Diagnostic value of PET imaging in clinically unresponsive patients.

Rapid advancements in the critical care management of acute brain injuries have facilitated the survival of numerous patients who may have otherwise succumbed to their injuries. The probability of conscious recovery hinges on the extent of structural brain damage and the level of metabolic and functional cerebral impairment, which remain challenging to assess via laboratory, clinical, or functional tests. Current research settings and guidelines highlight the potential value of fluorodeoxyglucose-PET (FDG-PET) for diagnostic and prognostic purposes, emphasizing its capacity to consistently illustrate a metabolic reduction in cerebral glucose uptake across various disorders of consciousness. Crucially, FDG-PET might be a pivotal tool for differentiating between patients in the minimally conscious state and those in the unresponsive wakefulness syndrome, a persistent clinical challenge. In patients with disorders of consciousness, PET offers utility in evaluating the degree and spread of functional disruption, as well as identifying irreversible neural damage. Further, studies that capture responses to external stimuli can shed light on residual or revived brain functioning. Nevertheless, the validity of these findings in predicting clinical outcomes calls for additional long-term studies with larger patient cohorts suffering from consciousness impairment. Misdiagnosis of conscious illnesses during bedside clinical assessments remains a significant concern. Based on the clinical research settings, current clinical guidelines recommend PET for diagnostic and/or prognostic purposes. This review article discusses the clinical categories of conscious disorders and the diagnostic and prognostic value of PET imaging in clinically unresponsive patients, considering the known limitations of PET imaging in such contexts.

An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.

The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing the correlation between COVID-19 lesions and the level of respiratory support provided to the patients. Computed tomography (CT) imaging will be used to analyze the three levels of respiratory support received by the patient: Level 0 (minimum support), Level 1 (non-invasive support such as soft oxygen), and Level 2 (invasive support such as mechanical ventilation). The system will begin by segmenting the COVID-19 lesions from the CT images and creating an appearance model for each lesion using a 2D, rotation-invariant, Markov-Gibbs random field (MGRF) model. Three MGRF-based models will be created, one for each level of respiratory support. This suggests that the system will be able to differentiate between different levels of severity in COVID-19 patients. The system will decide for each patient using a neural network-based fusion system, which combines the estimates of the Gibbs energy from the three MGRF-based models. The proposed system were assessed using 307 COVID-19-infected patients, achieving an accuracy of [Formula: see text], a sensitivity of [Formula: see text], and a specificity of [Formula: see text], indicating a high level of prediction accuracy.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

Traumatic aortic injury from pellet gun: A case report.

Acute traumatic aortic injuries are of the most lethal sequelae of penetrating thoracic injuries and require rapid detection and management. The American College of Radiology currently recommends the use of noncontrast CT, followed by computed tomography angiography (CTA) as the first-line imaging modalities when traumatic aortic injury is suspected. Direct signs of aortic injury on CTA include pseudoaneurysm, focal contour abnormality, intimal flap, intramural hematoma, an abrupt change in aortic caliber, and contrast extravasation. Aortic pseudoaneurysms are most often caused by blunt or penetrating trauma that results in damage to the vessel wall, turbulent blood flow, and formation of a surrounding hematoma contained by a wall of products from the clotting cascade. This wall is weaker than those of a true aneurysm and will ultimately rupture over time if not repaired. Traumatic aortic pseudoaneurysms are preferably treated by thoracic endovascular aortic repair using a prosthetic stent graft. Here, we present a 44-yearold female with a history of homelessness, polysubstance use disorder, and HIV who presented to the emergency department after being found down. She reported being shot by a pellet gun, and physical examination revealed a penetrating left-sided chest wound that appeared to be several days old. A STAT CTA was obtained and revealed a hemopneumothorax and possible thoracic aortic pseudoaneurysm. A left-sided chest tube was placed and the patient underwent thoracic endovascular aortic repair through right femoral arterial access and tolerated the procedure well. The patient was placed on daily aspirin postoperatively and discharged on post-op day 5.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends.

Bladder cancer (BC) is the 10th most common cancer globally and has a high mortality rate if not detected early and treated promptly. Non-muscle-invasive BC (NMIBC) is a subclassification of BC associated with high rates of recurrence and progression. Current tools for predicting recurrence and progression on NMIBC use scoring systems based on clinical and histopathological markers. These exclude other potentially useful biomarkers which could provide a more accurate personalized risk assessment. Future trends are likely to use artificial intelligence (AI) to enhance the prediction of recurrence in patients with NMIBC and decrease the use of standard clinical protocols such as cystoscopy and cytology. Here, we provide a comprehensive survey of the most recent studies from the last decade (N = 70 studies), focused on the prediction of patient outcomes in NMIBC, particularly recurrence, using biomarkers such as radiomics, histopathology, clinical, and genomics. The value of individual and combined biomarkers is discussed in detail with the goal of identifying future trends that will lead to the personalized management of NMIBC.

Correlation of Computed Tomography Measured Presacral Thickness with Body Mass Index.

Sacral colpopexy is often chosen as a reliable approach that effectively resolves vaginal vault prolapse. Advancements in minimally invasive technology, robotic and laparoscopic surgery, have helped facilitate surgical dissection and operation when performing this procedure. An increased presacral thickness can potentially present a surgical challenge when operating in the presacral space. We hypothesize that there is a correlation between body mass index and presacral thickness. Computed Tomography (CT) images of 241 patients were reviewed in this retrospective study. The presacral thickness was measured by taking the cross sectional distance from the sacral promontory to the upper aspect of the iliac arteries. The corresponding demographic information of age, body mass index (BMI), and comorbidities were evaluated using univariate analysis, linear regression, and multiple regression analysis. The mean age was 56.6 years, and BMI was 27.6. The mean presacral thickness measurement based on the CT scan was 21.08 mm. Univariate linear regression models demonstrated a positive correlation between presacral thickness and BMI and a negative correlation with age. When adjusting for both age and BMI on multivariate analysis, a positive correlation with hypertension was found. The surgeon should be aware of this potential change in anatomy when operating in the presacral space.

Correlation of patient age with CT-measured aorta-sacral promontory distance.

INTRODUCTION AND HYPOTHESIS: Pelvic floor disorders are becoming more prevalent in the elderly population. Since more patients are seeking definitive management for their prolapse, the number of elderly patients undergoing sacral colpopexies will likely increase. During sacral colpopexies, the surgeon must carefully dissect in the presacral space and avoid vital structures. In elderly patients the aorta potentially elongates and the vertebral body height decreases. Consequently, there is a potential for anatomical change of distance from the bifurcation of the aorta to the sacral promontory. This study aimed to correlate the aorta-sacral promontory distance with age. METHODS: From 1 January 2013 to 31 January 2014 computed tomography (CT) images of 241 patients were reviewed in this retrospective study. Radiologists measured the aorta-sacral promontory distance on sagittal acquisition. The corresponding demographic information of age, body mass index, and comorbidities was evaluated using univariate analysis and univariate linear regression. RESULTS: The mean age was 56.6 years, and BMI was 27.6. The mean aorta-sacral promontory measurement based on the CT scan was 63.11 mm. Univariate analysis using a t test and ANOVA demonstrated an inverse correlation with age (p < 0.0001) and hypertension (p = 0.0034) and a positive correlation with BMI categories (p < 0.0017) Under univariate linear regression, the weight of the patient in kilograms demonstrated positive correlation (p = 0.0413). CONCLUSIONS: Based on CT measurements, the aorta-sacral promontory distance is decreased in elderly and hypertensive patients. Heavier patients have an increased aorta-sacral promontory distance. These potential anatomical variants should be considered before operating in the presacral space.

The monocular-boundary-contour mechanism in binocular surface representation and suppression.

Boundary contours are important for representing binocular surfaces, including those in binocular rivalry. Ooi and He (2006, Perception 35 581-603) showed that a half-image with a boundary contour defined by abutting gratings predominates in binocular rivalry. We investigated the monocular-boundary-contour mechanism using Kanizsa square-like rivalry displays. In experiment 1, the left half-image had a vertical illusory contour on the right edge while the right half-image had a vertical illusory contour on the left edge. The Kanizsa elements (discs and pacmen) were filled with a 135 degree grating and placed on a 45 degree-grating background. When fused, observers experienced a strong predominance for perceiving an illusory rectangle in front of four discs. But this percept was replaced by robust rivalry alternations when the stimulus was manipulated by (i) switching the half-images between eyes, (ii)relocating the pacmen in each half-image to form horizontal illusory contours, or (iii) placing the pacmen diagonally (thus eliminating each monocular illusory contour). Such robust rivalry alternations were similar to those experienced when a 135 degree-grating disc was in rivalry with a 135 degree-grating pacman alone on the 45 degree-grating background (experiment 2). Experiment 3 showed that the relatively stable illusory-rectangle percept in experiment 1 is affected by the alignment of the images in the two eyes, in a manner consistent with adherence to the occlusion constraint in binocular surface formation.