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Prognosis and nomogram for predicting postoperative survival of duodenal adenocarcinoma: A retrospective study in China and the SEER database.

ABSTRACT: As primary duodenal adenocarcinoma is rare, the prognostic factors of this disease remain insufficiently explored, especially in China. We identified postoperative duodenal adenocarcinoma patients at a Chinese double-center (from 2006 to 2016) or who were registered with the Surveillance, Epidemiology, and End Results (SEER) database (from 2004 to 2014). Clinicopathological features and significant prognostic factors for cancer-specific survival (CSS) were reviewed and analyzed by using univariate and multivariate Cox proportional hazards regression. Then, a nomogram predicting CSS was constructed based on the SEER database and validated externally by using the separate Chinese cohort. Totally, 137 patients from the Chinese double-center and 698 patients from the SEER database were included for analysis. The multivariate analyses showed that age, tumor grade and TNM stage were independent prognostic factors. The nomogram constructed using these factors showed a clear prognostic superiority to the AJCC-TNM classification, 7^th ed. (C-index: SEER cohort, 0.693 vs 0.625, P?

SUBMITTER: Jiang S

PROVIDER: S-EPMC5962558 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Prognosis and nomogram for predicting postoperative survival of duodenal adenocarcinoma: A retrospective study in China and the SEER database.

Jiang Sujing S Zhao Rongjie R Li Yiran Y Han Xufeng X Liu Zhen Z Ge Weiting W Dong Ying Y Han Weidong W

Scientific reports 20180521 1

As primary duodenal adenocarcinoma is rare, the prognostic factors of this disease remain insufficiently explored, especially in China. We identified postoperative duodenal adenocarcinoma patients at a Chinese double-center (from 2006 to 2016) or who were registered with the Surveillance, Epidemiology, and End Results (SEER) database (from 2004 to 2014). Clinicopathological features and significant prognostic factors for cancer-specific survival (CSS) were reviewed and analyzed by using univaria ...[more]

PMID: 29786691

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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:Purpose The lung is the most common distant metastatic organ in patients with endometrial cancer (EC) but is rarely reported. This study examines the association between clinical characteristics and overall survival (OS) in EC with lung metastasis. Methods Patients with EC who had accompanying lung metastasis were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Univariate and multivariate Cox regression were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) and assess OS outcomes related to EC with lung metastasis. A Cox proportional hazards nomogram model for OS was constructed and validated. The calibration plot, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the discriminative ability and clinical benefit of the novel nomogram. Kaplan–Meier curves and scatter diagram analysis were used to investigate the risk stratifications of the nomogram. Results Overall, 1542 EC patients with lung metastasis between 2010 and 2017 were included and randomly divided into training and validation cohorts. A nomogram model was constructed using the clinical characteristics of tumor grade, histological type, surgery, adjuvant chemotherapy, adjuvant radiation, brain metastasis and liver metastasis. The concordance indexes (C-indexes) were 0.750 (95% CI, 0.732-0.767) and 0.743 (95% CI, 0.719-0.767) for the training cohort and validation cohort, respectively. Calibration plots and DCA showed good clinical applicability of the nomogram. The areas under the curves (AUCs) were 0.803 and 0.766 for 1-year and 3-year OS, respectively, indicating that the nomogram model had a stable discriminative ability. An online calculator of our nomogram is available on the internet at https://endometrialcancer.shinyapps.io/DynNomapp/. Additionally, patients in the high-risk group had a significantly worse OS than those in the low-risk group. Conclusion An easy-to-use, highly accurate nomogram was developed for predicting the prognosis of EC patients with lung metastasis.

Project description:ObjectiveTo identify prognostic factors and establish nomograms for predicting overall survival (OS) and cause specific survival (CSS) of patients with non-metastatic chondrosarcoma.MethodsWe collected information on patients with non-metastatic chondrosarcoma from the Surveillance, Epidemiology, and End Results (SEER) database between 2005 and 2014, together with data from the First Affiliated Hospital of Zhengzhou University from 2011 to 2016. Variables including patients' baseline demographics (age, race, and gender), tumor characteristics (tumor size and extension, histology subtype, primary site, and American Joint Committee on Cancer [AJCC] stage), therapy (surgery, chemotherapy, and radiotherapy), and socioeconomic status (SES) were extracted for further analysis. OS and CSS were retrieved as our researching endpoints. Patients from the database were regarded as the training set, and univariate analysis, Lasso regression and multivariate analysis as well as the random forest were used to explore the predictors and establish nomograms. To validate nomograms internally and externally, we applied bootstrapped validation internally with the training dataset, while the dataset for external validation was obtained from the First Affiliated Hospital of Zhengzhou University. We estimated the discriminative ability of nomograms based on Cox proportional hazard regression models by means of calibration curves and the concordance index (C-index) of internal and external validation.ResultsAfter the implementation of exclusion criteria, there were 1267 patients in the training set and 72 patients in the testing set with non-metastatic chondrosarcomas. Age, gender, grade, histological subtype, primary site, surgery, radiation, chemotherapy, being employed/unemployed, tumor size, and tumor extension were significantly associated with prognosis in the univariate analysis. Age, gender, tumor size and extension, primary site, surgery, radiotherapy, chemotherapy, histological grade, and subtype were independent prognostic factors in the Cox models. The C-index of nomograms (internal: OS, 0.787; CSS, 0.821; external: OS, 0.777; CSS, 0.821) were higher than following conventional systems: AJCC sixth (OS, 0.640; CSS, 0.673) and seventh edition (OS, 0.675; CSS, 0.711).ConclusionsAge, gender, tumor size and extension, surgery, histological grade, and subtype were independent prognostic factors for both OS and CSS. In addition, we revealed that chondrosarcomas in the trunk, radiotherapy, and chemotherapy were correlated with poor prognosis. Our nomograms based on significant clinicopathologic features can well predict the 3-year and 5-year survival probability of patients with non-metastatic chondrosarcoma and assist oncologists in making accurate survival evaluation.

Project description:BackgroundBreast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis. The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness.MethodsFrom 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and they were randomly assigned to the training cohort and the validation cohort. Data of another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.ResultsLASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically significantly correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The Risk Stratification System showed that the OS of low-risk patients was considerably better than that of high-risk patients ( P < 0.001).ConclusionThe nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.

Dataset Information

Prognosis and nomogram for predicting postoperative survival of duodenal adenocarcinoma: A retrospective study in China and the SEER database.

Publications

Prognosis and nomogram for predicting postoperative survival of duodenal adenocarcinoma: A retrospective study in China and the SEER database.

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