OBJECTIVES: To evaluate the effectiveness of single-balloon and double-balloon enteroscopy in diagnosing pediatric small bowel diseases and assess the diagnostic efficacy of computed tomography enterography (CTE) for small bowel diseases using enteroscopy as the reference standard. METHODS: Clinical data from 576 children who underwent enteroscopy at Hunan Children's Hospital between January 2017 and December 2023 were retrospectively collected. The children were categorized based on enteroscopy type into the single-balloon enteroscopy (SBE) group (n=457) and double-balloon enteroscopy (DBE) group (n=119), and the clinical data were compared between the two groups. The sensitivity and specificity of CTE for diagnosing small bowel diseases were evaluated using enteroscopy results as the standard. RESULTS: Among the 576 children, small bowel lesions were detected by enteroscopy in 274 children (47.6%).There was no significant difference in lesion detection rates or complication rates between the SBE and DBE groups (P>0.05), but the DBE group had deeper insertion, longer procedure time, and higher complete small bowel examination rate (P<0.05). The complication rate during enteroscopy was 4.3% (25/576), with 18 cases (3.1%) of mild complications and 7 cases (1.2%) of severe complications, which improved with symptomatic treatment, surgical, or endoscopic intervention. Among the 412 children who underwent CTE, the sensitivity and specificity for diagnosing small bowel diseases were 44.4% and 71.3%, respectively. CONCLUSIONS SBE and DBE have similar diagnostic efficacy for pediatric small bowel diseases, but DBE is preferred for suspected deep small bowel lesions and comprehensive small bowel examination. Enteroscopy in children demonstrates relatively good overall safety. CTE demonstrates relatively low sensitivity but comparatively high specificity for diagnosing small bowel diseases.
Retrospective Studies ; Treatment Outcome ; Double-Balloon Enteroscopy/statistics & numerical data* ; Single-Balloon Enteroscopy/statistics & numerical data* ; Humans ; Male ; Female ; Child ; Operative Time ; Tomography, X-Ray Computed/statistics & numerical data* ; Sensitivity and Specificity ; Intestine, Small/surgery* ; Intestinal Diseases/surgery*

A 39-year-old male patient was admitted to hospital with abdominal distension, unconsciousness, and anuria. Head computed tomography (CT) showed subarachnoid hemorrhage and diffuse cerebral edema. The high-density area of contrast accumulation region in the high-density CT plaque was 38 HU, and the preliminary diagnosis was SAH, incomplete intestinal obstruction, and sepsis caused by acute cerebrovascular disease. After admission, the patient displayed upturned eyes, limb convulsions, serum procalcitonin level exceeding 100 ng/mL, low blood pressure and septic shock. Imipenem was given for intensive anti-infection therapy. After treatment, procalcitonin levels showed a slow decline, renal function, and intra-abdominal pressure returned to normal, urine volume gradually increased, but platelets still showed a downward trend. Lumbar puncture showed colorless and clear cerebrospinal fluid, and the biochemical and routine results of cerebrospinal fluid were normal. SAH and intracranial infection were excluded, and it was considered that the head CT showed pseudo-subarachnoid hemorrhage. On the 3rd day of admission, laparoscopic exploratory laparotomy+appendectomy+abdominal drainage under general anesthesia were performed. During surgery, purulent gangrene in the appendix was found, with pus adhering to the surface of the intestines and a large amount of pus present in the abdominal cavity. Rhabdomyolysis syndrome developed after surgery. After continuous renal replacement therapy, the indicators gradually returned to normal. The patient was conscious, and the head CT results were normal. The patient was discharged from the hospital on the 19th day after surgery, and no special discomfort and abdominal pain and distension occurred during the 3-month follow-up.
Humans ; Male ; Adult ; Tomography, X-Ray Computed ; Sepsis/diagnostic imaging* ; Multiple Organ Failure/etiology* ; Subarachnoid Hemorrhage/complications*

Accurate staging is the fundamental basis for the treatment and prognosis of non-small cell lung cancer (NSCLC), and whether the tumor involves the pleura or chest wall is a critical aspect in assessing the staging of peripheral lung cancer. Imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) and positron emission tomography (PET) are widely used to determine pleural invasion in NSCLC. There has been an increasing number of studies evaluating whether NSCLC invades the pleura and the extent of such invasion. This article provides a review of the staging and the imaging diagnostic criteria of pleural invasion, aiming to offer references for peers in the precise diagnosis of pleural or chest wall invasion.
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Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Lung Neoplasms/diagnosis* ; Thoracic Wall/diagnostic imaging* ; Pleura/diagnostic imaging* ; Neoplasm Invasiveness ; Tomography, X-Ray Computed

Lung cancer is still one of the most common malignant tumors in the world. With the increase of its incidence and the development of medical technology, the overall survival of lung cancer patients has significantly extended compared to before. The incidence of brain and meningeal metastases from lung cancer has also been rising year by year, but patients with brain and meningeal metastases from lung cancer have a poor prognosis and a very high mortality rate, and the diagnosis is mainly based on computed tomography (CT), magnetic resonance imaging (MRI) and other imaging examinations. However, the imaging features are diverse and the specificity is low, which makes it easy to be misdiagnosed and missed. Therefore, accurately identifying brain and meningeal metastases and timely targeted treatment is crucial for improving patient prognosis. This paper analyzed the diagnosis and treatment of a case of lung cancer with no obvious recurrence and metastasis in nearly 7-year long-term follow-up after radical lung cancer surgery, but the patient with abnormal behavior, impaired consciousness and epilepsy in the past 5 months, and multiple punctate calcifications in the brain found by head CT and MRI. This paper consider that the patient's mental and behavioral symptoms were caused by brain and meningeal metastasis of lung cancer after excluding infectious disease and ineffective treatment of autoimmune encephalitis, and further pathological biopsy and genetic detection confirmed the diagnosis of metastatic lung adenocarcinoma with epidermal growth factor receptor (EGFR) L858R gene mutation, and the patient's symptoms were significantly improved after targeted therapy by Osimertinib. This paper also searched the relevant literatures of brain calcifications in databases such as China National Knowledge Infrastructure (CNKI), Wanfang, UpToDate, PubMed, etc., and found that intracerebral calcifications exist in a variety of diseases, including infectious, genetic and neurodegenerative diseases, vascular diseases, metabolic diseases and tumors. However, brain calcification in brain and meningeal metastases are often underestimated, and the consequent risk is misdiagnosis and delayed treatment. Therefore, brain and meningeal metastases manifested as brain calcification should not be ignored in patients with a history of previous tumors.
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Humans ; Lung Neoplasms/pathology* ; Brain Neoplasms/diagnostic imaging* ; Meningeal Neoplasms/diagnostic imaging* ; Calcinosis/diagnostic imaging* ; Male ; Middle Aged ; Tomography, X-Ray Computed ; Magnetic Resonance Imaging

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

In recent years, the widespread application of chest computed tomography (CT) screening has led to a significant increase in the detection rate of pulmonary nodules. As a critical diagnostic tool for early-stage lung cancer, video-assisted thoracic surgery (VATS) has emerged as the preferred therapeutic approach for pulmonary nodules. Clinical evidence demonstrates that precise preoperative localization significantly enhances surgical success rates (reducing conversion to thoracotomy), minimizes complications, and shortens operation time. This comprehensive review systematically evaluates six cutting-edge localization techniques: percutaneous puncture-assisted localization, electromagnetic navigation bronchoscopy (ENB) localization, 3D-printed auxiliary localization, basin-analysis-based localization, robotic navigation system localization, and mixed reality (MR)-guided localization. By critically analyzing their operational principles, efficacy, safety profiles, and clinical applicability, this paper aims to provide evidence-based recommendations for optimizing clinical decision-making in pulmonary nodule management.
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Humans ; Lung Neoplasms/diagnosis* ; Solitary Pulmonary Nodule/diagnostic imaging* ; Thoracic Surgery, Video-Assisted/methods* ; Multiple Pulmonary Nodules/diagnostic imaging* ; Tomography, X-Ray Computed

BACKGROUND: Lung adenocarcinoma is an important pathohistologic subtype of non-small cell lung cancer (NSCLC). Invasive non-mucinous pulmonary adenocarcinomas (INMA) tend to have a poor prognosis due to their significant heterogeneity and diverse histologic components. Establishing a histologic grading system for INMA is crucial for evaluating its malignancy. In 2021, the International Association for the Study of Lung Cancer (IASLC) proposed that a new histological grading system could better stratify the prognosis of INMA patients. The aim of this study was to establish a visualized nomogram model to predict INMA IASLC grading preoperatively by means of dual-energy computed tomography (DECT), fractal dimension (FD), clinical features and conventional CT parameters. METHODS: A total of 112 patients with INMA who underwent preoperative DECT were retrospectively enrolled from March 2021 to January 2025. Patients were categorized into low-intermediate grade and high grade groups based on IASLC grading. The clinical characteristics and conventional CT parameters, including baseline features, biochemical markers, and serum tumor markers, were collected. DECT-derived parameters, including iodine concentration (IC), effective atomic number (eff-Z), and normalized IC (NIC), were collected and determined as NIC ratio (NICr) and fractal dimension (FD). Univariate analysis was employed to compare differences in conventional characteristics and DECT parameters between the two groups. Variables demonstrating statistical significance were subsequently incorporated into a multivariate Logistic regression analysis. A nomogram model integrating clinical data, conventional CT parameters, and DECT parameters was developed to identify independent predictors for IASLC grading of INMA. The discriminatory performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Multivariate analysis identified smoking history [odds ratio (OR)=2.848, P=0.041], lobulation sign (OR=2.163, P=0.004), air bronchogram (OR=7.833, P=0.005), eff-Z in arterial phase (OR=4.266, P<0.001), and IC in arterial phase (OR=1.290, P=0.012) as independent and significant predictors for IASLC grading of INMA. The nomogram model constructed based on these indicators demonstrated optimal predictive performance, achieving an area under the curve (AUC) of 0.804 (95%CI: 0.725-0.883), with specificity and sensitivity of 85.3% and 65.7%, respectively. CONCLUSIONS The nomogram model based on clinical features, imaging features and spectral CT parameters have a large potential for application in the preoperative noninvasive assessment of INMA IASLC grading.
Humans ; Nomograms ; Female ; Male ; Middle Aged ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/pathology* ; Aged ; Retrospective Studies ; Adenocarcinoma of Lung/pathology* ; Neoplasm Grading ; Adult

In this paper, the technical basis and latest progress of X-ray tube for CT systems are introduced, with emphasis on material innovation, superconducting technology, liquid metal bearing, low dose scanning and electron beam filtration. As the core component of CT equipment, the technological innovation of CT X-ray tubes significantly improves image quality, reduces radiation dose, improves efficiency, and extends the service life of the X-ray tubes. Domestic and foreign manufacturers have made remarkable progress in the field of CT X-ray tubes. Domestic manufacturers, such as the 12th Institute of CLP, have made important progress in the development of CT X-ray tubes, and successfully achieved independent research and development of high-end CT X-ray tubes. Foreign manufacturers such as Siemens, GE, Philips and other manufacturers continue to innovate in liquid metal bearings, superconducting technology, new targets and other aspects to maintain technology leadership. In the future, CT X-ray tubes will develop in the direction of personalization, intelligence, long life, miniaturization and portability, providing more accurate and low-risk technical support for medical image diagnosis.
Tomography, X-Ray Computed/instrumentation* ; Equipment Design

This study analyzes the global patents development and clinical applications of photon counting CT (PCCT) technology in the past 20 years, including the trends of global patent application, the patent portfolio planning, key innovations and technological evolution of top medical device companies. The study utilizes patent retrievals in incoPat database, and then carries out patent navigation analysis. At the same time, with reports of clinical trials and research, the advantages and prospects of PCCT in clinical diagnosis are sorted out. With the rapid growth of the number of patent applications for PCCT, the technologies and clinical trials are becoming increasingly mature. It is expected that in the next few years, more PCCT products will be launched to the market, bringing a more accurate and safe diagnostic experience to radiologists and patients.
Patents as Topic ; Photons ; Tomography, X-Ray Computed ; Humans

OBJECTIVES: Renal cell carcinoma (RCC) is a malignant renal tumor that poses a significant threat to patient health. Accurate preoperative pathological grading plays a crucial role in determining the appropriate treatment for this disease. Currently, deep learning technology has become an important method for pathological grading of RCC. However, existing methods primarily rely on single-phase computed tomography (CT) imaging for analysis and prediction, which has limitations such as missing small lesions, one-sided evaluation, and local focusing issues. Therefore, this study proposes a multi-modal deep learning algorithm that integrates multi-phase enhanced CT images with clinical variable data, aiming to provide a basis for predicting the pathological grading of RCC. METHODS: First, the algorithm took four-phase enhanced CT images from the plain scan, arterial phase, venous phase, and delayed phase, along with clinical variables, as inputs. Then, an embedding encoding module was used to extract heterogeneous information from the clinical variables, and a 3-dimensional (3D) ResNet50 model was employed to capture spatial information from the multi-phase enhanced CT image data. Finally, a Fusion module deeply integrated the feature information from clinical variables and each phase's CT image features, further utilizing a cross-self-attention mechanism to achieve multi-phase feature fusion. This approach comprehensively captures the deep semantic information from the patient data, fully leveraging the complementary advantages of multi-modal and multi-phase data. To validate the effectiveness of the proposed method, a total of 1 229 RCC patients were approved by ethics review were included to train the model. RESULTS: Experimental results demonstrated superior performance compared to traditional radiomics and state-of-the-art deep learning methods, achieving an accuracy of 83.87%, a recall rate of 95.04%, and an F1-score of 82.23%. CONCLUSIONS The proposed algorithm exhibits strong stability and sensitivity, significantly enhancing the predictive performance of RCC pathological grading. It offers a novel approach for accurate RCC diagnosis and personalized treatment planning.
Humans ; Carcinoma, Renal Cell/pathology* ; Deep Learning ; Kidney Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Algorithms ; Neoplasm Grading ; Male ; Female ; Middle Aged