Objective To explore the potential indicators of cervical lymph node metastasis in papillary thyroid microcarcinoma (PTMC) patients and to develop a nomogram model. Methods The clinicopathologic features of PTMC patients in the SEER database from 2004 to 2015 and PTMC patients who were admitted to the Center for Thyroid and Breast Surgery of Xuanwu Hospital from 2019 to 2020 were retrospectively analyzed. The records of SEER database were divided into training set and internal verification set according to 7∶3. The patients data of Xuanwu Hospital were used as the external verification set. Logistic regression and Lasso regression were used to analyze the potential indicators for cervical lymph node metastasis. A nomogram was developed and whose predictive value was verified in the internal and external validation sets. According to the preoperative ultrasound imaging characteristics, the risk scores for PTMC patients were further calculated. The consistency between the scores based on pathologic and ultrasound imaging characteristics was verified. Results The logistic regression analysis results illustrated that male, age<55 years old, tumor size, multifocality, and extrathyroidal extension were associated with cervical lymph node metastasis in PTMC patients (P<0.001). The C index of the nomogram was 0.722, and the calibration curve exhibited to be a fairly good consistency with the perfect prediction in any set. The ROC curve of risk score based on ultrasound characteristics for predicting lymph node metastasis in PTMC patients was 0.701 [95%CI was (0.637 4, 0.765 6)], which was consistent with the risk score based on pathological characteristics (Kappa value was 0.607, P<0.001). Conclusions The nomogram model for predicting the lymph node metastasis of PTMC patients shows a good predictive value, and the risk score based on the preoperative ultrasound imaging characteristics has good consistency with the risk score based on pathological characteristics.
Objective The management of pulmonary nodules is a common clinical problem, and this study constructed a nomogram model based on FUT7 methylation combined with CT imaging features to predict the risk of adenocarcinoma in patients with pulmonary nodules. Methods The clinical data of 219 patients with pulmonary nodules diagnosed by histopathology at the First Affiliated Hospital of Zhengzhou University from 2021 to 2022 were retrospectively analyzed. The FUT7 methylation level in peripheral blood were detected, and the patients were randomly divided into training set (n=154) and validation set (n=65) according to proportion of 7:3. They were divided into a lung adenocarcinoma group and a benign nodule group according to pathological results. Single-factor analysis and multi-factor logistic regression analysis were used to construct a prediction model in the training set and verified in the validation set. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model, the calibration curve was used to evaluate the consistency of the model, and the clinical decision curve analysis (DCA) was used to evaluate the clinical application value of the model. The applicability of the model was further evaluated in the subgroup of high-risk CT signs (located in the upper lobe, vascular sign, and pleural sign). Results Multivariate logistic regression analysis showed that female, age, FUT7_CpG_4, FUT7_CpG_6, sub-solid nodules, lobular sign and burr sign were independent risk factors for lung adenocarcinoma (P<0.05). A column-line graph prediction model was constructed based on the results of the multifactorial analysis, and the area under the ROC curve was 0.925 (95%CI 0.877 - 0.972 ), and the maximum approximate entry index corresponded to a critical value of 0.562, at which time the sensitivity was 89.25%, the specificity was 86.89%, the positive predictive value was 91.21%, and the negative predictive value was 84.13%. The calibration plot predicted the risk of adenocarcinoma of pulmonary nodules was highly consistent with the risk of actual occurrence. The DCA curve showed a good clinical net benefit value when the threshold probability of the model was 0.02 - 0.80, which showed a good clinical net benefit value. In the upper lobe, vascular sign and pleural sign groups, the area under the ROC curve was 0.903 (95%CI 0.847 - 0.959), 0.897 (95%CI 0.848 - 0.945), and 0.894 (95%CI 0.831 - 0.956). Conclusions This study developed a nomogram model to predict the risk of lung adenocarcinoma in patients with pulmonary nodules. The nomogram has high predictive performance and clinical application value, and can provide a theoretical basis for the diagnosis and subsequent clinical management of pulmonary nodules.
Objective To determine the risk factors of anastomotic leakage after elective colectomy in elderly patients with colon cancer, and to establish a model for predicting the risk of postoperative anastomotic leakage based on these factors. Methods The clinical data of 122 over 65 years old elderly patients who underwent colon cancer surgery in the First Hospital of Lanzhou University from January 2018 to December 2021 were analyzed retrospectively. Single factor analysis and multivariate logistic regression were used to analyze the potential risk factors for anastomotic leakage. A nomogram predictive model was established based on the determined independent risk factors, and the predictive performance of the model was evaluated by the receiver operating characteristic curve. Results Among the 122 patients included in this study, 10 had postoperative anastomotic leakage and 112 had no anastomotic leakage. Single factor analysis results showed that the occurrence of anastomotic leakage was associated with body mass index, smoking, combined diabetes, age-adjusted Charlson comorbidity index, intraoperative and postoperative blood transfusion within 2 days, preoperative hemoglobin, preoperative albumin, and preoperative prognostic nutritional index (P＜0.05). The results of multivariate logistic regression analysis showed that smoking [OR=15.529, 95%CI (1.529, 157.690), P=0.020], age-adjusted Charlson comorbidity index [OR=1.742, 95%CI (1.024, 2.966), P=0.041], and intraoperative and postoperative blood transfusion within 2 days [OR=82.223, 95%CI (1.265, 5 343.025), P=0.038] were independent risk factors for anastomotic leakage. A nomogram predictive model was established based on three independent risk factors. The area under the receiver operating characteristic curve of the model was 0.897 [95%CI (0.804, 0.990)], and its corrected C-index value was 0.881, indicating that the model had good predictive ability for the risk of anastomotic leakage. Conclusions Smoking, higher age-adjusted Charlson comorbidity index, and intraoperative and postoperative blood transfusion within 2 days are important risk factors for anastomotic leak in elderly patients undergoing elective colon cancer resection. This nomogram predictive model based on the combination of the three factors is helpful for surgeons to optimize treatment decisions and postoperative monitoring.
ObjectiveTo study the differential expression of minichromosome maintenance protein (MCM) gene family in hepatocellular carcinoma (HCC) and to explore its survival predictive value.MethodsTranscriptome data, clinical data, and survival information of patients with HCC were extracted from The Cancer Genome Atlas (TCGA), and the differential expression of MCM gene was analyzed. The prognostic value of differentially expressed of MCM gene was studied by Cox proportional hazards regression model, the prognostic model and risk score system were constructed. On the basis of risk score, a number of indicators were included to construct a nomogram to predict the3- and 5-year survival probability of HCC patients, and to verify and evaluate their predictive ability and accuracy.ResultsThe expressions of MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, MCM8, and MCM10 in HCC tissues were higher than those of normal liver tissues (P<0.05), and univariate analysis showed that they were all related to prognosis (P<0.05). Multivariate analysis showed that MCM6 and MCM10 were independent factors affecting survival of HCC patients (P<0.05). Through multivariate analysis, a prognostic model consisting of MCM6, MCM8, and MCM10 was constructed, and a risk scoring system was established. It had been verified that this risk score was an independent risk factor affecting the prognosis of patients with HCC, and the prognosis of patients with high scores were worse than those of patients with low scores (P<0.001). We used TNM stage, T stage, and risk score to construct a nomogram with a consistency index (C index) of 0.723 and draw a time-dependent receiver operating characteristic curve, the results showed that area under the curve of 3- and 5-year were 0.731 and 0.704, respectively.ConclusionsMCM6,MCM8, and MCM10 in the MCM gene family have important prognostic value in HCC. The nomogram constructed in this study can better predict the survival probability of HCC patients.
Objective To explore independent risk factors for 30-day mortality in critical patients with pulmonary infection and sepsis, and build a prediction model. Methods Patients diagnosed with pulmonary infection and sepsis in the MIMIC-Ⅲ database were analyzed. The CareVue database was the training cohort (n=934), and the Metavision database was the external validation cohort (n=687). A COX proportional hazards regression model was established to screen independent risk factors and draw a nomogram. We conducted internal cross-validation and external validation of the model. Using the receiver operator characteristic (ROC) curve, Calibration chart, and decision curve analysis, we detected the discrimination, calibration, and benefit of the model respectively, comparing with the SOFA scoring model. Results Age, SOFA score, white blood cell count≤4×109/L, neutrophilic granulocyte percentage (NEU%)>85%, platelet count (PLT)≤100×109/L, PLT>300×109/L, red cell distribution width >15%, blood urea nitrogen, and lactate dehydrogenase were independent risk factors. The areas under the ROC curve of the model were 0.747 (training cohort) and 0.708 (external validation cohort), respectively, which was superior to the SOFA scoring model in terms of discrimination, calibration, and benefit. Conclusion The model established in this study can accurately and effectively predict the risk of the disease mortality, and provide a visual assessment method for early identification of high-risk patients.
Objective To develop a radiomics nomogram based on contrast-enhanced CT (CECT) for preoperative prediction of high-risk and low-risk thymomas. Methods Clinical data of patients with thymoma who underwent surgical resection and pathological confirmation at Northern Jiangsu People's Hospital from January 2018 to February 2023 were retrospectively analyzed. Feature selection was performed using the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) method. An ExtraTrees classifier was used to construct the radiomics signature model and the radiomics signature. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. The radiomics nomogram model was developed by combining the radiomics signature and clinical features. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. Calibration curves and decision curves were plotted to assess model accuracy and clinical values. Results A total of 120 patients including 59 females and 61 males with an average age of 56.30±12.10 years. There were 84 patients in the training group and 36 in the validation group, 62 in the low-risk thymoma group and 58 in the high-risk thymoma group. Radiomics features (1 038 in total) were extracted from the arterial phase of CECT scans, among which 6 radiomics features were used to construct the radiomics signature. The radiomics nomogram model, combining clinical-radiological characteristics and the radiomics signature, achieved an AUC of 0.872 in the training group and 0.833 in the validation group. Decision curve analysis demonstrated better clinical efficacy of the radiomics nomogram than the radiomics signature and clinical model. Conclusion The radiomics nomogram based on CECT showed good diagnostic value in distinguishing high-risk and low-risk thymoma, which may provide a noninvasive and efficient method for clinical decision-making.
ObjectiveCombined with long non-coding RNA (lncRNA) to find a regression model that can be used to predict the survival rate of patients with colon cancer before operation.MethodsThe clinical information and gene expression information of patients with colon cancer were downloaded by using TCGA database. The differentially expressed lncRNAs in tumor and paracancerous tissues were screened out, and then combined with the clinical information of patients to construct Cox proportional hazard regression model.ResultsA total of 26 kinds of lncRNAs with statistical difference in gene expression between paracancerous tissues and tumor tissues were selected (P<0.05). Through repeated screening and comparison of prediction efficiency, the prediction model was finally selected, which was constructed by patients’ age, M stage, N stage, and three kinds of lncRNAs (ZFAS1, SNHG25, and SNHG7) gene expression level: age [HR=4.00, 95%CI: (1.48, 10.84), P=0.006], M stage [HR=3.96, 95%CI: (2.23, 7.04), P<0.001], N stage [HR=1.87, 95%CI: (1.24, 2.84), P=0.003], ZFAS1 gene expression level [HR=0.60, 95%CI: (0.41, 0.86), P=0.006], SNHG25 gene expression level [HR=0.85, 95%CI: (0.73, 1.00), P=0.045], and SNHG7 gene expression level [HR=2.32, 95%CI: (1.53, 3.52), P<0.001] were all independent risk factors for postoperative survival of patients with colon cancer. The area under the ROC curves for predicting 1, 3, and 5-year overall survival were 0.802, 0.828, and 0.771, respectiely, which had a good prediction ability.ConclusionThe predictive model constructed by the combination of ZFAS1, SNHG25, SNHG7 genes expression level with M stage, N stage, and age can better predict the overall survival rate of patients before operation, which can effectively guide clinical decision-making and choose the most suitable treatment method for patients.
ObjectiveTo explore the association between the ZJU index and obstructive sleep apnea hypopnea syndrome (OSAHS) and to develop a prediction model based on ZJU index. MethodsClinical data of patients diagnosed by polysomnography were retrospectively collected from January 2021 to July 2024. Participants were categorized into OSAHS and non-OSAHS groups, and the general data of the two groups were compared. Regression analysis was performed to analyze the influencing factors of OSAHS, a prediction model of OSAHS was constructed based on the ZJU index, and the diagnostic efficacy was evaluated by using the subject's work characteristics (ROC) curve and calibration curve. Rusults A total of 211 patients were included in this study, including 165 in the OSAHS group and 46 in the non-OSAHS group. The multifactorial results showed that ZJU index and gender were the influencing factors for the occurrence of OSAHS (P<0.05), and a prediction model was constructed by combining the ZJU index with gender, and the area under the ROC curve (AUC) was 0.786 (95%CI: 0.717-0.85). The sensitivity was 51.5% and the specificity was 91.3%. The calibration curve showed good agreement between predicted and actual results. ConclusionZJU index is associated with OSAHS, and the prediction model constructed by ZJU index combined with gender could be well used to predict the occurrence of OSAHS.
ObjectiveTo explore the relation between preoperative serum gamma-glutamyl transpeptidase to platelet ratio (GPR) and overall survival (OS) of patients with hepatitis B virus-associated hepatocellular carcinoma (Abbreviated as “patients with HCC”), and to establish a nomogram for predicting OS. MethodsAccording to the inclusion and exclusion criteria, the clinicopathologic data of patients with HCC who underwent radical resection in the Department of Hepatobiliary Surgery of Xianyang Central Hospital, from January 15, 2012 to December 15, 2018, were retrospectively analyzed. The optimal critical value of GPR was determined by receiver operating characteristic curve, then the patients were divided into a low GPR group (GPR was optimal critical value or less ) and high GPR group (GPR was more optimal critical value). The Kaplan-Meier method was used to draw the survival curve and analyze the OS of patients. The univariate and multivariate Cox proportional hazards regression model were used to analyze the factors influencing prognosis in the patients with HCC. According to the risk factors of OS for patients with HCC, a nomogram was established. The consistency index and calibration curve in predicting the 3-year and 5-year accumulative OS rates of patients with HCC were evaluated. ResultsA total of 213 patients were gathered. The optimal critical value of GPR was 0.906. There were 114 patients in the low GPR group and 99 patients in the high GPR group. The Kaplan-Meier survival curve analysis showed that the 1-, 3- and 5-year accumulative OS rates were 99.1%, 81.8%, 60.6% in the low GPR group, respectively, which were 74.2%, 49.1%, 35.7% in the low GPR group, respectively. The OS curve of the low GPR group was better than that of the high GPR group (χ2=25.893, P<0.001). The multivariate analysis results showed that the microvascular invasion, incomplete capsule, intraoperative bleeding ＞1 000 mL, postoperative complications, GPR >0.906, low tumor differentiation, and late TNM stage did not contribute to accumulative OS in the patients with HCC (P<0.05). The consistency index (95%CI) of the nomogram in predicting accumulative OS rates at 3- and 5-year for patients with HCC were 0.761 (0.739, 0.783) and 0.735 (0.702, 0.838), respectively. The calibration curves of 3- and 5-year accumulative OS rates of the nomogram were in good agreement with the actual results. ConclusionsPreoperative GPR is associated with OS, and patients with higher GPR have worse prognosis. The nomogram based on GPR has a good accuracy and differentiation.
ObjectiveTo investigate the prognostic factors of primary gastric squamous cell carcinoma (SCC) and develop a nomogram for predicting the survival of gastric SCC.MethodsData of 199 cases of primary gastric SCC from 2004 to 2015 were collected in the National Cancer Institute SEER database by SEER Stat 8.3.5 software. X-tile software was used to determine the best cut-off value of the age, SPSS 25.0 software was used to analyze the prognostic factors of gastric SCC and draw a Kaplan-Meier curve, and then the Cox proportional hazard regression model analysis was performed to obtain independent prognostic factors of gastric SCC. We used R studio software to visualize the model and draw a nomogram. C-index was used to evaluate the prediction effect of the nomogram. Bootstrap analyses with 1 000 resamples were applied to complete the internal verification of the nomogram.ResultsAmong the 199 patients, survival rates for 1-, 3-, and 5-year were 40.7%, 22.4%, and 15.4%, respectively. Age (χ2=6.886, P=0.009), primary site (χ2=14.918, P=0.037), race (χ2=7.668, P=0.022), surgery (χ2=16.523, P<0.001), histologic type (χ2=9.372, P=0.009), T stage (χ2=11.639, P=0.009), and M stage (χ2=31.091, P<0.001) had a significant correlation with survival time of patients. The results of the Cox proportional hazard regression model showed that, age [HR=1.831, 95%CI was (1.289, 2.601)], primary site [HR=1.105, 95%CI was (1.019, 1.199)], M stage [HR=2.222, 95%CI was (1.552, 3.179)], and surgery [HR=0.561, 95%CI was (0.377, 0.835)] were independent prognostic factors affecting the survival of gastric SCC. Four independent prognostic factors contributed to constructing a nomogram with a C-index of 0.700.ConclusionIn this research, a reliable predictive model is constructed and drawn into a nomogram, which can be used for clinical reference.
