Magnetic Resonance Modalities Research Articles

Glutamate Signaling in Patients With Parkinson Disease With REM Sleep Behavior Disorder.

Clinical heterogeneity of patients with Parkinson disease (PD) is well recognized. PD with REM sleep behavior disorder (RBD) is a more malignant phenotype with faster motor progression and higher nonmotor symptom burden. However, the neural mechanisms underlying this clinical divergence concerning imbalances in neurotransmitter systems remain elusive. Combining magnetic resonance (MR) spectroscopy and [11C]ABP688 PET on a PET/MR hybrid system, we simultaneously investigated two different mechanisms of glutamate signaling in patients with PD. Patients were grouped according to their RBD status in overnight video-polysomnography and compared with age-matched and sex-matched healthy control (HC) participants. Total volumes of distribution (VT) of [11C]ABP688 were estimated with metabolite-corrected plasma concentrations during steady-state conditions between 45 and 60 minutes of the scan following a bolus-infusion protocol. Glutamate, glutamine, and glutathione levels were investigated with single-voxel stimulated echo acquisition mode MR spectroscopy of the left basal ganglia. We measured globally elevated VT of [11C]ABP688 in 16 patients with PD and RBD compared with 17 patients without RBD and 15 HC participants (F(2,45) = 5.579, p = 0.007). Conversely, glutamatergic metabolites did not differ between groups and did not correlate with the regional VT of [11C]ABP688. VT of [11C]ABP688 correlated with the amount of REM sleep without atonia (F(1,42) = 5.600, p = 0.023) and with dopaminergic treatment response in patients with PD (F(1,30) = 5.823, p = 0.022). Our results suggest that patients with PD and RBD exhibit altered glutamatergic signaling indicated by higher VT of [11C]ABP688 despite unaffected glutamate levels. The imbalance of glutamate receptors and MR spectroscopy glutamate metabolite levels indicates a novel mechanism contributing to the heterogeneity of PD and warrants further investigation of drugs targeting mGluR5.

A Comprehensive Review of Couvelaire Uterus: Diagnosis, and Management

Couvelaire uterus, also known as uteroplacental apoplexy or Couvelaire syndrome, is a rare and serious complication of pregnancy characterized by the extravasation of blood into the myometrium and the formation of a hematoma between the myometrium and the placenta. This review aims to provide a comprehensive overview of the pathophysiology, clinical manifestations, diagnosis, and management of Couvelaire uterus. We discuss the etiology and risk factors associated with the condition, as well as its potential complications and outcomes. Additionally, we outline the various diagnostic modalities available, including ultrasound and magnetic resonance imaging, and describe the current management strategies, which often involve close monitoring and, in severe cases, surgical intervention. By highlighting the key aspects of Couvelaire uterus, this review seeks to enhance clinicians' understanding of this rare condition and improve patient outcomes.

Mouse Anesthesia and Analgesia

AbstractProviding anesthesia and analgesia for mouse subjects is a common and critical practice in the laboratory setting. This practice is necessary for performing invasive procedures, achieving prolonged immobility for sensitive imaging modalities (magnetic resonance imaging, for instance), and providing intra‐ and post‐procedural pain relief. In addition to facilitating the procedures performed by the investigator, the provision of anesthesia and analgesia is crucial for the preservation of animal welfare and for humane treatment of animals used in research. Furthermore, anesthesia and analgesia are important components of animal use protocols reviewed by Institutional Animal Care and Use Committees, requiring careful consideration and planning for the particular animal model. In this article, we provide technical guidance for the investigator, covering the provision of anesthesia by two routes (injectable and inhalant), guidelines for monitoring anesthesia, current techniques for recognition of pain, considerations for administering preventative analgesia, and considerations for post‐operative care. © 2024 Wiley Periodicals LLC.Basic Protocol 1: Injectable anesthesiaBasic Protocol 2: Inhalant anesthesiaBasic Protocol 3: Assessing pain

VOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images.

Accurate and consistent contouring of organs-at-risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR-only RT, and MR-guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated. To analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the samepatients. In the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board-certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95-percentile Hausdorff distance (HD ). The mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3mm in terms of DC and HD , respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5mm, which was considered as valid for the analysis of intermodalityvariability. The contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR-only and MR-guidedRT.

Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study

BackgroundCognitive impairment is a hallmark of multiple sclerosis (MS) but is usually an under-recorded symptom of disease progression. Identifying the predictive signatures of cognitive decline in people with MS (pwMS) over time is important to ensure effective preventative treatment strategies. Structural and functional brain characteristics as measured by various magnetic resonance (MR) methods have been correlated with variation in cognitive function in MS, but typically these studies are limited to a single MR modality and/or are cross-sectional designs. Here we assess the predictive value of multiple different MR modalities in relation to cognitive decline in pwMS over 5 years. MethodsA cohort of 43 pwMS was assessed at baseline and 5 years follow-up. Baseline (input) data consisted of 70 multi-modal MRI measures for different brain regions including magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and standard volumetrics. Age, sex, disease duration and treatment were included as clinical inputs. Cognitive function was assessed using the Audio Recorded Cognitive Screen (ARCS) and the Symbol Digit Modalities Test (SDMT). Prediction modelling was performed using the machine learning package - GLMnet, where a penalised regression was applied to identify multi-modal signatures with the most predictive value (and the least error) for each outcome. ResultsThe multi-modal approach to neuroimaging was able to accurately predict cognitive decline in pwMS. The best performing model for change in total ARCS (tARCS) included 16 features from across the various MR modalities and explained 54 % of the variation in change over time (R2=0.54, 95 % CI=0.48–0.51). The features included nine MRS, four volumetric and two DTI parameters. The model also selected disease duration, but not treatment, as a predictive feature. By comparison, the best model for SDMT included several of the same above features and explained 39 % of the change over time (R2=0.39, 95 % CI=0.48–0.51). Conventional volumetric measures were about half as good at predicting change in tARCS score compared to the best multi-modal model (R2=0.26 95 % CI:0.22–0.29). The clinical interpretation of the best predictive model for change in tARCS showed that cognitive decline could be predicted with >90 % accuracy in this cohort (AUC=0.92, SE=0.86 - 0.94). ConclusionMulti-modal MRI signatures can predict cognitive decline in a cohort of pwMS over 5 years with high accuracy. Future studies will benefit from the inclusion of even more MR modalities e.g., functional MRI, quantitative susceptibility mapping, magnetisation transfer imaging, as well as other potential predictors e.g., genetic and environmental factors. With further validation, this signature could be used in future trials with high-risk patients to personalise the management of cognitive decline in pwMS, even in the absence of relapses.

A Transformable Mucoadhesive Microgel Network for Noninvasive Multimodal Imaging And Radioprotection of a Large Area of the Gastrointestinal Tract.

The lack of noninvasive imaging and modulation of a large area of the gastrointestinal (GI) tract constrain the diagnosis and treatment of many GI-related diseases. Recent advances use novel mucoadhesive materials to coat a part of the GI tract and then modulate its functions. High mucoadhesion is the key factor of the partial coating, but also the limitation for not spreading and covering the lower GI tract. Here, a bismuth-pectin organic-inorganic hybrid complex is screened and engineered into a transformable microgel network (Bi-GLUE) with high flowability and mucoadhesion, such that itcan quickly transit through and coat a large area of the GI tract. In murine and porcine models, Bi-GLUE delivers contrast agents to achieve real-time, large-area GI-tract imaging under X-ray or magnetic resonance modalities and to facilitate the non-invasive diagnosis of familial adenomatous polyposis. Moreover, Bi-GLUE, like an intracorporal radiation shield, decreases the radiotoxicity in a whole-abdomen irradiation rat model. This transformable microgel network offers a new direction that can modulate a large area of the GI tract and may have broad applications for GI-related conditions.

Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma.

The presence of microvascular invasion (MVI) is a crucial indicator of postoperative recurrence in patients with hepatocellular carcinoma (HCC). Detecting MVI before surgery can improve personalized surgical planning and enhance patient survival. However, existing automatic diagnosis methods for MVI have certain limitations. Some methods only analyze information from a single slice and overlook the context of the entire lesion, while others require high computational resources to process the entire tumor with a three-dimension (3D) convolutional neural network (CNN), which could be challenging to train. To address these limitations, this paper proposes a modality-based attention and dual-stream multiple instance learning(MIL) CNN. In this retrospective study, 283 patients with histologically confirmed HCC who underwent surgical resection between April 2017 and September 2019 were included. Five magnetic resonance (MR) modalities including T2-weighted, arterial phase, venous phase, delay phase and apparent diffusion coefficient images were used in image acquisition of each patient. Firstly, Each two-dimension (2D) slice of HCC magnetic resonance image (MRI) was converted into an instance embedding. Secondly, modality attention module was designed to emulates the decision-making process of doctors and helped the model to focus on the important MRI sequences. Thirdly, instance embeddings of 3D scans were aggregated into a bag embedding by a dual-stream MIL aggregator, in which the critical slices were given greater consideration. The dataset was split into a training set and a testing set in a 4:1 ratio, and model performance was evaluated using five-fold cross-validation. Using the proposed method, the prediction of MVI achieved an accuracy of 76.43% and an AUC of 74.22%, significantly surpassing the performance of the baseline methods. Our modality-based attention and dual-stream MIL CNN can achieve outstanding results for MVI prediction.

Cystic Adenoid Carcinoma Of The Trachea: A Rare Case Of Asymptomatic Tracheal Tumor

Aim : To present the imaging appearance of tracheal adenoid cystic carcinoma according to the different imaging modalities (ultrasound, computed tomography and magnetic resonance imaging). Method : We present the case of a 60-year-old patient, without symptoms guiding the diagnosis, in whom the diagnosis was suspected on CT scan imaging. Results : The patient was explored by CT angiography of the supra-aortic trunks as part of the etiologic investigation of an ischemic vascular accident. The examination objectified a tissular mass centered on the tracheoesophageal axis lateralized to the left invading the laryngeal cartilages and the left lobe of the thyroid. A cervical ultrasound was performed showing a heterogeneous hypoechoic area of the left lobe of the thyroid with no detected nodule. Fine needle aspiration of the suspicious area was performed, showing a tumoral lesion with basaloid cells evoking the diagnosis of adenoid cystic carcinoma. In order to assess the tumor extension and the patient's operability, a cervical MRI was performed given its better resolution in contrast. A bronchial fibroscopy was indicated showing a tracheal circumferential infiltrative stenosis with biopsies confirming the diagnosis. Conclusion: Adenoid cystic carcinomas of the trachea are rare tumors. Tracheobronchial endoscopy combined with biopsies allow the diagnosis of certainty. Cross-sectional imaging (CT and MRI) plays a key role in locoregional and distant extension assessment, thus guiding therapeutic management.

Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Volume Deviations in Alzheimer's Disease.

Normative modelling is a method for understanding the underlying heterogeneity within brain disorders like Alzheimer Disease (AD), by quantifying how each patient deviates from the expected normative pattern that has been learned from a healthy control distribution. Existing deep learning based normative models have been applied on only single modality Magnetic Resonance Imaging (MRI) neuroimaging data. However, these do not take into account the complementary information offered by multimodal M RI, which is essential for understanding a multifactorial disease like AD. To address this limitation, we propose a multi-modal variational autoencoder (mmVAE) based normative modelling framework that can capture the joint distribution between different modalities to identify abnormal brain volume deviations due to AD. Our multi-modal framework takes as input Freesurfer processed brain region volumes from T1-weighted (cortical and subcortical) and T2-weighed (hippocampal) scans of cognitively normal participants to learn the morphological characteristics of the healthy brain. The estimated normative model is then applied on AD patients to quantify the deviation in brain volumes and identify abnormal brain pattern deviations due to the progressive stages of AD. We compared our proposed mmVAE with a baseline unimodal VAE having a single encoder and decoder and the two modalities concatenated as unimodal input. Our experimental results show that deviation maps generated by mmVAE are more sensitive to disease staging within AD, have a better correlation with patient cognition and result in higher number of brain regions with statistically significant deviations compared to the unimodal baseline model.

Distilling sub-space structure across views for cardiac indices estimation.

Cardiac indices estimation in multi-view images attracts great attention due to its capability for cardiac function assessment. However, the variation of the cardiac indices across views causes that most cardiac indices estimation methods can only be trained separately in each view, resulting in low data utilization. To solve this problem, we have proposed distilling the sub-space structure across views to explore the multi-view data fully for cardiac indices estimation. In particular, the sub-space structure is obtained via building a n×n covariance matrix to describe the correlation between the output dimensions of all views. Then, an alternate convex search algorithm is proposed to optimize the cross-view learning framework by which: (i) we train the model with regularization of sub-space structure in each view; (ii) we update the sub-space structure based on the learned parameters from all views. In the end, we have conducted a series of experiments to verify the effectiveness of our proposed framework. The model is trained on three views (short axis, 2-chamber view and 4-chamber view) with two modalities (magnetic resonance imaging and computed tomography). Compared to the state-of-the-art methods, our method has demonstrated superior performance on cardiac indices estimation tasks.

MouseGAN++: Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain.

Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produce distinguished contrasts in different brain structures. Hence, we propose a novel disentangled and contrastive GAN-based framework, named MouseGAN++, to synthesize multiple MR modalities from single ones in a structure-preserving manner, thus improving the segmentation performance by imputing missing modalities and multi-modality fusion. Our results demonstrate that the translation performance of our method outperforms the state-of-the-art methods. Using the subsequently learned modality-invariant information as well as the modality-translated images, MouseGAN++ can segment fine brain structures with averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively, achieving around +10% performance improvement compared to the state-of-the-art algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data. We release our method as a mouse brain structural segmentation tool for free academic usage at https://github.com/yu02019.

Adrenal tumors: diagnostics, perioperative management and surgical treatment

Space-occupying lesions of the adrenal glands are one of the most frequent tumors; however, only a fraction of approximately 20% need further diagnostics and treatment. The diagnostic standard is native computed tomography (CT). For larger tumors and those that cannot be clearly classified as benign, the supplementary radiological modalities magnetic resonance imaging (MRI), contrast CT and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) should be used. Adrenal gland neoplasms can be hormone-active or hormone-inactive. The most important hormone-active adrenal gland neoplasms are those with autonomous cortisol secretion and autonomous aldosterone secretion as well as pheochromocytomas. Several laboratory tests are available each for the respective hormone activity. The indications for surgery are dependent on the dignity, size and hormone activity. Minimally invasive surgical techniques have become more important due to advantages such as lower blood loss; nevertheless, open surgery is still indispensable for large suspected tumors. Care must be taken preoperatively and postoperatively, particularly in cases of hormone-active tumors.

Hippocampal Neuronal Integrity and Functional Connectivity Within the Default Mode Network in Mild Cognitive Impairment: A Multimodal Investigation.

Background: In older people with mild cognitive impairment (MCI), the relationship between early changes in functional connectivity and in vivo changes in key neurometabolites is not known. Two established correlates of MCI diagnosis are decreased N-acetylaspartate (NAA) in the hippocampus, indicative of decreased neuronal integrity, and changes in the default mode network (DMN) functional network. If and how these measures interrelate is yet to be established, and such understanding may provide insight into the processes underpinning observed cognitive decline. Objectives: To determine the relationship between NAA levels in the left hippocampus and functional connectivity within the DMN in an aging cohort. Methods: In a sample of 51 participants with MCI and 30 controls, hippocampal NAA was determined using magnetic resonance spectroscopy, and DMN connectivity was quantified using resting-state functional MRI. The association between hippocampal NAA and the DMN functional connectivity was tested within the MCI group and separately within the control group. Results: In the DMN, we showed a significant inverse association between functional connectivity and hippocampal NAA in 20 specific brain connections for patients with MCI. This was despite no evidence of any associations in the healthy control group or group differences in either of these measures alone. Conclusions: This study suggests that decreased neuronal integrity in the hippocampus is associated with functional change within the DMN for those with MCI, in contrast to healthy older adults. These results highlight the potential of multimodal investigations to better understand the processes associated with cognitive decline. Impact statement This study measured activity within the default mode network (DMN) and quantified N-acetylaspartate (NAA), a measure of neuronal integrity, within the hippocampus in participants with mild cognitive impairment (MCI) and healthy controls. In participants with MCI, NAA levels were inversely associated with connectivity between specific regions of the DMN, a relationship not evident in healthy controls. This association was present even in the absence of group differences in DMN connectivity or NAA levels. This research illustrates the possibility of using multiple magnetic resonance modalities for more sensitive measures of early cognitive decline to identify and intervene earlier.

A Scoping Review of Magnetic Resonance Modalities Used in Detection of Persistent Postconcussion Symptoms in Pediatric Populations.

Up to 30% of youth with concussion experience PPCSs (PPCS) lasting 4 weeks or longer, and can significantly impact quality of life. Magnetic resonance imaging (MRI) has the potential to increase understanding of causal mechanisms underlying PPCS. However, there are no clear modalities to assist in detecting PPCS. This scoping review aims to synthesize findings on utilization of MRI among children and youth with PPCS, and summarize progress and limitations. Thirty-six studies were included from 4907 identified papers. Many studies used multiple modalities, including (1) structural (n = 27) such as T1-weighted imaging, diffusion weighted imaging, and susceptibility weighted imaging; and (2) functional (n = 23) such as functional MRI and perfusion-weighted imaging. Findings were heterogeneous among modalities and regions of interest, which warrants future reviews that report on the patterns and potential advancements in the field. Consideration of modalities that target PPCS prediction and sensitive modalities that can supplement a biopsychosocial approach to PPCS would benefit future research.

A layer-wise fusion network incorporating self-supervised learning for multimodal MR image synthesis.

Magnetic resonance (MR) imaging plays an important role in medical diagnosis and treatment; different modalities of MR images can provide rich and complementary information to improve the accuracy of diagnosis. However, due to the limitations of scanning time and medical conditions, certain modalities of MR may be unavailable or of low quality in clinical practice. In this study, we propose a new multimodal MR image synthesis network to generate missing MR images. The proposed model comprises three stages: feature extraction, feature fusion, and image generation. During feature extraction, 2D and 3D self-supervised pretext tasks are introduced to pre-train the backbone for better representations of each modality. Then, a channel attention mechanism is used when fusing features so that the network can adaptively weigh different fusion operations to learn common representations of all modalities. Finally, a generative adversarial network is considered as the basic framework to generate images, in which a feature-level edge information loss is combined with the pixel-wise loss to ensure consistency between the synthesized and real images in terms of anatomical characteristics. 2D and 3D self-supervised pre-training can have better performance on feature extraction to retain more details in the synthetic images. Moreover, the proposed multimodal attention feature fusion block (MAFFB) in the well-designed layer-wise fusion strategy can model both common and unique information in all modalities, consistent with the clinical analysis. We also perform an interpretability analysis to confirm the rationality and effectiveness of our method. The experimental results demonstrate that our method can be applied in both single-modal and multimodal synthesis with high robustness and outperforms other state-of-the-art approaches objectively and subjectively.

Brain tumor segmentation using multi-level Otsu thresholding and Chan-Vese active contour model

Research on brain tumor segmentation has been developed, ranging from threshold-based methods to the use of the deep learning algorithm. In this study, we proposed a region-based brain tumor segmentation method, namely the active contour model (ACM). Tumor segmentation was carried out using fluid attenuated inversion recovery (FLAIR) modality magnetic resonance imaging (MRI) image data obtained from the multimodal brain tumor image segmentation benchmark (BRATS) 2015 dataset of 86 images. The initial stage of our segmentation method is to find the initial initialization point/area for the ACM algorithm using multi-level Otsu thresholding, with the level used in this study is 3 levels. After the initial initialization area has been obtained, the segmentation process is continued with ACM which explores the tumor area to obtain a full and accurate tumor area result. The results of this study obtained dice similarity (DS) for our study of 0.7856 with a total time required of 28.080722 seconds, which better than other method that we also compared with ours, 0.75 compared to 0.78 in term of DS.

Worldwide Disparities in Recovery of Cardiac Testing 1 Year Into COVID-19

BackgroundThe extent to which health care systems have adapted to the COVID-19 pandemic to provide necessary cardiac diagnostic services is unknown. ObjectivesThe aim of this study was to determine the impact of the pandemic on cardiac testing practices, volumes and types of diagnostic services, and perceived psychological stress to health care providers worldwide. MethodsThe International Atomic Energy Agency conducted a worldwide survey assessing alterations from baseline in cardiovascular diagnostic care at the pandemic’s onset and 1 year later. Multivariable regression was used to determine factors associated with procedure volume recovery. ResultsSurveys were submitted from 669 centers in 107 countries. Worldwide reduction in cardiac procedure volumes of 64% from March 2019 to April 2020 recovered by April 2021 in high- and upper middle-income countries (recovery rates of 108% and 99%) but remained depressed in lower middle- and low-income countries (46% and 30% recovery). Although stress testing was used 12% less frequently in 2021 than in 2019, coronary computed tomographic angiography was used 14% more, a trend also seen for other advanced cardiac imaging modalities (positron emission tomography and magnetic resonance; 22%-25% increases). Pandemic-related psychological stress was estimated to have affected nearly 40% of staff, impacting patient care at 78% of sites. In multivariable regression, only lower-income status and physicians’ psychological stress were significant in predicting recovery of cardiac testing. ConclusionsCardiac diagnostic testing has yet to recover to prepandemic levels in lower-income countries. Worldwide, the decrease in standard stress testing is offset by greater use of advanced cardiac imaging modalities. Pandemic-related psychological stress among providers is widespread and associated with poor recovery of cardiac testing.

Explainability of deep neural networks for MRI analysis of brain tumors

PurposeArtificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice.MethodsIn this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent.ResultsNeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN.ConclusionDue to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI.

Multimodality MRI-based radiomics for aggressiveness prediction in papillary thyroid cancer

ObjectiveTo investigate the ability of a multimodality MRI-based radiomics model in predicting the aggressiveness of papillary thyroid carcinoma (PTC).MethodsThis study included consecutive patients who underwent neck magnetic resonance (MR) scans and subsequent thyroidectomy during the study period. The pathological diagnosis of thyroidectomy specimens was the gold standard to determine the aggressiveness. Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted. A machine learning model was established to evaluate the prediction of PTC aggressiveness.ResultsThe study cohort included 107 patients with PTC confirmed by pathology (cross-validation cohort: n = 71; test cohort: n = 36). A total of 1584 features were extracted from contrast-enhanced T1-weighted (CE-T1 WI), T2-weighted (T2 WI) and diffusion weighted (DWI) images of each patient. Sparse representation method is used for radiation feature selection and classification model establishment. The accuracy of the independent test set that using only one modality, like CE-T1WI, T2WI or DWI was not particularly satisfactory. In contrast, the result of these three modalities combined achieved 0.917.ConclusionOur study shows that multimodality MR image based on radiomics model can accurately distinguish aggressiveness in PTC from non-aggressiveness PTC before operation. This method may be helpful to inform the treatment strategy and prognosis of patients with aggressiveness PTC.

Bioresponsive Nanomaterials: Recent Advances in Cancer Multimodal Imaging and Imaging-Guided Therapy.

Cancer is a serious health problem which increasingly causes morbidity and mortality worldwide. It causes abnormal and uncontrolled cell division. Traditional cancer treatments include surgery, chemotherapy, radiotherapy and so on. These traditional therapies suffer from high toxicity and arouse safety concern in normal area and have difficulty in accurately targeting tumour. Recently, a variety of nanomaterials could be used for cancer diagnosis and therapy. Nanomaterials have several advantages, e.g., high concentration in tumour via targeting design, reduced toxicity in normal area and controlled drug release after various rational designs. They can combine with many types of biomaterials in order to improve biocompatibility. In this review, we outlined the latest research on the use of bioresponsive nanomaterials for various cancer imaging modalities (magnetic resonance imaging, positron emission tomography and phototacoustic imaging) and imaging-guided therapy means (chemotherapy, radiotherapy, photothermal therapy and photodynamic therapy), followed by discussing the challenges and future perspectives of this bioresponsive nanomaterials in biomedicine.