MR Imaging in the 21st Century: Technical Innovation over the First Two Decades

Recent advances in mitochondria-targeting theranostic agents

For its vital role in maintaining cellular activity and survival, mitochondrion is highly involved in various diseases, and several strategies to target mitochondria have been developed for specific imaging and treatment. Among these approaches, theranostic may realize both diagnosis and therapy with one integrated material, benefiting the simplification of treatment process and candidate drug evaluation. A variety of mitochondria-targeting theranostic agents have been designed based on the differential structure and composition of mitochondria, which enable more precise localization within cellular mitochondria at disease sites, facilitating the unveiling of pathological information while concurrently performing therapeutic interventions. Here, progress of mitochondria-targeting theranostic materials reported in recent years along with background information on mitochondria-targeting and therapy have been briefly summarized, determining to deliver updated status and design ideas in this field to readers.

Virtual Reality Head-Mounted Display (HMD) and Preoperative Patient-Specific Simulation: Impact on Decision-Making in Pediatric Urology: Preliminary Data

AIM OF THE STUDY

To assess how virtual reality (VR) patient-specific simulations can support decision-making processes and improve care in pediatric urology, ultimately improving patient outcomes.

PATIENTS AND METHODS

Children diagnosed with urological conditions necessitating complex procedures were retrospectively reviewed and enrolled in the study. Patient-specific VR simulations were developed with medical imaging specialists and VR technology experts. Routine CT images were utilized to create a VR environment using advanced software platforms. The accuracy and fidelity of the VR simulations was validated through a multi-step process. This involved comparing the virtual anatomical models to the original medical imaging data and conducting feedback sessions with pediatric urology experts to assess VR simulations' realism and clinical relevance.

RESULTS

A total of six pediatric patients were reviewed. The median age of the participants was 5.5 years (IQR: 3.5-8.5 years), with an equal distribution of males and females across both groups. A minimally invasive laparoscopic approach was performed for adrenal lesions (n = 3), Wilms' tumor (n = 1), bilateral nephroblastomatosis (n = 1), and abdominal trauma in complex vascular and renal malformation (ptotic and hypoplastic kidney) (n = 1). Key benefits included enhanced visualization of the segmental arteries and the deep vascularization of the kidney and adrenal glands in all cases. The high depth perception and precision in the orientation of the arteries and veins to the parenchyma changed the intraoperative decision-making process in five patients. Preoperative VR patient-specific simulation did not offer accuracy in studying the pelvic and calyceal anatomy.

CONCLUSIONS

VR patient-specific simulations represent an empowering tool in pediatric urology. By leveraging the immersive capabilities of VR technology, preoperative planning and intraoperative navigation can greatly impact surgical decision-making. As we continue to advance in medical simulation, VR holds promise in educational programs to include even surgical treatment of more complex urogenital malformations.

Technology-enabled patient care in medical radiation sciences: the two sides of the coin

This is an exciting time to be working in healthcare and medical radiation sciences. This article discusses the potential benefits and risks of new technological interventions for patient benefit and outlines the need for co-production, governance and education to ensure these are used for advancing patients' well-being.

Randomized controlled trials of non-pharmacological interventions for healthy seniors: Effects on cognitive decline, brain plasticity and activities of daily living-A 23-year scoping review

Little is known about the simultaneous effects of non-pharmacological interventions (NPI) on healthy older adults' behavior and brain plasticity, as measured by psychometric instruments and magnetic resonance imaging (MRI). The purpose of this scoping review was to compile an extensive list of randomized controlled trials published from January 1, 2000, to August 31, 2023, of NPI for mitigating and countervailing age-related physical and cognitive decline and associated cerebral degeneration in healthy elderly populations with a mean age of 55 and over. After inventorying the NPI that met our criteria, we divided them into six classes: single-domain cognitive, multi-domain cognitive, physical aerobic, physical non-aerobic, combined cognitive and physical aerobic, and combined cognitive and physical non-aerobic. The ultimate purpose of these NPI was to enhance individual autonomy and well-being by bolstering functional capacity that might transfer to activities of daily living. The insights from this study can be a starting point for new research and inform social, public health, and economic policies. The PRISMA extension for scoping reviews (PRISMA-ScR) checklist served as the framework for this scoping review, which includes 70 studies. Results indicate that medium- and long-term interventions combining non-aerobic physical exercise and multi-domain cognitive interventions best stimulate neuroplasticity and protect against age-related decline and that outcomes may transfer to activities of daily living.

Application of PET/MRI in Gynecologic Malignancies

The diagnosis, treatment, and management of gynecologic malignancies benefit from both positron emission tomography/computed tomography (PET/CT) and MRI. PET/CT provides important information on the local extent of disease as well as diffuse metastatic involvement. MRI offers soft tissue delineation and loco-regional disease involvement. The combination of these two technologies is key in diagnosis, treatment planning, and evaluating treatment response in gynecological malignancies. This review aims to assess the performance of PET/MRI in gynecologic cancer patients and outlines the technical challenges and clinical advantages of PET/MR systems when specifically applied to gynecologic malignancies.

Cutting Edge Developments in Spine Surgery at the University of Missouri

The treatment of spinal pathologies has evolved significantly from the times of Hippocrates and Galen to the current era. This evolution has led to the development of cutting-edge technologies to improve surgical techniques and patient outcomes. The University of Missouri Health System is a high-volume, tertiary care academic medical center that serves a large catchment area in central Missouri and beyond. The Department of Neurosurgery has sought to integrate the best available technologies to serve their spine patients. These technological advancements include intra-operative image guidance, robotic spine surgery, minimally invasive techniques, motion preservation surgery, and interdisciplinary care of metastatic disease to the spine. These advances have resulted in safer surgeries with enhanced outcomes at the University of Missouri. This integration of innovation demonstrates our tireless commitment to ensuring excellence in the comprehensive care of a diverse range of patients with complex spinal pathologies.

The Coproduction of Health Framework: Seeking Instructive Management Models and Theories

At the beginning of the 21st century, multiple and diverse social entities, including the public (consumers), private and nonprofit healthcare institutions, government (public health) and other industry sectors, began to recognize the limitations of the current fragmented healthcare system paradigm. Primary stakeholders, including employers, insurance companies, and healthcare professional organizations, also voiced dissatisfaction with unacceptable health outcomes and rising costs. Grand challenges and wicked problems threatened the viability of the health sector. American health systems responded with innovations and advances in healthcare delivery frameworks that encouraged shifts from intra- and inter-sector arrangements to multi-sector, lasting relationships that emphasized patient centrality along with long-term commitments to sustainability and accountability. This pathway, leading to a population health approach, also generated the need for transformative business models. The coproduction of health framework, with its emphasis on cross-sector alignments, nontraditional partner relationships, sustainable missions, and accountability capable of yielding return on investments, has emerged as a unique strategy for facing disruptive threats and challenges from nonhealth sector corporations. This chapter presents a coproduction of health framework, goals and criteria, examples of boundary spanning network alliance models, and operational (integrator, convener, aggregator) strategies. A comparison of important organizational science theories, including institutional theory, network/network analysis theory, and resource dependency theory, provides suggestions for future research directions necessary to validate the utility of the coproduction of health framework as a precursor for paradigm change.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics

ABSTRACT

With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

Recent Developments in PET and SPECT Radiotracers as Radiopharmaceuticals for Hypoxia Tumors

Hypoxia, a deficiency in the levels of oxygen, is a common feature of most solid tumors and induces many characteristics of cancer. Hypoxia is associated with metastases and strong resistance to radio- and chemotherapy, and can decrease the accuracy of cancer prognosis. Non-invasive imaging methods such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) using hypoxia-targeting radiopharmaceuticals have been used for the detection and therapy of tumor hypoxia. Nitroimidazoles are bioreducible moieties that can be selectively reduced under hypoxic conditions covalently bind to intracellular macromolecules, and are trapped within hypoxic cells and tissues. Recently, there has been a strong motivation to develop PET and SPECT radiotracers as radiopharmaceuticals containing nitroimidazole moieties for the visualization and treatment of hypoxic tumors. In this review, we summarize the development of some novel PET and SPECT radiotracers as radiopharmaceuticals containing nitroimidazoles, as well as their physicochemical properties, in vitro cellular uptake values, in vivo biodistribution, and PET/SPECT imaging results.

Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model

PURPOSE

The accurate preoperative histopathological grade diagnosis of adult gliomas is of great significance for the formulation of a surgical plan and the implementation of a subsequent treatment. The aim of this study is to establish a predictive model for classifying adult gliomas into grades 2-4 based on preoperative conventional multimodal MRI radiomics.

PATIENTS AND METHODS

Patients with pathologically confirmed gliomas at Huashan Hospital, Fudan University, between February 2017 and July 2019 were retrospectively analyzed. Two regions of interest (ROIs), called the maximum anomaly region (ROI1) and the tumor region (ROI2), were delineated on the patients' preoperative MRIs utilizing the tool ITK-SNAP, and Pyradiomics 3.0 was applied to execute feature extraction. Feature selection was performed utilizing a least absolute shrinkage and selection operator (LASSO) filter. Six classifiers, including Gaussian naive Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) with a linear kernel, adaptive boosting (AB), and multilayer perceptron (MLP) were used to establish predictive models, and the predictive performance of the six classifiers was evaluated through five-fold cross-validation. The performance of the predictive models was evaluated using the AUC and other metrics. After that, the model with the best predictive performance was tested using the external data from The Cancer Imaging Archive (TCIA).

RESULTS

According to the inclusion and exclusion criteria, 240 patients with gliomas were identified for inclusion in the study, including 106 grade 2, 68 grade 3, and 66 grade 4 gliomas. A total of 150 features was selected, and the MLP classifier had the best predictive performance among the six classifiers based on T2-FLAIR (mean AUC of 0.80 ± 0.07). The SVM classifier had the best predictive performance among the six classifiers based on DWI (mean AUC of 0.84 ± 0.05); the SVM classifier had the best predictive performance among the six classifiers based on CE-T1WI (mean AUC of 0.85 ± 0.06). Among the six classifiers, based on ROI1, the MLP classifier had the best prediction performance (mean AUC of 0.78 ± 0.07); among the six classifiers, based on ROI2, the SVM classifier had the best prediction performance (mean AUC of 0.82 ± 0.07). Among the six classifiers, based on the multimodal MRI of all the ROIs, the SVM classifier had the best prediction performance (average AUC of 0.85 ± 0.04). The SVM classifier, based on the multimodal MRI of all the ROIs, achieved an AUC of 0.81 using the external data from TCIA.

CONCLUSIONS

The prediction model, based on preoperative conventional multimodal MRI radiomics, established in this study can conveniently, accurately, and noninvasively classify adult gliomas into grades 2-4, providing certain assistance for the precise diagnosis and treatment of patients and optimizing their clinical management.

Clinical applications of artificial intelligence in liver imaging

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.