Non-contrast CT Scan Research Articles

Cerebrospinal fluid volume as an early radiological factor for clinical course prediction after aneurysmal subarachnoid hemorrhage. A pilot study

BackgroundThe pathological mechanisms following aneurysmal subarachnoid hemorrhage (SAH) are poorly understood. Limited clinical evidence exists on the association between cerebrospinal fluid (CSF) volume and the risk of delayed cerebral ischemia (DCI) or cerebral vasospasm (CV). In this study, we raised the hypothesis that the amount of CSF or its ratio to hemorrhage blood volume, as determined from non-contrast Computed Tomography (NCCT) images taken on admission, could be a significant predictor for CV and DCI. MethodsThe pilot study included a retrospective analysis of NCCT scans of 49 SAH patients taken shortly after an aneurysm rupture (33 males, 16 females, mean age 56.4 ± 15 years). The SynthStrip and Slicer3D software tools were used to extract radiological factors – CSF, brain, and hemorrhage volumes from the NCCT images. The “pure” CSF volume (VCSF) was estimated in the range of [−15, 15] Hounsfield units (HU). ResultsVCSF was negatively associated with the risk of CV occurrence (p = 0.0049) and DCI (p = 0.0069), but was not associated with patients’ outcomes. The hemorrhage volume (VSAH) was positively associated with an unfavorable outcome (p = 0.0032) but was not associated with CV/DCI. The ratio VSAH/VCSF was positively associated with, both, DCI (p = 0.031) and unfavorable outcome (p = 0.002). The CSF volume normalized by the brain volume showed the highest characteristics for DCI prediction (AUC = 0.791, sensitivity = 0.80, specificity = 0.812) and CV prediction (AUC = 0.769, sensitivity = 0.812, specificity = 0.70). ConclusionIt was demonstrated that “pure” CSF volume retrieved from the initial NCCT images of SAH patients (including CV, Non-CV, DCI, Non-DCI groups) is a more significant predictor of DCI and CV compared to other routinely used radiological biomarkers. VCSF could be used to predict clinical course as well as to personalize the management of SAH patients. Larger multicenter clinical trials should be performed to test the added value of the proposed methodology.

Contribution of Red Blood Cells and Platelets to Blood Clot Computed Tomography Imaging and Compressive Mechanical Characteristics

Thrombus computed tomography (CT) imaging characteristics may correspond with thrombus mechanical properties and thus predict thrombectomy success. The impact of red blood cell (RBC) content on these properties (imaging and mechanics) has been widely studied. However, the additional effect of platelets has not been considered. The objective of the current study was to examine the individual and combined effects of blood clot RBC and platelet content on resultant CT imaging and mechanical characteristics. Human blood clot analogues were prepared from a combination of preselected RBC volumes and platelet concentrations to decouple their contributions. The resulting clot RBC content (%) and platelet content (%) were determined using Martius Scarlet Blue and CD42b staining, respectively. Non-contrast and contrast-enhanced CT (NCCT and CECT) scans were performed to measure the clot densities. CECT density increase was taken as a proxy for clinical perviousness. Unconfined compressive mechanics were analysed by performing 10 cycles of 80% strain. RBC content is the major determinant of clot NCCT density. However, additional consideration of the platelet content improves the association. CECT density increase is influenced by clot platelet and not RBC content. Platelet content is the dominant component driving clot stiffness, especially at high strains. Both RBC and platelet content contribute to the clot’s viscoelastic and plastic compressive properties. The current in vitro results suggest that CT density is reflective of RBC content and subsequent clot viscoelasticity and plasticity, and that perviousness reflects the clot’s platelet content and subsequent stiffness. However, these indications should be confirmed in a clinical stroke cohort.

Using an artificial intelligence software improves emergency medicine physician intracranial haemorrhage detection to radiologist levels

BackgroundTools to increase the turnaround speed and accuracy of imaging reports could positively influence ED logistics. The Caire ICH is an artificial intelligence (AI) software developed for ED physicians to...

Superior Mesenteric Artery Thrombosis and Intestinal Ischemia in Chronic Kidney Disease

We describe the case of an elderly female with a complex medical history, including hypertension, chronic kidney disease, hypothyroidism, and multiple cardiovascular and pulmonary conditions, who presented to the emergency department with abdominal pain. Due to the patient’s chronic kidney disease, two non-contrast CT scans initially ordered showed unremarkable results. The decision to order a third CT scan with contrast finally demonstrated a thrombus in the superior mesenteric artery with potential bowel ischemia. The patient’s condition deteriorated rapidly during the discovery of mesenteric artery thrombosis and extensive gangrenous bowel during surgery. Due to the irreversibility of the bowel ischemia, the patient was placed on comfort measures and passed away.

The prognostic value of ASPECTS in specific regions following mechanical thrombectomy in patients with acute ischemic stroke from large-vessel occlusion.

The aim of this study is to investigate the relationship between the volume of specific regional infarction and the prognosis of patients who undergo mechanical thrombectomy (MT) for acute large vessel occlusion. In this study, we collected the clinical and imaging features of patients with unilateral acute anterior circulation ischemic stroke from January 2021 to June 2023 in the Second Affiliated Hospital of Nanchang University. All patients underwent CT perfusion and non-contrast CT scan before MT. The ASPECTS was assessed based on imaging data, and artificial intelligence was used to obtain the percentage of infarction in each of the 10 regions of ASPECTS. According to the modified Rankin Scale, the patients were divided into the good prognosis group and poor prognosis group at the 90-day follow-up. Various indicators in the two groups were compared. Multivariable logistic regression was used to assess the risk factors for poor prognosis. The relationship between core infarction volume and the probability of poor prognosis was plotted to analyze the trend of poor prognosis with changes in the proportion of infarction volume. Finally, a receiver operating characteristic curve was constructed to analyze the predictive ability on poor prognosis. A total of 91 patients were included, with 58 patients having a good prognosis (mRS ≤ 2) and 33 patients having a poor prognosis (mRS ≥ 3). Multivariate analysis showed that NIHSS score and core infarction involving the internal capsule and M6 region were independent risk factors for poor prognosis. According to the linear correlation, a higher ratio of core infarction volume in the internal capsule or M6 region was linked to an increased risk of a poor prognosis. However, the non-linear analysis revealed that the prognostic impact of core infarction volume was significant when the ratio was greater than 69.7%. The ROC curve indicated that the combination of NIHSS score, infarct location, and the ratio of infarct volume has an AUC of 0.87, with a sensitivity of 84.8% and a specificity of 84.5%. It is important to examine the location and volume of the infarct in the internal capsule and M6 when deciding whether to do a MT.

Enhanced Detection of Reversible Acute Ischemic Stroke: High-resolution Mapping of Net Water Uptake as a Tissue Clock in Non-contrast Computed Tomography Scans (P11-5.022)

Enhanced Detection of Reversible Acute Ischemic Stroke: High-resolution Mapping of Net Water Uptake as a Tissue Clock in Non-contrast Computed Tomography Scans (P11-5.022)

A Cross-Sectional Study on the Use of Computed Tomography Scan for Diagnosing Renal Masses in Pakistani Adults

Objective: The aim of this study is to assess the validity of Computed Tomography (CT) in evaluating solid renal masses by comparing CT findings with histopathological results.
 Methodology: This cross-sectional study involved 210 patients at the Department of Radiology PESSI Hospital Islamabad, spanning July 2022 to June 2023. Patients were selected based on predefined inclusion criteria, and their detailed medical histories were examined. Each patient underwent both non-contrast and contrast CT scans, and the radiological diagnoses were confirmed. The CT-based diagnoses were then compared with histopathological results, and measures such as positive predictive value (PPV), accuracy, sensitivity, specificity, and negative predictive value (NPV) of CT in diagnosing renal masses were calculated.
 Results: The mean age of the study participants was 48.00±810 years. Male to female ratio was 2:1. The majority of the study participants were belonging from the urban area and were under graduate. The results of the study further revealed the frequency of renal masses which was more in male (71.90%, n=151) as compared to female (28.09%, n=59). The study found that the right kidney was affected in 58.09% (n=122) of cases, the left kidney in 36.19% (n=76), and both kidneys in 5.71% (n=12). In our study, we observed variations in density among patients, with 39.06% exhibiting mixed density, followed by 23.80% with hypo-dense, 20.95% with hyper-dense, and 16.19% with iso-dense lesions. When it comes to enhancement, we found that 40.01% had a moderate degree, 24.76% had mild enhancement, 21.90% had intense enhancement, and 13.33% showed no enhancement.
 Conclusion: In this study, we found a strong correlation between histopathological diagnosis and computed tomography (CT) in identifying solid renal masses, with notably high validity test results. Given these robust validity parameters, we can confidently conclude that CT scans serve as an effective and reliable diagnostic modality for identifying and diagnosing renal masses.

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48mm or degree). The resulting motion-corrected image, , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 33HU to 37 9HU, structural similarity index (SSIM) increases from 0.60 0.06 to 0.98 0.01) and the phantom study (image MAE drops from 117 17HU to 42 19HU, SSIM increases from 0.83 0.04 to 0.98 0.02). We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

Assessing variability in non-contrast CT for the evaluation of stroke: The effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume.

To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume. The NCCT series image data of 87 patients imaged with a CT stroke protocol at our institution were reconstructed with 3 kernels (H10s-smooth, H40s-medium, H70h-sharp) and 2 slice thicknesses (1.5mm and 5mm) to create a reference condition (H40s/5mm) and 5 non-reference conditions. Each reconstruction for each patient was analyzed with the Brainomix e-Stroke software (Brainomix, Oxford, England) which yields an ASPECTS value and measure of total hypodense volume (mL). An ASPECTS value was returned for 74 of 87 cases in the reference condition (13 failures). ASPECTS in non-reference conditions changed from that measured in the reference condition for 59 cases, 7 of which changed above or below the clinical threshold of 7 for 3 non-reference conditions. ANOVA tests were performed to compare the differences in protocols, Dunnett's post-hoc tests were performed after ANOVA, and a significance level of p < 0.05 was defined. There was no significant effect of kernel (p = 0.91), a significant effect of slice thickness (p < 0.01) and no significant interaction between these factors (p = 0.91). Post-hoc tests indicated no significant difference between ASPECTS estimated in the reference and any non-reference conditions. There was a significant effect of kernel (p < 0.01) and slice thickness (p < 0.01) on hypodense volume, however there was no significant interaction between these factors (p = 0.79). Post-hoc tests indicated significantly different hypodense volume measurements for H10s/1.5mm (p = 0.03), H40s/1.5mm (p < 0.01), H70h/5mm (p < 0.01). No significant difference was found in hypodense volume measured in the H10s/5mm condition (p = 0.96). Automated ASPECTS and hypodense volume measurements can be significantly impacted by reconstruction kernel and slice thickness.

122 Use of Automated Active and Continuous CSF Irrigation and Drainage for Treatment of Intraventricular Hemorrhage a Retrospective Comparative Study With Volumetric Analysis

INTRODUCTION: Intraventricular hemorrhage (IVH) is a common neurosurgical pathological conditon that can occur in isolation or along with intraparenchymal and subarachnoid hemorrhage and is associated with poor outcome. Hydrocephalus associated with IVH is often treated with external ventricular drain (EVD) placement, although occlusion and replacement of the drain are common. METHODS: A prospectively maintained database was retrospectively searched for consecutive patients who presented with IVH between September 2020 and Febuary 2023 who were treated with an EVD or the IRRAflow. Pretreatment and posttreatment noncontrast computed tomography scans were segmented to determine IVH volumes. Change in hematoma volume, catheter related conversion and infections, in-hospital mortality, shunt dependency, and symptom resolution or improvement were recorded for outcome assessment. RESULTS: A total of 51 patients were included (21 in the active irrigation and drainage [IRRAflow] group vs. 30 in the passive EVD group. Demographic, disease severity, and presenting variables betweeen the groups were statistically similar. Mean hematoma volume change (8.8 ± 11.3 vs. 17.7 ± 19.4 mL, p = 0.0459) of IVH was significantly lower in the EVD group than the IRRAflow group. No. of days catheter was put in was longer in the EVD group compared to IRRAflow (15.2 ± 10.0 vs. 9.4 ± 4.5; p = 0.0019). On multivaritate logistic regression, shunt dependency on follow-up was higher in the passive EVD group compared to active irrigation and drainage group (p = 0.0215). CONCLUSIONS: Active irrigation with drainage along with CSF exchange for IVH patients appears to be superor to passive drainage alone using EVD.

The role of CT brain findings in the early diagnosis of infantile encephalitic beriberi.

Thiamine deficiency disease may occur in infants from thiamine-deficient mothers in developing countries, as well as in infants fed solely with soy-based formula. Thiamine deficiency in infants may present with acute neurological manifestations of infantile encephalitic beriberi. To review the role of noncontrast CT brain findings in infantile encephalitic beriberi in early diagnosis. A retrospective review of noncontrast CT scans of the brain in 21 infants with acute-onset infantile encephalitic beriberi was carried out. On noncontrast-enhanced CT brain, hypodense lesions were seen symmetrically in the putamen in all the babies; symmetric hypodensities were seen in the caudate nuclei in 14/21 (67%), in dorsomedial thalami/hypothalamic/subthalamic area in 4/21 (19%), and in the globi pallidi in 2/21 (9.5%) of the infants. Recognition of symmetrical hypodense lesions in the basal ganglia and medial thalami/hypothalamic/subthalamic area on noncontrast CT scan of the brain are important early features to recognize in encephalitic beriberi in at-risk infants. IEBB is a cause of hypodense bilateral basal ganglia and may be identified by this finding in the appropriate clinical settings.

Diagnostic Accuracy of Renal Artery Resistive Index in Diagnosing Obstructive Uropathy

Background: Obstructive uropathy, a common cause of acute and chronic kidney disease, poses diagnostic challenges, particularly when non-invasive methods are preferred to reduce patient exposure to ionizing radiation from CT scans. The renal artery resistive index (RI) measured by Doppler ultrasound presents a potential non-invasive diagnostic tool, yet its accuracy and utility in clinical practice require further elucidation. Objective: To assess the diagnostic accuracy of the renal artery resistive index (RI) as measured by Doppler ultrasound in identifying obstructive uropathy, using CT scans as the gold standard for comparison. Methods: This cross-sectional study included 201 patients presenting with symptoms indicative of obstructive uropathy at a tertiary care hospital. Participants underwent both Doppler ultrasound to measure the renal artery RI and non-contrast CT scans of the kidneys, ureters, and bladder (KUB). The diagnostic performance of the RI was evaluated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy, with CT scan findings serving as the benchmark. Results: The study cohort had a mean age of 43.02±15.62 years, with a male predominance (64.7%). The renal artery RI demonstrated a sensitivity of 81.13%, specificity of 85.81%, PPV of 67.19%, NPV of 92.70%, and an overall accuracy of 84.58% in diagnosing obstructive uropathy. Calculi were more frequently located in the right urinary tract (54.2%) than the left (45.8%), with the majority being smaller than 1 cm (79.1%). Conclusion: The renal artery resistive index (RI) measured by Doppler ultrasound is a highly accurate diagnostic tool for detecting obstructive uropathy, offering a viable, non-invasive alternative to CT scans. This method could significantly reduce unnecessary radiation exposure in patients, aligning with current healthcare priorities for safety and efficiency.

Abstract 6294: Subclinical atherosclerosis in newly diagnosed colorectal cancer patients

Abstract Introduction: Recent advances in early detection and treatment have improved survival rates for patients with colorectal cancer (CRC), and emerging data suggests patients may be at elevated risk of cardiovascular disease (CVD). However, there are no standard recommendations for assessing CVD risk in patients with CRC. Coronary artery calcium (CAC) is a measure of the quantity of coronary artery calcification using noninvasive scanning and has been shown to be predictive of CVD risk. This prospective cohort study utilizes CAC to evaluate the occurrence of subclinical atherosclerosis at the time of CRC diagnosis and the risk of major adverse cardiovascular events (MACE). Methods: The ColoCare Study is a prospective cohort study across six U.S. and European sites. Data from one of the ColoCare consortium sites in Los Angeles, California was analyzed. Adults with newly diagnosed stage I- IV colorectal cancer and available non-contrast CT scans were included. Electronic medical records were reviewed for assessment of baseline cardiovascular risk factors and adverse events. Subclinical atherosclerosis was defined as the presence of CAC (CAC&amp;gt;0). CAC was manually measured from routine oncologic diagnostic CT and PET-CT scans at the time of cancer diagnosis using the Agatston method. MACE was defined as hospitalization due to heart failure, cardiomyopathy, ischemic heart disease, or ischemic stroke after the diagnosis of CRC cancer. Multivariable-adjusted Cox regression was used to evaluate the association between CAC at diagnosis and the risk of MACE. Results: Among 109 individuals with CAC measured at the time of CRC diagnosis, 41 (37.6%) had subclinical atherosclerosis (CAC=1-399 [n=29], CAC&amp;gt;400 [n=12]). Individuals with CAC&amp;gt;0 were older (67.0 vs. 51.9 years, p&amp;lt;0.01), more likely to be former or current smokers (48.0 vs. 29.0 years, p=0.04), and to have hypertension (61% vs. 23%, p&amp;lt;0.01) compared to those without CAC (CAC=0). Among individuals with CAC (n=41), 51.2% had scores &amp;gt;100, and 29.2% had scores &amp;gt;400, indicating moderate and severe atherosclerosis respectively. Among patients with moderate or greater CAC burden (&amp;gt;100; statin therapy indicated), 28.6% were not on statin therapy. CAC&amp;gt;0 was associated with an increased incidence of MACE (13% vs. 53%, p&amp;lt;0.01). Discussion: Subclinical atherosclerosis was observed in nearly 40% of patients with CRC at diagnosis and MACE occurred in 38.2% of patients. Most patients were under-optimized for CVD risk, with nearly 30% of statin-eligible patients with at least moderate CAC burden not prescribed statin therapy. These findings highlight an opportunity for improved CVD risk mitigation in patients with CRC, including guidelines-based CVD risk factor modification. Citation Format: Julia A. Levy, Maimoona Nadri, Elham Kazemian, Katelyn M. Atkins, Jordan O. Gasho, Katrina D. Silos, Cody Ramin, Andriana P. Nikolova, Erin M. Siegel, Biljana Gigic, Adetunji T. Toriola, Jennifer Ose, Christopher I. Li, David Shibata, Cornelia M. Ulrich, Jane C. Figueiredo. Subclinical atherosclerosis in newly diagnosed colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6294.

Trabecular bone mineral density as measured by thoracic vertebrae predicts incident hip and vertebral fractures: the multi-ethnic study of atherosclerosis.

Osteoporosis affects an enormous number of patients, of all races and both sexes, and its prevalence increases as the population ages. Few studies have evaluated the association between the vertebral trabecular bone mineral density(vBMD) and osteoporosis-related hip fracture in a multiethnic population, and no studies have demonstrated the predictive value of vBMD for fractures. We sought to determine the predictive value of QCT-based trabecular vBMD of thoracic vertebrae derived from coronary artery calcium scan for hip fractures in the Multi-Ethnic Study of Atherosclerosis(MESA), a nationwide multicenter cohort included 6814 people from six medical centers across the USA and assess if low bone density by QCT can predict future fractures. Measures were done using trabecular bone measures, adjusted for individual patients, from three consecutive thoracic vertebrae (BDI Inc, Manhattan Beach CA, USA) from non-contrast cardiac CT scans. Six thousand eight hundred fourteen MESA baseline participants were included with a mean age of 62.2 ± 10.2years, and 52.8% were women. The mean thoracic BMD is 162.6 ± 46.8mg/cm3 (95% CI 161.5, 163.7), and 27.6% of participants (n = 1883) had osteoporosis (T-score 2.5 or lower). Over a median follow-up of 17.4years, Caucasians have a higher rate of vertebral fractures (6.9%), followed by Blacks (4.4%), Hispanics (3.7%), and Chinese (3.0%). Hip fracture patients had a lower baseline vBMD as measured by QCT than the non-hip fracture group by 13.6mg/cm3 [P < 0.001]. The same pattern was seen in the vertebral fracture population, where the mean BMD was substantially lower 18.3mg/cm3 [P < 0.001] than in the non-vertebral fracture population. Notably, the above substantial relationship was unaffected by age, gender, race, BMI, hypertension, current smoking, medication use, or activity. Patients with low trabecular BMD of thoracic vertebrae showed a 1.57-fold greater risk of first hip fracture (HR 1.57, 95% CI 1.38-1.95) and a nearly threefold increased risk of first vertebral fracture (HR 2.93, 95% CI 1.87-4.59) compared to normal BMD patients. There is significant correlation between thoracic trabecular BMD and the incidence of future hip and vertebral fracture. This study demonstrates that thoracic vertebrae BMD, as measured on cardiac CT (QCT), can predict both hip and vertebral fractures without additional radiation, scanning, or patient burden. Osteopenia and osteoporosis are markedly underdiagnosed. Finding occult disease affords the opportunity to treat the millions of people undergoing CT scans every year for other indications.

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis.

Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P=0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P=0.001). This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.

A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection

Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep learning methods for identifying hemorrhagic stroke on medical imaging scans has garnered significant interest in recent years. These methods are specifically designed to assist radiologists in identifying and highlighting abnormalities in medical scans. In this study, we conducted a systematic review and comprehensive gap analysis of various deep learning methodologies for automated hemorrhagic stroke identification. Our experiments were conducted using various datasets, particularly a publicly available large dataset of non-contrast computed tomography scans of the brain, in a regulated setting. Through an evaluation strategy that encompasses unexplored domain aspects, we identified the key challenges and areas for improvement. This strategy involved the utilization of multisite datasets and the evaluation based on the complexity in the appearance of hemorrhage. The subsequent analysis of the evaluation results revealed gaps in the current state of the art, emphasizing the need to bridge these gaps to achieve higher accuracy and reliability. This study offers valuable insights into the current state of automated hemorrhage stroke identification, highlighting areas for future research and development. The findings have significant implications for improving patient outcomes and alleviating the burden on radiology professionals, leading to more sustainable healthcare practices. The study concludes with recommendations for researchers in this field to foster the future development of automated tools for diagnosing hemorrhagic stroke.

Aortic Calcification is Associated With the Difference Between Invasive Central and Cuff-Measured Brachial Blood Pressure in Chronic Kidney Disease.

Chronic kidney disease (CKD) is associated with accelerated vascular calcification and increased central systolic blood pressure when measured invasively (invCSBP) relative to cuff-based brachial systolic blood pressure (cuffSBP). The contribution of aortic wall calcification to this phenomenon has not been clarified. We, therefore, examined the effects of aortic calcification on cuffSBP and invCSBP in a cohort of patients representing all stages of CKD. During elective coronary angiography, invCSBP was measured in the ascending aorta with a fluid-filled catheter with simultaneous recording of cuffSBP using an oscillometric device. Furthermore, participants underwent a non-contrast computed tomography scan of the entire aorta with observer-blinded calcification scoring of the aortic wall ad modum Agatston. We included 168 patients (mean age 67.0 ± 10.5, 38 females) of whom 38 had normal kidney function, while 30, 40, 28, and 32 had CKD stages 3a, 3b, 4, and 5, respectively. Agatston scores adjusted for body surface area ranged from 48 to 40,165. We found that invCSBP increased 3.6 (95% confidence interval 1.4-5.7) mm Hg relative to cuffSBP for every 10,000-increment in aortic Agatston score. This association remained significant after adjustment for age, diabetes, antihypertensive treatment, smoking, eGFR, and BP level. No such association was found for diastolic BP. Patients with advanced aortic calcification have relatively higher invCSBP for the same cuffSBP as compared to patients with less calcification. Advanced aortic calcification in CKD may therefore result in hidden central hypertension despite apparently well-controlled cuffSBP. ClinicalTrials.gov identifier: NCT04114695.

Ischemic Stroke in the Cardiac Surgery Intensive Care Unit: A Quality Improvement Study

ObjectiveTo investigate the frequency of stroke and code stroke activation, and the factors influencing code stroke management in postoperative cardiac surgical patients. DesignA retrospective quality improvement study conducted between January 1st, 2016, and December 31st, 2021. SettingCardiac Surgery Recovery Unit (CSRU) at London Health Sciences Centre (LHSC) in London, Ontario, Canada. ParticipantsPost-cardiac surgery patients aged 18 years or older who developed ischemic stroke during their admission to the CSRU. InterventionsNo specific interventions were administered as part of this study. “Code Stroke" activation mobilizes a specialized team. The objectives include assessment by a physician within 10 minutes, obtaining neuroimaging and interpretation within 45 minutes, and commencement of treatment within 60 minutes. Measurements and Main ResultsThe incidence rate of stroke in the CSRU was 1.3%, and 34% of these cases had code stroke activated. The last known well time was 11 +/- 8 hours. The most common reasons for not activating code stroke were not meeting both timing and clinical criteria. The average time to Computed Tomography (CT) scan was 36 +/- 22 minutes. Among patients who had code stroke activated, 24% had large vessel occlusion (LVO), and 67% of those with LVO had an established stroke on their initial CT. ConclusionCode stroke was activated in only one-third of patients who experienced a stroke following cardiac surgery. Additionally, out of those who had code stroke activated, only one-fourth were diagnosed with LVO. Among those with LVO, two-thirds were found to have a well-established stroke on non-contrast CT scans and were deemed ineligible for intervention.

Segmenting Ischemic Penumbra and Infarct Core Simultaneously on Non-Contrast CT of Patients with Acute Ischemic Stroke Using Novel Convolutional Neural Network.

Differentiating between a salvageable Ischemic Penumbra (IP) and an irreversibly damaged Infarct Core (IC) is important for therapy decision making for acute ischemic stroke (AIS) patients. Existing methods rely on Computed Tomography Perfusion (CTP) or Diffusion-Weighted Imaging-Fluid Attenuated Inversion Recovery (DWI-FLAIR). We designed a novel Convolutional Neural Network named I2PC-Net, which relies solely on Non-Contrast Computed Tomography (NCCT) for the automatic and simultaneous segmentation of the IP and IC. In the encoder, Multi-Scale Convolution (MSC) blocks were proposed to capture effective features of ischemic lesions, and in the deep levels of the encoder, Symmetry Enhancement (SE) blocks were also designed to enhance anatomical symmetries. In the attention-based decoder, hierarchical deep supervision was introduced to address the challenge of differentiating between the IP and IC. We collected 197 NCCT scans from AIS patients to evaluate the proposed method. On the test set, I2PC-Net achieved Dice Similarity Scores of 42.76 ± 21.84%, 33.54 ± 24.13% and 65.67 ± 12.30% and lesion volume correlation coefficients of 0.95 (p < 0.001), 0.61 (p < 0.001) and 0.93 (p < 0.001) for the IP, IC and IP + IC, respectively. The results indicated that NCCT could potentially be used as a surrogate technique of CTP for the quantitative evaluation of the IP and IC.

Weekly assessment of volumetric and dosimetric changes during volumetric modulated arc therapy of locally advanced head and neck carcinoma: Implications for adaptive radiation therapy-A prospective study.

Chemoradiation in head and neck carcinoma (HNC) shows significant anatomical resulting in erroneous dose deposition in the target or the organ at risk (OAR). Adaptive radiotherapy (ART) can overcome this. Timing of significant target and OAR changes with dosimetric impact; thus, most suitable time and frequency of ART is unclear. This dosimetric study used prospective weekly non-contrast CT scans in 12 HNC patients (78 scans). OARs and TVs were manually contoured after registration with simulation scan. Dose overlay done on each scan without reoptimization. Dosimetric and volumetric variations assessed. Commonest site was oropharynx. Gross Tumor Volume (GTV) reduced from 47.5 ± 19.2 to 17.8 ± 10.7 cc. Nodal GTV reduced from 15.7 ± 18.8 to 4.7 ± 7.1 cc. Parotid showed mean volume loss of 35%. T stage moderately correlated with GTV regression. Maximum GTV changes occurred after 3 weeks. Best time to do single fixed interval ART would be by the end of 3 weeks.