Dataset Information

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study.

ABSTRACT: Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

SUBMITTER: You H

PROVIDER: S-EPMC8316725 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundDNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients.MethodThe DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves.ResultWe identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47-3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19-3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively.ConclusionsThe present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

Project description:BackgroundThe increased use of computed tomography has brought a corresponding increase in the numbers of early-stage lung cancer patients receiving treatment. However, even for stage IA3 lung adenocarcinoma, many patients experience postoperative recurrence and metastasis. The existing TNM staging system for lung cancer does not take many clinical and pathological factors into consideration, resulting in the failure to detect and intervene as soon as possible in those with high recurrence risk. The purpose of this study was to explore the risk factors for postoperative recurrence-free survival (RFS) in patients with stage IA3 lung adenocarcinoma, and to construct and verify a nomogram model for predicting RFS in patients with the disease.MethodsThis study analyzed patients with stage IA3 lung adenocarcinoma who underwent surgical treatment. Univariate and multivariate analysis were used to analyze the independent risk factors for postoperative RFS and establish a nomogram model. Concordance index (C-index), receiver operating characteristic curve, clinical decision analysis, and calibration curve were used to evaluate the discrimination and calibration of the nomogram model. Data from two other institutions were used for external validation, and the nomogram scores were combined with X-tile software to screen high-risk groups of recurrence.ResultsThe internal cohort included 235 eligible patients with stage IA3 lung adenocarcinoma from 7,235 lung cancer. Multivariate analysis showed smoking, solid nodules, mucinous lung adenocarcinoma, and micropapillary component ≥5% were independent risk factors for RFS. A nomogram model was constructed based on the above results and the bootstrap method was used for internal validation. The internal and external validation C-indexes of the nomogram were 0.822 (95% CI: 0.751-0.891) and 0.812, respectively, indicating the obvious prediction performance was good. The X-tile software combined with nomogram scores showed the low-risk group (5-RFS rate, 0.65-0.99) had better RFS than the high-risk group (5-RFS rate, 0.20-0.65) (P<0.0001).ConclusionsWe constructed a nomogram model for predicting postoperative RFS in patients with stage IA3 lung adenocarcinoma which can individually evaluate the risk of postoperative recurrence, screen high-risk groups, and develop individualized follow-up and intervention strategies to improve the survival rate of the patients.

Project description:BackgroundGallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients.MethodWe extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram.ResultA total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system.ConclusionWe constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions.

Project description:BackgroundLung adenocarcinoma (LUAD) is the major cause of cancer mortality. Traditional prognostic factors have limited importance after including other parameters. Thus, developing a more credible prognostic model combined with genes and clinical parameters is necessary.MethodsThe messenger RNA (mRNA) expression and clinical information from The Cancer Genome Atlas (TCGA)-LUAD datasets and microarray data from three Gene Expression Omnibus (GEO) databases were obtained. We identified differentially-expressed genes (DEGs) between lung tumor and normal tissues through integrated analysis of the three GEO datasets. Univariate and multivariate Cox regression analyses were conducted to select survival-associated DEGs and to establish a prognostic gene signature which was associated with overall survival (OS). The expression of gene proteins was assessed in 180 LUAD tissue microarrays (TMAs) by immunohistochemistry (IHC). We verified its predictive performance with a Kaplan-Meier (KM) curve, receiver operating characteristic (ROC) curve, and Harrell's concordance index (C-index) and validated it in external GEO databases. Multivariate Cox regression analysis was performed to identify the significant prognostic indicators in LUAD. Furthermore, we established a prognostic nomogram based on TCGA-LUAD dataset.ResultsA three-gene signature was constructed to predict the OS of LUAD patients. The KM analysis, ROC curve, and C-index present a good predictive ability of the gene signature in TCGA dataset [P<0.0001; C-index 0.6375; 95% confidence interval (CI): 0.5632-0.7118; area under the ROC curve (AUC) 0.674] and the external GEO datasets (P=0.05, 0.004, and 0.04, respectively). Univariate and multivariate Cox regression analyses also verified that LUAD patients with low-risk scores had a decreased risk of death compared to those with a high-risk score in TCGA database [hazard ratio (HR) =0.3898; 95% CI: 0.1938-0.7842; P<0.05]. Finally, we constructed a nomogram integrating the gene signature and clinicopathological parameters (P<0.0001; C-index 0.762; 95% CI: 0.714-0.845; AUC 0.8136). Compared with conventional staging, a nomogram can effectively improve prognosis prediction.ConclusionsThe nomogram is closely associated to the OS of LUAD patients. This consequence may be beneficial to individualized treatment and clinical decision-making.

Project description:BackgroundPrimary retroperitoneal sarcoma (RPS) comprises over 70 histologic subtypes, yet there are limited studies that have developed prognostic nomograms for RPS patients to predict overall survival (OS) and cancer-specific survival (CSS). The objective of this study was to construct prognostic nomograms for predicting OS and CSS in RPS patients.MethodsWe identified a total of 1166 RPS patients from the Surveillance, Epidemiology and End Results (SEER) database, and an additional 261 cases were collected from a tertiary cancer center. The study incorporated various clinicopathological and epidemiologic features as variables, and prediction windows for overall survival (OS) and cancer-specific survival (CSS) were set at 3, 5, and 7 years. Multivariable Cox models were utilized to develop the nomograms, and variable selection was performed using a backward procedure based on the Akaike Information Criterion. To evaluate the performance of the nomograms in terms of calibration and discrimination, we used calibration plots, coherence index, and area under the curve.FindingsThe study included 818 patients in the development cohort, 348 patients in the internal validation cohort, and 261 patients in the external validation cohort. The backward procedure selected the following variables: age, French Federation of Cancer Centers Sarcoma Group (FNCLCC) grade, pre-/postoperative chemotherapy, tumor size, primary site surgery, and tumor multifocality. The validation results demonstrated that the nomograms had good calibration and discrimination, with C-indices of 0.76 for OS and 0.81 for CSS. Calibration plots also showed good consistency between the predicted and actual survival rates. Furthermore, the areas under the time-dependent receiver operating characteristic curves for the 3-, 5-, and 7-year OS (0.84, 0.82, and 0.78, respectively) and CSS (0.88, 0.88, and 0.85, respectively) confirmed the accuracy of the nomograms.InterpretationOur study developed accurate nomograms to predict OS and CSS in patients with RPS. These nomograms have important clinical implications and can assist healthcare providers in making informed decisions regarding patient care and treatment options. They may also aid in patient counseling and stratification in clinical trials.

Project description:BackgroundRenal cell carcinoma (RCC) is a common malignant tumor in the elderly, with an increasing trend in recent years. We aimed to construct a nomogram of cancer-specific survival (CSS) and overall survival (OS) in elderly patients with nonmetastatic renal cell carcinoma (nmRCC).MethodsClinicopathological information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program in elderly patients with nmRCC from 2010 to 2015. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression analyses were used to identify independent risk factors for patient outcomes in the training cohort. A nomogram was constructed based on these independent risk factors to predict the 1-, 3-, and 5-year CSS and OS in elderly patients with nmRCC. We used a range of methods to validate the accuracy and reliability of the model, including the calibration curve, consistency index (C-index), and the area under the receiver operating curve (AUC). Decision curve analysis (DCA) was used to test the clinical utility of the model.ResultsA total of 12,116 patients were enrolled in the study. Patients were randomly assigned to the training cohort (N = 8,514) and validation cohort (N = 3,602). In the training cohort, univariate and multivariate Cox regression analysis showed that age, marriage, tumor histological type, histological tumor grade, TN stage, tumor size, and surgery are independent risk factors for prognosis. A nomogram was constructed based on independent risk factors to predict CSS and OS at 1-, 3-, and 5- years in elderly patients with nmRCC. The C-index of the training and validation cohorts in CSS were 0.826 and 0.831; in OS, they were 0.733 and 0.734, respectively. The AUC results of the training and validation cohort were similar to the C-index. The calibration curve indicated that the observed value is highly consistent with the predicted value, meaning the model has good accuracy. DCA results suggest that the clinical significance of the nomogram is better than that of traditional TNM staging.ConclusionsWe built a nomogram prediction model to predict the 1-, 3- and 5-year CSS and OS of elderly nmRCC patients. This model has good accuracy and discrimination and can help doctors and patients make clinical decisions and active monitoring.

Dataset Information

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets