Diagnostic performance of computed tomography-digital subtraction angiography and conventional magnetic resonance imaging for evaluating the vascularity of osseous spinal tumors.

BACKGROUND AND PURPOSE: Patients with hypervascular spinal tumors may have severe blood loss during tumor resection, which increases the risks of perioperative morbidity and mortality. However, the preoperative evaluation of tumor vascularity may be challenging; moreover, the reliability of the data obtained in conventional preoperative noninvasive imaging is debatable. In this study, we compared conventional magnetic resonance imaging (MRI) and subtraction computed tomography angiography (CTA) in terms of their performance in vascularity evaluation. The catheter digital subtraction angiography (DSA) technique was used as a reference standard. METHODS: This study included 123 consecutive patients with spinal tumor who underwent subtraction CTA, catheter DSA, and subsequent surgery between October 2015 and October 2021. Data regarding qualitative and semiquantitative subtraction CTA parameters and conventional MRI signs were collected for comparison with tumor vascularity graded through catheter DSA. The diagnostic performance of qualitative CTA, quantitative CTA, and conventional MRI in assessing spinal tumor vascularity was analyzed. RESULTS: Qualitative subtraction CTA was the best noninvasive imaging modality in terms of diagnostic performance (area under the receiver operating characteristic curve [AUROC], 0.95). Quantitative CTA was relatively inferior (AUROC, 0.87). MRI results had low reliability (AUROC, 0.51 to 0.59). Intratumoral hemorrhage and prominent foraminal venous plexus were found to be the specific signs for hypervascularity (specificity 93.2%). CONCLUSIONS: Qualitative subtraction CTA offers the highest diagnostic value in evaluating spinal tumor vascularity, compared to quantitative CTA and MRI. Although conventional MRI may not be a reliable approach, certain MRI signs may have high specificity, which may be crucial for assessing spinal tumor vascularity.

Thrombus discrimination with electron density: potential implications for non-contrast computed tomography imaging.

The potential clinical usefulness of electron density (ED) imaging, that can be directly estimated using dual-layer spectral computed tomography (CT), has been poorly investigated. We explored whether ED imaging might improve thrombus identification compared to conventional imaging in vitro. We evaluated mechanical thrombectomy material obtained from patients with acute ischemic stroke (AIS) treated in a tertiary level stroke center and immediately fixed in 10% neutral buffered formalin and stored in polystyrene test tubes. The test tubes were immersed in a bucket of water for evaluation by spectral CT, along with scattered control tubes. All images were obtained using a dual-layer detector CT scanner. Each tube was assessed using multiparametric side-by-side view of conventional CT (120 kVp), low monoenergetic imaging (40 keV), and ED images. Fifty-eight polystyrene tubes were analyzed, comprising 52 tubes with thrombectomy material of at least 1 mm2 size obtained from 52 AIS patients, and six control tubes filled with formalin. ED imaging identified accurately the presence of material in all tubes, whereas 2 (3%) of the tubes containing thrombus were not identified by conventional CT, leading to a very good agreement between observers for the presence of material using conventional CT and ED imaging (kappa =0.84, P<0.001). Using ED imaging, thrombus material showed a mean density of 108.8±2.9 percent ED relative to water (%EDW), water had a mean density of 100.0±0.3 %EDW, and formalin a mean density of 103.5±1.2 %EDW. Compared to conventional imaging and 40 keV monoenergetic, ED imaging had a significantly higher signal-to-noise ratio (conventional 10.4±7.0, vs. 40 keV 11.5±8.4, vs. ED 490.0±304.5, P<0.001) and contrast-to-noise ratio (CNR) (conventional 4.3±4.3, vs. 40 keV 5.7±11.2, vs. ED 37.8±29.1, P<0.001). In this in-vitro study, we demonstrated improved visualization of thrombus with ED imaging compared to conventional imaging and low monoenergetic imaging, with a significant increase in CNR.

Consideration of Brain CT Imaging Standard for Mild Head Injuries.

It has been reported that various clinical criteria indicate computed tomography (CT) examination for mild head injury (MHI). However, the decision to perform CT for MHI largely depends on the physician. Data on severe head injuries is available in sources such as the Japan Neurotrauma Data Bank, but only a few data has been collected on MHI. A total of 1688 patients with MHI (Glasgow Coma Scale 14 and 15) treated at our hospital from June 2017 to May 2019 were reviewed. CT was performed in 1237 patients (73.28%), and intracranial hemorrhage was detected in 50 patients. Three patients deteriorated, and all were surgically treated. Statistical analysis of the presence or absence of acute intracranial hemorrhage and "risk factors for complications of intracranial lesions in MHI" showed significant differences in unclear or ambiguous accident history (p = 0.022), continued post-traumatic amnesia (p < 0.01), trauma above the clavicles including clinical signs of skull fracture (skull base or depressed skull fracture) (p = 0.012), age <60 years (p < 0.01), coagulation disorders (p < 0.01), and alcohol or drug intoxication (p < 0.01). The 453 patients who did not satisfy these risk factors included only one patient with intracranial hemorrhage, so the negative predictive value was 99.78%. This study shows that the "risk factors for complications of intracranial lesions in MHI" are effective criteria for excluding acute intracranial hemorrhage and CT should be actively considered for patients with the above factors that showed significant differences.

CT-based radiomics and clinical characteristics for predicting bone metastasis in lung adenocarcinoma patients.

Background: The occurrence of bone metastasis (BM) will seriously shorten the survival time of lung adenocarcinoma patients and aggravate the suffering of patients. Computed tomography (CT)-based clinical radiomics nomogram may help clinicians stratify the risk of BM in lung adenocarcinoma patients, thereby enabling personalized individualized clinical decision making. Methods: A total of 501 patients with lung adenocarcinoma from March 2017 to March 2019 were enrolled in the study. Based on plain chest CT images, 1130 radiomics features were extracted from each lesion. One-way analysis of variance (ANOVA) and least absolute shrinkage selection operator (LASSO) algorithm were used for radiomics features selection. Univariate and multivariate analyses were used to screen for clinical characteristics and identify independent predictors of BM. Three models (radiomics model, clinical model and combined model) were constructed to predict BM in lung adenocarcinoma patients. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the three models. The DeLong test was used to compare the performance of the models. Results: Finally, the clinical model for predicting BM in lung adenocarcinoma patients was constructed based on 5 independent predictors: cytokeratin 19-fragments (CYFRA21-1), stage, Ki-67, edge, and lobulation. The radiomics model was constructed based on 5 radiomics features. The combined model incorporating clinical independent predictors and radiomics was constructed. In the validation cohort, the area under the curve (AUC) of the clinical model, radiomics model and combined model was 0.824, 0.842 and 0.866, respectively. Delong test showed that in the training cohort, the AUC values of the radiomics model and the combined model were statistically different (P=0.03), and the AUC values of the other models were not statistically different. DCA showed that the nomogram had a highest net clinical benefit. Conclusions: The CT-based clinical radiomics nomogram can be used as a non-invasive and quantitative method to help clinicians stratify the risk of BM in patients with lung adenocarcinoma, thereby enabling personalized clinical decision making.

A validated model to predict spread through air space in lung adenocarcinoma.

Background: Spread through air space (STAS) is currently considered to be a significant predictor of a poor outcome of pulmonary adenocarcinoma. Preoperative prediction of STAS is of great importance for treatment planning. The aim of the present study was to establish a nomogram based on computed tomography (CT) features for predicting STAS in lung adenocarcinoma and to assess the prognosis of the patients with STAS. Methods: A retrospective cohort study was performed in Wuhan Union Hospital from December 2015 to March 2021. The sample was divided into training and testing cohorts. Clinicopathologic and radiologic variables were recorded. The independent risk factors for STAS were determined by stepwise regression and then incorporated into the nomogram. Receiver operating characteristic (ROC) curves and calibration curves analysed by the Hosmer-Lemeshow test were used to evaluate the performance of the model. Decision curve analysis (DCA) was conducted to determine the clinical value of the nomogram. The Kaplan-Meier method was used for survival analysis and the multivariable Cox proportional hazards regression model was used to identify independent predictors for recurrence-free survival (RFS) and overall survival (OS). Results: The sample included 244 patients who underwent surgical resection for primary lung adenocarcinoma. The training cohort included 199 patients (68 STAS-positive and 131 STAS-negative patients), and the testing cohort included 45 patients (15 STAS-positive and 30 STAS-negative patients). The preoperative CT features associated with STAS were shape, ground-glass opacity (GGO) ratio and spicules. The nomogram including these three factors had good discriminative power, and the areas under the ROC curve were 0.875 and 0.922 for the training and testing data sets, respectively, with well-fitted calibration curves. DCA showed that the nomogram was clinically useful. STAS-positive patients had significantly worse OS and RFS than STAS-negative patients (both P<0.01). OS and RFS at 5-year for STAS-positive patients were 63.1% and 59.5%, respectively. Multivariate analysis showed that age [hazard ratio (HR), 1.1; 95% confidence interval (CI): 1.035-1.169; P=0.002], diameter (HR, 1.06; 95% CI: 1.04-1.11; P=0.03) and surgical margin (HR, 32.8; 95% CI: 6.8-158.3; P<0.001) were independent risk factors for OS. Adjuvant therapy (HR, 7.345; 95% CI: 2.52-21.41; P<0.001), N stage (N2) (HR, 0.239; 95% CI: 0.069-0.828; P=0.02) and surgical margin (HR, 15.6; 95% CI: 5.9-41.1; P<0.001) were found to be independent risk factors for RFS. Conclusions: The outcome of STAS-positive patients was worse. The nomogram incorporating the identified CT features could be applied to facilitate individualized preoperative prediction of STAS and selection of rational therapy.

Assessment of tumor hypoxia in spontaneous canine tumors after treatment with OMX, a novel H-NOX oxygen carrier, with [18F]FMISO PET/CT.

BACKGROUND: Hypoxia is a detrimental factor in solid tumors, leading to aggressiveness and therapy resistance. OMX, a tunable oxygen carrier from the heme nitric oxide/oxygen-binding (H-NOX) protein family, has the potential to reduce tumor hypoxia. [18F]Fluoromisonidazole ([18F]FMISO) positron emission tomography (PET) is the most widely used and investigated method for non-invasive imaging of tumor hypoxia. In this study, we used [18F]FMISO PET/CT (computed tomography) to assess the effect of OMX on tumor hypoxia in spontaneous canine tumors. RESULTS: Thirteen canine patients with various tumors (n = 14) were randomly divided into blocks of two, with the treatment groups alternating between receiving intratumoral (IT) OMX injection (OMX IT group) and intravenous (IV) OMX injection (OMX IV group). Tumors were regarded as hypoxic if maximum tumor-to-muscle ratio (TMRmax) was greater than 1.4. In addition, hypoxic volume (HV) was defined as the region with tumor-to-muscle ratio greater than 1.4 on [18F]FMISO PET images. Hypoxia was detected in 6/7 tumors in the OMX IT group and 5/7 tumors in the OMX IV injection group. Although there was no significant difference in baseline hypoxia between the OMX IT and IV groups, the two groups showed different responses to OMX. In the OMX IV group, hypoxic tumors (n = 5) exhibited significant reductions in tumor hypoxia, as indicated by decreased TMRmax and HV in [18F]FMISO PET imaging after treatment. In contrast, hypoxic tumors in the OMX IT group (n = 6) displayed a significant increase in [18F]FMISO uptake and variable changes in TMRmax and HV. CONCLUSIONS: [18F]FMISO PET/CT imaging presents a promising non-invasive procedure for monitoring tumor hypoxia and assessing the efficacy of hypoxia-modulating therapies in canine patients. OMX has shown promising outcomes in reducing tumor hypoxia, especially when administered intravenously, as evident from reductions in both TMRmax and HV in [18F]FMISO PET imaging.

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.

Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. Results: The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

Accuracy of patient-specific CT organ doses from Monte Carlo simulations: influence of CT-based voxel models.

Monte Carlo simulations using patient CT images as input are the gold standard to perform patient-specific dosimetry. However, in standard clinical practice patient's CT images are limited to the reconstructed CT scan range. In this study, organ dose calculations were performed with ImpactMC for chest and cardiac CT using whole-body and anatomy-specific voxel models to estimate the accuracy of CT organ doses based on the latter model. When the 3D patient model is limited to the CT scan range, CT organ doses from Monte Carlo simulations are the most accurate for organs entirely in the field of view. For these organs only the radiation dose related to scatter from the rest of the body is not incorporated. For organs lying partially outside the field of view organ doses are overestimated by not accounting for the non-irradiated tissue mass. This overestimation depends strongly on the amount of the organ volume located outside the field of view. To get a more accurate estimation of the radiation dose to these organs, the ICRP reference organ masses and densities could form a solution. Except for the breast, good agreement in dose was found for most organs. Voxel models generated from clinical CT examinations do not include the overscan in the z-direction. The availability of whole-body voxel models allowed to study this influence as well. As expected, overscan induces slightly higher organ doses.

Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework.

Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR). The ensemble of noise realizations was generated by using a realistic projection domain noise insertion technique. The impact of varying dose levels and denoising strengths were investigated for a ResNet-based deep convolutional neural network (DCNN) model trained using patient images. On the uncertainty maps, DCNN shows more detailed structures than IR although its bias map has less structural dependency, which implies that DCNN is more sensitive to small changes in the input. Both visual examples and histogram analysis demonstrated that hotspots of uncertainty in DCNN may be associated with a higher chance of distortion from the truth than IR, but it may also correspond to a better detection performance for some of the small structures.

Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases.

The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 patients underwent CT of the abdomen within three months of surgery for spinal metastases. Automated body composition segmentation and quantifications of the cross-sectional areas (CSA) of abdominal visceral and subcutaneous adipose tissue and abdominal skeletal muscle was performed. From this, 31% (61) of patients had postoperative complications within 30 days, and 16% (31) of patients underwent reoperation. Lower muscle CSA was associated with increased postoperative complications within 30 days (OR [95% CI] = 0.99 [0.98-0.99], p = 0.03). Through multivariate analysis, it was found that lower muscle CSA was also associated with an increased postoperative complication rate after controlling for the albumin, ASIA score, previous systemic therapy, and thoracic metastases (OR [95% CI] = 0.99 [0.98-0.99], p = 0.047). LOS and reoperations were not associated with any body composition measures. Low muscle mass may serve as a biomarker for the prediction of complications in patients with spinal metastases. The routine assessment of muscle mass on opportunistic CTs may help to predict outcomes in these patients.

Photon-Counting Detector Computed Tomography (PCD-CT): A New Era for Cardiovascular Imaging? Current Status and Future Outlooks.

Photon-counting detector computed tomography (PCD-CT) represents a revolutionary new generation of computed tomography (CT) for the imaging of patients with cardiovascular diseases. Since its commercial market introduction in 2021, numerous studies have identified advantages of this new technology in the field of cardiovascular imaging, including improved image quality due to an enhanced contrast-to-noise ratio, superior spatial resolution, reduced artifacts, and a reduced radiation dose. The aim of this narrative review was to discuss the current scientific literature, and to find answers to the question of whether PCD-CT has yet led to a true step-change and significant progress in cardiovascular imaging.

Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study.

PURPOSE: This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan-Meier curves. SHAP analysis was applied to allow for post hoc interpretability. RESULTS: A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70). CONCLUSIONS: An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.

The Utilization of Computed Tomography in the Pediatric Emergency Department for Patients With Ventriculoperitoneal Shunts.

Introduction Despite all the advantages of computed tomography (CT) scanning, there is a significant concern due to the rising use of CT scans in children with ventriculoperitoneal (VP) shunts. High doses of radiation are absorbed by patients, raising their chance of acquiring cancer. Evaluating a potential VP shunt malfunction is a frequent encounter in the pediatric emergency room, often necessitating the utilization of a CT scan. This study aims to recognize and quantify the utilization of CT scans in an emergency setting for pediatric patients with a clinical suspicion of VP shunt malfunction. Methods This retrospective chart review was conducted on patients who visited the emergency department with suspected VP shunt malfunction in a pediatric tertiary care hospital (King Abdullah Specialist Children Hospital), Riyadh, Saudi Arabia. The study included the files of children between the years 2018 and 2019. Results A total of 119 children were included; the main indication for VP shunt insertion was congenital hydrocephalus at 46.8% (n=52). The median number of CT scans done per patient was seven (IQR=3-9). CT findings were abnormal among 55.6% (n=60). The univariate analysis examining the impact of different factors on CT findings showed an association between an abnormal CT finding and female gender (P=0.017), younger age (P=0.03), and the presence of a cerebral cyst (P=0.001); however, subsequent multivariate analysis was not significant for any of these factors. Twenty-two point three percent (n=25) of the patients required neurosurgical intervention, and the associated factors with neurosurgical intervention included changes in activity level (P=0.04), weakness (P=0.004), and altered mental status (P=0.001). Conclusion Children with VP shunts are susceptible to significant radiation exposure through the utilization of CT scans whenever they present to the ER with suspected shunt malfunction during their lifespan, which puts them at risk of radiation-related complications, such as cancers. CT imaging remains a helpful tool aiding physicians in making accurate decisions. However, in this study, almost half of the children had unremarkable CT findings. Thus, it is imperative to rationalize its use by establishing tailored guidelines that delineate the appropriate circumstances warranting its application.

Evaluation of overexposure risk when there is a space between the subject and the couch in computed tomography: a phantom study.

The purpose of this study was to investigate the risk of overexposure associated with automatic tube current modulation (ATCM) and automatic couch height positioning compensation mechanism (AHC) in computed tomography (CT) systems, particularly in scenarios involving a gap between the subject and the couch. Results revealed that when AHC was enabled, CT dose index volume (CTDIvol) increased by approximately 10% at 2.5 cm, 20% at 5.0 cm, and 40% at 10.0 cm gaps compared to close contact conditions. While the AHC function ensures consistent exposure doses and image quality regardless of subject positioning relative to the CT gantry isocenter, the study highlights a potential risk of overexposure when a gap exists between the subject and the couch. These findings offer valuable insights for optimizing CT imaging protocols and underscore the importance of carefully considering subject positioning in clinical practice.

COVID-19 Chest Manifestation on CT Scan and Associated Risk Factors for Developing Pulmonary Fibrosis.

PURPOSE: This retrospective study describes the imaging findings on chest computed tomography (CT) scans of coronavirus disease 2019 (COVID-19) patients as well as the prevalence of pulmonary fibrosis and the potential risk factors for the disease. METHODS: One of the major COVID-19 centers in the western province of Saudi Arabia, the King Abdullah Medical Complex in Jeddah, was the site of this study. All adult COVID-19 patients who got a CT chest scan between January 2020 and April 2022 were included in the trial. The imaging findings and pulmonary severity scores (PSS) were obtained from the patients' CT chest. Patients were divided into two groups according to the evidence of fibrotic-like lung changes; clinical and radiological data between the two groups were subsequently compared. Data from the patients' electronic records was collected. RESULTS: The average patient age was 56.4 years, and most (73.5%) patients were men. Two-thirds of the patients had comorbidities (69.1%). CT scans revealed that diffuse lung infiltration is reported in 61% of cases, followed by lower lobes in 19.9%. Ground glass opacity (94.1%), consolidation (76.5%), septal thickening, and/or reticulation (24.4%) were the main chest findings during the initial CT scan. Fibrotic-like lung changes were developed in 9.6% of patients. Patients known to have a positive history of hypertension (p-value = 0.031) and coronary artery disease (CAD) (p-value = 0.011) were found to be significantly more likely to develop lung fibrosis. The patients' pneumonia severity score was significantly higher among the lung fibrotic patients (p-value = 0.026). Also, patients who were diagnosed with pulmonary fibrosis stayed longer in the hospital (p-value 0.001). Sex and age did not correlate significantly with risk of lung fibrosis. CONCLUSION: Pulmonary fibrosis was observed in 9.6% of COVID-19 patients. A close follow-up of patients with severe pneumonia, prolonged hospitalization, and pre-existing CAD and hypertension was necessary, as pulmonary fibrosis was more likely to occur as a result of these factors. There is a need for a thorough, long-term investigation with a large sample size.

Establishment and verification of a prediction model based on clinical characteristics and computed tomography radiomics parameters for distinguishing benign and malignant pulmonary nodules.

Background: The radiographic classification of pulmonary nodules into benign versus malignant categories is a pivotal component of early lung cancer diagnosis. The present study aimed to investigate clinical and computed tomography (CT) clinical-radiomics nomogram for preoperative differentiation of benign and malignant pulmonary nodules. Methods: This retrospective study included 342 patients with pulmonary nodules who underwent high-resolution CT (HRCT) examination. We assigned them to a training dataset (n=239) and a validation dataset (n=103). There are 1781 tumor characteristics quantified by extracted features from the lesion segmented from patients' CT images. The features with poor reproducibility and high redundancy were removed. Then a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to further select features and build radiomics signatures. The independent predictive factors were identified by multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability. The performance and clinical utility of the clinical-radiomics nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: After dimension reduction by the LASSO algorithm and multivariate logistic regression, four radiomic features were selected, including original_shape_Sphericity, exponential_glcm_Maximum Probability, log_sigma_2_0_mm_3D_glcm_Maximum Probability, and ogarithm_firstorder_90Percentile. Multivariate logistic regression showed that carcinoembryonic antigen (CEA) [odds ratio (OR) 95% confidence interval (CI): 1.40 (1.09-1.88)], CT rad score [OR (95% CI): 2.74 (2.03-3.85)], and cytokeratin-19-fragment (CYFRA21-1) [OR (95% CI): 1.80 (1.14-2.94)] were independent influencing factors of malignant pulmonary nodule (all P<0.05). The clinical-radiomics nomogram combining CEA, CYFRA21-1 and radiomics features achieved an area of curve (AUC) of 0.85 and 0.76 in the training group and verification group for the prediction of malignant pulmonary nodules. The clinical-radiomics nomogram demonstrated excellent agreement and practicality, as evidenced by the calibration curve and DCA. Conclusions: The clinical-radiomics nomogram combined of CT-based radiomics signature, along with CYFRA21-1 and CEA, demonstrated strong predictive ability, calibration, and clinical usefulness in distinguishing between benign and malignant pulmonary nodules. The use of CT-based radiomics has the potential to assist clinicians in making informed decisions prior to biopsy or surgery while avoiding unnecessary treatment for non-cancerous lesions.

Case Report: A rare case of bilateral middle ear tophaceous gout.

Introduction: Hypacusia can be caused by various etiologies; however, hearing loss attributed to gouty tophi remains a rare occurrence. This case report presents, for the first time, a bilateral gouty tophi causing hearing impairment. Case presentation: This report describes a case study involving an 83-year-old Caucasian female patient who presented symptoms of hypacusia, pruritus, and a sensation of pressure in her right ear. A computed tomography scan revealed the presence of non-homogeneous calcified structures in both ears. Following a comprehensive assessment that included pure-tone audiometry and a thorough evaluation of the patient's clinical complaints, a tympanoplasty procedure was initially performed on the right ear. Pathological analysis revealed the presence of gouty tophi. After surgical removal of the tophus, a notable improvement in the patient's hearing threshold was observed. Four months later, a similar surgical intervention was performed on the contralateral ear, achieving a similar positive outcome. The substantial postoperative decrease of bone conduction indicates an inner ear affection by the gout tophi. Conclusion: Gout tophus in both ears is a very rare but possible cause of hypacusia, even in the absence of a pre-existing diagnosis of systemic gout disease. We report a case of gout tophi in both ears as a rare cause of hearing loss.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Background: Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). Methods: In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. Results: In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. Conclusions: The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

Background: SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen. Therefore, the aim of our study was to identify the clinicopathologic, computed tomography (CT), and positron emission tomography (PET)-CT imaging features of SD-NSCLC. Methods: Nine patients of pathologically confirmed SD-NSCLC were included in our analysis. We reviewed electronic medical records for clinical information, demographic features, CT, and PET-CT imaging features were analyzed. Results: Smoking history and male predominance are observed in all patients with SD-NSCLC (n=9). On CT, SD-NSCLC appeared as relatively well-defined masses with lobulated contour (n=8) and peripheral location (n=7). Invasion of adjacent pleura or chest wall (n=7) were frequently observed, regardless of small tumor size. Four cases showed lymph node metastases. Among nine patients, three patients showed multiple bone metastases, and one patient showed lung-to-lung metastases. Conclusions: In patient with SD-NSCLC, there was tendency for male smokers, peripheral location and invasion of adjacent pleural or chest wall invasion regardless of small tumor size, when compared to SD-UT.