Dataset Information

Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

ABSTRACT: As a special type of lung cancer, multiple primary lung cancer (MPLC) has unique biological characteristics, and its research remains limited. The aim of our research was to identify prognostic factors and construct a prognostic nomogram of dual primary lung cancer (DPLC). A population cohort study of patients with DPLC was conducted using the extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. Relevant survival variables were identified using the Cox proportional hazard model. Prognostic nomogram was performed and its predictive performance was validated via the modeling and validating cohort data. Additionally, propensity score matching (PSM) was also applied to evaluate whether surgery affected the OS of this study population. 5411 eligible DPLC patients were included in this study cohort, with 41.0% of 3-year OS rate and 27.7% of 5-year OS rate. Age, sex, race, grade, stage, lymph node (LN) metastasis, histological type, primary site, and surgery were considered to be prognostic factors of OS. The C-indexes of the established nomogram were 0.70 (95% CI (0.69, 0.71)) in the modeling group and 0.70 (95% CI (0.68, 0.72)) in the validation group, which showed an ideal model discrimination ability. AUC and calibration plots of 3- and 5-year OS also proved the good performance of the established nomogram. After 1?:?1 PSM, surgery can potentially reduce the risk of OS (HR?=?0.63, 95% CI: 0.56-0.72) of DPLC. The prognostic nomogram with reliable performance was developed to predict 3- and 5-year OS rates, which could assist clinicians to make more reasonable survival prediction for DPLC patients. For patients without absolute surgical contraindications, surgery should be actively considered.

SUBMITTER: Song CK

PROVIDER: S-EPMC7049836 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

Song Cong-Kuan CK Guo Zi-Xin ZX Shen Xiao-Yan XY Wang Yu-Jin YJ Wang Qing-Wen QW Yu Dong-Hu DH Chen Chen C Liu Xiao-Ping XP Huang Jing-Yu JY Li Sheng S Hu Weidong W

BioMed research international 20200219

As a special type of lung cancer, multiple primary lung cancer (MPLC) has unique biological characteristics, and its research remains limited. The aim of our research was to identify prognostic factors and construct a prognostic nomogram of dual primary lung cancer (DPLC). A population cohort study of patients with DPLC was conducted using the extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. Relevant survival variables were identified using the Cox proportiona ...[more]

PMID: 32149127

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Project description:BackgroundResearch on patients with lung cancer as a second primary malignancy (LCSPM) remains limited. This study aims to determine the clinical characteristics, prognosis, and temporal relationship of other cancers to lung cancer in these patients.MethodsThis study retrospectively analyzed 3465 patients with dual primary cancers from the 5253 patients with LCSPM retrieved from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015.Results2285 eligible patients were further analyzed in this study cohort with 59.3% of 1-year OS, 34.7% of 3-year OS, and 25.2% of 5-year OS. The most common first primary cancer (FPC) in dual primary cancer patients with LCSPM was prostate cancer, followed by female breast cancer and urinary bladder cancer. In the entire study population, the median interval between the two primary malignancies was 21 months (range: 3.5-52 months). Age, sex, FPC location, surgery, stage, and histology of lung cancer were regarded as independent prognostic factors for these patients. The prognosis of patients with urinary bladder cancer as FPC was the worst in the univariate (p = 0.024) and multivariate (p < 0.001) Cox analyses. Lung cancer-directed surgery could significantly improve long-term survival (HR = 0.22, p < 0.001). Additionally, the C-index of the established nomogram with acceptable calibration curves was 0.760 (95% CI: 0.744-0.776) in the training cohort and was 0.759 (95% CI: 0.737-0.781) in the validation cohort, showing an ideal model discrimination ability. The corresponding decision curve analysis (DCA) indicated the nomogram had relatively ideal clinical utility.ConclusionsCancer patients still have the risk of developing a new primary lung cancer. Close, lifelong follow-up is recommended for all these patients. Early detection for surgical treatment will significantly improve the prognosis of dual primary cancer patients with LCSPM. The nomogram developed to predict 1-, 3-, and 5-year OS rates has relatively good performance.

Project description:Purpose This study was launched to ascertain the independent prognostic factors influencing the overall survival (OS) prognosis of intracranial subependymoma and construct a prognostic model to predict OS time. Materials and methods We collected data from patients with intracranial subependymoma, including treatment data, follow-up data, and clinical and pathological characteristics from the SEER database within 2004 to 2016, and patients were randomly classified into training and validation cohorts. Univariate and multivariate analyses were applied to the training group through building a Cox proportional hazards model. According to the results of multivariate analysis, we established a nomogram to forecast the OS rate of the per-case patient graphically, then calculated the accuracy of verification in both training and validation cohorts by concordance index (C-index). Univariate and multivariate analyses were used for different subgroups of unoperated versus operated, gross total resection (GTR), subtotal resection (STR), and biopsy after using the propensity score matching (PSM) analyses. Results A total of 667 patients were enrolled, and we randomly assigned 535 patients (80.21%) into the training cohort and 132 patients (19.79%) into the validation cohort. Age [hazard ratio (HR) = 6.355; 95% confidence interval (CI), 2.240–18.029; p = 0.001] and sex (HR = 0.475; 95% CI, 0.232–0.974; p = 0.042) were the independent prognostic factors in the training cohort. On the basis of age and sex, the nomogram was established to predict the OS for every patient (C-index = 0.733 ± 0.065 in the training cohort and 0.850 ± 0.065 in the validation cohort), and calibration plots reflected the reliability of the nomogram. Age, gender, or laterality was the independent prognostic factor for OS in the different matched subgroups of unoperated versus operated, GTR, STR, and biopsy. Surgical treatment, race, year of diagnosis, insurance, tumor location, tumor size, pathology, tumor grade, and radiation were not statistically significantly different in OS for subependymoma in our research. Conclusion Age and sex were the independent prognostic variables for OS in intracranial subependymoma. According to our research, we should not be more inclined to choose conservative or surgical treatment. Nonetheless, the information that we present might be useful to suggest potential hypotheses to be tested in the clinical research setting.

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.

Project description:BackgroundBone is one of the most common metastatic sites of advanced lung cancer, and the median survival time is significantly shorter than that of patients without metastasis. This study aimed to identify prognostic factors associated with survival and construct a practical nomogram to predict overall survival (OS) in lung cancer patients with bone metastasis (BM).MethodsWe extracted the patients with BM from lung cancer between 2011 and 2015 from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate Cox regressions were performed to identify independent prognostic factors for OS. The variables screened by multivariate Cox regression analysis were used to construct the prognostic nomogram. The performance of the nomogram was assessed by receiver operating characteristic (ROC) curve, concordance index (C-index), and calibration curves, and decision curve analysis (DCA) was used to assess its clinical applicability.ResultsA total of 7861 patients were included in this study and were randomly divided into training (n=5505) and validation (n=2356) cohorts using R software in a ratio of 7:3. Cox regression analysis showed that age, sex, race, grade, tumor size, histological type, T stage, N stage, surgery, brain metastasis, liver metastasis, chemotherapy and radiotherapy were independent prognostic factors for OS. The C-index was 0.723 (95% CI: 0.697-0.749) in the training cohorts and 0.738 (95% CI: 0.698-0.778) in the validation cohorts. The AUC of both the training cohorts and the validation cohorts at 3-month (0.842 vs 0.859), 6-month (0.793 vs 0.814), and 1-year (0.776 vs 0.788) showed good predictive performance, and the calibration curves also demonstrated the reliability and stability of the model.ConclusionsThe nomogram associated with the prognosis of BM from lung cancer was a reliable and practical tool, which could provide risk assessment and clinical decision-making for individualized treatment of patients.

Dataset Information

Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

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Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study.

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