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

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8958 ◽  
Author(s):  
Min Shi ◽  
Biao Zhou ◽  
Shu-Ping Yang

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.

2020 ◽  
Vol 19 ◽  
pp. 153303382094771
Author(s):  
Yao Jiang ◽  
Tianyu Wang ◽  
Zizheng Wei

Background: Osteosarcoma is one of the most common malignant bone tumors, with a high incidence in adolescence. The objective of this study was to construct prognostic nomograms for predicting the prognosis of juvenile osteosarcoma. Methods: Patients with osteosarcoma diagnosed between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The essential clinical predictors were identified with univariate and multivariate Cox analysis. Nomograms were constructed to predict the 3- and 5-year cancer- specific survival (CSS) and overall survival (OS). Concordance index (C-index) and calibration plots were performed to validate the predictive performance of nomograms. Results: We enrolled 736 adolescents with osteosarcoma from the SEER database, with 516 samples grouped into a training cohort and 220 samples grouped into a validation cohort. In multivariate analysis of the training cohort, predictors including tumor size, surgery treatment and AJCC stage were found to be associated with OS and CSS, while age was only associated with CSS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of adolescents with osteosarcoma. The C-index and calibration curves also showed the satisfactory performance of these nomograms for prognosis prediction. Conclusion: The developed nomograms are useful tools for precisely predicting the prognosis of adolescents with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.


Author(s):  
Xiaoxiao Liu ◽  
Wei Guo ◽  
Xiaobo Shi ◽  
Yue Ke ◽  
Yuxing Li ◽  
...  

This study aimed to build up nomogram models to evaluate overall survival (OS) and cancer-specific survival (CSS) in early-onset esophageal cancer (EOEC). Patients diagnosed with esophageal cancer (EC) from 2004 to 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Clinicopathological characteristics of younger versus older patients were compared, and survival analysis was performed in both groups. Independent related factors influencing the prognosis of EOEC were identified by univariate and multivariate Cox analysis, which were incorporated to construct a nomogram. The predictive capability of the nomogram was estimated by the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). A total of 534 younger and 17,243 older patients were available from the SEER database. Younger patients were randomly segmented into a training set (n=266) and a validation set (n=268). In terms of the training set, the C-index of the OS nomogram was 0.740 (95% CI: 0.707-0.773), and that of the CSS nomogram was 0.752 (95% CI: 0.719-0.785). In view of the validation set, the C-index of OS and CSS were 0.706 (95% CI: 0.671-0.741) and 0.723 (95%CI: 0.690-0.756), respectively. Calibration curves demonstrated the consistent degree of fit between actual and predicted values in nomogram models. From the perspective of DCA, the nomogram models were more beneficial than the tumor-node-metastasis (TNM) and the SEER stage for EOEC. In brief, the nomogram model can be considered as an individualized quantitative tool to predict the prognosis of EOEC patients to assist clinicians in making treatment decisions.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fengkai Yang ◽  
Hangkai Xie ◽  
Yucheng Wang

Background. The objective of this study was to develop a nomogram model and risk classification system to predict overall survival in elderly patients with fibrosarcoma. Methods. The study retrospectively collected data from the Surveillance, Epidemiology, and End Results (SEER) database relating to elderly patients diagnosed with fibrosarcoma between 1975 and 2015. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses on the training set to construct a nomogram model for predicting the overall survival of patients at 3, 5, and 10 years. The receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the discrimination and predictive accuracy of the model. Decision curve analysis was used for assessing the clinical utility of the model. Result. A total of 357 elderly fibrosarcoma patients from the SEER database were included in our analysis, randomly classified into a training set (252) and a validation set (105). The multivariate Cox regression analysis of the training set demonstrated that age, surgery, grade, chemotherapy, and tumor stage were independent prognostic factors. The ROC showed good model discrimination, with AUC values of 0.837, 0.808, and 0.806 for 3, 5, and 10 years in the training set and 0.769, 0.779, and 0.770 for 3, 5, and 10 years in the validation set, respectively. The calibration curves and decision curve analysis showed that the model has high predictive accuracy and a high clinical application. In addition, a risk classification system was constructed to differentiate patients into three different mortality risk groups accurately. Conclusion. The nomogram model and risk classification system constructed by us help optimize patients’ treatment decisions to improve prognosis.


2020 ◽  
Author(s):  
Wenwen Zheng ◽  
Weiwei Zhu ◽  
Shengqiang Yu ◽  
Kangqi Li ◽  
Yuexia Ding ◽  
...  

Abstract Background: The prognosis of metastatic renal cell carcinoma (RCC) patients vary widely because of clinical and pathological heterogeneity. We aimed to develop a novel nomogram to predict overall survival (OS) for this population. Methods: Metastatic RCC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. These patients were randomly assigned to a training set and a validation set at a ratio of 1:1. Significant prognostic factors of survival were identified through Cox regression models and then integrated to form a nomogram to predict 1-, 3- and 5-year OS. The nomogram was subsequently subjected to validations via the training and the validation sets. The performance of this model was evaluated by using Harrell’s concordance index (C-index), calibration curve, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results: Overall, 2315 eligible metastatic RCC patients were enrolled from the SEER database. A nomogram of survival prediction for patients of newly diagnosed with metastatic RCC was established, in which eight clinical factors significantly associated with OS were involved, including Fuhrman grade, lymph node status, sarcomatoid feature, cancer-directed surgery, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. The new model presented better discrimination power than the American Joint Committee on Cancer (AJCC) staging system (7th edition) in the training set (C-indexes, 0.701 vs. 0.612, P <0.001) and the validation set (C-indexes, 0.676 vs. 0.600, P <0.001). The calibration plots of the nomogram exhibited optimal agreement between the predicted values and the observed values. The results of NRI and IDI also indicated the superior predictive capability of the nomogram relative to the AJCC staging system. The DCA plots revealed higher clinical use of our model in survival prediction. Conclusions: We developed and validated an effective nomogram to provide individual OS prediction for metastatic RCC patients, which would be beneficial to clinical trial design, patient counseling, and therapeutic modality selection.


2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Yi Yang ◽  
Mingze Yao ◽  
Shengrong Long ◽  
Chengran Xu ◽  
Lun Li ◽  
...  

Purpose. In our study, we aimed to screen the risk factors that affect overall survival (OS) and cancer-specific survival (CSS) in adult glioma patients and to develop and evaluate nomograms. Methods. Primary high-grade gliomas patients being retrieved from the surveillance, epidemiology and end results (SEER) database, between 2004 and 2015, then they randomly assigned to a training group and a validation group. Univariate and multivariate Cox analysis models were used to choose the variables significantly correlated with the prognosis of high-grade glioma patients. And these variables were used to construct the nomograms. Next, concordance index (C-index), calibration plot and receiver operating characteristics (ROCs) curve were used to evaluate the accuracy of the nomogram model. In addition, the decision curve analysis (DCA) was used to analyze the benefit of nomogram and prognostic indicators commonly used in clinical practice. Results. A total of 6395 confirmed glioma patients were selected from the SEER database, divided into training set (n =3166) and validation set (n =3229). Age at diagnosis, tumor grade, tumor size, histological type, surgical type, radiotherapy and chemotherapy were screened out by Cox analysis model. For OS nomogram, the C-index of the training set was 0.741 (95% CI: 0.751-0.731), and the validation set was 0.738 (95% CI: 0.748-0.728). For CSS nomogram, the C-index of the training set was 0.739 (95% CI: 0.749-0.729), and the validation set was 0.738 (95% CI: 0.748-0.728). The net benefit and net reduction in inverventions of nomograms in the decision curve analysis (DCA) was higher than histological type. Conclusions. We developed nomograms to predict 3- and 5-year OS rates and 3- and 5-year CSS rates in adult high-grade glioma patients. Both the training set and the validation set showed good calibration and validation, indicating the clinical applicability of the nomogram and good predictive results.


2021 ◽  
Author(s):  
Guangrong Lu ◽  
Jiajia Li ◽  
Limin Wu ◽  
Yuning Shi ◽  
Xuchao Zhang ◽  
...  

Background: This study aimed to develop and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in small intestinal gastrointestinal stromal tumours (SI GISTs). Methods: Patients diagnosed with SI GISTs were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and further randomly divided into the training and validating cohorts. Univariate and multivariate cox analyses were conducted in the training set to determine independent prognostic factors to build nomograms for predicting 3- and 5-year OS and CSS. The performance of the nomograms was assessed by concordance index (C-index), calibration plot and the area receiver operating characteristic (ROC) curve (AUC). Results: A total of 776 patients with SI GISTs were retrospectively collected from the SEER database. OS nomogram was constructed based on age, surgery, imatinib treatment and AJCC stage, while CSS nomogram incorporated age, surgery, tumor grade and AJCC stage. In the training set, C-index for the OS nomogram was 0.773 [95% confidence intervals (95% CI): 0.722–0.824], CSS nomogram 0.806 (95% CI: 0.757–0.855). In internal validation cohort, the C-index for the OS nomogram was 0.741, while for the CSS nomogram 0.819. Well-corresponded calibration plots both in OS and CSS nomogram models were noticed. The comparisons of AUC values showed that the established nomograms exhibited superior discrimination power than 7th TNM staging system. Conclusion: Our nomogram can effectively predict 3- and 5-year OS and CSS in patients with SI GISTs, and its use can help improve the accuracy of personalized survival prediction and facilitate to provide constructive therapeutic suggestions.


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