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

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

INTRODUCTION: Out-of-hospital-cardiac-arrest (OHCA) is a major public health challenge and post-return-of-spontaneous-circulation (ROSC) goals have shifted from just survival to survival with intact neurology. Although post-ROSC care is crucial for survival with intact neurology, there are insufficient well-established protocols for post-resuscitation care. We aimed to evaluate post-resuscitation care in the emergency department (ED) of adult (aged ≥16 years) OHCA patients with sustained ROSC and its associated neurologically intact survival. METHODS: A retrospective review of electronic medical records was conducted for OHCA patients with sustained ROSC at the ED. Data including demographics, pre-hospital resuscitation, ED resuscitation, post-resuscitation care and eventual outcomes were analysed. RESULTS: Among 921 OHCA patients, 85 (9.2%) had sustained ROSC at the ED. Nineteen patients (19/85, 22.4%) survived, with 13 (13/85, 15.3%) having intact neurology at discharge. Electrocardiogram and chest X-ray were performed in all OHCA patients, whereas computed tomography (CT) was performed inconsistently, with CT brain being most common (74/85, 87.1%), while CT pulmonary angiogram (6/85, 7.1%), abdomen and pelvis (4/85, 4.7%) and aortogram (2/85, 2.4%) were done infrequently. Only four patients (4.7%) had all five neuroprotective goals of normoxia, normocarbia, normotension, normothermia and normoglycaemia achieved in the ED. The proportion of all five neuroprotective goals being met was significantly higher ( P = 0.01) among those with neurologically intact survival (3/13, 23.1%) than those without (1/72, 1.4%). CONCLUSION Post-resuscitation care at the ED showed great variability, indicating gaps between recommended guidelines and clinical practice. Good quality post-resuscitation care, centred around neuroprotection goals, must be initiated promptly to achieve meaningful survival with intact neurology.
Humans ; Out-of-Hospital Cardiac Arrest/mortality* ; Retrospective Studies ; Male ; Female ; Middle Aged ; Emergency Service, Hospital ; Cardiopulmonary Resuscitation/methods* ; Return of Spontaneous Circulation ; Aged ; Adult ; Treatment Outcome ; Electrocardiography ; Tomography, X-Ray Computed ; Aged, 80 and over

OBJECTIVES: This retrospective study aimed to investigate factors influencing positional changes of the condyle and temporomandibular joint (TMJ) following mandibular defect reconstruction with bone flaps, and to evaluate the biomechanical impacts of flap reconstruction on condylar positioning, thereby providing evidence for optimizing surgical protocols and TMJ functional rehabilitation. METHODS: A retrospective study was conducted on 90 patients undergoing mandibular segmental resection with immediate bone flap reconstruction at Guizhou Medical University Affiliated Stomatological Hospital (June 2019 to May 2024). After strict screening, 50 cases with complete data were analyzed. Clinical parameters (defect size, location, reconstruction method) and craniofacial CT scans at four timepoints [preoperative (T0), 7-10 days (T1), 3 months (T2), and 6 months (T3) postoperatively] were collected. Mimics 20 software facilitated 3D reconstruction for measuring TMJ anterior/posterior/superior joint spaces (Kamelchuk method) and calculating condylar position via the Pullinger index [Ln (posterior/anterior space)]. Vitral and Krisjane methods quantified mandibular linear parameters (ramus length, condylar pole distances to the sagittal plane, angulation) and glenoid fossa morphology. Statistical analyses were performed using SPSS 21.0. RESULTS: Mandibular defect size and location were significant factors influencing postoperative condylar position changes (P<0.05). Compared to preoperative measurements, postoperative condylar anterior, posterior, and superior joint spaces were significantly increased (P<0.001). The most pronounced anterior condylar displacement occurred within 7-10 days postoperatively (P<0.05). In patients with condyle resection, postoperative joint space and angle changes were significant; in patients with condyle preservation, only superior and anterior joint space changes were statistically significant (P<0.05). Additionally, from T1 to T2, the changes in condylar medial-lateral distance, superior joint space, and anterior joint space were negatively correlated with the preoperative condylar position. Compared with preoperative,in the T0-T1 period, condylar medial-lateral distance, posterior joint space, and articular tubercle angle changes were significantly negatively correlated with time (P<0.05). Notably, the angle between the condylar long axis and the coronal axis showed a sustained negative trend from T1 to T3 (P<0.05). CONCLUSIONS Condylar position changes after mandibular defect repair with bone flap reconstruction are associated with the size and location of the defect. Additionally, adaptive remodeling of the temporomandibular joint (TMJ) joint space occurs postoperatively. The phenomenon of anterior displacement of the condyle in the early postoperative period (7-10 days) shows a trend of reduction with prolonged follow-up time, and further sample size research is needed.
Humans ; Retrospective Studies ; Temporomandibular Joint/surgery* ; Mandibular Condyle/surgery* ; Male ; Female ; Adult ; Middle Aged ; Mandibular Reconstruction/methods* ; Mandible/surgery* ; Surgical Flaps ; Tomography, X-Ray Computed ; Young Adult ; Biomechanical Phenomena ; Aged ; Adolescent ; Imaging, Three-Dimensional

A nodule in the right middle lobe of the lung was treated by a combination of cone-beam CT,three-dimensional registration for fusion imaging,and electromagnetic navigation bronchoscopy-guided thermal ablation.The procedure lasted for 90 min,with no significant bleeding observed under the bronchoscope.The total radiation dose during the operation was 384 mGy.The patient recovered well postoperatively,with only a small amount of blood in the sputum and no pneumothorax or other complications.A follow-up chest CT on the first day post operation showed that the ablation area completely covered the lesion,and the patient was discharged successfully.
Humans ; Bronchoscopy/methods* ; Catheter Ablation/methods* ; Cone-Beam Computed Tomography ; Electromagnetic Phenomena ; Imaging, Three-Dimensional ; Lung Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed

Quantitative CT (QCT) is a method of measuring bone mineral density (BMD) of human based on a CT machine,calibrated by QCT body model and analyzed by professional software.Compared with dual-energy X-ray absorptiometry,QCT can not only assess the cortical and cancellous BMD but also exclude the influences of osteophytes and aortic/vascular calcification,thus being capable of accurately reflecting patients' bone mass.In recent years,increasing studies on QCT and osteoporosis (OP) have been carried out,and the application of QCT in the diagnosis of OP,evaluation of vertebral bone conditions,prediction of fracture risks,and assessment of anti-OP treatment is garnering increasing attention from researchers at home and abroad.This article reviews the research progress in this field,aiming to provide a reference for the research on QCT in the diagnosis and treatment of OP.
Humans ; Osteoporosis/diagnosis* ; Tomography, X-Ray Computed/methods* ; Bone Density

Objective To explore the application value of multi-model adaptive statistical iterative reconstruction-Veo (ASiR-V) in ultra-low dose chest CT examination of children in the plateau area. Methods The children who underwent chest CT examination in Xizang Autonomous Region People's Hospital were enrolled in this study and assigned into two groups according to the scanning conditions.Group A underwent scanning at a tube voltage of 100 kV and ASiR-V 50% reconstruction,and group B underwent scanning at a tube voltage of 80 kV and ASiR-V 0 (Group B1) and ASiR-V 50% (Group B2) reconstruction.The image quality of each group was evaluated objectively and subjectively.The radiation dose and image quality were compared between groups. Results Groups A and B showed the volume CT dose indexes of (2.33±0.62) mGy and (0.86±0.01) mGy and the dose length products of (65.01±25.12) mGy·cm and (23.55±3.38) mGy·cm,respectively,which presented differences between groups (both P<0.001).The image noise in the bilateral upper and middle lung areas in group B2 was lower than that in group B1 but higher than that in group A (all P<0.001).There was no significant difference in image quality score of the lung window among groups (all P>0.05).Groups A,B1,and B2 had no significant differences in ascending aorta (P=0.538) or liver CT value (P=0.175) in the mediastinal window.The signal-to-noise ratios and contrast-to-noise ratios of ascending aorta and liver in group B2 were higher than those in group B1 (all P<0.001) and lower than those in group A (all P<0.05).The image quality score of the mediastinal window followed a descending order of group A>group B2>group B1 (all P<0.001)。Conclusion ASiR-V combined with low tube voltage can effectively reduce the radiation dose and guarantee the image quality of chest CT of children in the plateau area.
Humans ; Radiation Dosage ; Tomography, X-Ray Computed/methods* ; Child ; Male ; Female ; Child, Preschool ; Radiography, Thoracic/methods* ; Infant ; Models, Statistical ; Image Processing, Computer-Assisted/methods*

Objective To develop a diagnostic model combining the CT angiography(CCTA)-derived myocardial radiomics signatures with the CT-derived fractional flow reserve(CT-FFR)based on coronary CCTA and investigate the diagnostic accuracy of the hybrid model for hemodynamically significant coronary artery disease(CAD).Methods The patients presenting stable angina pectoris,diagnosed with CAD,and clinically referred for CCTA examination and invasive coronary angiography were prospectively recruited.Radiomics features of the left ventricular myocardium were extracted from the three main perfusion territories demarcated according to the coronary blood supply.The extracted features were first selected by the minimum redundancy maximum relevance feature ranking method.A least absolute shrinkage and selection operator Logistic regression algorithm with leave-one-out cross-validation was then employed to construct a radiomics model.The CT-FFR value was generated for each blood vessel.The area under the receiver operating characteristics curve(AUC_ROC),sensitivity,and specificity were adopted to evaluate the performance of each model against the reference standard invasive coronary angiography/FFR.Results A total of 70 patients[42 men and 28 women;(61±10) years old] were included in this study and complemented CCTA examination,with 175 vessels and the corresponding myocardial territories undergoing invasive coronary angiography/FFR.A total of 1 656 specific radiomics parameters were extracted,from which 14 features were selected to establish the radiomics model.The AUC_ROC,sensitivity,and specificity were 0.797(95%CI=0.732-0.861),77.1%,and 73.7%for the radiomics model,0.892(95%CI=0.841-0.943),81.4%,and 88.8%for the CT-FFR model,and 0.928(95%CI=0.890-0.965),83.3%,and 88.4%for the hybrid model,respectively.The hybrid model outperformed the radiomics model and CT-FFR alone(P=0.040).Conclusions The radiomics signatures of the vessel-related myocardium from CCTA could provide incremental value to the diagnostic performance of CT-FFR and improve vessel-specific ischemia detection.The hybrid model combining CT-FFR with radiomics signatures is potentially feasible for improving the diagnostic accuracy for hemodynamically significant CAD.
Coronary Angiography/methods* ; Tomography, X-Ray Computed ; Humans ; Hemodynamics ; Coronary Artery Disease/diagnostic imaging* ; Male ; Female ; Middle Aged ; Aged ; Radiomics ; Angina Pectoris/diagnostic imaging* ; China ; Image Processing, Computer-Assisted ; Coronary Vessels/diagnostic imaging*

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult