ABSTRACT Purpose Bone metastasis significantly affects the prognosis of lung adenocarcinoma (LUAD) patients. This study aims to construct and validate a risk prediction model for bone metastasis in LUAD with a T1 primary tumor based on intratumoral and peritumoral radiomics features. Materials and Methods A total of 392 patients pathologically diagnosed with LUAD and a T1 primary tumor from two medical centers were retrospectively included (training cohort: n = 217, internal validation cohort: n = 93, external validation cohort: n = 82). Univariate and multivariate analyses identified independent risk factors for the clinicoradiologic model. Radiomics features were extracted from the gross tumor volume (GTV) and peritumoral tumor volume (PTV) in the training cohort CT images to establish intratumoral and peritumoral radiomics models. The optimal radiomics model was combined with clinicoradiologic features to develop a nomogram. Model performance was assessed using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results Among 392 LUAD patients with a T1 primary tumor, 147 had bone metastasis. The clinicoradiologic model incorporated three predictors: lymph node enlargement, pleural effusion, and carcinoembryonic antigen (CEA) levels. The PTV (−3 to 3 mm) radiomics model showed high discrimination performance, with an AUC of 0.810 (95% CI: 0.712–0.908) in the external validation cohort. The nomogram model demonstrated the highest discrimination performance, with an AUC of 0.884 (95% CI: 0.715–0.946), and showed acceptable calibration. Conclusion In this retrospective two‐center cohort of patients with LUAD and a T1 primary tumor, the combined clinicoradiologic‐radiomics nomogram showed potential for stratifying the risk of synchronous bone metastasis at baseline evaluation. Further validation in larger and more representative cohorts is warranted before broader clinical application.

Background:Intracranial atherosclerosis is the predominant etiology of ischemic stroke in East Asian populations. Atherosclerotic progression frequently involves concomitant vascular remodeling, and stroke risk profiles may vary across distinct vascular remodeling morphologies. However, the intracranial vertebrobasilar plaque features of different remodeling patterns have not been extensively characterized. In this study, high-resolution magnetic resonance vessel wall imaging (HR-MRI) was used to identify vertebrobasilar plaque phenotypes across different remodeling patterns in order to generate deeper insights into the mechanisms of ischemic stroke in this population. Methods:From January 2020 to July 2021, 208 patients with posterior circulation atherosclerosis were retrospectively evaluated. Demographic data, atherosclerosis risk factors, blood glucose and lipid profiles, homocysteine level, and imaging data were collected. The characteristics of vascular plaques were analyzed via HR-MRI. Parameters including vascular remodeling index (RI), degree of stenosis, plaque burden, plaque enhancement, surface morphology, distribution pattern, and plaque location were recorded. A RI value ≤0.95 indicated negative remodeling (NR), ≥1.05 indicated positive remodeling (PR), and a value between 0.95 and 1.05 indicated intermediate remodeling (IR). Comparative analyses assessed differences in demographics, risk factors, laboratory indices, and plaque features across remodeling groups. Subsequently, stratification into positive and non-PR cohorts was followed by univariate and multivariate logistic regression to identify risk factors for stroke. Predictive performance was evaluated through receiver operating characteristic (ROC) curve analysis. Results:The cohort comprised 133 cases of PR (63.9%), 35 cases of IR (16.8%), and 40 cases of NR (19.2%), indicating a predominance of PR in intracranial vertebrobasilar atherosclerosis. Degree of vascular stenosis, plaque burden, and distribution patterns were associated with vascular remodeling type (P<0.001, P<0.001, and P=0.008, respectively). However, no significant association was found between plaque enhancement and remodeling patterns. The multivariate analysis revealed that the independent risk factors for stroke in the non-PR group were plaque enhancement [adjusted odds ratio (aOR) 6.01; 95% confidence interval (CI): 1.48-24.38; P=0.012] and plaque location in the basilar artery (aOR: 5.41; 95% CI: 1.21-24.19; P=0.027); the area under the ROC curve (AUC) of the combined model was 0.802 (95% CI: 0.698-0.907). In the PR group, the independent risk factors for stroke were plaque enhancement (aOR 6.93; 95% CI: 2.5-19.19; P<0.001), diffuse distribution (aOR 3.3; 95% CI: 1.12-9.77; P=0.031), and irregular surface morphology (aOR 3.7; 95% CI: 1.7-11.7; P=0.026); the AUC of the combined model was 0.812 (95% CI: 0.729-0.896). Notably, stenosis severity and plaque burden were not independently associated with stroke risk in either group (P>0.05). Conclusions:Our study established remodeling pattern-specific stroke risk profiles in intracranial vertebrobasilar atherosclerosis. Marked plaque enhancement consistently predicted stroke across remodeling subtypes. Plaque enhancement assessment should be prioritized regardless of remodeling phenotype, and different preventive strategies may be needed depending on the remodeling classification.

Objective To investigate the association between maternal metabolic parameters and quantitative CT texture features of the lung during pregnancy. Methods This multi-center retrospective cross-sectional study included 898 pregnant women. Maternal metabolic parameters including triglycerides (TG), total cholesterol (TC), fasting plasma glucose (FPG), TyG index, TyGBMI index etc. Lung CT texture features were extracted by quantitative CT texture analysis including mean attenuation (Mean), standard deviation (Std), and skewness (Skewness), etc. Spearman correlation was used for preliminary variable selection. Multivariable linear regression was employed to evaluate independent associations between metabolic parameters and CT texture features. LASSO regression and variance inflation factor (VIF) were used to assess model robustness and multicollinearity. Results Multivariable linear regression revealed that TG (β = 6.23, 95%CI: 4.46–8.00, P < 0.001) and FPG (β = 8.18, 95%CI: 4.81–11.55, P < 0.001) were independently positively associated with Std, while TC was independently negatively associated with Std (β=-3.70, 95%CI: -4.94–-2.46, P < 0.001). TyGBMI index showed independent associations with both Mean (β = 0.39, 95%CI: 0.11–0.68, P = 0.007) and Skewness (β=-0.006, 95%CI: -0.009–-0.002, P = 0.002). LASSO cross-validation selected identical variables as the primary models, and all VIF values were below 5, indicating robust models without severe multicollinearity. Conclusions this study is the first to systematically evaluate the association between maternal metabolic parameters and lung CT texture features. TG and FPG were independent risk factors for lung heterogeneity, TC was negatively correlated with heterogeneity, and TyGBMI, as a composite metabolic index, had a robust predictive value for mean and skewness.This study provides new evidence for understanding the pulmonary imaging manifestations of metabolic disorders during pregnancy.

Objectives:To evaluate the effectiveness of delta radiomics in predicting high-grade components in lung adenocarcinoma and to develop a robust machine learning model for clinical application. Methods:This retrospective multi-center cohort study included lung cancer patients from three hospitals who had pre-surgery CT follow-up scans. Training (n = 491) and validation (n = 210) were performed using cases from Center 1, and testing was conducted using cases from Centers 2 and 3 (n = 92). Radiomic features were extracted from baseline and follow-up CT images, and delta radiomic features were calculated. The LASSO algorithm was used for radiomic feature selection, and rad-score and delta rad-score were constructed. Significant clinical and radiomic features were combined to build the final machine learning model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), DeLong test, decision curve analysis (DCA), and integrated discrimination improvement (IDI) analysis. Results:In the external test cohort, the integrated machine learning model constructed based on clinical features (CTR, smoking status, maximum diameter of the solid component), rad-score, and delta rad-score showed that the random forest model performed the best, with an AUC of 0.91. The random forest model outperformed the clinical model (AUC = 0.80), rad-score (AUC = 0.79), and delta rad-score (AUC = 0.81). DCA and IDI indicated that the random forest model provides superior clinical benefit and improvement. Conclusion:Delta radiomics significantly aids in identifying high-grade subtypes of lung adenocarcinoma. The integrated machine learning model offers an effective approach for prediction of high-grade components, with potential clinical implications. Clinical Relevance Statement:This study presents a novel application of delta radiomics to predict high-grade lung adenocarcinoma, which may influence surgical management and improve patient outcomes.

The accurate detection of circulating tumor DNA (ctDNA) in patients’ biological fluids can facilitate cancer diagnosis and prognosis. This study reports the development of a differential pulse voltammetric (DPV) biosensor for the detection of ctDNA coding for the key T790M lung cancer mutation of the epidermal growth factor receptor (EGFR). The biosensor was built with a working electrode (WE) made of optimized electroconductive particles comprising covalent organic frameworks (COF) displaying Au nanoparticles (Au NPs) modified with polyethyleneimine (COF@Au-PEI). Au NPs accelerated electron transfer while COFs and PEI maximized the architecture of the WE to improve electrochemical reactions and molecular probe binding. Next, hairpin DNA probes (haiDNA) specific to the EGFR T790M ctDNA analyte were bonded to COF@Au-PEI. Lastly, Au@Pt nanohybrids were attached to the WE to enhance its electrochemical performance. In this system, the long polynucleotide EGFR T790M ctDNA analyte interferes with electrochemical reactions upon binding to the haiDNA probes, resulting in a proportional decrease of the DPV signal. The device detected EGFR T790M ctDNA in blood plasma and exhibited good sensitivity, accuracy, stability, and reproducibility. The sensor was also highly selective with no DPV signal for the EGFR wildtype allele harboring a difference of only one base pair.

To establish a model integrates clinical, traditional radiologic, intratumoral and peritumoral radiomics (ITR and PTR), and intratumoral and peritumoral heterogeneity (ITH and PTH) features to diagnose lymphovascular invasion (LVI) status for early stage non small cell lung cancer (NSCLC). Clinical data and chest CT imaging data of NSCLC patients who underwent surgical resection of the lungs from January 2019 to May 2021 were collected. Surgical pathology were the diagnostic gold standard to clarify the LVI status. ITR and PTR features and ITH and PTH features from the total tumor volume and peritumoral tumor volume were extracted. Then clinical, traditional radiologic, ITR and PTR, ITH and PTH models were established to diagnose LVI status. Finally, a column chart diagnostic model was constructed and the diagnostic efficacy was evaluated. 366 NSCLC patients were enrolled in this retrospective study from 2 institutions, in which Institution 1 served as the basis for training (n = 154) and internal validation (n = 154) sets, while Institution 2 served as the external validation set (n = 58). In the three cohorts of PTR_(0–3, -3–3 and 0–6), the PTR_0–6 model has better predictive performance, with area under the curve (AUC) of 0.882 and 0.824 for the training and validation groups, respectively. Gender, Vascular Convergence Sign, and N stage were significantly related to LVI status, Finally, the combined model integrated ITH, PTR_0–6, and PTH_0–6 models, N stage and Vascular Convergence Sign has the highest diagnostic accuracy. The AUCs for training set, internal validation set, and external validation set were 0.963, 0.882, and 0.743, respectively. A comprehensive diagnostic model based on clinical features, traditional radiological features, radiomic features, and heterogeneity features of NSCLC were established to diagnose LVI for early stage NSCLC, which has the highest diagnostic efficiency and can help to guide treatment decisions.

Objective:To summarize the clinical characteristics of patients on maintenance hemodialysis (MHD) with the novel coronavirus omicron variant and explore the risk factors for severe cases. Methods:We retrospectively analyzed the data of 158 patients on MHD from Zhongnan Hospital of Wuhan University between December 7, 2022 and January 31, 2023. We collected clinical data, described clinical characteristics, and analyzed the relationships between these factors and critical illness using univariate and multivariate logistic regression analyses. Results: The median age of the 158 patients was 63 (interquartile range: 52–71) years, and 128 (63.7%) were men. Fever (62.7%) and cough (60.1%) were the two most common symptoms. Hypertension (80.4%) was the most common comorbidity, followed by diabetes (31.0%), cardiovascular disease (22.8%), and cerebrovascular disease (15.2%). Unvaccinated patients constituted the majority of the enrolled patients (88.6%, 140/158), whereas only a small proportion (11.4%, 18/158) had been vaccinated (including fully vaccinated and partially vaccinated patients). Multivariate logistic regression analysis indicated that an elevated C-reactive protein (CRP) level (odds ratio [OR]: 1.03, 95% confidence interval [CI], 1.014–1.046], p<0.001) and a decreased platelet count (OR: 0.986, 95% CI, 0.986 (0.976–0.997), p=0.013) during hospitalization were risk factors for the severe group. Conclusions:This study demonstrated a high mortality rate among patients on MHD infected with omicron variant. Furthermore, advanced age, increased CRP levels, and decreased platelet count were predictors of critical illness.

Objectives: The aim of this study was to demonstrate the interaction and influence of gestational age and parity on the lung volume and lung density through Computed Tomography (CT) imaging data among healthy pregnant women. Design: Retrospective cross-sectional study Setting: Two clinical sites in Wuhan Hubei Province. Sample: pregnant women and non-pregnant women. Methods： Linear mixed-effects regression model and generalized additive mixed model (GAMM) were taken to control the potential confounders and evaluate the interact effects of lung volume and lung density. Univariate analysis estimated the influence of gestational age and parity on the lung volume and lung density. Main Outcome Measures: Lung volume (left, right, total), lung density (left, right, total). Results: The total lung density of all patients in GAMM was significantly increased with gestational age (p<0.001). Univariate analysis showed that the absolute value of total lung volume decreases with the progression of gestation ( p=0.040), this tendency is also displayed in both the left lung and right volume. Lung volume of multipara in the third trimester was lower than the first trimester, and it had significant difference between T1 and T3 ( p=0.0196). Furthermore, this study found that the lung volume of multiparous women was lower that primiparous in the third trimester ( p=0.009). Nevertheless, the lung density increased with gestational age in primiparous, particularly in the third trimester ( p=0.007), the lung density of multipara was lower than nullipara ( p<0.001), more delivery times follow lower lung density, and there was a significant difference when compared with each parity state. During the third trimester, the lung density of multiparous declined when compared with primiparous ( p<0.001). Conclusion: Parity and gestation age tended to have an impact on the alterations of lung volume and lung density in physiological pregnant women. Lung density of multipara was lower than nullipara, more delivery times follow lower lung density, indicating parity was associated with lung density.

Purpose This study aimed to systematically assess the quality and performance of computed tomography (CT) radiomics studies in predicting brain metastasis (BM) among patients with lung cancer. Methods The PubMed, Embase and Web of Science were searched for studies predicting BM in patients with lung cancer using CT-based radiomics features. Information regarding patients, imaging, and radiomics analysis was extracted from eligible studies. We assessed the quality of included studies using the Radiomics Quality Scoring (RQS) tool and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A meta-analysis of studies regarding the prediction of BM in patients with lung cancer was performed. Results Thirteen studies were identified, with sample sizes ranging from 75 to 602. The mean RQS of the studies was 12 (range 9–16), and the corresponding percentage of the score was 33.55 % (range 25.00–44.44 %). Four studies (30.8 %) were considered as low risk of bias, while the remaining nine studies (69.2 %) were considered to have unclear risks. The meta-analysis included twelve studies. The pooled sensitivity, specificity and Area Under the Curve (AUC) value with 95 % confidence intervals were 0.75 [0.69, 0.80], 0.76 [0.68, 0.82], and 0.81 [0.77–0.84], respectively. Conclusion CT radiomics-based models show promising results as a non-invasive method to predict BM in lung cancer patients. However, multicenter and prospective studies are warranted to enhance the stability and acceptance of radiomics.

Rationale and Objectives: Bone metastasis (BM) significantly affects the prognosis of lung adenocarcinoma (LUAD) patients. Currently, no effective clinical model exists for predicting early BM in category T1 LUAD. This study aims to develop a model for timely BM detection by analyzing relevant influencing factors. Materials and Methods This retrospective study analyzed data from 478 patients with category T1 LUAD from August 2017 to August 2023. Of these, 334 patients were assigned to a training cohort and 144 to an internal validation cohort. Univariate and multivariate analyses identified BM risk factors, leading to a nomogram model. Model performance was evaluated using area under the curve (AUC), calibration curves, and decision curve analysis (DCA). An online calculator was also created to assess BM risk. Results Multivariate analysis revealed that alkaline phosphatase (ALP), carcinoembryonic antigen (CEA), nodule type, CT-reported N staging, and pleural effusion are independent BM risk factors. The nomogram showed strong accuracy, with AUC values of 0.929 in the training cohort and 0.954 in the validation cohort. Calibration analyses confirmed reliability, with DCA indicating high clinical benefit for both cohorts. Conclusion This nomogram effectively identifies high-risk patients for BM in category T1 LUAD, aiding personalized clinical decision-making.

Coronavirus disease 2019(COVID-19)is a newly emerged acute respiratory infectious disease that is rapidly spreading worldwide,becoming a significant global public health concern.Imaging assessment is the primary and commonly utilized method for clinical diagnosis,differential diagnosis,treatment monitoring,and follow-up.To achieve early detection,reporting,isolation,and treatment of COVID-19,and ultimately improve the cure rate and reduce mortality,it is crucial to establish imaging diagnostic criteria for COVID-19.

目的 分析肺磨玻璃结节(GGN)肺腺癌中微浸润性腺癌(MIA)与浸润性腺癌(IAC)病例临床及CT影像特征,建立IAC诊断模型并评估其诊断价值.方法 回顾性分析2017年7月至2021年7月本院肺GGN中MIA与IAC病例影像及临床资料,按照7:3的比例随机分为训练组和验证组,训练组中IAC为实验组,MIA为对照组,比较两组临床及影像学特征,采用多因素Logistic回归分析筛选IAC的危险因素并建立IAC诊断模型,采用受试者工作特征(ROC)曲线分析模型的诊断效能,采用列线图量化危险因素.结果 479例GGN病灶纳入研究,其中训练组335例,验证组144例.病灶质量(P=0.001)、深分叶征(P=0.029)和空泡征(P=0.014)是IAC的独立危险因素,训练组曲线下面积(AUC)为0.852,验证组AUC为0.762,IAC列线图诊断模型预测概率的截断值为0.634.结论 基于临床及影像特征建立IAC列线图诊断模型对IAC有较好的诊断价值.

PurposeExploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis.MethodsThis retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models.ResultsFive clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical–radiographic model (P=0.049).ConclusionsThe proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

Background More and more pulmonary ground-glass nodules (GGNs) are screened with the extensive usage of low-dose computed tomography (CT). The need of CT-guided percutaneous puncture biopsy of GGN remains controversial. Purpose To explore the diagnostic accuracy of CT-guided percutaneous puncture biopsy of GGNs. Material and Methods We searched PubMed, EMBASE, the Cochrane Library, and CNKI. Included studies reported the puncture biopsy results of pulmonary GGNs, including the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) cases. After evaluating the studies, statistical analysis, and quality assessment, the pooled diagnostic sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was constructed and the area under the curve (AUC) was calculated. Subgroup analysis was performed according to whether spiral CT or fluoroscopy-guided CT was used in the study. Results This meta-analysis included 14 studies with a total of 759 patients (702 samples). The pooled SEN, SPE, and DOR of CT-guided puncture biopsy of pulmonary GGNs were 0.91 (95% confidence interval [CI] = 0.89-0.94), 0.99 (95% CI = 0.95-1.00), and 138.72 (95% CI = 57.98-331.89), respectively. The AUC was 0.97. Conclusion Our results indicated that CT-guided puncture biopsy of GGNs has high SEN, SPE, and DOR, which proved that CT-guided puncture biopsy was a good way to determine the pathological nature of GGN.

目的:报道肺腺癌病例多基因检测的结果,分析肺腺癌驱动基因突变状态的相关因素.方法:回顾性总结武汉大学中南医院2017年1月至2021年7月肺腺癌检测EGFR、ALK、KRAS、HRAS、NRAS、PIK3CA、BRAF、PTEN、AKT1、HER2、ROS1、RET等12个驱动基因突变的病例资料,探讨肺腺癌驱动基因突变状态的相关因素.结果:181例肺腺癌病例纳入研究,94例为穿刺活检标本,87例手术切除标本.81.21％(147/181)的病例存在驱动基因突变,3个主要突变是EGFR(60.8％,110/181)、KRAS(16.6％,30/181)、NRAS(2.2％,4/181).6例多基因突变,EGFR/NRAS、EGFR/KRAS、EGFR/PIK3CA、HER2/NRAS、HER2/PTEN 和 EGFR/KRAS/NRAS 各 1例.EGFR突变与女性(P＜0.001)和非吸烟(P＜0.001)相关,最常见的突变位点是Exon 19-del和Exon 21 L858R;KRAS突变与男性(P＜0.001)和吸烟(P＜0.001)相关,最常见的突变位点是Exon 2 G12C和Exon 2 G12V.穿刺标本与手术标本驱动基因突变阳性率无显著性差异(P＞0.05),早期(Ⅰ+Ⅱ)和晚期(Ⅲ+Ⅳ)肺腺癌基因突变阳性率无显著性差异(P＞0.05).结论:EGFR、KRAS基因突变状态与性别、吸烟状态相关,经皮肺穿刺活检与手术切除标本基因突变率无差异,早期和晚期肺腺癌基因突变率无差异.

目的 基于临床及CT影像组学特征建立周围型小细胞肺癌(SCLC)与肺腺癌(ADC)诊断模型,并评估其诊断价值.方法 回顾性搜集周围型肺癌患者临床及CT影像资料,选取治疗前2周内有薄层CT影像的病例分为SCLC组和ADC组,以SCLC组为实验组,采用倾向性评分匹配按1∶2匹配ADC对照组,两组按照7∶3比例随机分为训练集和验证集.依据训练集病例资料采用多因素Logistic回归分析筛选有意义的变量,建立临床、影像组学及临床组学联合预测周围型SCLC诊断模型.采用受试者工作特征(ROC)曲线评价模型诊断效能,建立个体化诊断列线图.结果 周围型SCLC和ADC两组间NSE和13个组学特征有显著性差异.训练集和验证集ROC曲线下面积,临床诊断模型分别为0.793和0.750,影像组学模型分别为0.857和0.838,联合模型分别为0.905和0.882.结论 基于临床及CT影像组学特征建立周围型SCLC与ADC诊断模型可鉴别诊断周围型SCLC及ADC.

Objective: To measure and analyze the shoulder circumferences of adults' permanent teeth crown preparations based on data collected through the intraoral scanning, so as to provide dental anatomy data for clinical diagnosis and analysis. Methods: Intraoral scanning data of 840 complete crown preparations were collected, and were entrusted to the World Dental Laboratory Co., Ltd. in Fuzhou between March 2021 and June 2022. Except the data of the third molar, the rest data were categorized in terms of 14 tooth positions in the upper and lower jaw (each category involved 30 samples from male group and 30 samples from female group). Image measurement software was used to measure the shoulder circumferences of permanent teeth crown preparations. And analysis was conducted to reveal the difference of shoulder circumference diameters between male and female groups. And then they were grouped according to the mean value at each tooth position, on the premise that the difference between the maximum and minimum values and the mean value of the entire group was≤±1.00 mm. Analysis were further conducted to determine the differences of shoulder circumference diameters between each dental position and the differences between male and female in the same groups. Results: Bivariate analysis of variance showed that gender had no effect on the shoulder circumference of full crown preparations (F=0.55, P=1.457), while tooth position had a significant impact on the shoulder circumference of full crown preparations (F=273.15, P<0.001). The samples were classified into 5 groups according to the mean values of shoulder circumference diameters relating to each tooth position. Statistical analysis showed that Group 1, covering maxillary lateral incisor, mandibular central incisor and mandibular lateral incisor, had shoulder circumference with diameters of (16.62±2.21) mm; Group 2, consisting of maxillary central incisor, maxillary cusp, mandibular cusp, mandibular first premolar and mandibular second premolar, had diameters of (20.78±2.48) mm; Group 3, consisting of maxillary first premolar and maxillary second premolar, had diamerters of (22.09±2.72) mm; Group 4, covering maxillary first molar, maxillary second molar and mandibular first molar, had diamerters of (30.21±2.67) mm; while group 5, with mandibular second molar alone its member, had diamerters of (31.34±3.18) mm. The difference among the 5 groups was statistically significant (P<0.05). Conclusions: Significant differences of shoulder circumference diameters could be found between different tooth positions, while at the same tooth position, the differences between male and female are not significant. The 14 tooth positions could be grouped into 5 groups according to their shoulder circumference diameters. Future research could take the grouping as reference.

The purpose of this meta-analysis was to investigate the relationship between serum carcinoembryonic antigen (CEA) expression and epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC). Databases such as PubMed, Cochrane, EMBASE and Google Scholar were systematically searched to identify studies assessing the association of serum CEA expression with EGFR mutations. Across 19 studies, 4168 patients were included between CEA expression and EGFR mutations odds ratio (OR) conjoint analysis of correlations. Compared with CEA-negative NSCLC, CEA-positive tumors had an increased EGFR mutation rate (OR = 1.85, 95

To quantitatively analyze the risk factors for air embolism following computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) and qualitatively review their char-acteristics. The databases of PubMed, Embase, Web of Science, Wanfang Data, VIP information, and China Na-tional Knowledge Infrastructure were searched on January 4, 2021, for studies reporting the oc-currence of air embolisms following CT-guided PTNB. After study selection, data extraction, and quality assessment, the characteristics of the included cases were qualitatively and quantitatively analyzed. A total of 154 cases of air embolism following CT-guided PTNB were reported. The reported inci-dence was 0.06% to 4.80%, and 35 (22.73%) patients were asymptomatic. An unconscious or unre-sponsive state was the most common symptom (29.87%). Air was most commonly found in the left ventricle (44.81%), and 104 (67.53%) patients recovered without sequelae. Air location (P < 0.001), emphysema (P = 0.061), and cough (P = 0.076) were associated with clinical symptoms. Air location (P = 0.015) and symptoms (P < 0.001) were significantly associated with prognosis. Lesion location [odds ratio (OR): 1.85, P = 0.017], lesion subtype (OR: 3.78, P = 0.01), pneumothorax (OR: 2.16, P = 0.003), hemorrhage (OR: 3.20, P < 0.001), and lesions located above the left atrium (OR: 4.35, P = 0.042) were significant risk factors for air embolism. Based on the current evidence, a subsolid lesion, being located in the lower lobe, the presence of pneumothorax or hemorrhage, and lesions located above the left atrium were significant risk factors for air embolism.

Abstract Background Metastases are uncommon in intraventricular tumors. Intraventricular metastases from lung cancer are extremely rare and present challenges in terms of diagnosis and management. Case Presentation: We present the case of a 52-year-old man who presented with multiple lesions on CT and MRI. A lung CT scan, needle biopsy, and pathological examination confirmed the diagnosis of small cell lung cancer. The patient received chemotherapy with anti-PD-L1 blockade, along with etoposide and platinum (EP). As a result, the size of the intraventricular tumor was significantly reduced. Conclusions We summarized nine cases of intraventricular metastasis of lung cancer, including our case. Patients are often challenging to diagnose initially, especially in cases of undiagnosed lung cancer, and their prognosis is often unfavorable. On CT or MRI, intraventricular metastases of lung cancer appear as multiple lesions that are distributed along the ventricular wall. Intraventricular metastases should be considered when multiple neoplastic lesions are found within the ventricles. Conducting a search for primary tumor lesions or performing an intraventricular tumor biopsy can help confirm the diagnosis.