Objective·To develop an early diagnostic model for small cell lung cancer(SCLC)based on differences in serum metabolite expression profiles between patients with SCLC and those with benign pulmonary diseases,using machine learning algorithms.Methods·Serum samples were collected from 29 SCLC patients and 67 patients with benign lung diseases at Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,as the training cohort.An independent external validation cohort included 20 SCLC patients and 40 patients with benign lung diseases from Gansu Provincial Cancer Hospital.A total of 69 serum metabolites were quantitatively analyzed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).The XGBoost Classifier was employed to rank metabolite importance,and a forward feature selection strategy based on XGBoost was used to identify a subset of key metabolites.Diagnostic models were constructed using AdaBoost,random forest(RF),and light gradient boosting machine(LGBM)algorithms.Model performance was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC),and validated on the external test cohort.Results·Principal component analysis(PCA)and orthogonal projections to latent structures-discriminant analysis(OPLS-DA)of the training cohort revealed distinct metabolic profiles between SCLC and benign lung disease patients.Based on feature importance rankings,six key metabolites were selected to construct the MTB-6 diagnostic model.Among the models,AdaBoost achieved the best performance,with an AUC of 0.943,sensitivity of 75.0%,and specificity of 90.9%in the training cohort.In the external test cohort,the model demonstrated robust performance with an AUC of 0.921,sensitivity of 80.0%,and specificity of 87.5%.Conclusion·The MTB-6 model,based on six serum metabolites and the AdaBoost algorithm,exhibits excellent diagnostic performance and holds potential for the differential diagnosis of SCLC and benign pulmonary diseases.

Objective·To develop an early diagnostic model for small cell lung cancer(SCLC)based on differences in serum metabolite expression profiles between patients with SCLC and those with benign pulmonary diseases,using machine learning algorithms.Methods·Serum samples were collected from 29 SCLC patients and 67 patients with benign lung diseases at Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,as the training cohort.An independent external validation cohort included 20 SCLC patients and 40 patients with benign lung diseases from Gansu Provincial Cancer Hospital.A total of 69 serum metabolites were quantitatively analyzed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).The XGBoost Classifier was employed to rank metabolite importance,and a forward feature selection strategy based on XGBoost was used to identify a subset of key metabolites.Diagnostic models were constructed using AdaBoost,random forest(RF),and light gradient boosting machine(LGBM)algorithms.Model performance was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC),and validated on the external test cohort.Results·Principal component analysis(PCA)and orthogonal projections to latent structures-discriminant analysis(OPLS-DA)of the training cohort revealed distinct metabolic profiles between SCLC and benign lung disease patients.Based on feature importance rankings,six key metabolites were selected to construct the MTB-6 diagnostic model.Among the models,AdaBoost achieved the best performance,with an AUC of 0.943,sensitivity of 75.0%,and specificity of 90.9%in the training cohort.In the external test cohort,the model demonstrated robust performance with an AUC of 0.921,sensitivity of 80.0%,and specificity of 87.5%.Conclusion·The MTB-6 model,based on six serum metabolites and the AdaBoost algorithm,exhibits excellent diagnostic performance and holds potential for the differential diagnosis of SCLC and benign pulmonary diseases.

Objective·To verify the performance and explore the clinical application value of a multi-modal pulmonary nodule diagnosis model combined with metabolic fingerprints,protein biomarker CEA and Image-AI via random forest(MPI-RF).Methods·This study enrolled 289 patients with pulmonary nodules who were admitted to the Shanghai Chest Hospital,Shanghai Jiao Tong University School of Medicine and were detected by low-dose helical computed tomography(LDCT).The patients were divided into malignant nodule group(n=197)and benign nodule group(n=92)based on postoperative pathological results,and the basic information of the two groups was collected and compared.Electrochemiluminescence was used to detect the preoperative serum CEA levels of the patients in the two groups,matrix-assisted laser desorption/ionization mass spectrometry(MALDI-MS)was used to detect the serum metabolic fingerprints,and the CT image artificial intelligence model Image-AI was used to calculate the image scores.CEA data,serum metabolic fingerprints data and image scores were integrated and input into MPI-RF to calculate the malignant probability score of each patient.The receiver operator characteristic curve(ROC curve)and area under the curve(AUC)were used to evaluate the performance of different models,and the DeLong test was used for comparative analysis,including the diagnostic performance of MPI-RF in different types(solid nodule,pure ground-glass nodule and part-solid nodule)and sizes(diameter<8 mm and diameter≥8 mm)of pulmonary nodules,the diagnostic performance comparison of MPI-RF with Mayo Clinic model,veterans administration(VA)model and Brock model,and the diagnostic performance comparison of MPI-RF with lung imaging reporting and data system(Lung-RADS)in benign and malignant nodules.Results·MPI-RF had good diagnostic performance in the differentiation of benign and malignant pulmonary nodules(AUC=0.887,95%CI 0.848?0.925,sensitivity 81.22%,specificity 83.70%).Among them,the AUC of MPI-RF for solid nodules was 0.877(95%CI 0.820?0.934),for part-solid nodules was 0.858(95%CI 0.771?0.946),and for pure ground-glass nodules was 0.978(95%CI 0.923?1.000).The AUC of MPI-RF was 0.840(95%CI 0.716?0.963)for nodules within 8 mm diameter and 0.891(95%CI 0.849?0.933)for nodules larger than 8 mm diameter.Compared with the existing models,the diagnostic performance of MPI-RF was better than that of Mayo Clinic model,VA model and Brock model(all P=0.000).Compared with Lung-RADS,MPI-RF had better diagnostic performance in the total samples and different types of nodules(all P=0.000).Conclusion·MPI-RF is a model for the differential diagnosis of benign and malignant pulmonary nodules with excellent performance,and has potential clinical application value.

Objective To evaluate the clinical application value of bronchoalveolar lavage fluid(BALF)as a specimen for epidermal growth factor receptor(EGFR)gene mutation detection.Methods Droplet digital polymerase chain reaction(ddPCR)was used to detect the mutation situation of relevant sites(L858R and E746_A750del)in EGFR gene in BALF of totally 52 lung cancer patients,and the corresponding molecular pathological results were compared.Results With molecular pathological as a gold standard,the sensitivity of BALF for the detection of E746_A750del was 58.3％(14/24),specificity was 100.0％(14/14)and concord-ance was 73.7％(28/38,Kappa=0.508,P＜0.01),while the sensitivity of BALF for the detection of L858R was 64.3％(9/14),specificity was 100.0％(14/14)and concordance was 82.1％(23/28,Kappa=0.643,P＜0.01).Compared with the molecular pathological results,it showed high concordance in determining mu-tations of E746_A750del and L858R in EGFR gene in BALF(0.40＜Kappa＜0.75).The detection rate of EGFR gene mutation in BALF was related with tumor stage(P＜0.05).The abundance of mutations could predict to some extent the distant metastasis of lung cancer.Conclusion BALF specimens have important clinical application value in detecting EGFR gene mutations in lung cancer patients and could serve as a sup-plement to molecular pathology detection.

Aim To explore inhibitory effects and mechanism of engineering endostatin on growth and metastasis of melanoma cells in mice. Methods Melanoma cells(2× 106/mouse)were inoculated sabcutaneously to C57BL/6 mice. After tumorigenesis,endostatin(8mg/kg.d)was administrated to tumor-bearing mice,once a day ,twenty-one in all.Dietetic state and weight change of the tumor-bearing mice were observed and tumorous sige was measured during administration of endostatin. On 26thday,the tumor-bearing mice were sacrificed,subcutaneously tumorous weight was weighed and brain,lung ,liver,spleen and kidney were excised and sections were made to supply the pathological examination. Results Area under curve in the endostatin-treated group was obviously less than that in tamor control group(P∨ 0.01). Pathological study revealed that lavge areal necrosis arose in tumor and newborn cappillaries around the tumor disapeared. Conclusion Endostatin possosses strongly inhibitory effects on growth and metastasis of mouse melanoma and formation of newborn capillaries around tumor.