Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

To analyze the prevalence, drug resistance and whole genome characteristics of Mycoplasma pneumoniae (MP) in respiratory throat swab samples of hospitalized children with pneumonia in Suzhou City from 2023 to 2024. Throat swab samples of hospitalized children aged 0-14 years old with pneumonia in Suzhou were collected from September 2023 to September 2024. Real-time fluorenscence quantitative PCR technology was used to detect MP nucleic acid. The results showed that the positive rate of MP in 3 235 samples was 22.44% (726/3 235), with a rate of 55.00% in week 47 of 2023. The positive rate of MP increased with age ( χ2=45.842, P<0.001). The study selected MP nucleic acid test positive samples from week 20 (5.13-5.19) to week 23 (6.3-6.9) of 2024 for isolation, culture and resistance phenotype detection. About 31 MP strains were successfully isolated and cultured, all of which were resistant to macrolides. The next-generation sequencing technology and nanopore sequencing technology were used for genome sequencing. All 31 strains carried the A2063G mutation, with the main prevalent genotype being the P1-1, and the main mlST type being the ST3. Despite the overall genomic similarity between strains being over 99%, there were significant differences between the P1-1 and P1-2 strains in the P1 gene region. In summary, from 2023 to 2024, the main MP type prevalent in Suzhou City is the P1-1 genotype. All isolated MP strains carry an A2063G resistance site mutation and are resistant to macrolides, requiring continuous monitoring and further research.

Objective To evaluate the value of ultrasound and magnetic resonance imaging(MRI)in the diagnosis of knee osteoarthritis(KOA)by Meta-analysis.Methods Multiple databases for literature on KOA ultrasound and MRI diagnosis were screened.After data extraction and study quality assessment,14 articles involving 1613 patients were finally included.Meta-analysis were used on the extracted data to comprehensively evaluate the effectiveness of ultrasound and MRI in the diagnosis of KOA.Results All included studies used arthroscopy or surgical pathology as the gold standard for diagnosis.The pooled sensitivity,specificity,and diagnostic odds ratio(DOR)for ultrasound in diagnosing KOA were 0.79,0.82,and 20.30,respectively,with area under the curve(AUC)was 0.89.In comparison,the pooled sensitivity,specificity,and DOR for MRI in diagnosing KOA were 0.88,0.85,and 42.35,respectively,with AUC of 0.92.Conclusion Ultrasound and MRI have good accuracy in the diagnosis of KOA,and MRI is superior to ultrasound.

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

To analyze the prevalence, drug resistance and whole genome characteristics of Mycoplasma pneumoniae (MP) in respiratory throat swab samples of hospitalized children with pneumonia in Suzhou City from 2023 to 2024. Throat swab samples of hospitalized children aged 0-14 years old with pneumonia in Suzhou were collected from September 2023 to September 2024. Real-time fluorenscence quantitative PCR technology was used to detect MP nucleic acid. The results showed that the positive rate of MP in 3 235 samples was 22.44% (726/3 235), with a rate of 55.00% in week 47 of 2023. The positive rate of MP increased with age ( χ2=45.842, P<0.001). The study selected MP nucleic acid test positive samples from week 20 (5.13-5.19) to week 23 (6.3-6.9) of 2024 for isolation, culture and resistance phenotype detection. About 31 MP strains were successfully isolated and cultured, all of which were resistant to macrolides. The next-generation sequencing technology and nanopore sequencing technology were used for genome sequencing. All 31 strains carried the A2063G mutation, with the main prevalent genotype being the P1-1, and the main mlST type being the ST3. Despite the overall genomic similarity between strains being over 99%, there were significant differences between the P1-1 and P1-2 strains in the P1 gene region. In summary, from 2023 to 2024, the main MP type prevalent in Suzhou City is the P1-1 genotype. All isolated MP strains carry an A2063G resistance site mutation and are resistant to macrolides, requiring continuous monitoring and further research.

Objective:To establish a genomic nucleic acid mass spectrometry detection platform for allelic risk associated with Alzheimer's disease.Methods:Whole blood samples of 61 patients diagnosed as Alzheimer's disease in the Affiliated Brain Hospital of Guangzhou Medical University from December 28th, 2023 to 31st, March 2024 were collected and deoxynucleic acid (DNA) was extracted, including 22 males and 39 females, aged (67.36 ± 8.18) years old. After screening out 17 risk gene loci in Chinese population, multiplex polymerase chain reaction primers, single-base extension primers and Sanger sequencing primers were designed. Ten samples were used for primer optimization and debugging through Sanger sequencing and time-of-flight mass spectrometry to establish a detection system. The remaining samples were genotyped using a time-of-flight mass spectrometer and verified by Sanger sequencing for accuracy evaluation. Five samples were selected for gradient dilution and then subjected to time-of-flight mass spectrometry detection to evaluate the detection limit. Three clinical samples, one case of Escherichia coli and one case of Staphylococcus aureus genomic DNA samples were selected for cross-reaction research. The anti-interference ability of the detection system was evaluated against hemolysis, chylous substances and conventional anticoagulants in the samples. Two samples, one wild and one homozygous mutation sample with representative peak shapes, were selected to evaluate the anti-interference ability. Four samples containing the common genotypes of all gene loci in the system were selected and repeated 10 times to evaluate the precision.Results:The minimum intensity of single-base extension primers on mass spectrometry is greater than half of the maximum intensity. All 17 risk gene loci screened were successfully typed. The time-of-flight mass spectrometry detection results of 1,037 loci from 61 samples showed that the genotyping detection rate was 100%. The genotypes of the 20 DNA samples were completely consistent with the results of Sanger sequencing, with an accuracy rate of 100%. The mass spectrometry detection results of five samples after gradient dilution indicated that the low detection limit was 5 ng of DNA. The reaction system has a strong anti-interference ability against hemolysis of samples, chylous substances, conventional anticoagulants and DNA cross-contamination. Homologous allele interference and no cross-reaction between the bacterial genome and 17 gene loci do not affect the risk genome detection results. The results of 10 repeated mass spectrometry tests on 4 samples showed that the precision was 100%.Conclusion:The genomic detection system of Alzheimer's disease risk has been successfully established to provide an auxiliary mean for disease diagnosis and risk assessment.

Objective To evaluate the value of ultrasound and magnetic resonance imaging(MRI)in the diagnosis of knee osteoarthritis(KOA)by Meta-analysis.Methods Multiple databases for literature on KOA ultrasound and MRI diagnosis were screened.After data extraction and study quality assessment,14 articles involving 1613 patients were finally included.Meta-analysis were used on the extracted data to comprehensively evaluate the effectiveness of ultrasound and MRI in the diagnosis of KOA.Results All included studies used arthroscopy or surgical pathology as the gold standard for diagnosis.The pooled sensitivity,specificity,and diagnostic odds ratio(DOR)for ultrasound in diagnosing KOA were 0.79,0.82,and 20.30,respectively,with area under the curve(AUC)was 0.89.In comparison,the pooled sensitivity,specificity,and DOR for MRI in diagnosing KOA were 0.88,0.85,and 42.35,respectively,with AUC of 0.92.Conclusion Ultrasound and MRI have good accuracy in the diagnosis of KOA,and MRI is superior to ultrasound.