Computed Tomography Scans Of Patients Research Articles

Post-contrast acute kidney injury in the super-elder patients: a CT-scan perspective.

The administration of intravenous (IV) contrast media during computed tomography (CT) examinations is essential to enhance diagnostic accuracy in various clinical scenarios. Traditionally, older age is considered a risk factor for the development of post-contrast Acute Kidney Injury (PC-AKI); however, there is limited information available for the super-elderly population (aged ≥ 85). This study aims to investigate the incidence and risk factors associated with PC-AKI in individuals aged 85 and older undergoing CT scans with IV contrast. A retrospective cohort study, including all hospitalized patients aged 85 or older who underwent CT scans between the years 2005 and 2021. Patients were categorized into IV contrast and non-IV contrast groups. Baseline demographic and clinical data, along with kidney function parameters, were collected. The final cohort included 7,078 patients who underwent CT scans, with 40% receiving IV contrast. The overall AKI occurrence within 72 h post-CT was 5.72%, slightly elevated in the non-IV contrast group (6.25% vs. 4.94%, p = 0.02). However, multivariate analysis revealed no significant difference between the groups (OR 1, CI 0.8-1.2, p = 0.92), even after stratifying by kidney function. A secondary analysis, using a less strict AKI definition, supported these findings. Baseline creatinine levels emerged as prominent risk factor associated with PC- AKI. The current study provides reassurance regarding the safety of contrast-enhanced CT scans in super-elderly patients, particularly those with baseline normal to mild kidney dysfunction. These findings may contribute to the ongoing discussion on the risk-benefit balance of contrast-enhanced CT scans in the super-elderly population.

Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.

This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists. We retrospectively included abdominopelvic CT images with the following inclusion criteria: a) CT images from patients with solid organ malignancies between March 1 and March 31, 2019, in a single institution; and b) abdominal CT images interpreted as negative for basal lung metastases. Reference standards for diagnosis of lung metastases were confirmed by reviewing medical records and subsequent CT images. An AI system that could automatically detect lung nodules on CT images was applied retrospectively. A radiologist reviewed the AI detection results to classify them as lesions with the possibility of metastasis or clearly benign. The performance of the initial AI results and the radiologist's review of the AI results were evaluated using patient-level and lesion-level sensitivities, false-positive rates, and the number of false-positive lesions per patient. A total of 878 patients (580 men; mean age, 63 years) were included, with overlooked basal lung metastases confirmed in 13 patients (1.5%). The AI exhibited an area under the receiver operating characteristic curve value of 0.911 for the identification of overlooked basal lung metastases. Patient- and lesion-level sensitivities of the AI system ranged from 69.2% to 92.3% and 46.2% to 92.3%, respectively. After a radiologist reviewed the AI results, the sensitivity remained unchanged. The false-positive rate and number of false-positive lesions per patient ranged from 5.8% to 27.6% and 0.1% to 0.5%, respectively. Radiologist reviews significantly reduced the false-positive rate (2.4%-12.6%; all P values < 0.001) and the number of false-positive lesions detected per patient (0.03-0.20, respectively). The AI system could accurately identify basal lung metastases detected in abdominopelvic CT images that were overlooked by radiologists, suggesting its potential as a tool for radiologist interpretation. The AI system can identify missed basal lung lesions in abdominopelvic CT scans in patients with malignancy, providing feedback to radiologists, which can reduce the risk of missing basal lung metastasis.

Is time to first CT scan in patients with isolated severe traumatic brain injury prolonged when prehospital arterial cannulation is performed? A retrospective non-inferiority study

BackgroundInvasive blood pressure measurement is the in-hospital gold standard to guide hemodynamic management and consecutively cerebral perfusion pressure in patients with traumatic brain injury (TBI). Its prehospital use is controversial since it may delay further care. The primary aim of this study was to test the hypothesis that patients with severe traumatic brain injury who receive prehospital arterial cannulation, compared to those with in-hospital cannulation, do not have a prolonged time between on-scene arrival and first computed tomography (CT) of the head by more than ten minutes.MethodsThis retrospective study included patients 18 years and older with isolated severe TBI and prehospital induction of emergency anaesthesia who received treatment in the resuscitation room of the University Hospital of Graz between January 1st, 2015, and December 31st, 2022. A Wilcoxon rank-sum test was used to test for non-inferiority (margin = ten minutes) of the time interval between on-scene arrival and first head CT.ResultsWe included data of 181 patients in the final analysis. Prehospital arterial line insertion was performed in 87 patients (48%). Median (25–75th percentile) durations between on-scene arrival and first head CT were 73 (61–92) min for prehospital arterial cannulation and 75 (60–93) min for arterial cannulation in the resuscitation room. Prehospital arterial line insertion was significantly non-inferior within a margin of ten minutes with a median difference of 1 min (95% CI − 6 to 7, p = 0.003).ConclusionTime-interval between on-scene arrival and first head CT in patients with isolated severe traumatic brain injury who received prehospital arterial cannulation was not prolonged compared to those with in-hospital cannulation. This supports early out-of-hospital arterial cannulation performed by experienced providers.

Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.

Nontuberculous mycobacterial pulmonary disease presenting as bronchiolitis pattern on CT without cavity or bronchiectasis

BackgroundThis study aimed to investigate the radiological changes in patients with nontuberculous mycobacterial pulmonary disease (NTM-PD) having bronchiolitis patterns on computed tomography (CT).MethodsWe retrospectively reviewed the final diagnosis and radiologic changes of patients suspected of having NTM-PD without cavity or bronchiectasis on CT image, between January 1, 2005 and March 31, 2021. NTM-PD was diagnosed based on the American Thoracic Society and Infectious Diseases Society of America criteria. The initial and final CT findings (bronchiectasis, cellular bronchiolitis, cavity formation, nodules, and consolidation) were compared between patients diagnosed with and without NTM-PD.ResultsThis study included 96 patients and 515 CT images. The median CT follow-up duration was 1510.5 (interquartile range: 862.2–3005) days. NTM-PD was recognized in 43 patients. The clinical variables were not significantly different between patients with and without NTM-PD, except for underlying chronic airway disease (P < 0.001). Nodule and consolidation were more frequently observed on the initial CT scans of patients with NTM-PD compared with those without (P < 0.05). On the final follow-up CT scan, bronchiectasis (P < 0.001), cavity (P < 0.05), nodule (P < 0.05), and consolidation (P < 0.05) were more frequently observed in patients with NTM-PD. Among the 43 patients with NTM-PD, 30 showed a radiological progression on CT, with bronchiectasis (n = 22) being the most common finding. The incidence of bronchiectasis increased over time.ConclusionThe bronchiolitis pattern on CT images of patients with NTM-PD showed frequent radiological progression during the follow-up period.

Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI. Computed tomography (CT) is the first-line diagnostic imaging modality typically utilized in patients with suspected acute mild, moderate and severe TBI. Radiology reports play a crucial role in the diagnostic process, providing critical information about the location and extent of brain injury, as well as factors that could prevent secondary injury. However, the complexity and variability of radiology reports can make it challenging for healthcare providers to extract the necessary information for diagnosis and treatment planning. In this article, we report the efforts of an international group of TBI imaging experts to develop a clinical radiology report template for CT scans obtained in patients suspected of TBI and consisting of fourteen different subdivisions (CT technique, mechanism of injury or clinical history, presence of scalp injuries, fractures, potential vascular injuries, potential injuries involving the extra-axial spaces, brain parenchymal injuries, potential injuries involving the cerebrospinal fluid spaces and the ventricular system, mass effect, secondary injuries, prior or coexisting pathology).

Evaluation of treatment planning discrepancies: CT versus plain radiographic findings in patients with foot and ankle trauma

This study aimed to compare the findings of plain radiography and computed tomography (CT) of foot and ankle in patients submitted to the emergency department with high-energy foot and ankle trauma, to demonstrate if missing fractures on plain radiographs will significantly alter the treatment plan considered for each patient based on the findings of each imaging modality. We retrospectively observed standard radiological foot and ankle x-rays and CT scans in patients who presented to our center from April 2019 to June 2020 with a history of foot and ankle trauma with either loss of consciousness, a history of high-energy trauma, or clinical presentation disproportionate to plain radiographic findings. We investigated the number of fractures of each bone detected on plain radiographs and CT scans and the treatments based on each modality’s findings in patients admitted to our center. Sixty-five out of 163 (39.87%) included in our study had at least one missed fracture on plain radiography that was detected on CT. Thirty-one (19%) patients had normal radiography despite actually having fractures. In 38 (23.31%) patients CT changed the treatment plan decided by our surgeons (P < 0.001). The two imaging modalities had a moderate agreement for detecting foot and ankle fractures overall (κ = 0.432). The failure to detect fractures in patients with high-energy trauma can significantly impact treatment effectiveness. Integrating CT scans into the diagnostic process can lead to changes in treatment planning and ultimately improve patient outcomes.Level of clinical evidenceIV.

IA-Body Composition CT at T12 in Idiopathic Pulmonary Fibrosis: Diagnosing Sarcopenia and Correlating with Other Morphofunctional Assessment Techniques.

Body composition (BC) techniques, including bioelectrical impedance analysis (BIVA), nutritional ultrasound® (NU), and computed tomography (CT), can detect nutritional diagnoses such as sarcopenia (Sc). Sc in idiopathic pulmonary fibrosis (IPF) is associated with greater severity and lower survival. Our aim was to explore the correlation of BIVA, NU and functional parameters with BC at T12 level CT scans in patients with IPF but also its relationship with degree of Sc, malnutrition and mortality. This bicentric cross-sectional study included 60 IPF patients (85.2% male, 70.9 ± 7.8 years). Morphofunctional assessment (MFA) techniques included BIVA, NU, CT at T12 level (T12-CT), handgrip strength, and timed up and go. CT data were obtained using FocusedON®. Statistical analysis was conducted using JAMOVI version 2.3.22 to determine the cutoff points for Sc in T12-CT and to analyze correlations with other MFA techniques. the cutoff for muscle area in T12-CT was ≤77.44 cm2 (area under the curve (AUC) = 0.734, sensitivity = 41.7%, specificity = 100%). The skeletal muscle index (SMI_T12CT) cutoff was ≤24.5 cm2/m2 (AUC = 0.689, sensitivity = 66.7%, specificity = 66.7%). Low SMI_T12CT exhibited significantly reduced median survival and higher risk of mortality compared to those with normal muscle mass (SMI cut off ≥ 28.8 cm/m2). SMI_T12CT was highly correlated with body cell mass from BIVA (r = 0.681) and rectus femoris cross-sectional area (RF-CSA) from NU (r = 0.599). Cronbach's α for muscle parameters across different MFA techniques and CT was 0.735, confirming their validity for evaluating muscle composition. T12-CT scan is a reliable technique for measuring low muscle mass in patients with IPF, specifically when the L3 vertebrae are not captured. An SMI value of <28.8 is a good predictor of low lean mass and 12-month mortality in IPF patients.

Relationship Between Brain CT Scan Findings, Consciousness Levels, and Outcomes in Brain Trauma Patients: A Cross-Sectional Study

Background: Trauma is the fourth leading cause of mortality worldwide. The Glasgow Coma Scale (GCS) is a crucial tool for evaluating the consciousness levels of trauma patients, serving as a diagnostic instrument for assessing injury severity and the condition of brain trauma patients. Additionally, Computed Tomography (CT) Scans are the most practical and accurate imaging modality for diagnosing the types and locations of lesions in cases of brain trauma in the emergency department. The study was designed to explore the correlation between pathological findings in Brain CT scans and the consciousness levels of brain trauma patients, as assessed by the GCS. Methods: This cross-sectional descriptive-analytical study investigated 200 brain trauma patients admitted to the emergency department of Khatam Al-Anbia Hospital in Zahedan. Upon arrival, an emergency medicine specialist recorded the patients’ consciousness levels based on the GCS criteria. After performing a CT scan and determining the type and location of the lesion, a radiologist, emergency medicine, and neurosurgery specialists determined the continuation of the treatment process. Subsequently, patients requiring admission to the neurosurgery department or intensive care unit were monitored. The final patient status (deceased/survivor) was tracked at the end of the treatment period and added to the checklist. Following data coding and entry into the computer, descriptive statistics, including mean, standard deviation, and confidence intervals, were used for evaluation. Additionally, the Chi-square and independent t-tests, along with SPSS 22, were employed to examine the relationship between consciousness levels and CT scan results. Results: Three out of 200 patients were excluded from the study due to transfers to other medical facilities during treatment. The mean age of the remaining patients was 27.94 ± 11.25. Results showed that 67.5% of all patients survived, while 32.4% succumbed to injuries. The initial GCS score was 14-15 for 83 patients (42.1%), 9-13 for 69 patients (35%), and 3-8 for 45 patients (22.8%). Brain CT scans of trauma patients predominantly revealed subdural hematomas in 48 patients (24.3%). In the examination of the relationship between the mechanism of brain trauma and mortality, 75% of patients involved in falls and 65.5% in accidents survived, while all patients who experienced other causes of trauma survived. Ultimately, no significant difference was observed between the mechanism of brain trauma and patient mortality (p=0.318). Furthermore, all patients with a GCS score of 14-15 (100%), 85.6% with a GCS score of 9-13, and 35.5% with a GCS score of 3-8 survived. A significant relationship between initial consciousness levels and mortality rates in the emergency department was evident (p=0.001). Statistical analysis indicated that 66.7% of patients with subdural hematomas, 75% with epidural hematomas, 81% with cerebral contusions, 35.3% with intracerebral hemorrhages (ICH), and 92.3% with diffuse axonal injuries (DAI) ultimately survived, signifying a significant relationship between CT scan results and mortality rates (p=0.01). Moreover, the highest mortality rate was observed in patients with ICH, with a frequency of 64.7%. Conclusions: Simultaneously evaluating consciousness levels using the GCS, along with considering the type of pathology identified in CT scans of brain trauma patients admitted to the emergency department, significantly aids in determining patient mortality rates. Promptly initiating the patient’s treatment process can lead to reduced complications from brain trauma and, in some cases, decreased mortality.

Machine Learning Models Can Define Clinically Relevant Bone Density Subgroups based on Patient Specific Calibrated CT Scans in Patients Undergoing Reverse Shoulder Arthroplasty

Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and glenoid planning based on preoperative computed tomography (CT) scans assist in implant selection and position, reproducible methods for quantifying the patients' bone density are currently not available. The purpose of this study was to perform bone density analyses including patient specific calibration in an RSA cohort based on preoperative CT imaging. It was hypothesized that preoperative CT bone density measures would provide objective quantification of the patients' humeral bone quality. This study consisted of three parts, (1) analysis of a patient-specific calibration method in cadaveric CT scans, (2) retrospective application in a clinical RSA cohort, and (3) clustering and classification with machine learning models. Forty cadaveric shoulders were scanned in a clinical CT and compared regarding calibration with density phantoms, air muscle, and fat (patient-specific) or standard Hounsfield unit. Post-scan patient-specific calibration was used to improve the extraction of three-dimensional regions of interest for retrospective bone density analysis in a clinical RSA cohort (n=345). Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of low bone densities in the respective patients. The patient-specific calibration method demonstrated improved accuracy with excellent intraclass correlation coefficients (ICC) for cylindrical cancellous bone densities (ICC>0.75). Clustering partitioned the training data set into a high-density subgroup consisting of 96 patients and a low-density subgroup consisting of 146 patients, showing significant differences between these groups. The SVM showed optimized prediction accuracy of low and high bone densities compared to conventional statistics in the training (accuracy=91.2%; AUC=0.967) and testing (accuracy=90.5 %; AUC=0.958) data set. Preoperative CT scans can be used to quantify the proximal humeral bone quality in patients undergoing RSA. The use of machine learning models and patient-specific calibration on bone mineral density demonstrated that multiple 3D bone density scores improved the accuracy of objective preoperative bone quality assessment. The trained model could provide preoperative information to surgeons treating patients with potentially poor bone quality.

Validation of an automated segmentation method for body composition analysis in colorectal cancer patients using diagnostic abdominal computed tomography images

Background & AimsSeveral automated programs have been developed to facilitate body composition analysis of images from abdominal computed tomography (CT) scans. External validation in patients with colorectal cancer is necessary for use in research and clinical practice. Our aim was to validate an automatic method (AutoMATiCA) of segmenting CT images at the third lumbar level (L3) from patients with colorectal cancer, by comparing with manual segmentation. MethodsDiagnostic abdominal CT scans of consecutive patients with stage I-III colorectal cancer were analysed to measure cross-sectional areas and tissue densities of skeletal muscle and intra-muscular, visceral, and subcutaneous adipose tissue. Trained analysts performed manual segmentation of L3 CT images using SliceOmatic. Automatic segmentation was performed using AutoMATiCA, an open-source software. The Dice similarity coefficient (DSC) was calculated to assess segmentation accuracy. Agreement of automatic with manual segmentation was evaluated using intra-class correlation coefficients (ICCs) and Bland-Altman plots with limits of agreement. ResultsA total of 292 scans were included, of which 62% were from male patients. The agreement of AutoMATiCA with the manual segmentation was excellent, with median DSC values ranging from 0.900-0.991 and ICCs above 0.95 for all segmented areas. No systematic deviations were observed in Bland-Altman plots for all segmented areas, with overall narrow limits of agreement. ConclusionsAutoMATiCA provides an accurate segmentation of abdominal CT images from patients with colorectal cancer. Our findings support its use as a highly efficient automated tool for body composition analysis in research and potentially also in clinical practice.

Prognostic factors for interstitial lung disease progression in rheumatoid arthritis: May methotrexate protect against progression?

Objective Lung computed tomography (CT) is a valid method for the detection and assessment of the progression of interstitial lung disease (ILD) in rheumatoid arthritis (RA) patients. The objective of this study is to conduct a comparative analysis of the characteristics of individuals with RA-ILD, with and without radiographic progression, determined using lung CT scans. Method In this retrospective observational study, three radiologists re-evaluated CT scans of RA-ILD patients who had at least one follow-up CT. The lungs were divided into upper, middle, and lower zones, with equal slices. Progression was defined as the involvement of more zones in the vertical extent by the same elementary findings or the emergence of more severe findings in the same zones compared to the previous examination. Logistic regression analysis was used to assess the possible factors identified in univariate analysis. Results This study included 104 patients with 215 lung CT scans for analysis. Radiographic progression was seen in 43 patients (41.3%). Male sex, findings compatible with ILD on the last X-ray, age at diagnosis of ILD > 50 years, and presence of ground-glass opacity on CT were more common in the group with progression. In multivariate analysis (adjusted for ILD disease duration), findings consistent with ILD on chest X-ray and male sex were independent risk factors for progression, while taking methotrexate (ever) was an independent protective factor for progression. Conclusion Our findings indicate a negative association between methotrexate use and ILD progression. These results should be confirmed in further studies.

Differential Impact of Subcutaneous and Visceral Fat on Bone Changes after Gastrectomy.

Osteoporosis and fragility fractures are crucial musculoskeletal complications in long-term survivors of gastric cancer. However, the relationship between changes in body composition after gastrectomy and bone loss has not been investigated. Therefore, this study aimed to explore whether computed tomography (CT)-derived body composition parameters are associated with bone loss after gastrectomy in patients with gastric cancer. We retrospectively reviewed medical records and abdomen CT scans of patients who underwent gastrectomy at Yonsei University Severance Hospital between 2009 and 2018. Patients with non-metastatic gastric adenocarcinoma and preoperative and postoperative non-contrast CT scans were analyzed. Section area of skeletal muscle (SMA), visceral fat (VFA), and subcutaneous fat (SFA) were assessed using semi-automatic segmentation software. Changes in trabecular bone attenuation of L1 mid-vertebra level (L1 Hounsfield units [HU]) were measured. Fifty-seven patients (mean age, 65.5±10.6; 70.2% males) were analyzed, and the median duration was 31 months. Fortyseven patients (82.5%) lost weight after gastrectomy. Baseline SMA and VFA did not differ between the bone loss and preserved groups; however, baseline SFA was significantly higher in the bone preserved group than in the bone loss group (P=0.020). In a multivariable linear regression model adjusted for confounding factors, one standard deviation higher VFA at baseline was associated with greater annualized L1 HU loss (%) (P=0.034). However, higher preoperative SFA was associated with protection against bone loss after gastrectomy (P=0.025). Higher preoperative SFA exhibited a protective effect against bone loss after gastrectomy in patients with non-metastatic gastric cancer, whereas VFA exhibited a negative effect.

Peritonsillar Abscess Outcomes with and Without Computed Tomography: A Retrospective Cohort Study.

Peritonsillar abscess (PTA) is a common deep space head and neck infection, which can be diagnosed with or without computed tomography (CT). CT poses a risk for false positives, leading to unnecessary abscess drainage attempts without benefit, whereas needle or open aspiration without imaging could potentially lead to missed abscess in need of treatment. This study considered the utility and impact of obtaining CT scans in patients with suspected PTA by comparing outcomes between CT and non-CT usage. Retrospective cohort analysis using TriNetX datasets compared the outcomes of two cohorts: PTA without CT and PTA with CT. Measured outcomes included incision and drainage; quinsy adenotonsillectomy; recurrent PTA; airway emergency/obstruction; repeat emergency department (ED) visits; and need for antibiotics, opiates, or steroids. Odds ratios (OR) were calculated using a cohort analysis. The CT usage group had increased odds of receiving antibiotics (OR 3.043, [2.043-4.531]), opiates (OR 1.614, [1.138-1.289]), and steroids (OR 1.373, [1.108-1.702]), as well as a higher likelihood of returning to the ED (OR 5.900, [3.534-9.849]) and developing a recurrent PTA (OR 1.943, [1.410-2.677]). No significant differences were observed in the incidence of incision and drainage, quinsy adenotonsillectomy, or airway emergency/obstruction. Our study indicated that CT scans for PTA diagnosis were associated with increased prescription of antibiotics, opioids, steroids, return ED visits, and recurrent PTA. Future prospective trials are needed to determine if the use of CT scans indicates higher patient acuity that explains the potential negative outcomes. Level II Laryngoscope, 2024.

An Advanced Knee Simulator Model Can Reproducibly Be Used for Ligament Balancing Training during Total Knee Arthroplasty.

A critical and difficult aspect of total knee arthroplasty (TKA) is ligamentous balancing for which cadavers and models have played a large role in the education and training of new arthroplasty surgeons, although they both have several shortcomings including cost, scarcity, and dissimilarity to in vivo ligament properties. An advanced knee simulator (AKS) model based on computed tomography (CT) scans was developed in the setting of these challenges with cadavers and previous models. In this study, we compared the ligament balancing between AKS and human cadaveric knees to assess the validity of using the AKS for ligament balancing training during TKA. A CT scan of a TKA patient with varus deformity was used to design the AKS model with modular components, using three-dimensional printing. Three fellowship-trained arthroplasty surgeons used technology-assisted TKA procedure to plan and balance three cadaver knees and the AKS model. Medial and lateral laxity data were captured using manual varus and valgus stress assessments for cadavers and the model in an extension pose (10 degrees of flexion from terminal extension) and between 90 and 95 degrees for flexion. After preresection assessments, surgeons planned a balanced cruciate-retaining TKA. Following bony cuts and trialing, extension and flexion ligament laxity values were recorded in a similar manner. Descriptive statistics and Student's t-tests were performed to compare the cadavers and model with a p-value set at 0.05. Preresection medial/lateral laxity data for both extension and flexion were plotted and showed that the highest standard deviation (SD) for the cadavers was 0.67 mm, whereas the highest SD for the AKS was 1.25 mm. A similar plot for trialing demonstrated that the highest SD for the cadavers was 0.6 mm, whereas the highest SD for the AKS was 0.61 mm. The AKS trialing data were highly reproducible when compared with cadaveric data, demonstrating the value of the AKS model as a tool to teach ligament balancing for TKA and for future research endeavors.

Kidney, ureter, and urinary bladder segmentation based on non-contrast enhanced computed tomography images using modified U-Net

This study was performed to segment the urinary system as the basis for diagnosing urinary system diseases on non-contrast computed tomography (CT). This study was conducted with images obtained between January 2016 and December 2020. During the study period, non-contrast abdominopelvic CT scans of patients and diagnosed and treated with urinary stones at the emergency departments of two institutions were collected. Region of interest extraction was first performed, and urinary system segmentation was performed using a modified U-Net. Thereafter, fivefold cross-validation was performed to evaluate the robustness of the model performance. In fivefold cross-validation results of the segmentation of the urinary system, the average dice coefficient was 0.8673, and the dice coefficients for each class (kidney, ureter, and urinary bladder) were 0.9651, 0.7172, and 0.9196, respectively. In the test dataset, the average dice coefficient of best performing model in fivefold cross validation for whole urinary system was 0.8623, and the dice coefficients for each class (kidney, ureter, and urinary bladder) were 0.9613, 0.7225, and 0.9032, respectively. The segmentation of the urinary system using the modified U-Net proposed in this study could be the basis for the detection of kidney, ureter, and urinary bladder lesions, such as stones and tumours, through machine learning.

Lesion-aware cross-phase attention network for renal tumor subtype classification on multi-phase CT scans

Multi-phase computed tomography (CT) has been widely used for the preoperative diagnosis of kidney cancer due to its non-invasive nature and ability to characterize renal lesions. However, since enhancement patterns of renal lesions across CT phases are different even for the same lesion type, the visual assessment by radiologists suffers from inter-observer variability in clinical practice. Although deep learning-based approaches have been recently explored for differential diagnosis of kidney cancer, they do not explicitly model the relationships between CT phases in the network design, limiting the diagnostic performance. In this paper, we propose a novel lesion-aware cross-phase attention network (LACPANet) that can effectively capture temporal dependencies of renal lesions across CT phases to accurately classify the lesions into five major pathological subtypes from time-series multi-phase CT images. We introduce a 3D inter-phase lesion-aware attention mechanism to learn effective 3D lesion features that are used to estimate attention weights describing the inter-phase relations of the enhancement patterns. We also present a multi-scale attention scheme to capture and aggregate temporal patterns of lesion features at different spatial scales for further improvement. Extensive experiments on multi-phase CT scans of kidney cancer patients from the collected dataset demonstrate that our LACPANet outperforms state-of-the-art approaches in diagnostic accuracy.

Prevalence and clinical significance of incidental findings in chest and abdominopelvic CT scans of trauma patients; A cross-sectional study

BackgroundImaging may inadvertently reveal pathologies unrelated to their performing purpose, known as incidental findings (IF). This study aimed to assess the prevalence, clinical significance, and documentation of IFs in chest and abdominopelvic computed tomography (CT) scans of trauma patients. MethodsThis observational study was conducted at two urban level-1 trauma centers from March 2019 through April 2022. Official radiology reports of trauma patients who underwent chest and/or abdominopelvic CT scans at the emergency department (ED) were explored, and IF were extracted. Predictive factors of the presence of IFs and their documenting were investigated. ResultsOut of 656 chest and 658 abdominopelvic CT scans, 167 (25.37%) and 212 (32.31%) scans harbored at least one IF, respectively. Patients with IFs tended to be of higher age and female in both chest (age: 48 [IQR: 35–62] vs. 34 [IQR: 25–42.5]; female: 31.14% vs 14.66%, p < 0.001 for both) and abdominopelvic CT scans (age: 41 [IQR: 30–57.5] vs 33 [IQR: 25–43], female: 26.42% vs. 13.96%, p < 0.001 for both). As for documentation of significant IFs, only 49 of 112 chest IFs (43.8%) and 55 of 176 abdominopelvic IFs (31.3%) were documented. Investigating factors associated with documentation of clinically significant IFs, shorter length of hospital stay (1.5 (IQR: 0–4) vs. 3 (IQR: 2–8), p = 0.003), and discharging by ED physicians (documentation rate: 13.2% vs 42.6%, p < 0.001) were associated with poorer documentation of IFs only in abdominopelvic scans. ConclusionCT imaging in ED trauma patients often reveals incidental findings, especially in older patients. Over 50% of these findings are clinically significant, yet they are frequently ignored and not documented. Physicians need to be more vigilant in recognizing and documenting these incidental findings and informing patients of the need for further evaluation.

#2439 The association between vitamin D deficiency and sarcopenia in patients with pre-dialysis CKD

Abstract Background and Aims Sarcopenia is characterized by a progressive and widespread loss of skeletal muscle, leading to muscle weakness and frailty. While primary sarcopenia is linked to aging, secondary sarcopenia can independently occur, especially in chronic conditions like chronic kidney disease (CKD). Investigating sarcopenia in CKD patients has become a focal point in research due to its significant impact on the quality of life, morbidity, and mortality of these individuals. Despite reports connecting vitamin D deficiency, prevalent in CKD, to sarcopenia in the general population, there is limited information regarding the specific relationship between vitamin D and sarcopenia in CKD. This study aims to explore the association between vitamin D deficiency and sarcopenia in patients with pre-dialysis CKD. Method This cross-sectional study enrolled 569 patients with pre-dialysis chronic kidney disease (CKD), defined as having an estimated glomerular filtration rate (eGFR) less than 60 ml/min/m2. Sarcopenia was assessed using computed tomography (CT)-derived skeletal muscle index (SMI), calculated based on the cross-sectional area of skeletal muscles at the 3rd lumbar vertebra. Using Korean-specific SMI cutoffs, patients were categorized into sarcopenic and non-sarcopenic groups. Logistic regression analyses were employed to explore associations between sarcopenia and baseline variables, including active vitamin D, specifically 1,25-dihydroxyvitamin D [1,25(OH)2D] level. Results Among the 569 patients, 278 had CKD stage 3, 198 had CKD stage 4, and 93 had CKD stage 5. The prevalence of sarcopenia increased with advancing CKD stage (CKD stage 3 vs. stage 4 vs. stage 5: 20.1% vs. 31.3% vs. 41.9%, P &amp;lt; 0.001). Univariate logistic regression analysis revealed an association between 1,25(OH)2D levels and sarcopenia. Additionally, age, male sex, diabetes, body mass index, eGFR, urinary albumin excretion levels, and serum levels of albumin, high-sensitive C-reactive protein (hsCRP), and intact parathyroid hormone were associated with sarcopenia. In multivariable logistic regression analysis, 1,25(OH)2D levels remained independently associated with sarcopenia (per 1 pg/dl increase, odds ratio [OR]: 0.84; 95% confidence interval [CI]: 0.80–0.87, P &amp;lt; 0.001). Age (per 1 year increase, OR: 1.04, 95% CI: 1.02–1.07, P &amp;lt; 0.001), diabetes (vs. non-diabetes, OR: 2.33, 95% CI: 1.39–3.89, P = 0.001), serum albumin (per 1 mg/dl increase, OR: 0.39, 95% CI: 0.22–0.71, P = 0.0023), and hsCRP (per 1 mg/dl increase, OR: 1.91, 95% CI: 1.25–2.92, P = 0.003) were also independently associated with sarcopenia. Conclusion This study revealed an independent association between 1,25(OH)2D levels and sarcopenia derived from CT scans in pre-dialysis CKD patients. This association retained significance even after adjusting for traditional risk factors for sarcopenia, such as age, diabetes, serum albumin, and hsCRP. Further prospective studies are necessary to determine whether therapy with exogenous 1,25(OH)2D drugs can improve sarcopenia in these patients.

Machine learning classifier is associated with mortality in interstitial lung disease: a retrospective validation study leveraging registry data

BackgroundMortality prediction in interstitial lung disease (ILD) poses a significant challenge to clinicians due to heterogeneity across disease subtypes. Currently, forced vital capacity (FVC) and Gender, Age, and Physiology (GAP) score are the two most utilized metrics in prognostication. Recently, a machine learning classifier system, Fibresolve, designed to identify a variety of computed tomography (CT) patterns associated with idiopathic pulmonary fibrosis (IPF), was demonstrated to have a significant association with mortality across multiple subtypes of ILD. The purpose of this follow-up study was to retrospectively validate these findings in a large, external cohort of patients with ILD.MethodsIn this multi-center validation study, Fibresolve was applied to chest CT scans of patients with confirmed ILD that had available follow-up data. Fibresolve scores categorized by tertile were analyzed using Cox regression analysis adjusted for tobacco use and modified GAP (mGAP) score.ResultsOf 643 patients included, 446 (69.3%) died over a median follow-up time of 144 [1-821] weeks. The median [range] mGAP score was 5 [3–7]. In multivariable analysis, Fibresolve score categorized by tertile was significantly associated with mortality (Tertile 2 HR 1.47, 95% CI 0.82–2.37, p = 0.11; Tertile 3 HR 3.12, 95% CI 1.98–4.90, p < 0.001). Subgroup analyses revealed significant associations amongst those with non-IPF ILDs (Tertile 2 HR 1.95, 95% CI 1.28–2.97, Tertile 3 HR 4.66, 95% CI 2.94–7.38) and severe disease, defined by a FVC ≤ 75% (Tertile 2 HR 2.29, 95% CI 1.43–3.67, Tertile 3 HR 4.80, 95% CI 2.93–7.86).ConclusionsFibresolve is independently associated with mortality in ILD, particularly amongst patients with non-IPF ILDs and in those with severe disease.