Dataset Information

A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study.

ABSTRACT: BACKGROUND:Squamous cell carcinoma (SCC) is a main pathological type of non-small cell lung cancer. It is common among elderly patients with poor prognosis. We aimed to establish an accurate nomogram to predict survival for elderly patients (??60 years old) with SCC based on the Surveillance, Epidemiology, and End Results (SEER) database. METHODS:The gerontal patients diagnosed with SCC from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The independent prognostic factors were identified using multivariate Cox proportional hazards regression analysis, which were utilized to conduct a nomogram for predicting survival. The novel nomogram was evaluated by Concordance index (C-index), calibration curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS:32,474 elderly SCC patients were included in the analysis, who were randomly assigned to training cohort (n?=?22,732) and validation cohort (n?=?9742). The following factors were contained in the final prognostic model: age, sex, race, marital status, tumor site, AJCC stage, surgery, radiation and chemotherapy. Compared to AJCC stage, the novel nomogram exhibited better performance: C-index (training group: 0.789 vs. 0.730, validation group: 0.791 vs. 0.733), the areas under the receiver operating characteristic curve of the training set (1-year AUC: 0.846 vs. 0.791, 3-year AUC: 0.860 vs. 0.801, 5-year AUC: 0.859 vs. 0.794) and the validation set (1-year AUC: 0.846 vs. 0.793, 3-year AUC: 0.863 vs. 0.806, 5-year AUC: 0.866 vs. 0.801), and the 1-, 3- and 5-year calibration plots. Additionally, the NRI and IDI and 1-, 3- and 5-year DCA curves all confirmed that the nomogram was a great prognosis tool. CONCLUSIONS:We constructed a novel nomogram that could be practical and helpful for precise evaluation of elderly SCC patient prognosis, thus helping clinicians in determining the appropriate therapy strategies for individual SCC patients.

SUBMITTER: Chen S

PROVIDER: S-EPMC7670679 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study.

Chen Siying S Gao Chunxia C Du Qian Q Tang Lina L You Haisheng H Dong Yalin Y

Journal of translational medicine 20201116 1

<h4>Background</h4>Squamous cell carcinoma (SCC) is a main pathological type of non-small cell lung cancer. It is common among elderly patients with poor prognosis. We aimed to establish an accurate nomogram to predict survival for elderly patients (≥ 60 years old) with SCC based on the Surveillance, Epidemiology, and End Results (SEER) database.<h4>Methods</h4>The gerontal patients diagnosed with SCC from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) da ...[more]

PMID: 33198777

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Project description:BackgroundMetastatic non-small cell lung cancer (NSCLC) is mostly seen in older patients and is associated with poor prognosis. There is no reliable method to predict the prognosis of elderly patients (≥60 years old) with metastatic NSCLC. The aim of our study was to develop and validate nomograms which accurately predict survival in this group of patients.MethodsNSCLC patients diagnosed between 2010 and 2015 were all identified from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed by significant clinicopathological variables (p < 0.05) selected in multivariate Cox analysis regression.ResultsA total of 9584 patients met the inclusion criteria and were randomly allocated in the training (n = 6712) and validation (n = 2872) cohorts. In training cohort, independent prognostic factors included age, gender, race, grade, tumor site, pathology, T stage, N stage, radiotherapy, surgery, chemotherapy, and metastatic site (p < 0.05) for lung cancer-specific survival (LCSS) and overall survival (OS) were identified by the Cox regression. Nomograms for predicting 1-, 2-, and 3-years LCSS and OS were established and showed excellent predictive performance with a higher C-index than that of the 7th TNM staging system (LCSS: training cohort: 0.712 vs. 0.534; p < 0.001; validation cohort: 0.707 vs. 0.528; p < 0.001; OS: training cohort: 0.713 vs. 0.531; p < 0.001; validation cohort: 0.710 vs. 0.528; p < 0.001). The calibration plots showed good consistency from the predicted to actual survival probabilities both in training cohort and validation cohort. Moreover, the decision curve analysis (DCA) achieved better net clinical benefit compared with TNM staging models.ConclusionsWe established and validated novel nomograms for predicting LCSS and OS in elderly patients with metastatic NSCLC with desirable discrimination and calibration ability. These nomograms could provide personalized risk assessment for these patients and assist in clinical decision.

Project description:Non-small cell lung cancer (NSCLC) is a malignant disease with a significant morbidity rate. For patients diagnosed with borderline resectable locally advanced (T3-4 invasion and N0-1) NSCLC, the optimal treatment and prognosis are still under debate. This study aimed to develop a predictive nomogram that could assess the prognosis of these patients and optimize clinical decision-making. Between 2010 to 2015, the survival, demographic and clinical characteristics of patients with borderline resectable locally advanced T3-4N0-1 NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Cox proportional hazard regression analyses were conducted to identify potential factors, which were further utilized to develop a dynamic nomogram for personalized prediction. Internal and external validation were conducted to verify the predictive accuracy of the nomogram. Totally, 5,054 eligible records were enrolled into the study cohort. The included patients were divided into a training cohort (n=3,538) and a validation cohort (n=1,516) in a 7:3 ratio. Nine independent prognostic factors (including age, gender, primary site, lymph node removal, differentiation grade, T stage, N stage, histology and adjuvant chemotherapy) were finally included into the nomogram. The developed nomogram exhibited favorable discriminative ability with the C-index =0.71. Moreover, the calibration curves demonstrated excellent agreement between predicted and observed outcomes in both the training and validation cohorts. Notably, subgroup analyses revealed that neoadjuvant chemotherapy was significantly associated with a better overall survival (OS) (P<0.05) in patients staged as T3-4N1. In this study, we developed and validated a prognostic model to assist in clinical decision-making for patients with borderline resectable locally advanced T3-4N0-1 NSCLC. Our findings suggested that patients with T3-4N1 stage disease may derive significant benefits from neoadjuvant chemotherapy.

Project description:Bioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC. The transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database ( http://www.immport.org/ ); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed. A total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P < 0.05). After including 19 differentially expressed immune genes with P < 0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan-Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic model curve confirmed that the prediction model had a certain accuracy (AUC = 0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment. The NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.

Project description:BackgroundThe aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC).MethodsA total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts.ResultsEleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients' OS (HR = 3.913, p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables (age, sex, chronic obstructive pulmonary disease, T stage, N stage, and M stage) had a better prognostic performance than the clinical nomogram both in the training cohort (C-index, 0.861, 95% CI: 0.843-0.879 vs. C-index, 0.851, 95% CI: 0.832-0.870; p < 0.001) and in the validation cohort (C-index, 0.868, 95% CI: 0.841-0.896 vs. C-index, 0.854, 95% CI: 0.824-0.884; p = 0.002). The calibration curves demonstrated optimal alignment between the prediction and actual observation.ConclusionThe established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting.

Dataset Information

A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study.

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A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study.

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