Purpose To explore the independent predictors of malignant solitary pulmonary nodule (SPN) manifesting as ground-glass nodule (GGN),and to establish a prediction model.Materials and Methods The clinical data and CT images of 362 patients (group A) with pathological-confirmed SPN appearing as GGN in Shanghai Chest Hospital Shanghai Jiaotong University from January 2014 to December 2015 were retrospectively analyzed.The independent predictors of malignant SPN were identified,and the clinical prediction model was established.Another 119 SPN patients in Affiliated Zhoushan Hospital of Wenzhou Medical University were selected as group B to verify the diagnostic efficiency of the prediction model.Results Using multivariate Logistic regression analysis,clear border (OR=6.274,P＜0.01),smooth edge (OR=0.391,P＜0.01),lobulation (OR=3.387,P＜0.01),pleural retraction sign (OR=2.430,P＜0.01),and vocule sign (OR=3.076,P＜0.01)were identified as independent predictors of malignant SPN.The area of the model under the ROC curve was 0.859 with 95％ CI (0.804-0.903).The diagnostic accuracy rate,sensitivity,specificity,positive predictive value and negative predictive value were 85.92％,91.03％,81.97％,92.03％ and 73.53％,respectively.Conclusion In this study,the independent predictors of malignant SPN appearing as GGN were identified,and the prediction model was established.The model can accurately identify SPN and provide effective help for early diagnosis of SPN.

Purpose To evaluate the CT features and pathological manifestations of the solid components of mixture ground-glass opacity (GGO) in adenocarcinoma in situ (AIS), minimally invasive adenocarcinom (MIA) and invasive adenocarcinoma (IAC), to analyze the qualitative diagnosis value of solid components of mixture GGO in the diagnosis of AIS, MIA and IAC, to provide reference for the selection of clinical treatment.Materials and Methods Eighteen patients with AIS, 53 patients with MIA and 28 patients with IAC (the maximum diameter smaller than 2 cm) proved by surgery and pathology with CT features appearing as mixture GGO were retrospectively analyzed, CT features of the solid components in three groups were analyzed and compared with pathology.Results The solid components in AIS mainly appeared as punctiform or polygon, with extensive distribution, solid nodules were usually single (17 cases, 94.44%), located in the middle of the lesion (14 cases, 77.78%), with clear binderies (16 cases, 88.89%) and the same density with vessels in the same axis (13 cases, 72.22%); the majority of solid components in MIA appeared as circular or elliptical (33 cases, 62.26%), less than or equal to 5 mm (48 cases, 90.57%), with eccentric or multi-point distribution (45 cases, 84.90%), the boundaries were less sharp (40 cases, 75.47%), with slightly lower density than that of the vasculars in the same level (34 cases, 64.15%); the solid components in IAC mainly appeared as irregular lesions (21 cases, 75.00%), lager than 5 mm (24 cases, 85.71%), with eccentric growth (20 cases, 71.43%) and less sharp boundary (15 cases, 53.57%), the integration of multiple nodules could also be observed. There were statistically significant differences in the CT features of solid components within the lesions among the three groups (P<0.01).Conclusion It is possible to predict the pathological typing and the prognosis of pulmonary mixture GGO in a certain extent according to the different CT features of the solid components in it, and to guide clinical treatment principles.

Background and objective hTe solitary pulmonary nodule (SPN) is a common and challenging clini-cal problem, especially solid SPN. hTe object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs.Methods We had a retrospective review of 317 sol-id SPNs (group A) having a ifnal diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, letf or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air broncho-gram sign, vocule sign, cavity and calciifcation. By using univariate and multivariate analysis, we found the independent predic-tors of malignancy of solid SPNs and subsequently established a clinical prediction model. hTen, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model.Results MultivariateLogistic regression analysis was used to identify eight clini-cal characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calciifcation) as independent predictors of malignancy of in solid SPNs. hTe area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and speciifcity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%.Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.

Objective:To systematically evaluate the efficacy and safety of precision thoracic radiotherapy (TRT) in the limited-stage small cell lung cancer (LS-SCLC) patients by network meta-analysis.Methods:Randomized controlled trials (RCTs) of TRT regimes in the LS-SCLC were electronically searched from PubMed, Web of Science, The Cochrane Library, CNKI and Wanfang Data from inception to September 1 st, 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Statistical analysis was performed by Stata 17 and R 4.1.1 software. Results:A total of 6 RCTs involving 1730 patients with six radiation regimens including hyperfractionated radiotherapy (HFRT): HFRT 45(45 Gy/30 F) and HFRT 60(60 Gy/40 F); conventional fractionated radiotherapy (CFRT): CFRT 70(70 Gy/35 F) and CFRT 66(66 Gy/33 F); moderately hypofractionated radiotherapy (MHFRT): MHFRT 65(65 Gy/26 F) and MHFRT 42(42 Gy/15 F) were included. The network meta-analysis showed that: in terms of improving progression-free survival and overall survival, there was no statistically significant difference among the six radiotherapy regimens. The probabilistic ranking results were: MHFRT 65> HFRT 60>CFRT 66>CFRT 70>MHFRT 42>HFRT 45, and HFRT 60>MHFRT 65>CFRT 66>CFRT 70>HFRT 45>MHFRT 42, respectively. The HFRT 60 regimen was superior to other regimens in reducing the incidence of grade ≥3 pneumonia, and there was no difference between the regimens in causing grade ≥3 radiation esophagitis, and the results of ranking probability were: HFRT 60> MHFRT 42>CFRT 66>CFRT 70>HFRT 45>MHFRT 65, and HFRT 60>CFRT 70>CFRT 66>HFRT 45>MHFRT 42>MHFRT 65, respectively. Conclusions:HFRT 60 radiotherapy regimen may be more effective and safer in the treatment of LS-SCLC patients as a priority choice for LS-SCLC TRT. Limited by the number and quality of included studies, the above conclusions need to be verified by more high-quality studies.

Objective To evaluate the acute exacerbation of chronic obstructive pulmonary disease(COPD)(AECOPD)status via combining clinical data,lung function parameters with CT radiomic features based on machine learning method.Methods A total of 343 COPD patients,including 158 AECOPD patients and 185 non-AECOPD patients were retrospectively selected and randomly divided into training and testing sets at a ratio of 7∶3.The radiomics features were calculated after automatically delineating the whole lung volume of interest(VOI).Five machine learning methods were used to construct the AECOPD diagnostic model,then the corresponding Radiomics score(Rad-score)was calculated in the training set and was validated in the testing set.The logistic-combined model was established after integrating age,Global Initiative for Chronic Obstructive Lung Disease(GOLD)classification,vital capacity(VC),forced vital capacity(FVC),forced expiratory volume in one second(FEV1),FEV1％pred,FEV1/FVC％,peak expiratory flow(PEF),maximum ventilatory volume(MVV),and Rad-score value.The area under the curve(AUC)of receiver operating characteristic(ROC)curve was calculated to evaluate the evaluated performance of all models.Results The logistic regression model had the best diagnostic performance,with AUC of 0.724 and 0.758 in the training and testing sets,respectively.The performance of the logistic-combined model to diagnose AECOPD was superior to that of the single logistic regression model,with the AUC of 0.777 and 0.760 in the training and testing sets,respectively.Conclusion A combination strategy including clinical data,lung function parameters,and CT radiomics may be helpful to diagnose AECOPD status,with moderate diagnostic performance.