Dataset Information

Nomograms for predicting overall survival and cancer-specific survival in young patients with pancreatic cancer in the US based on the SEER database.

ABSTRACT: Background:The incidence of young patients with pancreatic cancer (PC) is on the rise, and there is a lack of models that could effectively predict their prognosis. The purpose of this study was to construct nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of young patients with PC. Methods:PC patients younger than 50 years old from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were selected and randomly divided into training set and validation set. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent factors affecting OS. The Fine and Gray competing risk regression model was used to determine the independent factors affecting CSS. We used significant variables in the training set to construct nomograms predicting prognosis. The discrimination and calibration power of models were evaluated by concordance index (C-index), calibration curve and 10-flod cross-validation. Results:A total of 4,146 patients were selected. Multivariable Cox analysis showed that gender, race, grade, pathological types, AJCC stage and surgery were independent factors affecting OS. The C-index of the nomogram predicting OS in training and validation was 0.733 (average = 0.731, 95% CI [0.724-0.738]) and 0.742 (95% CI [0.725-0.759]), respectively. Competing risk analysis showed that primary site, pathological types, AJCC stage and surgery were independent factors affecting CSS. The C-index of the nomogram predicting CSS in training and validation set was 0.792 (average = 0.765, 95% CI [0.742-0.788]) and 0.776 (95% CI [0.773-0.779]), respectively. C-index based on nomogram was better in training and validation set than that based on AJCC stage. Calibration curves showed that these nomograms could accurately predict the 1-, 3- and 5-year OS and CSS both in training set and validation set. Conclusions:The nomograms could effectively predict OS and CSS in young patients with PC, which help clinicians more accurately and quantitatively judge the prognosis of individual patients.

SUBMITTER: Shi M

PROVIDER: S-EPMC7164422 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Nomograms for predicting overall survival and cancer-specific survival in young patients with pancreatic cancer in the US based on the SEER database.

Shi Min M Zhou Biao B Yang Shu-Ping SP

PeerJ 20200414

<h4>Background</h4>The incidence of young patients with pancreatic cancer (PC) is on the rise, and there is a lack of models that could effectively predict their prognosis. The purpose of this study was to construct nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of young patients with PC.<h4>Methods</h4>PC patients younger than 50 years old from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were selected and randomly divide ...[more]

PMID: 32322444

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Project description:Background:To establish and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with papillary renal cell carcinoma (pRCC). Methods:Patients diagnosed with pRCC between 2010 and 2014 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively included in this study and divided into training and validation groups randomly. Uni- and multivariate Cox regression analyses were used to identify significant variables related to OS and CSS in the training group. Based on results of multivariate Cox regression analysis, nomograms for 3- and 5-year CSS and OS were established, respectively. Additionally, Kaplan-Meier (KM) survival curves were produced to learn the actual effects of different variables. Finally, the nomograms were evaluated both in the training group and the validation group using the area under the receiver operating characteristic (ROC) curve, the concordance index (C-index) and calibration curves. Results:A total of 4,859 eligible patients were enrolled, with 3,403 categorized into the training group and 1,456 into the validation group. Seven factors [age, T stage, N stage, M stage, use of surgery/lymph node removal (LNR) and insurance status] were significantly related to OS and seven factors (age, T stage, N stage, M stage and use of surgery/chemotherapy/LNR) were significantly associated with CSS. These factors were eventually included in the predictive nomograms. The C-indexes for OS in the training and validation groups were 0.764 and 0.723 respectively, and 0.859 and 0.824 for CSS. The 3- and 5-year AUCs for OS were 0.779 and 0.752 in the training cohort, and 0.749 and 0.722 in the validation cohort. Similarly, 3- and 5-year AUCs for OS were 0.871 and 0.844 in the training cohort, and 0.853 and 0.822 in the validation group. Finally, the calibration curves suggested that the predictive nomograms had a good consistency between the observed and the predicted survival. Conclusions:It was the first time to develop nomograms to predict the survival outcomes of pRCC patients. The prognostic nomograms were reliable with high accuracy, which might have guiding significance for clinical practice.

Project description:BackgroundCurrently, the prognosis of kidney cancer depends mainly on the pathological grade or tumor stage. Clinicians have few effective tools that can personalize and adequately evaluate the prognosis of kidney cancer patients.MethodsA total of 70 481 kidney cancer patients were selected from the Surveillance, Epidemiology, and End Results database, among which patients diagnosed in 2005-2011 (n = 42 890) were used to establish nomograms for overall survival (OS) and cancer-specific survival (CSS), and those diagnosed in 2012-2015 (n = 24 591) were used for external validation. Univariate and multivariate Cox analyses were used to determine independent prognostic factors. Concordance index (C-index), receiver operating characteristic curve, and calibration curve were used to evaluate the predictive capacity of the nomograms. We further reduced subgroup classification and used propensity score matching to balance clinical informations, and analyzed the effect of other variables on survival. We established a new kidney cancer prognostic score system based on the effect of all available variables on survival. Cox proportional hazard model and Kaplan-Meier curves were used for survival comparison.ResultsAge, gender, marital status, surgery, grade, T stage, and M stage were included as independent risk factors in the nomograms. The favorable area under the curve (AUC) value (for OS, AUC = 0.812-0.858; and for CSS, AUC = 0.890-0.921), internal (for OS, C-index = 0.776; and for CSS, C-index = 0.856), and external (for OS, C-index = 0.814-0.841; and for CSS, C-index = 0.894-0.904) validation indicated that the proposed nomograms could accurately predict 1-, 3-, and 5-year OS and CSS of kidney cancer patients. The Aggtrmmns prognostic scoring system based on age, gender, race, marital status, grade, TNM stage, and surgery of kidney cancer patients could stage patients more explicitly than the AJCC staging system.ConclusionThe nomogram and Aggtrmmns scoring system can predict OS and CSS in kidney cancer patients effectively, which may help clinicians personalize prognostic assessments and clinical decisions.

Dataset Information

Nomograms for predicting overall survival and cancer-specific survival in young patients with pancreatic cancer in the US based on the SEER database.

Publications

Nomograms for predicting overall survival and cancer-specific survival in young patients with pancreatic cancer in the US based on the SEER database.

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