UNASSIGNED: Distinguishing benign from malignant sub-centimeter solid pulmonary nodules (SSPNs) continues to be challenging in clinical practice. Earlier diagnosis is crucial for improving patient survival and prognosis. This study aimed to investigate the risk factors of malignant SSPNs and establish and validate a prediction model based on computed tomography (CT) characteristics to assist in their early diagnosis.
UNASSIGNED: A total of 261 consecutive participants with 261 SSPNs were retrospectively recruited between January 2012 and July 2023 from National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (Center 1), including 161 malignant lesions and 100 benign lesions. Patients were randomly assigned to the training set (n=183) and validation set (n=78) according to a 7:3 ratio. Malignant nodules were confirmed by pathology; and benign nodules were confirmed by follow-up or pathology. Clinical data and CT features were collected to estimate the independent predictors of malignancy of SSPN with multivariate logistic analysis. A clinical prediction model was subsequently established by logistic regression. Furthermore, an additional 69 consecutive patients with 69 SSPNs from The Fourth Hospital of Hebei Medical University (Center 2) between January 2022 and December 2022 were retrospectively included as an external cohort to validate the predictive efficacy of the model. The performance of the prediction model was assessed by sensitivity, specificity, and the area under the receiver operating characteristic curve.
UNASSIGNED: There were 113 (61.7%), 48 (61.5%) and 28 (40.6%) malignant SSPNs in the training, internal and external validation sets, respectively. Multivariate logistic analysis revealed four independent predictors of malignant SSPNs: tumor-lung interface (P=0.002), spiculation (P=0.04), air bronchogram (P=0.047), and invisible at the mediastinal window (P=0.003). The area under the curve (AUC) for the prediction model in the training set was 0.875 [95% confidence interval (CI): 0.818, 0.933]; and the sensitivity and specificity were 94.7% and 68.6%, respectively. The AUCs in the internal and external validation set were (0.781; 95% CI: 0.664, 0.897) and (0.873; 95% CI: 0.791, 0.955), respectively; the sensitivity and specificity were 66.7% and 83.3% for the internal validation data, and 100.0% and 61.0% for the external validation data, respectively.
UNASSIGNED: The prediction model based on CT characteristics could be helpful for distinguishing malignant SSPNs from benign ones.

UNASSIGNED: Varicella pneumonia is one of the most common and severe complications of chickenpox infection. This study aimed to describe the clinical and radiological features of varicella pneumonia in a cluster of immunocompetent adolescents.
UNASSIGNED: A retrospective analysis was conducted on a cohort of adolescent patients diagnosed with varicella pneumonia at Wuhan Jinyintan Hospital between February 2023 and May 2023. The clinical and imaging data were collected and analyzed. A total of 116 patients were divided into two groups by the absence (group 1, n=57) or presence (group 2, n=59) of lower respiratory symptoms for data comparison.
UNASSIGNED: Among 116 patients (median age, 16 years; 60 males), rash (100%) was the most prevalent clinical symptom. The most common respiratory symptom and sign were fever (42.2%) and coarse breath sounds (41.4%). Chest computed tomography (CT) performed within five days of symptoms onset revealed multiple (89.7%), peripheral (51.7%), and ill-defined (73.3%) lung nodules in most patients, which gradually improved 6-10 days after symptom onset. Group 2 had higher levels of interleukin-6 (P<0.001), C-reactive protein (P=0.02), serum amyloid-A protein (P=0.002), longer hospital stays (P=0.04), more involved lung lobes (P=0.02), and a higher incidence of multiple nodules (P=0.043) than those of group 1.
UNASSIGNED: In immunocompetent adolescents, clustered varicella pneumonia often presents as mild and more uniform in clinical and radiological presentations than sporadic cases. The most common CT findings were multiple pulmonary nodules. Patients with lower respiratory symptoms exhibited more severe clinical and radiological manifestations. Generally, it is not recommended that patients undergo frequent CT scans in a short period.

UNASSIGNED: Patients with lung cancer accompanied by sarcopenia may have a poor prognosis. Normally, low muscle mass associated with sarcopenia is assessed using the skeletal muscle index (SMI). It remains unclear whether the standardized skeletal muscle area (SMA) using 2-dimensional (2D) vertebral metrics (called the skeletal muscle vertebral related index, SMVI) could substitute for SMI when it is missing. The aim of this study was to investigate the feasibility of SMVI as an alternative to SMI, and their associations with overall survival (OS) in patients with non-small cell lung cancer (NSCLC).
UNASSIGNED: In this single-center study, a retrospective analysis was conducted on 433 NSCLC patients who underwent computed tomography (CT) scans. At the third lumbar vertebra (L3) level, measurements were taken for SMA, vertebral body area, transverse vertebral diameter (TVD), longitudinal vertebral diameter (LVD), and vertebral height (VH). The 4 SMVIs were skeletal muscle vertebral ratio (SMVR) (SMA/vertebral body area), skeletal muscle transverse vertebral diameter index (SMTVDI) (SMA/TVD2), skeletal muscle longitudinal vertebral diameter index (SMLVDI) (SMA/LVD2), and skeletal muscle vertebral height index (SMVHI) (SMA/VH2). The patients were categorized into low and high muscle mass groups based on SMI, and the differences in SMVIs between the 2 groups were compared to assess their correlation with SMI. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were utilized to assess the discriminatory ability. Kaplan-Meier curves were employed to compare the survival disparity between the 2 groups.
UNASSIGNED: We included 191 male and 242 female patients in this study. Compared to the high muscle mass group, patients in the low muscle mass group exhibited significantly lower SMVR, SMTVDI, SMLVDI, and SMVHI (all P<0.05). All 4 SMVIs showed a positive correlation with SMI, with Spearman correlation coefficients of 0.83, 0.76, 0.75, and 0.67, respectively (all P<0.001). The AUC for diagnosing low muscle mass was higher than 0.8 for all 4 SMVI parameters. The Kaplan-Meier curve revealed that the low-risk group had a better survival probability than the high-risk group in the SMVR, SMTVDI, and SMLVDI.
UNASSIGNED: The SMVI functions as an alternative metric for evaluating skeletal muscle mass in the assessment of NSCLC based on SMI.

UNASSIGNED: The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm).
UNASSIGNED: A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined.
UNASSIGNED: A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively.
UNASSIGNED: The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.

UNASSIGNED: The musculoskeletal system participates in the pathology of metabolic disorders. Several studies have focused on body composition changes; however, the adipose tissue between muscle bundles with different metabolic statuses has rarely been studied. This study sought to explore the association between body compositions and metabolic disorders in Asians, and identify whether these body compositions can be used to detect metabolic disorders with different waist circumferences (WCs) by computed tomography (CT).
UNASSIGNED: A total of 116 subjects were included in the study and categorized into the following four groups according to WC and metabolic syndrome (MS): (I) the healthy control group; (II) the normal WC with metabolic disorder group; (III) the normal WC with MS group; and (IV) the larger WC with MS group. The International Diabetes Federation (IDF) criteria based on WC, laboratory tests, body mass index (BMI), and medical history was used to diagnose MS. Body composition parameters, such as muscle attenuation, the cross-sectional area of subcutaneous adipose tissue (SAT), muscle, extramyocellular lipid (EMCL), visceral adipose tissue (VAT), and the ratios between different compositions [e.g., the SMR (SAT/muscle), EMR (EMCL/muscle), and VMR (VAT/muscle)] were calculated for the thigh and abdomen. The areas under the curve (AUCs) of the receiver operating characteristic (ROC) curves adjusted for multiple comparisons were used to discriminate among metabolic disorders.
UNASSIGNED: The groups with metabolic disorders had more SAT (P=0.001) and EMCL (P=0.040) in the thigh, and more VAT (P=0.001) and a higher SMR (P<0.001) in the abdomen. EMCL and muscle attenuation in the thigh (AUCs =0.790 and 0.791), and the VMR and SMR in the abdomen were better able to diagnose metabolic disorders (AUCs =0.752 and 0.746) than other body composition parameters. While SAT and EMCL in the thigh (AUCs =0.768 and 0.760), and VAT and the VMR in the abdomen (AUCs =0.788 and 0.775) were better able to diagnose MS than other parameters.
UNASSIGNED: Body composition parameters for the thigh and abdomen could assist in detecting patients with an increased risk of MS.

UNASSIGNED: Noninvasively detecting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients before targeted therapy remains a challenge. This study aimed to develop a 3-dimensional (3D) convolutional neural network (CNN)-based deep learning model to predict EGFR mutation status using computed tomography (CT) images.
UNASSIGNED: We retrospectively collected 660 patients from 2 large medical centers. The patients were divided into training (n=528) and external test (n=132) sets according to hospital source. The CNN model was trained in a supervised end-to-end manner, and its performance was evaluated using an external test set. To compare the performance of the CNN model, we constructed 1 clinical and 3 radiomics models. Furthermore, we constructed a comprehensive model combining the highest-performing radiomics and CNN models. The receiver operating characteristic (ROC) curves were used as primary measures of performance for each model. Delong test was used to compare performance differences between different models.
UNASSIGNED: Compared with the clinical [training set, area under the curve (AUC) =69.6%, 95% confidence interval (CI), 0.661-0.732; test set, AUC =68.4%, 95% CI, 0.609-0.752] and the highest-performing radiomics models (training set, AUC =84.3%, 95% CI, 0.812-0.873; test set, AUC =72.4%, 95% CI, 0.653-0.794) models, the CNN model (training set, AUC =94.3%, 95% CI, 0.920-0.961; test set, AUC =94.7%, 95% CI, 0.894-0.978) had significantly better predictive performance for predicting EGFR mutation status. In addition, compared with the comprehensive model (training set, AUC =95.7%, 95% CI, 0.942-0.971; test set, AUC =87.4%, 95% CI, 0.820-0.924), the CNN model had better stability.
UNASSIGNED: The CNN model has excellent performance in non-invasively predicting EGFR mutation status in patients with lung adenocarcinoma and is expected to become an auxiliary tool for clinicians.

Pulmonary artery aneurysm (PAA) is a rare pulmonary vascular disease with nonspecific symptoms and various etiologies. As the disease progresses, in addition to the dilation of the pulmonary arteries, it may be accompanied by remodeling of the cardiac structure and changes in the morphology of the aorta. Recognizing the cause of PAA is therefore a clinically challenging task. In this review article, we provide an overview of various causes of PAA with the support of corresponding imaging findings on computed tomography pulmonary angiography (CTPA) examination. Firstly, from the perspective of hemodynamics, a logical diagnosis is provided according to whether the main pulmonary artery (MPA) is dilated, and whether the PA is dilated locally or diffusely. Secondly, for the imaging examination of vascular wall lesions, due to the limitations of ultrasound examination and interventional procedures, the irreplaceability of dual-phase CTPA examination in disease assessment is especially emphasized. Finally, for highly suspected disorders, it is necessary to comprehensively check with the patient whether there is a family history or past medical history. For patients with PAA, especially those with Marfan syndrome (MFS) or arteritis, adequate preoperative imaging evaluation, regular postoperative radiographic follow-up, and concurrent treatment of the underlying disease (if necessary) are crucial, which are related to the prognosis and long-term quality of life of such patients. Despite the nonspecific features of PAA presentation, a thorough examination of the patient\'s clinical history and imaging characteristics will play an important role in diagnosing PAA and planning patient management strategies.

BACKGROUND: Acute appendicitis (AA) is the most common emergency general surgical condition worldwide. Diagnosis is challenging and incorporates clinical, biochemical and radiological investigations. Our aim was to provide data from routine practice investigating widely utilised diagnostic methods from a single centre within the United Kingdom.
METHODS: We conducted a retrospective observational cohort study of patients who underwent a laparoscopic appendicectomy for AA between April 2022 and March 2023. AA was defined as the presence of transmural polymorphonuclear leukocytes in histology. Subgroup analysis was performed on paediatric patients. Factors associated with AA were investigated, and the diagnostic utility of biochemical and radiological investigations was examined.
RESULTS: A total of 330 appendicectomies were analysed. We found an overall negative appendicectomy rate (NAR) of 38% and 48% in paediatric patients. Independent factors associated with AA on the multivariate analysis included elevated neutrophil counts (>7 × 109/L) (OR 4.04), elevated CRP (>5 mg/L) (OR 3.04) and a radiological diagnosis (OR 8.0). Computerised tomography (CT) and ultrasound had sensitivity/specificity of 98%/47% and 35%/86%, respectively. The positive-predictive values were 85% for CT and 50% for ultrasound, and the negative-predictive values were 86% for CT and 77% for ultrasound.
CONCLUSIONS: Our study has highlighted the importance of utilising a combination of factors to improve the diagnostic certainty of AA. However, our routine practice data have shown different sensitivities and specificities of imaging in comparison to existing literature, resulting in a high NAR. Further real-world data are needed to understand whether these differences from the existing data are seen in other clinical settings.

OBJECTIVE: This study aimed to compare the efficacy of computed tomography (CT)-guided minimally invasive puncture and drainage (MIPD) and craniotomy for hematoma evacuation in the treatment of cerebellar hemorrhage.
METHODS: This single-center prospective cohort study was conducted from January 2020 to February 2023. During the study period, 40 patients with cerebellar hemorrhage who underwent CT-guided MIPD treatment were enrolled in the CT-guided MIPD (CTGMIPD) group, and 40 patients with the cerebellar hemorrhage who had a propensity score matching that of the CTGMIPD group and who underwent craniotomy for hematoma evacuation were enrolled in the standard craniotomy (SC) group. The primary outcome indicators were the 6-month mortality of the patients and the proportion of survivors with a modified Rankin Scale (mRS) scores of 1 or 2. The secondary outcome indicators were the cerebellar hematoma volume, National Institutes of Health Stroke Scale (NIHSS) score, Glasgow Coma Scale (GCS) score, incidence of postoperative complications, length of hospital stay, and medical costs. In addition, data concerning the patients who died during the study period were further analyzed.
RESULTS: At the 6-month follow-up, there was no significant difference in mortality between the two groups, although the proportion of patients with an mRS scores of 1 or 2 was significantly higher in the CTGMIPD group when compared with the SC group (P = 0.015). No significant differences were observed in the hematoma volume, NIHSS score, and GCS score between the two groups. By contrast, the incidence of postoperative complications, length of hospital stay, and medical costs were significantly lower in the CTGMIPD group than in the SC group (all P < 0.05). When compared with the SC group, the proportion of dead patients with a hematoma volume greater than 30 ml was higher in the CTGMIPD group (P = 0.03). Moreover, after stratification of the patients with a preoperative GCS score ≤8, the CTGMIPD group had a significantly higher mortality rate than the SC group (P = 0.04).
CONCLUSIONS: The efficacy of CT-guided MIPD in the treatment of cerebellar hemorrhage is close to that of craniotomy for hematoma excavation, although the complication and disability rates of the former are significantly lower than those of the latter. When the preoperative hematoma volume is less than 30 mL or the preoperative GCS score is greater than 8, CT-guided MIPD represents a better choice for the treatment of cerebellar hemorrhage than craniotomy for hematoma evacuation.

UNASSIGNED: Although the thymus undergoes degeneration with the advancement of age, recent studies have continuously revealed that the thymus possesses the potential for regeneration and may reverse this aging trend. Furthermore, an increasing number of studies indicate an association between thymus function and immunotherapy. Considering that lung cancer patients typically undergo chest computed tomography (CT) scans during treatment, this provides convenient conditions for us to observe thymic remodeling through imaging data. Therefore, exploring the changes in the thymus on CT images is of great significance for understanding its relationship with the efficacy of immunotherapy in non-small cell lung cancer (NSCLC) patients. This study investigated the CT imaging characteristics of thymic density changes in patients with advanced NSCLC after immunotherapy. The primary objective was to determine whether changes in thymic density are predictors of response to immunotherapy in patients with NSCLC.
UNASSIGNED: A total of 412 patients with advanced NSCLC who underwent immunotherapy were included. Thymic density measurements were taken initially and after immunotherapy, with the annualized change calculated. Comprehensive analysis, including disease progression, survival, and subgroup assessments, was conducted. The primary outcome was overall survival (OS), and the secondary outcomes were progression-free survival (PFS), objective response rate (ORR) and disease control rate (DCR).
UNASSIGNED: The annual change in density of the thymic region ranged from -108 to 108 HU after the initiation of ICIs. Patients were categorized into \"loss\" or \"non-loss\" groups (210 vs. 202) based on thymic density changes. Analysis of short-term progression of solid tumors revealed no statistically significant differences in ORR (P=0.55) and DCR (P=0.67) between the two groups. Throughout the entire follow-up period, 41 patients (19.5%) in the \"loss\" group and 64 patients (31.7%) in the \"non-loss\" group died. Thymic density reduction was not associated with PFS (P=0.08), but it was positively associated with increased OS (P=0.003). The results were consistent across subgroups.
UNASSIGNED: Thymic density changes were observed in nearly all NSCLC patients undergoing immunotherapy, with decreased density associated with longer OS. These findings suggest a potential association between thymic density changes and immune efficacy in NSCLC immunotherapy.