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PurposeTo evaluate the epidemiology of systematic reviews (SRs) published in imaging journals.MethodsA MEDLINE search identified SRs published in imaging journals from 1 January 2000–31 December 2016. Articles retrieved were screened against inclusion criteria. Demographic and methodological characteristics were extracted from studies. Temporal trends were evaluated using linear regression and Pearson’s correlation coefficients.Results921 SRs were included that reported on 27,435 primary studies, 85,276,484 patients and were cited 26,961 times. The SR publication rate increased 23-fold (r=0.92, p<0.001) while the proportion of SRs to non-SRs increased 13-fold (r = 0.94, p<0.001) from 2000 (0.10%) to 2016 (1.33%). Diagnostic test accuracy (DTA) SRs were most frequent (46.5%) followed by therapeutic SRs (16.6%). Most SRs did not report funding status (54.2%). The median author team size was five; this increased over time (r=0.20, p<0.001). Of the studies, 67.3% included an imaging specialist co-author; this decreased over time (r=-0.57, p=0.017). Most SRs included a meta-analysis (69.6%). Journal impact factor positively correlated with SR publication rates (r=0.54, p<0.001). Magnetic resonance imaging (MRI) and ‘vascular and interventional radiology’ were the most frequently studied imaging modality and subspecialty, respectively. The USA, UK, China, Netherlands and Canada were the top five publishing countries.ConclusionsThe SR publication rate is increasing rapidly compared with the rate of growth of non-SRs; however, they still make up just over 1% of all studies. Authors, reviewers and editors should be aware of methodological and reporting standards specific to imaging systematic reviews including those for DTA and individual patient data.Key Points• Systematic review publication rate has increased 23-fold from 2000–2016.• The proportion of systematic reviews to non-systematic reviews has increased 13-fold.• The USA, UK and China are the most frequent published countries; those from the USA and China are increasing the most rapidly.

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

Computational pathology 1 , 2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders 3 , 4 . However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots 5 tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref. 6 ). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making. PathChat, a multimodal generative AI copilot for human pathology, has been trained on a large dataset of visual-language instructions to interactively assist users with diverse pathology tasks.

Intraoperative imaging (IOI) is performed to guide delineation and localization of regions of surgical interest. While oncological surgical planning predominantly utilizes x-ray computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US), intraoperative guidance mainly remains on surgeon interpretation and pathology for confirmation. Over the past decades however, intraoperative guidance has evolved significantly with the emergence of several novel imaging technologies, including fluorescence-, Raman, photoacoustic-, and radio-guided approaches. These modalities have demonstrated the potential to further optimize precision in surgical resection and improve clinical outcomes for patients. Not only can these technologies enhance our understanding of the disease, they can also yield large imaging datasets intraoperatively that can be analyzed by deep learning approaches for more rapid and accurate pathological diagnosis. Unfortunately, many of these novel technologies are still under preclinical or early clinical evaluation. Organizations like the Intra-Operative Imaging Study Group of the European Society for Molecular Imaging (ESMI) support interdisciplinary interactions with the aim to improve technical capabilities in the field, an approach that can succeed only if scientists, engineers, and physicians work closely together with industry and regulatory bodies to resolve roadblocks to clinical translation. In this review, we provide an overview of a variety of novel IOI technologies, discuss their challenges, and present future perspectives on the enormous potential of IOI for oncological surgical navigation.

Imaging is an essential tool in research, diagnostics and the management of endocrine disorders. Ultrasonography, nuclear medicine techniques, MRI, CT and optical methods are already used for applications in endocrinology. Optoacoustic imaging, also termed photoacoustic imaging, is emerging as a method for visualizing endocrine physiology and disease at different scales of detail: microscopic, mesoscopic and macroscopic. Optoacoustic contrast arises from endogenous light absorbers, such as oxygenated and deoxygenated haemoglobin, lipids and water, or exogenous contrast agents, and reveals tissue vasculature, perfusion, oxygenation, metabolic activity and inflammation. The development of high-performance optoacoustic scanners for use in humans has given rise to a variety of clinical investigations, which complement the use of the technology in preclinical research. Here, we review key progress with optoacoustic imaging technology as it relates to applications in endocrinology; for example, to visualize thyroid morphology and function, and the microvasculature in diabetes mellitus or adipose tissue metabolism, with particular focus on multispectral optoacoustic tomography and raster-scan optoacoustic mesoscopy. We explain the merits of optoacoustic microscopy and focus on mid-infrared optoacoustic microscopy, which enables label-free imaging of metabolites in cells and tissues. We showcase current optoacoustic applications within endocrinology and discuss the potential of these technologies to advance research and clinical practice.Optoacoustic imaging enables the non-invasive and label-free imaging of the structure and function of organs, tissues and cells. This Review highlights key progress with optoacoustic imaging technology for applications in endocrinology and metabolism, with a specific focus on multispectral optoacoustic tomography and raster-scan optoacoustic mesoscopy.

Myopia is rapidly increasing in Asia and around the world, while it is recognised that complications from high myopia may cause significant visual impairment. Thus, imaging the myopic eye is important for the diagnosis of sight-threatening complications, monitoring of disease progression and evaluation of treatments. For example, recent advances in high-resolution imaging using optical coherence tomography may delineate early myopic macula pathology, optical coherence tomography angiography may aid early choroidal neovascularisation detection, while multimodal imaging is important for monitoring treatment response. However, imaging the eye with high myopia accurately has its challenges and limitations, which are important for clinicians to understand in order to choose the best imaging modality and interpret the images accurately. In this review, we present the current imaging modalities available from the anterior to posterior segment of the myopic eye, including the optic nerve. We summarise the clinical indications, image interpretation and future developments that may overcome current technological limitations. We also discuss potential biomarkers for myopic progression or development of complications, including basement membrane defects, and choroidal atrophy or choroidal thickness measurements. Finally, we present future developments in the field of myopia imaging, such as photoacoustic imaging and corneal or scleral biomechanics, which may lead to innovative treatment modalities for myopia.

Current methodologies and data analysis techniques in vibrational spectroscopy and their biomedical applications As with many other spectroscopic techniques, Raman and infrared spectroscopy have made great progress due to recent developments in optics, detectors, nanotechnology, and computer science.

Background and purposeStudying the imaging and histopathologic characteristics of thrombi in ischemic stroke could provide insights into stroke etiology and ideal treatment strategies. We conducted a systematic review of imaging and histologic characteristics of thrombi in acute ischemic stroke.Materials and methodsWe identified all studies published between January 2005 and December 2015 that reported findings related to histologic and/or imaging characteristics of thrombi in acute ischemic stroke secondary to large vessel occlusion. The five outcomes examined in this study were (1) association between histologic composition of thrombi and stroke etiology; (2) association between histologic composition of thrombi and angiographic outcomes; (3) association between thrombi imaging and histologic characteristics; (4) association between thrombi imaging characteristics and angiographic outcomes; and (5) association between imaging characteristics of thrombi and stroke etiology. A meta-analysis was performed using a random effects model.ResultsThere was no significant difference in the proportion of red blood cell (RBC)-rich thrombi between cardioembolic and large artery atherosclerosis etiologies (OR 1.62, 95% CI 0.1 to 28.0, p=0.63). Patients with a hyperdense artery sign had a higher odds of having RBC-rich thrombi than those without a hyperdense artery sign (OR 9.0, 95% CI 2.6 to 31.2, p<0.01). Patients with a good angiographic outcome had a mean thrombus Hounsfield unit (HU) of 55.1±3.1 compared with a mean HU of 48.4±1.9 for patients with a poor angiographic outcome (mean standard difference 6.5, 95% CI 2.7 to 10.2, p<0.001). There was no association between imaging characteristics and stroke etiology (OR 1.13, 95% CI 0.32 to 4.00, p=0.85).ConclusionsThe hyperdense artery sign is associated with RBC-rich thrombi and improved recanalization rates. However, there was no association between the histopathological characteristics of thrombi and stroke etiology and angiographic outcomes.

The advancement of medical imaging has profoundly impacted our understanding of the human body and various diseases. It has led to the continuous refinement of related technologies over many years. Despite these advancements, several challenges persist in the development of medical imaging, including data shortages characterized by low contrast, high noise levels, and limited image resolution. The U-Net architecture has significantly evolved to address these challenges, becoming a staple in medical imaging due to its effective performance and numerous updated versions. However, the emergence of Transformer-based models marks a new era in deep learning for medical imaging. These models and their variants promise substantial progress, necessitating a comparative analysis to comprehend recent advancements. This review begins by exploring the fundamental U-Net architecture and its variants, then examines the limitations encountered during its evolution. It then introduces the Transformer-based self-attention mechanism and investigates how modern models incorporate positional information. The review emphasizes the revolutionary potential of Transformer-based techniques, discusses their limitations, and outlines potential avenues for future research.

Radiography is a field of medicine inherently intertwined with technology. The dependency on technology is very high for obtaining images in ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although the reduction in radiation dose is not applicable in US and MRI, advancements in technology have made it possible in CT, with ongoing studies aimed at further optimization. The resolution and diagnostic quality of images obtained through advancements in each modality are steadily improving. Additionally, technological progress has significantly shortened acquisition times for CT and MRI. The use of artificial intelligence (AI), which is becoming increasingly widespread worldwide, has also been incorporated into radiography. This technology can produce more accurate and reproducible results in US examinations. Machine learning offers great potential for improving image quality, creating more distinct and useful images, and even developing new US imaging modalities. Furthermore, AI technologies are increasingly prevalent in CT and MRI for image evaluation, image generation, and enhanced image quality.