.88% and 6.0%, respectively, compared with Unet. The experimental results show that MRS‐Net achieves better brain tumor image segmentation.

MRI and CT imaging characteristics in parotid tumors with false-negative fine-needle aspirations

BackgroundsPreoperative imaging, particularly with magnetic resonance imaging (MRI) and computed tomography (CT) scans, plays a crucial role in distinguishing between benign and malignant parotid gland tumors, while the reliability of Ultrasound-Guided Fine Needle Aspiration (FNA) in diagnosing these masses remains a topic of debate.MethodsThis two-center retrospective analysis was conducted on 347 patients with parotid gland tumors who had FNA and preoperative imaging (CT or MRI). All patients underwent surgery and final histopathological examination was available, along with complete medical records between January 2008 and May 2023.ResultsAmong the 347 patients, 318 (92%) had benign and 10 (3%) had malignant tumors based on FNA, with 19 (5%) unsatisfactory specimens. Final histological diagnosis revealed 303 (87%) benign and 44 (13%) malignant lesions, with a false-negative rate of 10.6% for FNA. Multivariate analysis identified irregular shape and invasion as independent predictors of malignancy in patient with benign or unsatisfactory FNA results. The odds ratio for irregular shape was 3.06 and for invasion was 12.73.ConclusionImaging characteristics, such as irregular shape and invasion may indicate towards malignant parotid tumors, even in patients with false-negative benign findings in FNA.

Robust brain MRI image classification with SIBOW-SVM

Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) is the most effective technique for brain tumor detection, generating comprehensive brain scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning methods, such as Convolutional Neural Networks (CNNs), have demonstrated significantly higher accuracy than manual screening. However, deep learning-based image classification methods, including CNNs, face challenges in estimating class probabilities without proper model calibration (Guo et al., 2017; Minderer et al., 2021). In this paper, we propose a novel brain tumor image classification method called SIBOW-SVM, which integrates the Bag-of-Features model with SIFT feature extraction and weighted Support Vector Machines. This new approach can effectively extract hidden image features, enabling differentiation of various tumor types, provide accurate label predictions, and estimate probabilities of images belonging to each class, offering high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive image datasets. To benchmark our method, we apply SIBOW-SVM to a public dataset of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results demonstrate that the new method outperforms state-of-the-art techniques, including CNNs, in terms of uncertianty quantification, classification accuracy, computational efficiency, and data robustness.

HQNet: A hybrid quantum network for multi-class MRI brain classification via quantum computing

The complex and nonlinear nature of brain tumor morphology and texture pose significant challenges for representative feature extraction from magnetic resonance imaging (MRI) images, ultimately leading to inaccurate diagnoses of brain tumors. To tackle this problem, we propose a novel approach termed the hybrid quantum–classical convolutional network (HQNet) for detecting MRI brain tumors. Firstly, we use data augmentation techniques to deal with the issues of class imbalance and the sample scarcity within brain tumor MRI images. Secondly, we integrate channel attention and self-attention mechanisms to capture both channel and spatial dependencies from MRI images. This integration enables us to effectively fuse these dependencies and mitigate the discrepancies between them. However, the varying dependence of brain tumors at different scales can result in a decline in classification performance. To address this issue, we extract brain tumor features at various scales and combine them to alleviate this effect. Thirdly, a new parameterized quantum circuit is designed based on the principles of quantum computing to maximize the potential advantages of quantum computing and improve feature extraction. Finally, we compress the features and pass them through the fully connected layer to generate the final classification result. The effectiveness of our model is demonstrated through its superior performance when evaluated using three benchmark datasets.

Correlation between post-chemotherapy MRI and histopathology of malignant bone tumors treated with extra-articular resection.

Magnetic resonance imaging (MRI) remains the gold standard for diagnosing tumoral joint involvement; however, its interpretation remains uncertain due to the presence of perilesional edema that can lead to unjustified arthrectomy. The aim of the study is to identify precise MRI signs that are predictive of joint involvement. This retrospective multicenter study included 25 patients who underwent extra-articular resection for malignant bone tumor with suspected joint involvement at the shoulder, hip, or knee, between January 2004 and July 2023. Joint resection was indicated based on preoperative MRI examination. MRI signs of joint involvement were joint effusion, capsuloligamentous invasion, disruption of articular cartilage, and intra-articular tumor mass. We then compared histopathological "true" joint involvement of the resected specimen with its preoperative MRI diagnosis. Extra-articular resection was performed in 9 shoulders, 4 hips, and 12 knees. Histological analysis confirmed wide resections for all patients. Based on histopathological analysis, extra-articular resection was unjustified in 40% (44%, 0%, and 50%, respectively). The most specific iconographic criteria were intra-capsular cortical breach and the presence of a tumor mass inside the joint. Articular cartilage disruption is often the most accurate sign. Our results suggest that MRI has a poor ability to accurately diagnose joint involvement. To date, however, this is the best tool available. In addition to obvious signs of joint involvement, MRI analysis must be performed with a thorough understanding of capsular anatomy and its particularities for each joint so as not to miss other signs, such as intra-capsular cortical effraction.

MODIFIED VGG16 FOR ACCURATE BRAIN TUMOR DETECTION IN MRI IMAGERY

Brain tumors are one of the most severe medical conditions that require immediate attention and treatment. The early detection of brain tumors is of utmost importance, as it can significantly improve the chances of successful treatment outcomes and increase the patient's quality of life. This study proposes a novel methodology for the early detection of brain tumors in magnetic resonance imaging (MRI) images using a modified VGG16 neural network architecture. The dataset comprises both tumor and non-tumor MRI images collected from Kaggle and has preprocessing techniques applied to optimize the model's performance. The proposed approach delivers an impressive accuracy rate of 99.08%, demonstrating its efficacy in precise brain tumor detection. This new methodology is expected to aid doctors in accurate diagnosis and treatment planning, thereby helping to save more lives and improve the quality of life of patients suffering from brain tumors.

Enhancing the accuracy of preoperative and intraoperative evaluation of malignant ovarian germ cell tumors with a focus on fertility preservation in young women.

To analyze and improve the accuracy of preoperative assessment and intraoperative frozen-section analysis (FSA) for malignant ovarian germ cell tumors (MOGCTs), especially in the context of fertility preservation. A retrospective review of 48 women aged under 40 years, diagnosed with MOGCTs, and treated at Chonnam National University Hospital between July and December 2022 was conducted. The results of preoperative magnetic resonance imaging (MRI), measurement of serum tumor markers (α-fetoprotein [AFP], β-human chorionic gonadotropin, lactate dehydrogenase [LDH], cancer antigen [CA] 125, CA 19-9, CA 72-4, carcinoembryonic antigen), and intraoperative FSA were compared with the final pathology diagnosis. MRI demonstrated a sensitivity of 95.5%, whereas FSA showed a sensitivity of 72.9% for all MOGCTs. Sensitivities varied according to the subtype, but were consistently higher in MRI (100% for dysgerminoma, 88.9% for immature teratoma, 100% for endodermal sinus tumor, 100% for others). However, there were differences in FSA according to subtype (100% for dysgerminoma, 50.0% for immature teratoma, 100% for endodermal sinus tumor, 25.0% for others). Serum tumor markers also provided diagnostic insights, particularly LDH for dysgerminoma (82.4%) and AFP for immature teratoma (75.0%) and endodermal sinus tumor (100%). Preoperative MRI and serum tumor marker measurement may be effective in guiding fertility-sparing surgical decisions. MRI could outperform FSA in terms of accuracy, especially for immature teratoma.

Synthesis of Antifouling Poly(ethylene glycol) Brushes via "Grafting to" Approach for Improved Biodistribution.

Polyethylene glycol (PEG) modification of materials has been identified to mitigate the challenge of biofouling. However, the practical application of PEGylation has been hampered by a low PEGylation density on the material surface. Therefore, developing efficient strategies to promote the PEGylation density is crucial. In this study, PEG brushes (PBs) with various structures were synthesized and their physicochemical properties and biomedical applications were investigated. Compared to benzaldehyde (BA), o-phthalaldehyde (OPA) exhibited higher reactivity with amine groups, resulting in increased grafting density (as high as 96.3%) and improved antifouling properties of PEG brushes. Bottlebrushes fabricated by PEG-OPA and polylysine demonstrated a prolonged circulation time in blood and enhanced potential for magnetic resonance imaging of tumors. Furthermore, the rigidity of the backbone was found to be crucial for the antifouling properties of PEG brushes both in vitro and in vivo. These findings are significant and provide valuable insights into designing biomaterials with superior antifouling performance.

The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer.

The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as 'watch-and-wait'. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the 'watch-and-wait' approach, highlighting important practical aspects in selecting patients for non-surgical management.

Biomimetic copper-containing nanogels for imaging-guided tumor chemo-chemodynamic-immunotherapy

Developing multifunctional nanoplatforms to comprehensively modulate the tumor microenvironment and enhance diagnostic and therapeutic outcomes still remains a great challenge. Here, we report the facile construction of a multivariate nanoplatform based on cancer cell membrane (CM)-encapsulated redox-responsive poly(N-vinylcaprolactam) (PVCL) nanogels (NGs) co-loaded with Cu(II) and chemotherapeutic drug toyocamycin (Toy) for magnetic resonance (MR) imaging-guided combination tumor chemodynamic therapy/chemoimmunotherapy. We show that redox-responsive PVCL NGs formed through precipitation polymerization can be aminated, conjugated with 3,4-dihydroxyhydrocinnamic acid for Cu(II) complexation, physically loaded with Toy, and finally camouflaged with CMs. The created ADCT@CM NGs with an average size of 113.0 nm are stable under physiological conditions and can efficiently release Cu(II) and Toy under tumor microenvironment with a high level of glutathione. Meanwhile, the developed NGs are able to enhance cancer cell oxidative stress and endoplasmic reticulum stress by synergizing the effects of chemodynamic therapy mediated by Cu-based Fenton-like reaction and Toy-mediated chemotherapy, thereby triggering significant immunogenic cell death (ICD). In a melanoma mouse model, the NGs show potent immune activation effects to reinforce tumor therapeutic efficacy through ICD induction and immune modulation including high levels of immune cytokine secretion, increased tumor infiltration of CD8+ cytotoxic T cells, and reduced tumor infiltration of regulatory T cells. With the CM coating and Cu(II) loading, the developed NG platform demonstrates homologous tumor targeting and T1-weighted MR imaging, hence providing a general biomimetic NG platform for ICD-facilitated tumor theranostic nanoplatform. Statement of significanceDeveloping multifunctional nanoplatforms to comprehensively modulate the tumor microenvironment (TME) and enhance theranostic outcomes remains a challenge. Here, a cancer cell membrane (CM)-camouflaged nanoplatform based on aminated poly(N-vinylcaprolactam) nanogels (NGs) co-loaded with Cu(II) and toyocamycin (Toy) was prepared for magnetic resonance (MR) imaging-guided combination tumor chemodynamic therapy/chemoimmunotherapy. The tumor targeting specificity and efficient TME-triggered release of Cu(II) and Toy could enhance tumor cell oxidative stress and endoplasmic reticulum stress by synergizing the effects of chemodynamic therapy mediated by Cu-based Fenton-like reaction and Toy-mediated chemotherapy, respectively, thereby leading to significant immunogenic cell death (ICD) and immune response. With the CM coating and Cu(II) loading, the developed NG platform also demonstrates good T1-weighted tumor MR imaging performance. Hence, this study provides a general biomimetic NG platform for ICD-facilitated tumor theranostics.

MRI features of breast cancer immunophenotypes with a focus on luminal estrogen receptor low positive invasive carcinomas

To compare the magnetic resonance imaging (MRI) features of different immunophenotypes of breast carcinoma of no special type (NST), with special attention to estrogen receptor (ER)-low-positive breast cancer. This retrospective, single-centre, Institutional Review Board (IRB)-approved study included 398 patients with invasive breast carcinoma. Breast carcinomas were classified as ER-low-positive when there was ER staining in 1–10% of tumour cells. Pretreatment MRI was reviewed to assess the tumour imaging features according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. Of the 398 cases, 50 (12.6%) were luminal A, 191 (48.0%) were luminal B, 26 (6.5%) were luminal ER-low positive, 64 (16.1%) were HER2-overexpressing, and 67 (16.8%) were triple negative. Correlation analysis between MRI features and tumour immunophenotype showed statistically significant differences in mass shape, margins, internal enhancement and the delayed phase of the kinetic curve. An oval or round shape and rim enhancement were most frequently observed in triple-negative and luminal ER-low-positive tumours. Spiculated margins were most common in luminal A and luminal B tumours. A persistent kinetic curve was more frequent in luminal A tumours, while a washout curve was more common in the triple-negative, HER2-overexpressing and luminal ER-low-positive immunophenotypes. Multinomial regression analysis showed that luminal ER-low-positive tumours had similar results to triple-negative tumours for almost all variables. Luminal ER-low-positive tumours present with similar MRI findings to triple-negative tumours, which suggests that MRI can play a fundamental role in adequate radiopathological correlation and therapeutic planning in these patients.

Comparison of arterial spin labeling and dynamic susceptibility contrast perfusion MR imaging in pediatric brain tumors: A systematic review and meta-analysis.

Brain tumors are a leading cause of mortality in children. Accurate tumor grading is essential to plan treatment and for prognostication. Perfusion imaging has been shown to correlate well with tumor grade in adults, however there are fewer studies in pediatric patients. Moreover, there is no consensus regarding which MR perfusion technique demonstrates the highest accuracy in the latter population. To compare the diagnostic test accuracy of dynamic-susceptibility contrast and arterial spin-labelling, in their ability to differentiate between low-and high-grade pediatric brain tumors at first presentation. Articles were retrieved from online electronic databases: MEDLINE (Ovid), Web of Science Core Collection and SCOPUS. Studies in pediatric patients with a treatment naïve diagnosed brain tumor and imaging including either ASL or DSC or both, together with a histological diagnosis were included. Studies involving adult patient or mixed age populations, studies with incomplete data and those which used dynamic contrast enhanced perfusion were excluded. The sensitivities and specificities obtained from each study were used to calculate the true-positive, true-negative, false-positive, and false-negative count. A case was defined as a histologically proven high-grade tumor. The random-effect model was used to merge statistics. Significance level was set at p < 0.05. Forest plots showing pairs of sensitivity and specificity, with their 95% confidence intervals, were constructed for each study. The bivariate model was applied in order to account for between-study variability. The SROC plots were constructed from the obtained data-sets. The AUC for the SROC of all studies was estimated to determine the overall diagnostic test accuracy of perfusion MRI, followed by a separate comparison of the SROC of ASL versus DSC studies. Small and heterogenous sample size. The diagnostic accuracy of ASL was found to be comparable and not inferior to DSC, thus its use in the diagnostic assessment of pediatric patients should continue to be supported. ASL = arterial spin labelling, DSC = dynamic susceptibility contrast, DCE = dynamic contrast-enhanced, rCBF = relative cerebral blood flow, rCBV = relative cerebral blood volume, MTT = mean transfer time, TR = repetition time, TE = echo time, SROC = summary receiver operating characteristics, HG= high-grade, LG = low-grade, AUC = area under the curve, PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Integrating a deep neural network and Transformer architecture for the automatic segmentation and survival prediction in cervical cancer.

Automated tumor segmentation and survival prediction are critical to clinical diagnosis and treatment. This study aimed to develop deep-learning models for automatic tumor segmentation and survival prediction in magnetic resonance imaging (MRI) of cervical cancer (CC) by combining deep neural networks and Transformer architecture. This study included 406 patients with CC, each with comprehensive clinical information and MRI scans. We randomly divided patients into training, validation, and independent test cohorts in a 6:2:2 ratio. During the model training, we employed two architecture types: one being a hybrid model combining convolutional neural network (CNN) and ransformer (CoTr) and one of pure CNNs. For survival prediction, the hybrid model combined tumor image features extracted by segmentation models with clinical information. The performance of the segmentation models was evaluated using the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). The performance of the survival models was assessed using the concordance index. The CoTr model performed well in both contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) imaging segmentation tasks, with average DSCs of 0.827 and 0.820, respectively, which outperformed other the CNN models such as U-Net (DSC: 0.807 and 0.808), attention U-Net (DSC: 0.814 and 0.811), and V-Net (DSC: 0.805 and 0.807). For survival prediction, the proposed deep-learning model significantly outperformed traditional methods, yielding a concordance index of 0.732. Moreover, it effectively divided patients into low-risk and high-risk groups for disease progression (P<0.001). Combining Transformer architecture with a CNN can improve MRI tumor segmentation, and this deep-learning model excelled in the survival prediction of patients with CC as compared to traditional methods.

Self-Supervised Contrastive Learning for Automated Segmentation of Brain Tumor MRI Images in Schizophrenia

Schizophrenic patients’ brain tumor magnetic resonance imaging (MRI) images are important references for doctors to diagnose and treat schizophrenia. However, automatic segmentation of these images is a professional and tedious task. Existing methods suffer from problems such as large model parameters, long computation time, and inadequate image processing. To achieve more accurate segmentation of brain tumors, we propose brain tumor MRI images for automatic segmentation using self-supervised contrastive learning in schizophrenia patients (BTCSSSP). First, a denoising algorithm based on progressive principal component analysis approximation and adaptive clustering is designed to process the noisy MRI images. Second, a brightness-aware image enhancement algorithm is developed to address the problems of non-uniformity, unclear boundaries, and poor spatial resolution of the MRI images. Finally, a cross-scale U-Net network with selective feature fusion attention module is designed based on self-supervised contrastive learning to achieve automatic segmentation of brain tumor MRI images. The results show that the BTCSSSP method yields higher Recall and Precision than existing methods. The maximum recall is 95%, and the image segmentation precision is 95%, thus indicating good practical applicability.

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors

Brain metastases (BMs) and high-grade gliomas (HGGs) are the most common and aggressive types of malignant brain tumors in adults, with often poor prognosis and short survival. As their clinical symptoms and image appearances on conventional magnetic resonance imaging (MRI) can be astonishingly similar, their accurate differentiation based solely on clinical and radiological information can be very challenging, particularly for “cancer of unknown primary”, where no systemic malignancy is known or found. Non-invasive multiparametric MRI and radiomics offer the potential to identify these distinct biological properties, aiding in the characterization and differentiation of HGGs and BMs. However, there is a scarcity of publicly available multi-origin brain tumor imaging data for tumor characterization. In this paper, we introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized DICOM files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting.

MR–CT image fusion method of intracranial tumors based on Res2Net

BackgroundInformation complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation.PurposeThe purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method.MethodsThis paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image.ResultsQualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics.ConclusionThe proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from The Cancer Imaging Archive (TCIA) to test the tumor segmentation performance of this software. Regions of interest (ROIs) corresponding to contrast-enhancing lesions, necrotic portions, and non-enhancing T2 high signal intensity components were segmented for each tumor. TumorPrism3D demonstrated high accuracy in segmenting all three tumor components in cases of glioblastoma multiforme. They achieved a better Dice similarity coefficient (DSC) ranging from 0.83 to 0.91 than 3DSlicer with a DSC ranging from 0.80 to 0.84 for the accuracy of segmented tumors. Comparative analysis with the widely used 3DSlicer software revealed TumorPrism3D to be approximately 37.4% faster in the segmentation process from initial contour drawing to final segmentation mask determination. The semi-automated nature of TumorPrism3D facilitates reproducible tumor segmentation at a rapid pace, offering the potential for quantitative analysis of tumor characteristics and artificial intelligence-assisted segmentation in brain MR imaging.

Magnetic resonance imaging-based prognostic model for subsequent distant metastasis in patients with ipsilateral breast tumor recurrence following breast-conserving surgery.

Ipsilateral breast tumor recurrence (IBTR) following breast-conserving surgery (BCS) has been considered a risk factor for distant metastasis (DM). Limited data are available regarding the subsequent outcomes after IBTR. Therefore, this study aimed to determine the clinical course after IBTR and develop a magnetic resonance imaging (MRI)-based predictive model for subsequent DM. We retrospectively extracted quantitative features from MRI to construct a radiomics cohort, with all eligible patients undergoing preoperative MRI at time of primary tumor and IBTR between 2010 and 2018. Multivariate Cox analysis was performed to identify factors associated with DM. Three models were constructed using different sets of clinicopathological, qualitative, and quantitative MRI features and compared. Additionally, Kaplan-Meier analysis was performed to assess the prognostic value of the optimal model. Among the 183 patients who experienced IBTR, 47 who underwent MRI for both primary and recurrent tumors were enrolled. Multivariate analysis demonstrated that the independent prognostic factors were human epidermal growth factor receptor 2 (HER2) status [hazard ratio (HR) =5.40] and background parenchymal enhancement (BPE) (HR =7.94) (all P values <0.01). Furthermore, four quantitative MRI features of recurrent tumors were selected through the least absolute shrinkage and selection operator (LASSO) method. The combined model exhibited superior performance [concordance index (C-index) 0.77] compared to the clinicoradiological model (C-index 0.71; P=0.006) and radiomics model (C-index 0.70; and P=0.01). Furthermore, the combined model successfully categorized patients into low- and high-risk subgroups with distinct prognoses (P<0.001). The clinicopathological and MRI features of IBTR were associated with secondary events following surgery. Additionally, the MRI-based combined model exhibited the highest predictive efficacy. These findings could be helpful in risk stratification and tailoring follow-up strategies in patients with IBTR.