Nomograms Predict Overall Survival and Cancer-Specific Survival in Patients with Fibrosarcoma: A SEER-Based Study
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ABSTRACT:
SUBMITTER: Xiang G
PROVIDER: S-EPMC7533781 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:Background For patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making. Methods A retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results The LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging. Conclusion Two prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.
| S-EPMC9530359 | biostudies-literature
Project description:BackgroundOur study aimed to explore the prognostic factors of bladder cancer with bone metastasis (BCBM) and develop prediction models to predict the overall survival (OS) and cancer-specific survival (CSS) of BCBM patients.MethodsA total of 1438 patients with BCBM were obtained from the SEER database. Patients from 2010 to 2016 were randomly divided into training and validation datasets (7:3), while patients from 2017 were divided for external testing. Nomograms were established using prognostic factors identified through Cox regression analyses and validated internally and externally. The concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discrimination and calibration of nomogram models, while decision curve analyses (DCA) and Kaplan-Meier (KM) curves were used to estimate the clinical applicability.ResultsMarital status, tumor metastasis (brain, liver, and lung), primary site surgery, and chemotherapy were indicated as independent prognostic factors for OS and CSS. Calibration plots and the overall C-index showed a novel agreement between the observed and predicted outcomes. Nomograms revealed significant advantages in OS and CSS predictions. AUCs for internal and external validation were listed as follows: for OS, 3-month AUCs were 0.853 and 0.849; 6-month AUCs were 0.873 and 0.832; 12-month AUCs were 0.825 and 0.805; for CSS, 3-month AUCs were 0.849 and 0.847; 6-month AUCs were 0.870 and 0.824; 12-month AUCs were 0.815 and 0.797, respectively. DCA curves demonstrated good clinical benefit, and KM curves showed distinct stratification performance.ConclusionThe nomograms as web-based tools were proved to be accurate, efficient, and clinically beneficial, which might help in patient management and clinical decision-making for BCBM patients.
| S-EPMC9865586 | biostudies-literature
Project description:BackgroundTo establish and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with papillary renal cell carcinoma (pRCC).MethodsPatients 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.ResultsA 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.ConclusionsIt 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.
| S-EPMC7354311 | biostudies-literature
Project description:BackgroundThe 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.MethodsPC 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.ResultsA 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.ConclusionsThe 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.
| S-EPMC7164422 | biostudies-literature
Project description:ObjectiveProstate cancer (PC) is the most common non-cutaneous malignancy in men worldwide. Accurate predicting the survival of elderly PC patients can help reduce mortality in patients. We aimed to construct nomograms to predict cancer-specific survival (CSS) and overall survival (OS) in elderly PC patients.MethodsInformation on PC patients aged 65 years and older was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for PC patients. Nomograms were developed to predict the CSS and OS of elderly PC patients based on a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested by the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to test the clinical value of the nomograms compared with the TNM staging system and D'Amico risk stratification system.Results135183 elderly PC patients in 2010-2018 were included. All patients were randomly assigned to the training set (N=94764) and the validation set (N=40419). Univariate and multivariate Cox regression model analysis revealed that age, race, marriage, histological grade, TNM stage, surgery, chemotherapy, radiotherapy, biopsy Gleason score (GS), and prostate-specific antigen (PSA) were independent risk factors for predicting CSS and OS in elderly patients with PC. The C-index of the training set and the validation set for predicting CSS was 0.883(95%CI:0.877-0.889) and 0.887(95%CI:0.877-0.897), respectively. The C-index of the training set and the validation set for predicting OS was 0.77(95%CI:0.766-0.774)and 0.767(95%CI:0.759-0.775), respectively. It showed that the proposed model has excellent discriminative ability. The AUC and the calibration curves also showed good accuracy and discriminability. The DCA showed that the nomograms for CSS and OS have good clinical potential value.ConclusionsWe developed new nomograms to predict CSS and OS in elderly PC patients. The models have been internally validated with good accuracy and reliability and can help doctors and patients to make better clinical decisions.
| S-EPMC9259789 | biostudies-literature
Project description:BackgroundTo develop and validate survival nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in upper tract urothelial carcinoma (UTUC) patients.MethodPatients diagnosed with UTUC from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively enrolled. Clinical characteristics and survival outcomes were respectively collected from the included patients. Then, eligible patients were divided into the training cohort and the validation cohort. Additionally, survival nomograms were developed based on the results of multivariate Cox analysis in the training cohort. Furthermore, Kaplan-Meier (KM) survival curves were generated to assess the actual effect of each variable. Lastly, the nomograms were validated using the concordance index (C-index), the area under the receiver operating characteristic (ROC) curve and calibration curves.ResultsTotally, 3,556 patients were included, with 2,492 in the training cohort and 1,064 in the validation cohort. No significant differences were detected in comparisons in clinical characteristics between two cohorts. Based on the results of uni- and multivariate Cox regression analysis, seven factors (age, TNM stage, use of surgery/radiation and marital status) for OS and six factors (age, TNM stage and use of surgery/radiation) for CSS were selected to develop the survival nomograms. The C-index for OS and CSS was 0.763 and 0.793 in the training cohort, and 0.759 and 0.784 in the validation cohort. Additionally, the 3- and 5-year AUCs for OS were 0.808 and 0.780 in the training cohort, and 0.785 and 0.778 in the validation group. As for CSS, it was 0.833 and 0.803 in the training cohort, and 0.815 and 0.810 in the validation cohort. Lastly, the calibration curves indicated a good consistency between the actual survival and the predictive survival.ConclusionsIt was the first time to conduct survival models for UTUC patients with predictive performance. It might be valuable of clinical application and further exploration with more studies in the future.
| S-EPMC7354328 | biostudies-literature
Project description:BackgroundConsidering that the knowledge of adenocarcinoma in villous adenoma of the colorectum is limited to several case reports, we designed a study to investigate independent prognostic factors and developed nomograms for predicting the survival of patients.MethodsUnivariate and multivariate Cox regression analyses were used to evaluate prognostic factors. A nomogram predicting cancer-specific survival (CSS) was performed; internally and externally validated; evaluated by receiver operating characteristic (ROC) curve, C-index, and decision curve analyses; and compared to the 7th TNM stage.ResultsPatients with adenocarcinoma in villous adenoma of the colorectum had a 1-year overall survival (OS) rate of 88.3% (95% CI: 87.1-89.5%), a 3-year OS rate of 75.1% (95% CI: 73.3-77%) and a 5-year OS rate of 64.5% (95% CI: 62-67.1%). Nomograms for 1-, 3- and 5-year CSS predictions were constructed and performed better with a higher C-index than the 7th TNM staging (internal: 0.716 vs 0.663; P?<?0.001; external: 0.713 vs 0.647; P?<?0.001). Additionally, the nomogram showed good agreement between internal and external validation. According to DCA analysis, compared to the 7th TNM stage, the nomogram showed a greater benefit across the period of follow-up regardless of the internal cohort or external cohort.ConclusionAge, race, T stage, pathologic grade, N stage, tumor size and M stage were prognostic factors for both OS and CSS. The constructed nomograms were more effective and accurate for predicting the 1-, 3- and 5-year CSS of patients with adenocarcinoma in villous adenoma than 7th TNM staging.
| S-EPMC7325241 | biostudies-literature
Project description:Background: Different proportions of Gleason pattern 3 and Gleason pattern 4 lead to various prognosis of prostate cancer with Gleason score 7. The objective of this study was to compare the survival outcomes of Gleason score 3+4 and 4+3 based on data from the Surveillance, Epidemiology, and End Results cancer registry database, and to investigate independent prognosis-associated factors and develop nomograms for predicting survival in Gleason score 7 prostate cancer patients. Methods: A retrospective study was conducted on 69,116 cases diagnosed as prostate adenocarcinoma with Gleason score 7 between 2004 and 2009. Prognosis-associated factors were evaluated using univariate and multivariate Cox regression analysis, and a 1:1 ratio paired cohort by propensity score matching with the statistical software IBM SPSS, to evaluate prognostic differences between Gleason score 3+4 and 4+3. The primary cohort was randomly divided into training set (n = 48,384) and validation set (n = 20,732). Based on the independent factors of prognosis, nomograms for prognosis were established by the training group and validated by the validation group using R version 3.5.0. Results: After propensity score matching, Cox regression analysis showed that Gleason 4+3 had an increased mortality risk both for overall survival (HR: 1.235, 95% CI: 1.179-1.294, P < 0.001) and cancer-specific survival (HR: 1.606, 95% CI: 1.468-1.762, P < 0.001). Nomograms for overall survival and cancer-specific survival were established with C-index 0.786 and 0.842, respectively. The calibration plot indicated an optimal agreement between the actual observation and nomogram prediction for overall survival and cancer-specific survival probability at 5 or 10 year. Conclusions: Prostate cancer with Gleason score 4+3 had worse overall survival and cancer-specific survival than Gleason score 3+4. Nomograms were formulated to predict 5-year and 10-year OS and CSS in patients with prostate cancer of Gleason score 7.
| S-EPMC6646708 | biostudies-literature
Project description:PurposeThis study aimed to identify independent prognosis-associated factors of bone-metastatic prostate cancer. The nomograms were further developed to obtain indicators for the prognostic evaluation.MethodsA total of 7315 bone-metastatic prostate cancer (PCa) patients from 2010 to 2016 were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training cohort (n=5,120) and test cohort (n=2,195) in a ratio of 7:3. Univariate and multivariate Cox regression models were applied to evaluate potential risk factors. A 1:1 propensity score matching (PSM) was further performed to decrease the confounding effect and re-evaluate the influence of radical prostatectomy and chemotherapy on prognosis. Combining these potential prognosis factors, the nomograms of cancer-specific survival (CSS) and overall survival (OS) at different times were established. C-indexes, calibration curves, and decision curves were developed to evaluate the discrimination, calibration, and clinical benefit of the nomograms.ResultsEleven independent prognosis factors for CSS and twelve for OS were utilized to conduct the nomograms respectively. The C-indexes of nomograms for CSS and OS were 0.712 and 0.702, respectively. A favorable consistency between the predicted and actual survival probabilities was demonstrated by adopting calibration curves. Decision curves also exhibited a positive clinical benefit of the nomograms.ConclusionsNomograms were formulated successfully to predict 3-year and 5-year CSS and OS for bone-metastatic PCa patients. Radical prostatectomy and chemotherapy were strongly associated with the bone-metastatic PCa prognosis.
| S-EPMC9764338 | biostudies-literature
Project description:ObjectiveThis study was designed to establish and validate promising and reliable nomograms for predicting the survival of angiosarcoma (AS) patients.MethodsThe Surveillance, Epidemiology, and End Results database was queried to collect the clinical information of 785 AS patients between 2004 and 2015. Data were split into a training cohort (n = 549) and a validation cohort (n = 236) without any preference. Univariate Cox and multivariate Cox regression analyses were performed to analyze the clinical parameters. Independent prognostic factors were then identified. Two nomograms were constructed to predict overall survival (OS) and cancer-specific survival (CSS) at 3 and 5 years. Finally, the models were evaluated using concordance indices (C-indices), calibration plots, and decision curve analysis (DCA).ResultsBased on the inclusion and exclusion criteria, 785 individuals were included in this analysis. Univariate and multivariate Cox regression analyses revealed that age, tumor size, and stage were prognostic factors independently associated with the OS of AS. Tumor site, tumor size, and stage were associated with the CSS of AS. Based on the statistical results and clinical significance of variables, nomograms were built. The nomograms for OS and CSS had C-indices of 0.666 and 0.654, respectively. The calibration curves showed good agreement between the predictive values and the actual values. DCA also indicated that the nomograms were clinically useful.ConclusionWe established nomograms with good predictive ability that could provide clinicians with better predictions about the clinical outcomes of AS patients.
| S-EPMC8704180 | biostudies-literature