ObjectiveTo explore the independent risk factors for benign and malignant subsolid pulmonary nodules and establish a malignant probability prediction model.MethodsA retrospective analysis was performed in 443 patients with subsolid pulmonary nodules admitted to Subei People's Hospital of Jiangsu Province from 2014 to 2018 with definite pathological findings. The patients were randomly divided into a modeling group and a validation group. There were 296 patients in the modeling group, including 125 males and 171 females, with an average age of 55.9±11.1 years. There were 147 patients in the verification group, including 68 males and 79 females, with an average age of 56.9±11.6 years. Univariate and multivariate analysis was used to screen the independent risk factors for benign and malignant lesions of subsolid pulmonary nodules, and then a prediction model was established. Based on the validation data, the model of this study was compared and validated with Mayo, VA, Brock and PKUPH models.ResultsUnivariate and multivariate analysis showed that gender, consolidation/tumor ratio (CTR), boundary, spiculation, lobulation and carcinoembryonic antigen (CEA) were independent risk factors for the diagnosis of benign and malignant subsolid pulmonary nodules. The prediction model formula for malignant probability was: P=ex/(1+ex). X=0.018+(1.436×gender)+(2.068×CTR)+(−1.976×boundary)+ (2.082×spiculation)+(1.277×lobulation)+(2.296×CEA). In this study, the area under the curve was 0.856, the sensitivity was 81.6%, the specificity was 75.6%, the positive predictive value was 95.4%, and the negative predictive value was 39.8%. Compared with the traditional model, the predictive value of this model was significantly better than that of Mayo, VA, Brock and PKUPH models.ConclusionCompared with Mayo, VA, Brock and PKUPH models, the predictive value of the model is more ideal and has greater clinical application value, which can be used for early screening of subsolid nodules.
ObjectiveTo explore the risk factors affecting the prognosis of patients with metastatic breast cancer (MBC) and construct a nomogram survival prediction model.MethodsThe patients with MBC from 2010 to 2013 were collected from surveillance, epidemiology, and end results (SEER) database, then were randomly divided into training group and validation group by R software. SPSS software was used to compare the survival and prognosis of MBC patients with different metastatic sites in the training group by log-rank method and construct the Kaplan-Meier survival curve. The Cox proportional hazards model was used to analyze the factors of 3-year overall survival, then construct a nomogram survival prediction model by the independent prognostic factors. The C-index was used to evaluate its predictive value and the calibration curve was used to verify the nomogram survival prediction model by internal and external calibration graph.ResultsA total of 3 288 patients with MBC were collected, including 2 304 cases in the training group and 984 cases in the validation group. The data of the two groups were comparable. The median follow-up time of training group and validation group was 34 months and 34 months, respectively. In the training group, the results of Cox proportional hazards model showed that the older, black race, higher histological grading, without operation, ER (–), PR (–), HER-2 (–), and metastases of bone, brain, liver and lung were the risk factors of survival prognosis (P<0.05) and constructed the nomogram survival prediction model with these independent prognostic factors. The nomogram survival prediction showed a good accuracy with C-index of 0.704 [95%CI (0.691, 0.717)] in internal validation (training group) and C-index of 0.691 [95%CI (0.671, 0.711)] in external validation (validation group) in predicting 3-year overall survival. All calibration curves showed excellent consistency.ConclusionNomogram for predicting 3-year overall survival of patients with MBC in this study has a good predictive capability, and it is conducive to development of individualized clinical treatment.
Objective To establish and validate a risk prediction model for post-thrombotic syndrome (PTS) in patients after interventional treatment for acute lower extremity deep vein thrombosis (LEDVT). MethodsA retrospective study was conducted to collect data from 234 patients with acute LEDVT who underwent interventional treatment at Xuzhou Central Hospital from December 2017 to June 2022, serving as the modeling set. Factors influencing the occurrence of PTS were analyzed, and a nomogram was developed. An additional 98 patients from the same period treated at the Xuzhou Cancer Hospital were included as an external validation set to assess the reliability of the model. ResultsAmong the patients used to establish the model, the incidence of PTS was 25.2% (59/234), while in the validation set was 31.6% (31/98). Multivariate logistic regression analysis of the modeling set identified the following factors as influencing PTS: age (OR=1.076, P=0.001), BMI (OR=1.163, P=0.004), iliac vein stent placement (OR=0.165, P<0.001), history of varicose veins (OR=5.809, P<0.001), and preoperative D-dimer level (OR=1.341, P<0.001). These 5 factors were used to construct the risk prediction model. The area under the receiver operating characteristic (ROC) curve (AUC) of the model was 0.869 [95%CI (0.819, 0.919)], with the highest Youden index of 0.568, corresponding to a sensitivity of 79.7% and specificity of 77.1%. When applied to the validation set, the AUC was 0.821 [95%CI (0.734, 0.909)], with sensitivity of 77.4%, specificity of 76.1%, and accuracy of 76.6%. ConclusionsThe risk prediction model for PTS established in this study demonstrates good predictive performance. The included parameters are simple and practical, providing a useful reference for clinicians in the preliminary screening of high-risk PTS patients.
ObjectiveTo reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. MethodsPublications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. ResultsA marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. ConclusionOver the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.
Objective To investigate the influencing factors for the clinical remission of advanced esophageal squamous cell carcinoma (ESCC) after neoadjuvant chemotherapy, establish an individualized nomogram model to predict the clinical remission of advanced ESCC with neoadjuvant chemotherapy and evaluate its efficacy, providing serve for the preoperative adjuvant treatment of ESCC.Methods The clinical data of patients with esophageal cancer who underwent neoadjuvant chemotherapy (nedaplatin 80 mg/m2, day 3+docetaxel 75 mg/m2, day 1, 2 cycles, 21 days per cycle interval) in the Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College from February 2016 to August 2020 were analyzed retrospectively. According to the WHO criteria for efficacy assessment of solid tumors, tumors were divided into complete remission (CR), partial remission (PR), stable disease (SD) and progressive disease (PD). CR and PR were defined as effective neoadjuvant chemotherapy, and SD and PD were defined as ineffective neoadjuvant chemotherapy. Univariate and multivariate analyses were used to analyze the influencing factors for the short-term efficacy of neoadjuvant chemotherapy. The R software was used to establish a nomogram model for predicting the clinical remission of advanced ESCC with neoadjuvant chemotherapy, and Bootstrap method for internal verification of the model. C-index, calibration curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the nomogram.Results Finally 115 patients were enrolled, including 93 males and 22 females, aged 40-75 (64.0±8.0) years. After receiving docetaxel+nedaplatin neoadjuvant chemotherapy for 2 cycles, there were 9 patients with CR, 56 patients with PR, 43 patients with SD and 7 patients with PD. Among them, chemotherapy was effective (CR+PR) in 65 patients and ineffective (SD+PD) in 50 patients, with the clinical effective rate of about 56.5% (65/115). Univariate analysis showed that there were statistical differences in smoking history, alcoholism history, tumor location, tumor differentiation degree, and cN stage before chemotherapy between the effective neoadjuvant chemotherapy group and the ineffective neoadjuvant chemotherapy group (P<0.05). Logistic regression analysis showed that low-differentiation advanced ESCC had the worst clinical response to neoadjuvant chemotherapy, moderately-highly differentiated ESCC responded better (P<0.05). Stage cN0 advanced ESCC responded better to neoadjuvant chemotherapy than stage cN1 and cN2 (P<0.05). The C-index and the area under the ROC curve of the nomogram were both 0.763 (95%CI 0.676-0.850), the calibration curve fit well, the best critical value of the nomogram calculated by the Youden index was 70.04 points, and the sensitivity and specificity of the critical value were 80.0% and 58.0%, respectively.ConclusionThe established clinical prediction model has good discrimination and accuracy, and can provide a reference for individualized analysis of the clinical remission of advanced ESCC with neoadjuvant chemotherapy and the screening of new adjuvant treatment subjects.
Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
ObjectiveTo investigate factors influencing the results of bronchodilator reversibility tests (BDT) in mild to moderate asthma, and to develop a model predicting the result of BDT in this population. Methods A cross-sectional study was designed to recruit patients with forced expiratory volume in the first second (FEV1) ≥ 70% predicted from the Australasian Severe Asthma Network during 2014 to 2021, whose asthma diagnosis was confirmed by a positive bronchial challenge test. Structural questionnaires, BDT, fractional exhaled nitric oxide (FeNO), induced sputum and peripheral blood sampling were conducted. Patients were further divided into positive group and negative group according to their BDT result. Then the comparative analysis between two groups, correlation analysis, and multivariate logistical regression were performed. Logistic models for predicting BDT result were developed using variables screened through LASSO regression. Results A total of 334 patients were included. Compared with the BDT negative group (n=240), the BDT positive group (n=94) was found to have worse airway obstruction in lung function, asthma control and quality of life, higher eosinophil counts in both peripheral blood and induced sputum, and higher FeNO. According to the multivariate regression, the positive BDT results significantly correlated with Asthma Control Questionnaire score, Asthma Questionnaire of Life Quality score, FEV1%pred, MMEF%pred, FEV1/FVC, blood and sputum eosinophil counts and FeNO. A total of 326 patients were included in the training set, and FEV1%pred, MMEF%pred, FEV1/FVC, smoking pack years, blood and sputum eosinophil counts and FeNO were then screened out by LASSO regression as stable predictors. The areas under the receiver operating characteristic curve of the 3 prediction models (P<0.001) constructed using the variables above ranged from 0.810 to 0.834. Internal validation was performed, and both the discrimination (0.810, 0.834 and 0.812, respectively) and the calibration (0.135, 0.133 and 0.192, respectively) of the models were acceptable. Conclusion The BDT results of patients with mild to moderate asthma were associated with asthma control, lung function, systemic or airway eosinophilia and FeNO, and models including lung function, eosinophils, and FeNO, etc. could predict the BDT results well.
Objective To explore the factors influencing 2-month sputum smear conversion (2m-SSC) in patients with systemic lupus erythematosus (SLE) and tuberculosis, and to establish a prediction model for 2m-SSC. Methods The initial and follow-up medical records of inpatients with SLE and sputum smear-positive tuberculosis in West China Hospital of Sichuan University from December 2013 to September 2019 were retrospectively reviewed. Single factor analyses and multivariable Firth’s logistic regression were used to determine the influencing factors of 2m-SSC, and a prediction model for 2m-SSC was established. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to evaluate the performance of the model. Results A total of 91 patients with SLE and sputum smear-positive tuberculosis were ultimately included, with 8 cases in the non-conversion group and 83 in the conversion group. There were statistically significant differences in white blood cell count, total bilirubin, indirect bilirubin (IBIL), triglyceride, and rheumatoid factor (RF) between the two groups (P<0.05). Serum RF [odds ratio (OR)=2.271, 95% confidence interval (CI) (1.312, 4.350), P=0.003], IBIL [OR=2.363, 95%CI (1.206, 5.045), P=0.013], and glucose [OR=2.058, 95%CI (1.016, 4.280), P=0.045] were identified as risk factors unfavorable to 2m-SSC outcomes. The constructed model (including three variables: RF, IBIL, and glucose) had a good ability in predicting 2m-SSC [AUC=0.893, 95%CI (0.744, 1.000)], with a sensitivity of 87.5% and a specificity of 84.3%. Conclusion This study established a prediction model for 2m-SSC in patients with SLE and tuberculosis, and found the value of serum RF, IBIL, and glucose in predicting 2m-SSC, providing certain guidance for clinicians in treatment decisions.
ObjectiveTo analyze risk factors associated with prognosis of appendiceal adenocarcinoma using data from the Surveillance, Epidemiology, and End Results (SEER) database. MethodsThe patients pathologically diagnosed with appendiceal adenocarcinoma from 2005 to 2015 were extracted from the SEER database and then randomly divided into a training cohort and validation cohort in a 7∶3 ratio. The univariate and multivariate Cox regression analyses were performed in the training cohort to identify the independent risk factors for overall survival and cancer-specific survival. Based on these factors, a nomogram prediction model was constructed and subsequently internally validated. The statistical significance was defined as α=0.05. ResultsA total of 749 patients with appendiceal adenocarcinoma were enrolled, with 524 in the training cohort and 225 in the validation cohort. The multivariate Cox regression analysis identified that the T, N, M stages, and surgery as the independent prognostic factors for both overall survival and cancer-specific survival. Additionally, the age was identified as an independent prognostic factor for overall survival, and tumor size for cancer-specific survival. Based on these factors, the nomogram prediction models for the overall survival rate and cancer-specific survival rate were developed. The nomogram of overall survival rate achieved a C-index of 0.716 [95%CI=(0.689, 0.743)] in the training cohort and 0.695 [95%CI=(0.649, 0.740)] in the validation cohort, while the nomogram of cancer-specific survival rate showed C-index values of 0.749 [95%CI=(0.716,0.782)] and 0.746 [95%CI=(0.699, 0.793)], respectively. The area under the receiver operating characteristic curves (AUCs) for 3- and 5-year overall survival rates were 0.780 [95%CI=(0.739, 0.821)] and 0.773 [95%CI=(0.732, 0.814)] respectively in the training cohort, were 0.789 [95%CI=(0.726, 0.852)] and 0.776 [95%CI=(0.715, 0.837)] respectively in the validation cohort, which for 3- and 5-year cancer-specific survival rates were 0.813 [95%CI=(0.768, 0.858)] and 0.796 [95%CI=(0.753, 0.839)] respectively in the training cohort, were 0.813 [95%CI=(0.750, 0.876)] and 0.811 [95%CI=(0.750, 0.872)] respectively in the validation cohort. The calibration curves demonstrated good agreements between predicted and observed outcomes for both overall survival rate and cancer-specific survival rate. ConclusionsThrough analysis results of appendiceal adenocarcinoma patients from the SEER database reveal that advanced T, N, and M stages, as well as lack of surgery are significant risk factors for both overall survival and cancer-specific survival. The constructed nomograms for predicting overall survival and cancer-specific survival rates, which incorporate these risk factors, demonstrate strong predictive accuracy.
ObjectiveTo construct a prediction model for the postoperative recurrence risk of granulomatous lobular mastitis (GM) based on multiple systemic inflammatory indicators and clinicopathologic characteristics, with the aim of guiding clinical treatment. MethodsThe GM patients who underwent lesion resection at Sichuan Provincial Hospital for Women and Children from January 2017 to March 2024 were retrospectively collected. The univariate and multivariate logistic regression analyses were used to screen the risk factors for recurrence after GM lesion resection, and a nomogram prediction model was constructed based on the risk factors. The test level was set at α=0.05. ResultsA total of 533 patients with GM were included in this study, of whom 118 cases (22.1%) developed postoperative recurrence. The results of multivariate analysis showed that the not taking oral bromocriptine, having microabscess formation in postoperative pathological examination, systemic immune inflammation index (SII) >789.0×109/L, and immunoglobulin E (IgE) >64.4 U/mL were the independent risk factors for recurrence after GM lesion resection. Based on the risk factors, the nomogram predicting recurrence risk was constructed. The area under the receiver operating characteristic curve (95%CI) was 0.913 (0.895, 0.932), and its sensitivity and specificity were 90.5% and 88.9%, respectively. The calibration curve showed that the probability of recurrence after GM lesion resection predicted by using the nomogram was highly consistent with the actual recurrence probability. The decision curve analysis showed that the nomogram had a good clinical net benefit. ConclusionsThe findings of this study suggest that close postoperative monitoring for recurrence is warranted in patients who did not receive oral bromocriptine treatment, presented with microabscess formation on pathological examination, and exhibited elevated SII and IgE level. The postoperative GM recurrence prediction nomogram model constructed based on risk factors demonstrates a good predictive performance, providing a valuable reference for early treatment and management strategies of GM.