3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma.

Despite tremendous gains in the molecular understanding of exocrine pancreatic cancer, the prognosis for this disease remains very poor, largely because of delayed disease detection and limited effectiveness of systemic therapies. Both incidence rates and mortality rates for pancreatic cancer have increased during the past decade, in contrast to most other solid tumor types. Recent improvements in multimodality care have substantially improved overall survival, local control, and metastasis-free survival for patients who have localized tumors that are amenable to surgical resection. The widening gap in prognosis between patients with resectable and unresectable or metastatic disease reinforces the importance of detecting pancreatic cancer sooner to improve outcomes. Furthermore, the developing use of therapies that target tumor-specific molecular vulnerabilities may offer improved disease control for patients with advanced disease. Finally, the substantial morbidity associated with pancreatic cancer, including wasting, fatigue, and pain, remains an under-addressed component of this disease, which powerfully affects quality of life and limits tolerance to aggressive therapies. In this article, the authors review the current multidisciplinary standards of care in pancreatic cancer with a focus on emerging concepts in pancreatic cancer detection, precision therapy, and survivorship.

3D Cinematic rendering for evaluating femoral pseudoaneurysms in injection drug users.

The inguinal region, specifically the femoral vasculature, is a commonly used site of injection for intravenous drug users (IVDU). Repeated puncture of the vessel wall results in breakdown and subsequent arterial pseudoaneurysm- dilatations or outpouching of blood vessels, which, if left untreated, can result in fatal complications such as rupture with hemorrhage, sepsis, or even limb loss. The current modalities for arterial pseudoaneurysms include Doppler ultrasound and computed tomography (CT) angiography, both of which play important roles in management and surgical planning. However, 3D cinematic rendering (CR), a novel CT post-processing technique, offers timely, highly detailed photorealistic images that more clearly display the relation of anatomical structures, allowing for greater diagnostic confidence and precise surgical planning, particularly useful in the emergency setting. In this pictorial review, we demonstrate role of 3D CR in diagnosis and management of femoral pseudoaneurysms in IVDU through 9 illustrative cases.

The role of 3D cinematic rendering in the evaluation of upper extremity trauma.

Traumatic upper extremity injuries are a common cause of emergency department visits, comprising between 10-30% of traumatic injury visits. Timely and accurate evaluation is important to prevent severe complications such as permanent deformities, ischemia, or even death. Computed tomography (CT) and CT angiography (CTA) are the favored non-invasive imaging techniques for assessing upper extremity trauma, playing a crucial role in both the treatment planning and decision-making processes for such injuries. In CT postprocessing, a novel 3D rendering method, cinematic rendering (CR), employs sophisticated lighting models that simulate the interaction of multiple photons with the volumetric dataset. This technique produces images with realistic shadows and improved surface detail, surpassing the capabilities of volume rendering (VR) or maximal intensity projection (MIP). Considering the benefits of CR, we demonstrate its use and ability to achieve photorealistic anatomic visualization in a series of 11 cases where patients presented with traumatic upper extremity injuries, including bone, vascular, and skin/soft tissue injuries, adding to diagnostic confidence and intervention planning.

Cinematic rendering of non-traumatic thoracic aorta emergencies: a new look at an old problem.

Non-traumatic thoracic aorta emergencies are acute conditions associated with substantial morbidity and mortality. In the emergency setting, timely detection of aortic injury through radiological imaging is crucial for prompt treatment planning and favorable patient outcomes. 3D cinematic rendering (CR), a novel rendering algorithm for computed tomography (CT) image processing, allows for life-like visualization of spatial details and contours of highly complex anatomic structures such as the thoracic aorta and its vessels, generating a photorealistic view that not just adds to diagnostic confidence, but is especially useful for non-radiologists, including surgeons and emergency medicine physicians. In this pictorial review, we demonstrate the utility of CR in the setting of non-traumatic thoracic aorta emergencies through 10 cases that were processed at a standalone 3D CR station at the time of presentation, including its role in diagnosis and management.

Expanded experience with cardiovascular black blood cinematic rendering.

Black blood cinematic rendering (BBCR) is a newly described preset for cinematic rendering, which creates photorealistic displays from volumetric data sets with the contrast-enhanced blood pool displayed as dark and transparent. That set of features potentially provides for enhanced visualization of endomyocardial and intraluminal pathology, as well as cardiac devices. The similarity of the images to black-blood magnetic resonance imaging (MRI) may allow for expansion of the evaluation of certain types of pathology into patient populations unable to undergo MRI. In the emergency setting, the rapid acquisition time and reasonable post-processing time make this technique clinically feasible. In this expanded experience, we demonstrate an expanded clinical experience with the BBCR technique, highlighting the applications for intraluminal cardiovascular evaluation, especially focused on current and potential emergency radiology applications.

Applications of Artificial Intelligence in Acute Abdominal Imaging.

Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.

Cinematic Rendering of Pancreatic Neuroendocrine Tumours: Opportunities for Clinical Implementation: Part 1: Tumour Detection and Characterization.

Pancreatic neuroendocrine tumours (PNETs) are a rare subset of pancreatic tumours that have historically comprised up to 3% of all clinically detected pancreatic tumours. In recent decades, however, advancements in imaging have led to an increased incidental detection rate of PNETs and imaging has played an increasingly central role in the initial diagnostics and surgical planning of these tumours. Cinematic rendering (CR) is a 3D post-processing technique that generates highly photorealistic images through more realistically modelling the path of photons through the imaged volume. This allows for more comprehensive visualization, description, and interpretation of anatomical structures. In this 2-part review article, we present the first description of the various CR appearances of PNETs in the reported literature while providing commentary on the unique clinical opportunities afforded by the adjunctive utilization of CR in the workup of these rare tumours. The first of these 2 instalments highlights the utility of CR in optimizing PNET detection and characterization.

Cinematic Rendering of Pancreatic Neuroendocrine Tumours: Opportunities for Clinical Implementation: Part 2: Preoperative Planning and Evaluation of Metastatic Disease.

Pancreatic neuroendocrine tumours (PNETs) are a rare subset of pancreatic tumours that have historically comprised up to 3% of all clinically detected pancreatic tumours. In recent decades, however, advancements in imaging have led to an increased incidental detection rate of PNETs and imaging has played an increasingly central role in the initial diagnostics and surgical planning of these tumours. Cinematic rendering (CR) is a 3D post-processing technique that generates highly photorealistic images through more realistically modelling the path of photons through the imaged volume. This allows for more comprehensive visualization, description, and interpretation of anatomical structures. In this 2-part review article, we present the first description of the various CR appearances of PNETs in the reported literature while providing commentary on the unique clinical opportunities afforded by the adjunctive utilization of CR in the workup of these rare tumours. This second instalment focuses on the applications of CR in optimizing preoperative planning of PNETs.

Augmented Reality With Cinematic Rendered 3-Dimensional Images From Volumetric Computed Tomography Data.

ABSTRACT: Recent advances in 3-dimensional visualization of volumetric computed tomography data have led to the novel technique of cinematic rendering (CR), which provides photorealistic images with enhanced surface detail and realistic shadowing effects that are generally not possible with older methods such as volume rendering. The emergence of CR coincides with the increasingly widespread availability of virtual reality (VR)/augmented reality (AR) interfaces including wearable headsets. The intersection of these technologies suggests many potential advances, including the ability of interpreting radiologists to look at photorealistic images of patient pathology in real time with surgeons and other referring providers, so long as VR/AR headsets are deployed and readily available. In this article, we will present our initial experience with viewing and manipulating CR images in the context of a VR/AR headset. We include a description of key aspects of the software and user interface, and provide relevant pictorial examples that may help potential adopters understand the initial steps of using this exciting convergence of technologies. Ultimately, trials evaluating the added value of the combination of CR with VR/AR will be necessary to understand the potential impact of these methods on medical practice.

Radiologists' Expectations of Artificial Intelligence in Pancreatic Cancer Imaging: How Good Is Good Enough?

BACKGROUND: Existing (artificial intelligence [AI]) tools in radiology are modeled without necessarily considering the expectations and experience of the end user-the radiologist. The literature is scarce on the tangible parameters that AI capabilities need to meet for radiologists to consider them useful tools. OBJECTIVE: The purpose of this study is to explore radiologists' attitudes toward AI tools in pancreatic cancer imaging and to quantitatively assess their expectations of these tools. METHODS: A link to the survey was posted on the www.ctisus.com website, advertised in the www.ctisus.com email newsletter, and publicized on LinkedIn, Facebook, and Twitter accounts. This survey asked participants about their demographics, practice, and current attitudes toward AI. They were also asked about their expectations of what constitutes a clinically useful AI tool. The survey consisted of 17 questions, which included 9 multiple choice questions, 2 Likert scale questions, 4 binary (yes/no) questions, 1 rank order question, and 1 free text question. RESULTS: A total of 161 respondents completed the survey, yielding a response rate of 46.3% of the total 348 clicks on the survey link. The minimum acceptable sensitivity of an AI program for the detection of pancreatic cancer chosen by most respondents was either 90% or 95% at a specificity of 95%. The minimum size of pancreatic cancer that most respondents would find an AI useful at detecting was 5 mm. Respondents preferred AI tools that demonstrated greater sensitivity over those with greater specificity. Over half of respondents anticipated incorporating AI tools into their clinical practice within the next 5 years. CONCLUSION: Radiologists are open to the idea of integrating AI-based tools and have high expectations regarding the performance of these tools. Consideration of radiologists' input is important to contextualize expectations and optimize clinical adoption of existing and future AI tools.

Evaluation of extensive inflammatory conditions of the bowel using three-dimensional CT cinematic rendering: focus on inflammatory bowel disease.

Inflammatory conditions that affect long segments of bowel and/or the mesentery and mesenteric vasculature are a common cause of emergency department visits and evaluation by cross-sectional imaging. Inflammatory bowel disease, specifically Crohn disease and ulcerative colitis, can be unsuspected at presentation and may only be eventually diagnosed based on initial imaging findings. Traditional 2D axial reconstructions and multi-planar reformations can be limited in their ability to globally assess the extent of disease. 3D methods such as volume rendering (VR) are often used as adjunctive means of visualizing the pathology in such patients. Recently, a novel technique known as cinematic rendering (CR) has emerged, utilizing advanced lighting models and ray tracing to simulate photon interactions with tissues, resulting in realistic shadows and enhanced surface detail compared to VR. Generating CR images from select presets takes an experienced radiologist approximately 5 min, meaning that the technique can be incorporated into meaningful emergency department workflows. Given the apparent advantages of CR, we highlight its application in a series of cases in which patients had inflammatory conditions that affected long segments of bowel and/or involved the mesentery, particularly those patients with inflammatory bowel disease, but also including patients with mesenteric venous thrombosis and lymphedema. Those conditions included inflammatory bowel disease, mesenteric venous thrombosis, and bowel lymphedema. We present examples of those conditions in this pictorial essay and describe the potential of CR to visualize key findings. As CR exhibits possible advantages, further studies are warranted to support its broader clinical adoption and assess its efficacy in diagnosing and guiding managing of inflammatory conditions in emergency settings.

Cinematic rendering in the evaluation of complex vascular injury of the lower extremities: how we do it.

Lower extremity trauma is one of the most common injury patterns seen in emergency medical and surgical practice. Vascular injuries occur in less than one percent of all civilian fractures. However, if not treated promptly, such injuries can lead to ischemia and death. Computed tomography angiography (CTA) is the non-invasive imaging gold standard and plays a crucial part in the decision-making process for treating lower extremity trauma. A novel, FDA-approved 3D reconstruction technique known as cinematic rendering (CR) yields photorealistic reconstructions of lower extremity vascular injuries depicting clinically important aspects of those injuries, aiding in patient workup and surgical planning, and thus improving patient outcomes. In this article, we provide clinical examples of the use of CR in evaluating lower extremity vascular injuries, including the relationship of these injuries to adjacent osseous structures and overlying soft tissues, and its role in management of lower extremity trauma.

Is AI the Ultimate QA?

We are among the many that believe that artificial intelligence will not replace practitioners and is most valuable as an adjunct in diagnostic radiology. We suggest a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps. This AI Quality workflow uses a visual classifier to predict the likelihood of a finding of interest, such as a lung nodule, and then leverages natural language processing to review a radiologist's report, identifying discrepancies between imaging and documentation. Comparing artificial intelligence predictions with natural language processing report extractions with artificial intelligence in the background of computer-aided detection decisions may offer numerous potential benefits, including streamlined workflow, improved detection quality, an alternative approach to thinking of AI, and possibly even indemnity against malpractice. Here we consider early indications of the potential of artificial intelligence as the ultimate quality assurance for radiologists.

CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma.

OBJECTIVE. Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predicting postoperative survival of patients with PDAC. MATERIALS AND METHODS. A total of 153 patients with surgically resected PDAC who underwent preoperative CT between 2011 and 2017 were retrospectively identified. Demographic, clinical, and survival information was collected from the medical records. Survival time after the surgical resection was used to stratify patients into a low-risk group (survival time > 3 years) and a high-risk group (survival time < 1 year). The 3D volume of the whole pancreatic tumor and background pancreas were manually segmented. A total of 478 radiomics features were extracted from tumors and 11 extra features were computed from pancreas boundaries. The 10 most relevant features were selected by feature reduction. Survival analysis was performed on the basis of clinical parameters both with and without the addition of the selected features. Survival status and time were estimated by a random survival forest algorithm. Concordance index (C-index) was used to evaluate performance of the survival prediction model. RESULTS. The mean age of patients with PDAC was 67 ± 11 (SD) years. The mean tumor size was 3.31 ± 2.55 cm. The 10 most relevant radiomics features showed 82.2% accuracy in the classification of high-risk versus low-risk groups. The C-index of survival prediction with clinical parameters alone was 0.6785. The addition of CT radiomics features improved the C-index to 0.7414. CONCLUSION. Addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC.

Current Status of Radiomics and Deep Learning in Liver Imaging.

ABSTRACT: Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.

Cinematic rendering enhancements to virtual bronchoscopy: assessment of emergent tracheal pathology.

Utilizing complex lighting models, cinematic rendering is a novel technique for demonstrating computed tomography data with exquisite 3D anatomic detail. The tracheal lumen, tracheal wall, and adjacent soft tissue structures are represented with photorealistic detail exceeding that of conventional volume rendering or virtual bronchoscopy techniques. We applied cinematic rendering to a spectrum of emergent tracheal pathologies: traumatic tracheal tears, tracheoesophageal fistulas, tracheal foreign bodies, tracheal stenosis (intrinsic and extrinsic causes), tracheal neoplasms, and tracheomalacia. Cinematic rendering images enable visually accessible evaluation and comprehensive understanding of acute tracheal pathology, which is likely to be of value to both interventional pulmonologists and thoracic surgeons who are determining patient treatment plans.

The first use of artificial intelligence (AI) in the ER: triage not diagnosis.

Predictions related to the impact of AI on radiology as a profession run the gamut from AI putting radiologists out of business to having no effect at all. The use of AI appears to show significant promise in ER triage in the present. We briefly discuss the emerging effectiveness of AI in the ER imaging setting by looking at some of the products approved by the FDA and finding their way into "practice." The FDA approval process to date has focused on applications that affect patient triage and not necessarily ones that have the computer serve as the only or final reader. We describe a select group of applications to provide the reader with a sense of the current state of AI use in the ER setting to assess neurologic, pulmonary, and musculoskeletal trauma indications. In the process, we highlight the benefits of triage staging using AI, such as accelerating diagnosis and optimizing workflow, with few downsides. The ability to triage patients and take care of acute processes such as intracranial bleed, pneumothorax, and pulmonary embolism will largely benefit the health system, improving patient care and reducing costs. These capabilities are all available now. This first wave of AI applications is not replacing radiologists. Rather, the innovative software is improving throughput, contributing to the timeliness in which radiologists can get to read abnormal scans, and possibly enhances radiologists' accuracy. As for what the future holds for the use of AI in radiology, only time will tell.

Imaging of urinary bladder injury: the role of CT cystography.

Although conventional radiographic cystography has been traditionally considered the reference standard in detecting bladder injuries, computed tomography (CT) cystography has become the initial imaging method of choice in the acute setting. CT cystography has been shown to provide comparable accuracy as conventional cystography, and can be easily performed in conjunction with trauma CT surveys in patients with suspected bladder injuries. Despite increasing enthusiasm toward CT cystography in dealing with patients with suspected bladder injuries, there is little information in this regard in the literature. This article aims to discuss the role of CT cystography in the evaluation of bladder injuries.