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.

Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model.

BACKGROUND: PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs). PATIENTS AND METHODS: An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally. RESULTS: Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm). CONCLUSIONS: Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.

Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst.

BACKGROUND/OBJECTIVES: Pancreatic cyst management can be distilled into three separate pathways - discharge, monitoring or surgery- based on the risk of malignant transformation. This study compares the performance of artificial intelligence (AI) models to clinical care for this task. METHODS: Two explainable boosting machine (EBM) models were developed and evaluated using clinical features only, or clinical features and cyst fluid molecular markers (CFMM) using a publicly available dataset, consisting of 850 cases (median age 64; 65 % female) with independent training (429 cases) and holdout test cohorts (421 cases). There were 137 cysts with no malignant potential, 114 malignant cysts, and 599 IPMNs and MCNs. RESULTS: The EBM and EBM with CFMM models had higher accuracy for identifying patients requiring monitoring (0.88 and 0.82) and surgery (0.66 and 0.82) respectively compared with current clinical care (0.62 and 0.58). For discharge, the EBM with CFMM model had a higher accuracy (0.91) than either the EBM model (0.84) or current clinical care (0.86). In the cohort of patients who underwent surgical resection, use of the EBM-CFMM model would have decreased the number of unnecessary surgeries by 59 % (n = 92), increased correct surgeries by 7.5 % (n = 11), identified patients who require monitoring by 122 % (n = 76), and increased the number of patients correctly classified for discharge by 138 % (n = 18) compared to clinical care. CONCLUSIONS: EBM models had greater sensitivity and specificity for identifying the correct management compared with either clinical management or previous AI models. The model predictions are demonstrated to be interpretable by clinicians.

CarD is an essential regulator of rRNA transcription required for Mycobacterium tuberculosis persistence.

Mycobacterium tuberculosis is arguably the world's most successful infectious agent because of its ability to control its own cell growth within the host. Bacterial growth rate is closely coupled to rRNA transcription, which in E. coli is regulated through DksA and (p)ppGpp. The mechanisms of rRNA transcriptional control in mycobacteria, which lack DksA, are undefined. Here we identify CarD as an essential mycobacterial protein that controls rRNA transcription. Loss of CarD is lethal for mycobacteria in culture and during infection of mice. CarD depletion leads to sensitivity to killing by oxidative stress, starvation, and DNA damage, accompanied by failure to reduce rRNA transcription. CarD can functionally replace DksA for stringent control of rRNA transcription, even though CarD associates with a different site on RNA polymerase. These findings highlight a distinct molecular mechanism for regulating rRNA transcription in mycobacteria that is critical for M. tuberculosis pathogenesis.

Flank pain, hypertension, and hematuria: CT and 3D cinematic rendering in the evaluation of renal artery emergencies-a pictorial essay.

Non-traumatic acute renal artery emergencies encompass a spectrum of etiologies, including renal artery stenosis, arteriovenous malformations, aneurysms and pseudoaneurysms, dissections, thrombosis, and vasculitis. Prompt and accurate diagnosis in the emergency setting is crucial due to the potential for significant morbidity and mortality. Computed tomography (CT) and CT angiography (CTA) are the mainstay imaging modalities, offering rapid acquisition and high diagnostic accuracy. The integration of 3D postprocessing techniques, such as 3D cinematic rendering (CR), improves the diagnostic workflow by providing photorealistic and anatomically accurate visualizations. This pictorial essay illustrates the diagnostic utility of CT and CTA, supplemented by 3D CR, through a series of 10 cases of non-traumatic renal artery emergencies. The added value of 3D CR in improving diagnostic confidence, surgical planning, and understanding of complex vascular anatomy is emphasized.

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.

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 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.

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.

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.

Cinematic Rendering of Gastrointestinal Stromal Tumors: A Review of Current Possibilities and Future Developments.

Gastrointestinal stromal tumors (GISTs) are defined as CD117-positive primary, spindled or epithelioid, mesenchymal tumors of the gastrointestinal tract, omentum, or mesentery. While computed tomography (CT) is the recommended imaging modality for GISTs, overlap in imaging features between GISTs and other gastrointestinal tumors often make radiological diagnosis and subsequent selection of the optimal therapeutic approach challenging. Cinematic rendering is a novel CT post-processing technique that generates highly photorealistic anatomic images based on a unique lighting model. The global lighting model produces high degrees of surface detail and shadowing effects that generate depth in the final three-dimensional display. Early studies have shown that cinematic rendering produces high-quality images with enhanced detail by comparison with other three-dimensional visualization techniques. Cinematic rendering shows promise in improving the visualization of enhancement patterns and internal architecture of abdominal lesions, local tumor extension, and global disease burden, which may be helpful for lesion characterization and pretreatment planning. This article discusses and illustrates the application of cinematic rendering in the evaluation of GISTs and the unique benefit of using cinematic rendering in the workup of GIST with a specific emphasis on tumor characterization and preoperative planning.

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances.

Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.

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.